&EPA
           United States
           Environmental Protection
           Agency
            Office of Research and
            Development
            Washington, DC 20460
EPA/600/R-93/046
March 1993
PRZM-2, A Model for
Predicting Pesticide
Fate in the Crop
Root and Unsaturated
Soil Zones:
           Users Manual for
           Release 2.0

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                                            EPA/600/R-93/046
                                                March 1993
  PRZM-2, A Model for Predicting Pesticide Fate
  in the Crop Root and Unsaturated Soil Zones:
         Users Manual for Release 2.0

                     by

           J.A. Mullins.'R.F. Carsel,2
       J.E. Scarbrough.'and A.M. Ivery1
              AScI  Corporation
            Athens, GA 30605-2720
      Environmental Research  Laboratory
     U.S. Environmental Protection Agency
            Athens, GA 30605-2720
 ENVIRONMENTAL RESEARCH LABORATORY
 OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
          ATHENS, GA 30605-2720
                                      iXO Printed on Recycled Paper

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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-CO-0054 to AScI It has
been subject to the Agency's peer and administrative review, and it has been approved for
publication as an EPA document. Mention of trade names of commercial products does
not constitute endorsement or recommendation for use by the U.S.  Environmental
Protection Agency.

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                                    FOREWORD

       As environmental controls become more costly to implement and the penalties of
judgment errors become more severe, environmental quality management requires more
efficient analytical tools based on greater knowledge of the environmental phenomena to
be managed. As part of this Laboratory's research on the occurrence, movement, transfor-
mation, impact, and control of environmental contaminants, the Assessment Branch
develops management or engineering tools to help pollution control officials reach
decisions  on the registration and restriction of pesticides used for agricultural purposes.

       The pesticide regulatory process requires that the potential  risk to human health
resulting from the introduction or continued use of these chemicals be evaluated.
Recently much of this attention has been focused on exposure through leaching of
pesticides to groundwater and subsequent ingestion of contaminated water. To provide a
tool for evaluating this exposure, the PRZM-2 model was developed. PRZM-2  simulates
the transport of field-applied pesticides in the crop root zone and the vadose zone taking
into account the effects of agricultural management practices. The model further provides
estimates of probable exposure concentrations by taking into account the variability in the
natural systems and the uncertainties in system properties and processes.
                                       Rosemarie C. Russo, Ph.D.
                                       Director
                                       Environmental Research Laboratory
                                       Athens, Georgia
                                         111

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                                    ABSTRACT

       This publication contains documentation for the PRZM-2 model. PRZM-2 links two
subordinate models-PRZM and VADOFT-- in order to predict pesticide transport and
transformation down through the crop root, and unsaturated zone. PRZM is a one-
dimensional, finite-difference model that accounts for pesticide fate in the crop root zone,
This release of PRZM-2 incorporates several features in addition to those simulated in the
original PRZM code-specifically, soil temperature simulation, volatilization  and vapor
phase transport in soils, irrigation simulation, microbial transformation, and a method of
characteristics (MOC) algorithm to eliminate numerical dispersion. PRZM is now capable
of simulating transport and transformation of the parent compound and as many as two
daughter species. VADOFT is a one-dimensional, finite-element code that solves the Rich-
ard's equation for flow in the unsaturated zone. The user may make use of  constitutive
relationships between pressure, water content, and hydraulic conductivity to solve the
flow equations. VADOFT may also simulate the fate of two parent and 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 that are tailored to site-
specific situations. In order to perform probability-based exposure assessments, the code
is also  equipped with a Monte Carlo pre- and post-processor.
                                        IV

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                               TABLE OF CONTENTS
                                                                               Page
 Disclaimer	ii
 Foreword	iii
 Abstract	iv
 Figures	x
 Tables	xiv
 Acknowledgments	xix
Section

  1.0 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-2	    1-7
            1.3.1  Overview of PRZM	    1-8
                  1.3.1.1       Features	    1-8
                  1.3.1.2       Limitations	    1-9
            1.3.2  Overview of the Vadose Zone Flow and
                  Transport Model (VADOFT)	1-11
                  1.3.2.1       Features	1-11
                  1.3.2.2       Limitations	1-11
            1.3.3  Overview of the Monte Carlo Simulation Module	1-12
            1.3.4  Model Linkage	1-12
                  1.3.4.1       Temporal Model Linkage	1-12
                  1.3.4.2       Spatial Linkages	1-13
            1.3.5  Monte Carlo  Processor	1-13
            1.3.6  Overview Summary	1-14

  2.0 MODEL DEVELOPMENT, DISTRIBUTION, AND SUPPORT	    2-1

      2.1    Development and Testing	    2-1
      2.2    Distribution	    2-2
      2.3    Obtaining a copy of the PRZM-2 Model	    2-3
            2.3.1 Diskette	    2-3
            2.3.2 Electronic Bulletin Board System (BBS)	    2-3
      2.4    General/Minimum Hardware and Software
            Installation and Run-Time Requirements	    2-4
            2.4.1 Installation Requirements	    2-4
            2.4.2 Run Time Requirements	    2-4
      2.5    Installation	    2-4
      2.6    Installation  Verification and Routine Execution	    2-5
      2.7    Code Modification	    2-5
      2.8    Technical Help	    2-5
      2.9    Disclaimer	    2-7

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                         TABLE OF CONTENTS (continued)


Section                                                                       Page

      2.10 Trademarks	    2-7

 3.0 MODULES AND LOGISTICS	3-1

 4.0 INPUT PARAMETERS FOR PRZM-2	4-1

      4.1    Input File Descriptions	    4-1
            4.1.1   Meteorological File	    4-2
            4.1.2   Execution Supervisor File...,	    4-2
                  4.1.2.1       Example Execution Supervisor	    4-3
                  4.1.2.2       Example Execution Supervisor	    4-4
                  4.1.2.3       Execution Supervisor Input Guide	    4-5
            4.1.3   PRZM Input File	    4-7
                  4.1.3.1       Example PRZM Input File	    4-7
                  4.1.3.2       PRZM Input Guide	    4-8
            4.1.4   VADOFT Input File	  4-24
                  4.1.4.1       Example VADOFT Input File	  4-24
                  4.1.4.2       VADOFT Input Guide for FLOW	  4-25
                  4.1.4.3       VADOFT Input Guide for TRANSPORT	  4-32
            4.1.5   MONTE CARLO  Input File	  4-39
                  4.1.5.1       Example MONTE CARLO Input File	  4-39
                  4.1.5.2       MONTE CARLO Input Guide	  4-40

 5.0 PARAMETER ESTIMATION	    5-1

      5.1    EXESUP (Execution Supervisor)	    5-1
      5.2    PRZM (Pesticide Root Zone Model)	    5-2
      5.3    VADOFT Parameters	  5-62

 6.0 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 Equations	    6-6
            6.3.1  Transport in Soil	    6-6
            6.3.2 Water Movement	  6-14
                  6.3.2.1       Option	  6-20
                  6.3.2.2       Option 2	  6-20
            6.3.3 Soil Erosion	  6-21

                                       vi

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                          TABLE OF CONTENTS (continued)


Section                                                                          Page

            6.3.4 Volatilization	6-22
                   6.3.4.1       Soil Vapor Phase and Volat. Flux	6-23
                   6.3.4.2       Volat. Flux Through the Plant Canopy	6-25
                   6.3.4.3       Volat. Flux from Plant Surfaces	6-30
                   6.3.4.4       Soil Temperature Simulation	6-31

            6.3.5 Irrigation Equations	6-40
                   6.3.5.1       Soil Moisture Deficit	6-41
                   6.3.5.2       Sprinkler Irrigation	6-41
                   6.3.5.3       Flood Irrigation	6-42
                   6.3.5.4       Furrow Irrigation	6-42
      6.4   Numerical Solution Techniques	6-44
            6.4.1 Chemical Transport Equations	6-44
            6.4.2 Volatilization	6-46
            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	6-53
                   6.5.1.1       High Peclet Number	6-53
                   6.5.1.2       Low Peclet Number	6 53
            6.5.2 Testing Results of Volatilization Subroutines	6-54
                   6.5.2.1       Comparison with Analytical Sol	6-54
                   6.5.2.2       Comparison with Field Data	6-67
                   6.5.2.3       Conclusions from Volatilization Model Testing ...... 6-65
            6.5.3 Testing Results of Soil Temp. Simulation Subroutine	6-68
            6.5.4 Testing of Daughter Products Simulation	6-69
      6.6   Biodegradation Theory and Assumptions	6-79

 7.0 VADOFT CODE AND THEORY	    7-1

      7.1   Introduction	    7-1
      7.2   Overview of VADOFT	
            7.2.1 Features	    7-1
                   7.2.1.1       General Description	    7-1
                   7.2.1.2       Process and Geometry	    7-1
                   7.2.1.3       Assumptions	    7-1
                   7.2.1.4       Data Requirements	    7-2
            7.2.2 Limitations	    7-2
      7.3   Description of the Flow Module	    7-3
            7.3.1 Flow Equation	    7-3
            7.3.2 Numerical Solution	    7-6
                   7.3.2.1       Numerical Appr. of the Flow Eq	    7-6
                                         VII

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                           TABLE OF CONTENTS (continued)


Section                                                                           Page

                   7.3.2.2       General Guidance on Selection of Grid Spacings
                               and Time Steps, and the Use of
                               Solution Algorithms	7-15
      7.4    Description of the Transport Module	7-15
             7.4.1 Transport Equation	7-15
             7.4.2 Numerical Solution of the Transport Equation	7-16
                   7.4.2.1       Numerical Appr. of the Transport
                               Equation	7-16
      7.5    Results of VADOFT Testing Simulations	7-19
             7.5.1 Flow Module	   7-19
                   7.5.1.1       Transient  Upward  Flow	   7-19
                   7.5.1.2       Steady Infiltration	   7-19
             7.5.2 Transport Module	   7-20
                   7.5.2.1       Transport in a Semi-Infinite
                               Soil Column	   7-20
                   7.5.2.2       Transport in a Finite Soil Column	   7-20
                   7.5.2.3       Transport in a Layered Soil Column	   7-20
             7.5.3 Combined Nonlinear Flow and Transport Modules	   7-32
                   7.5.3.1       Transport During Absorption of
                               Water in a Soil Tube	   7-32
                   7.5.3.2       Transient Infiltration and Contam-
                               inant Transport in the Vadose Zone	   7-32

 8.0 UNCERTAINTY PREPROCESSOR	    8-1

      8.1    Introduction	    8-1
      8.2    Overview of the Preprocessor	    8-1
             8.2.1 Description of the Method	    8-2
             8.2.2 Uncertainty in the Input Variables	    8-3
      8.3    Description  of Available Parameter Distributions	    8-4
             8.3.1 Uniform Distribution	    8-4
             8.3.2 Normal Distribution	    8-5
             8.3.3 Log-Normal  Distribution	    8-6
             8.3.4 Exponential Distribution	    8-6
             8.3.5 The Johnson System of Distributions	    8-7
             8.3.6 Triangular Distribution	    8-7
             8.3.7 Empirical Distribution	    8-8
             8.3.8 Uncertainty in Correlated Variables	    8-8
             8.3.9 Generation of Random Numbers	   8-13
      8.4    Analysis of Output and Estimation of Distribution
             Quantizes	   8-13
             8.4.1 Estimating Distribution Quantizes	   8-14
             8.4.2 Confidence of up	   8-16

                                         viii

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                       TABLE OF CONTENTS (continued)
Section

 9.0 REFERENCES
 10.0 APPENDICES	10-1
      10.1 Error Messages and Warnings	10-1
      10.2 Variable Glossary	10-1
      10.3 PRZM and VADOFT Example Input Files	10-70
                                     IX

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                          LIST OF FIGURES
 1.1  DECISION PATH FOR RISK ASSESSMENT	1-3

 1.2  TIME SERIES PLOT OF TOXICANT CONCENTRATIONS	1-5

 1.3  FREQUENCY DISTRIBUTION OF TOXICANT CONCENTRATIONS	1-5

 1.4  CUMULATIVE FREQUENCY DISTRIBUTION OF TOXICANT
    CONCENTRATIONS	1-5

 1.5  TIME SERIES OF TOXICANT CONCENTRATIONS WITH MOVING
    AVERAGE WINDOW OF DURATION!^	1-6

 1.6  LINKED MODELING SYSTEM CONFIGURATION	1-6

 5.1  PAN EVAPORATION CORRECTION FACTORS	5-18

 5.2  DIAGRAM FOR ESTIMATING SOIL EVAPORATION LOSS	5-19

 5.3  REPRESENTATIVE REGIONAL MEAN STORM DURATION VALUES
    FOR THE UNITED STATES	5-20

 5.4  DIAGRAM FOR ESTIMATING SOIL CONSERVATION SERVICE SOIL
    HYDROLOGIC GROUPS	5-21

 5.5  NUMERICAL DISPERSION ASSOCIATED WITH SPACE STEP	5-22

 5.6  PHYSICAL DISPERSION ASSOCIATED WITH ADJECTIVE TRANSPORT . . .5-23

 5.7  AVERAGE TEMPERATURE OF SHALLOW GROUNDWATER	5-24

 5.8  1/3-BAR SOIL MOISTURE BY VOLUME	5-25

 5.9  15-BAR SOIL MOISTURE BY VOLUME	5-26

5.10  MINERAL BULK DENSITY	5-27

5.11  ESTIMATION OF DRAINAGE RATE AD VERSUS NUMBER OF
    COMPARTMENTS	5-28

 6.1  PESTICIDE ROOT ZONE MODEL	6-3

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                          LIST OF FIGURES

Figure                                                           Page

 6.2  SCHEMATIC REPRESENTATION OF A SINGLE CHEMICAL IN A SOIL
     LAYER [[[ 6-7

 6.3  SCHEMATIC OF PESTICIDE VAPOR AND VOLATILIZATION PROCESSES . .6-24

 6.4  VARIABILITY OF INFILTRATION DEPTHS WITHIN AN IRRIGATION
     FURROW [[[ 6-43

 6.5  SCHEMATIC OF THE TOP TWO SOIL COMPARTMENTS AND THE
     OVERLYING SURFACE COMPARTMENT .............................. 6-48

 6.6  COMPARISON OF SIMULATION RESULTS AT HIGH PECLET NUMBER ..... 6-55

 6.7  COMPARISON OF SIMULATION RESULTS AT LOW PECLET NUMBER ...... 6-56

 6.8  COMPARISON OF VOLATILIZATION FLUX PREDICTED BY PRZM AND
     JURY'S ANALYTICAL SOLUTION: TEST CASES #1 AND #2 ................ 6-59

 6.9  COMPARISON OF VOLATILIZATION FLUX PREDICTED BY PRZM AND
     JURY'S ANALYTICAL SOLUTION: TEST CASES #3 AND #4 ................ 6-60

6.10  SENSITIVITY OF CUMULATIVE VOLATILIZATION FLUX TO
           DECAY RATE, ............................................. 6-63
6.11  EFFECTS OF DELX ON VOLATILIZATION FLUX AND PESTICIDE
     DECAY [[[ 6-64

6.12  COMPARISON OF CONSTANT AND TWO-STEP DECAY RATES ............ 6-66

6.13  EFFECTS OF TWO-STEP DECAY RATES ON VOLATILIZATION
     FLUX AND PESTICIDE DECAY  ................................. 6-67

6.14  COMPARISON OF SOIL TEMPERATURE PROFILES PREDICTED BY
     ANALYTICAL AND FINITE DIFFERENCE SOLUTIONS (Time Step=l HR) . . . .6-70

6.15  COMPARISON OF SOIL TEMPERATURE PROFILES PREDICTED BY

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                     LIST OF FIGURES (Continued)

                                                             Page

6.17  CONVERSION OF d TO C$ TO Q WITH NO ADSORPTION
     AND NO DECAY	6-76

6.18  CONVERSION OF CJ TO C2 TO Oj WITH DECAY BUT NO
     ADSORPTION	6-77

6.19  CONVERSION OF ALDICARB TO ALDICARB SULFOXIDE TO
     ALDICARB SULFONE	6-78

 7.1   LOGARITHMIC PLOT OF CONSTITUTIVE RELATIONS FOR CLAY, CLAY
     LOAM, AND LOAMY SAND	7-7

 7.2   LOGARITHMIC PLOT OF CONSTITUTIVE RELATIONS FOR SILT, SILTY
     CLAY LOAM, SILTY CLAY, AND SILTY LOAM	7-8

 7.3   LOGARITHMIC PLOT OF CONSTITUTIVE RELATIONS FOR SANDY CLAY,
     SANDY CLAY LOAM, SANDY LOAM, ND SAND	7-9

 7.4   STANDARD PLOT OF RELATIVE PERMEABILITY VS. SATURATION FOR
     CLAY, CLAY LOAM, LOAM, AND LOAMY SAND	7-10

 7.5   STANDARD PLOT OF RELATIVE PERMEABILITY VS. SATURATION FOR
     SILT, SILT CLAY LOAM, SILTY CLAY, AND SILTY LOAM	7-11

 7.6   STANDARD PLOT OF RELATIVE PERMEABILITY VS. SATURATION FOR
     SANDY CLAY, SANDY CLAY LOAM, SANDY LOAM, AND SAND	7-12

 7.7   FINITE ELEMENT DISCRETIZATION OF SOIL COLUMN SHOWING NODE
     AND ELEMENT NUMBERS	7-13

 7.8   SIMULATED PRESSURE HEAD PROFILES FOR THE PROBLEM OF
     TRANSIENT UPWARD FLOW IN A SOIL COLUMN	7-22

 7.9   SIMULATED PROFILE OF WATER SATURATION FOR THE PROBLEM OF
     TRANSIENT UPWARD FLOW IN A SOIL COLUMN	7-23

7.10  SIMULATED PRESSURE HEAD PROFILES FOR FIVE CASES OF
     THE PROBLEM OF STEADY INFILTRATION IN A SOIL COLUMN	7-25
                               xn

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                      LIST OF FIGURES (Continued)

Figure                                                         Page


 7.11  SIMULATED PROFILES OF WATER SATURATION FOR FIVE CASES
     OF THE PROBLEM OF STEADY INFILTRATION IN A SOIL COLUMN	7-26

 7.12  SIMULATED CONCENTRATION PROFILES FOR THE PROBLEM OF SOLUTE
     TRANSPORT IN A SEMI-INFINITE SOIL COLUMN	7-27

 7.13  SIMULATED CONCENTRATION PROFILES FOR TWO CASES OF
     THE PROBLEM OF SOLUTE TRANSPORT IN A SOIL COLUMN OF
     FINITE LENGTH	7-30

 7.14  SIMULATED OUTFLOW BREAKTHROUGH CURVE FOR CASE 1
     OF THE PROBLEM OF SOLUTE TRANSPORT IN A LAYERED
     SOIL COLUMN	7-33

 7.15  SIMULATED OUTFLOW BREAKTHROUGH CURVE FOR CASE 2
     OF THE PROBLEM OF SOLUTE TRANSPORT IN A LAYERED
     SOIL COLUMN	7-34

 7.16  ONE-DIMENSIONAL SOLUTE TRANSPORT DURING ABSORPTION
     OF WATER IN A SOIL TUBE	7-39

 7.17  SIMULATED PROFILES OF WATER SATURATION DURING ABSORP-
     TION OF WATER IN A SOIL TUBE	7-40

 7.18  SIMULATED CONCENTRATION PROFILES FOR THE PROBLEM OF
     ONE-DIMENSIONAL SOLUTE TRANSPORT DURING ABSORPTION
     OF WATER IN A SOIL TUBE	7-41

 7.19  PROBLEM DESCRIPTION FOR TRANSIENT INFILTRATION AND
     TRANSPORT IN THE VADOSE ZONE	7-42

 7.20  INFILTRATION RATE VS. TIME RELATIONSHIP USED IN
     NUMERICAL SIMULATION	7-43

 7.21  SIMULATED WATER SATURATION PROFILES	7-44

 7.22  SIMULATED PRESSURE HEAD PROFILES	7-45

 7.23  SIMULATED VERTICAL DARCY VELOCITY PROFILES	7-46

 7.24  SIMULATED SOLUTE CONCENTRATION PROFILES	7-47

 8.1   TRIANGULAR PROBABILITY DISTRIBUTION	8-9

                               xiii

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                          LIST OF TABLES
Table                                                         page

 3-1  LIST OF SUBROUTINES AND FUNCTIONS AND A BRIEF DESCRIPTION OF
     THEIR PURPOSE	3-2

 3-2  LIST OF ALL PARAMETER FILES, PARAMETER DIMENSIONS, AND
     A BRIEF DESCRIPTION	3-6

 4-1  VARIABLE DESIGNATIONS FOR PLOTTING FILES	4-21

 4-2  MONTE CARLO INPUT AND OUTPUT LABELS	4-43

 5-1  TYPICAL VALUES OF SNOWMELT (SFAC) AS RELATED
     TO FOREST COVER	5-29

 5-2  MEAN DURATION (HOURS) OF SUNLIGHT FOR LATITUDES 0° TO 50° IN
     THE NORTHERN AND SOUTHERN HEMISPHERES	5-29

 5-3  INDICATIONS OF THE GENERAL MAGNITUDE OF THE
     SOIL/ERODIBILITY FACTOR, K	5-30

 5-4  INTERCEPTION STORAGE FOR MAJOR CROPS	5-30

 5-5  VALUES OF THE EROSION EQUATION'S TOPOGRAPHIC FACTOR, LS,
     FOR SPECIFIED COMBINATIONS OF SLOPE LENGTH AND STEEPNESS . .5-31

 5-6  VALUES OF SUPPORT-PRACTICE FACTOR, P	5-32

 5-7  GENERALIZED VALUES OF THE COVER AND WAGEMENT FACTOR, C,
     IN THE 37 STATES EAST OF THE ROCKY MOUNTAINS .,	5-33

 5-8  MEAN STORM DURATION (TR) VALUES FOR SELECTED CITIES	5-33

 5-9  AGRONOMIC DATA FOR MAJOR AGRICULTURAL CROPS IN THE
     UNITED STATES	5-37

 5-10  RUNOFF CURVE NUMBERS FOR HYDROLOGIC SOIL-
     COVER COMPLEXES	5-38

 5-11  METHOD FOR CONVERTING CROP YIELDS TO RESIDUE	5-39

 5-12  RESIDUE REMAINING FROM TILLAGE OPERATIONS	5-39

 5-13  REDUCTION IN RUNOFF CURVE NUMBERS CAUSED BY
     CONSERVATION TILLAGE AND RESIDUE MANAGEMENT	5-40

                               xiv

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                       LIST OF TABLES (Continued)
Table                                                          Page

 5-14  VALUES FOR ESTIMATING WFMAX IN EXPONENTIAL
     FOLIAR MODEL	5-40

 5-15  PESTICIDE SOIL APPLICATION METHODS AND DISTRIBUTION	5-41

 5-16  MAXIMUM CANOPY HEIGHT AT CROP MATURATION	5-41

 5-17  DEGRADATION RATE CONSTANTS OF SELECTED PESTICIDES
     ON FOLIAGE	5-42

 5-18  ESTIMATED VALUES OF HENRY'S CONSTANT FOR SELECTED
     PESTICIDES	5-43

 5-19  PHYSICAL CHARACTERISTICS OF SELECTED PESTICIDES
     FOR USE IN DEVELOPMENT OF PARTITION COEFFICIENTS
     AND REPORTED DEGRADATION RATE CONSTANTS IN SOIL
     ROOT ZONE	5-44

 5-20  OCTANOL WATER DISTRIBUTION COEFFICIENTS AND SOIL
     DEGRADATION RATE CONSTANTS FOR SELECTED CHEMICALS	5-47

 5-21  ALBEDO FACTORS OF NATURAL SURFACES FOR SOLAR
     RADIATION	5-49

 5-22  EMISSMTY VALUES FOR NATURAL SURFACES AT NORMAL
     TEMPERATURES	5-50

 5-23  COEFFICIENTS FOR LINEAR REGRESSION EQUATIONS FOR
     PREDICTION OF SOIL WATER CONTENTS AT SPECIFIC
     MATRIC POTENTIALS	5-50

 5-24  THERMAL PROPERTIES OF SOME SOIL AND REFERENCE
     MATERIALS	 5-51

 5-25  HYDROLOGIC PROPERTIES BY SOIL TEXTURE	5-52

 5-26  DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR
     FIELD CAPACITY	5-53

 5-27  DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR
     WILTING POINT,	5-54
                                xv

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                       LIST OF TABLES (Continued)


Table                                                           Page

 5-28  CORRELATIONS AMONG TRANSFORMED VARIABLES OF ORGANIC
     MATTER, FIELD CAPACITY, AND WILTING POINT	5-55

 5-29  MEAN BULK DENSITY FOR FIVE SOIL TEXTURAL
     CLASSIFICATIONS	5-56

 5-30  DESCRIPTIVE STATISTICS  FOR BULK DENSITY	5-56

 5-31  DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR
     ORGANIC MATTER	5-57

 5-32  ADAPTATIONS AND LIMITATIONS OF COMMON IRRIGATION
     METHODS	5-58

 5-33  WATER REQUIREMENTS FOR VARIOUS IRRIGATION AND
     SOIL TYPES	5-58

 5-34  REPRESENTATIVE FURROW PARAMETERS DESCRIBED IN THE
     LITERATURE	5-59

 5-35  FURROW IRRIGATION RELATIONSHIPS FOR VARIOUS SOILS,
     SLOPES, AND DEPTHS OF APPLICATION	5-59

 5-36  SUITABLE SIDE SLOPES FOR CHANNELS BUILT IN VARIOUS
     KINDS OF MATERIALS	5-60

 5-37  VALUE OF "N" FOR DRAINAGE DITCH DESIGN	5-60

 5-38  REPRESENTATIVE PERMEABILITY RANGES FOR SEDIMENTARY
     MATERIALS	5-61

 5-39  VALUES OF GREEN-AMPT PARAMETERS FOR SCS HYDROLOGIC
     SOIL GROUPS	5-61

 5-40  DESCRIPTIVE STATISTICS FOR SATURATED HYDRAULIC
     CONDUCTIVITY	5-64

 5-41  DESCRIPTIVE STATISTICS FOR VAN GENUCHTEN WATER
     RETENTION MODEL PARAMETERS, a, & y	5-66

 5-42  DESCRIPTIVE STATISTICS FOR SATURATION WATER CONTENT
       AND RESIDUAL WATER CONTENT (65	5-67
                                xvi

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                      LIST OF TABLES (Continued)
Table                                                          Page

 5-43  STATISTICAL PARAMETERS USED FOR DISTRIBUTION
     APPROXIMATION	5-68

 5-44  CORRELATIONS AMONG TRANSFORMED VARIABLES PRESENTED
     WITH THE FACTORED COVARIANCE MATRIX	5-70

 6-1  SUMMARY OF SOIL TEMPERATURE MODEL CHARACTERISTICS	6-33

 6-2  INPUT PARAMETERS FOR THE TEST CASES - ANALYTICAL SOLUTION	6-58

 6-3  TRIFLURALIN VOLATILIZATION LOSSES, AMOUNTS REMAINING IN
     SOIL, AND ESTIMATED LOSSES VIA OTHER PATHWAYS FOR THE 120-
     DAY FIELD TEST	6-62

 6-4  INPUT PARAMETERS FOR THE TEST CASES - WATKINSVILLE SITE	6-62

 6-5  SIMULATION RESULTS OF USING DIFFERENT COMPARTMENT DEPTH . . .6-65

 6-6  SIMULATED SOIL TEMPERATURE PROFILE AFTER ONE DAY FOR
     DIFFERENT COMPARTMENT THICKNESSES	6-72

 7-1  SOIL PROPERTIES AND DISCRETIZATION DATA USED IN SIMULATING
     TRANSIENT FLOW IN A SOIL COLUMN	7-21

 7-2  SOIL PROPERTIES USED IN SIMULATING STEADY-STATE
     INFILTRATION	7-21

 7-3  ITERATIVE PROCEDURE PERFORMANCE COMPARISON	7-24

 7-4  VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA USED
     IN SIMULATING ONE-DIMENSIONAL TRANSPORT IN A SEMI-INFINITE
     SOIL COLUMN	7-24

 7-5  CONCENTRATION PROFILE CURVES AT t = 25 hr AND t = 50 hr
     SHOWING COMPARISON OF THE ANALYTICAL SOLUTION AND RESULTS
     FROM VADOFT	7-28

 7-6  VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA USED
     IN SIMULATING ONE-DIMENSIONAL TRANSPORT IN A FINITE
     SOIL COLUMN	7-29
                                xvn

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                      LIST OF TABLES (Concluded)
Table                                                         page

 7-7  CONCENTRATION PROFILE CURVES SHOWING COMPARISON OF THE
     ANALYTICAL SOLUTION AND VADOFT	7-31

 7-8  VALUES OF PHYSICAL PARAMETERS USED IN THE SIMULATION OF
     TRANSPORT IN A LAYERED SOIL COLUMN	7-35

 7-9  BREAKTHROUGH CURVES COMPUTED USING THE ANALYTICAL SOLUTION
     AND VADOFT (CASE1)	7-36

7-10  BREAKTHROUGH CURVES COMPUTED USING THE ANALYTICAL
     SOLUTION AND VADOFT (CASE 2)	7-37

7-11  VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION
     DATA USED IN SIMULATING TRANSPORT IN A VARIABLY
     SATURATED SOIL TUBE	7-38

7-12  VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA
     USED IN SIMULATING TRANSIENT INFILTRATION AND
     CONTAMINANT TRANSPORT IN THE VADOSE ZONE	7-38

10-1  PRZM-2 ERROR MESSAGES, WARNINGS, AND TROUBLESHOOTING
     APPROACHES	10-2

10-2  EXESUP PROGRAM VARIABLES	10-13

10-3  PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
     DESIGNATION	10-16

10-4  VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
     VARIABLE DESIGNATIONS 	10-55

10-5  MONTE-CARLO PROGRAM VARIABLES	10-68
                               XVIII

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                                ACKNOWLEDGMENTS

      A number of individuals contributed to this effort. Their roles are acknowledged in the
following paragraphs.

      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 Ms. Meeks (WCC) wrote the irrigation and MOC
algorithms. The volatilization routines were written by Dr. J. Lin and Dr. S. Raju of Aqua
Terra Consultants, Mr. Dean wrote the daughter products algorithms that were implemented
by Dr. Lin. Mr. J.L. 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.
Modifications were developed and implemented by the authors of this document.

      Final assembly of the model code,  documentation and model testing were performed by
AScI.  The  support of the graphics staff of AScI is appreciated. Also a special thanks to Ms. T.
Robinson (AScI) and Ms. S. Tucker  (CSC)  for their typing of text, equations, and  tables.

      The authors would like to acknowledge Mr. D.S. Brown, Chief, Assessment Branch,
ERL-Athens, for his suggestions, input, and helpful comments,
                                         xix

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                                      SECTION 1

                                   INTRODUCTION
This publication contains documentation for a linked groundwater loading model, known as
PRZM-2, for organic chemical contaminant transport down through the crop in root and vadose
zones. A brief section on background and objectives for the model development effort follows in
this introduction (Section 1.1). Section 1.2 gives a synopsis of risk and exposure assessment
concepts. The reader who has sufficient background in these concepts may proceed to Section
1.3, which provides an overview of the PRZM-2 modeling system, including 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 to groundwater and subsequent
ingestion of contaminated water.

The capability to simulate the potential exposure to pesticides via this pathway has two major
facets:

    o    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.

    o    Evaluation of the probability of the occurrence of concentrations of various magni-
         tudes at various depths.

Several 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  have
been linked together in such  a way that a complete simulation package, which takes into
account the effects of agricultural management practices on contaminant fate is available  for
use either by the Agency or the agricultural chemical industry to address potential  groundwa-
ter contamination problems.  Without such a package,  the decision maker must rely on
modeling scenarios that are either incomplete or potentially incorrect. Each time a new
scenario arises,  recurring questions must be answered:

    o    What models should be used?

    o    How should mass transfer between models be handled?
                                          1-1

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The resolution of these issues for each scenario is both expensive and time consuming.
Furthermore, it precludes consistency of approach to evaluation of contamination potential for
various scenarios.

The modeling package described in this report seeks to overcome these problems by providing a
consistent set of linked unsaturated zone models that have the flexibility to handle a wide
variety of hydrogeological, soils, climate, and pesticide scenarios. However, the formulation  of
the risk analysis problem requires more than a simple, deterministic evaluation of potential
exposure concentrations.  The inherent variability of force, capacitance and resistance in
natural systems, combined with the inability to exactly describe these attributes of the system,
suggests that exposure concentrations cannot be predicted with certainty. Therefore, the
uncertainty associated with the predictions must be quantified. Consequently, this simulation
package also seeks to provide this capability by utilizing Monte Carlo simulation techniques.

Stated more concisely, the objectives of this model development effort were to provide a
simulation package that can:

    o    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 agricul-
         tural management practices

    o    Provide probabilistic estimates of exposure concentrations by taking  into account the
         variability in the natural systems and the uncertainly in system 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 software development for facile model imple-
mentation.

1.2 CONCEPT OF RISK AND EXPOSURE ASSESSMENT

Exposure assessment, as defined in the Federal Register (1984) for human impacts, is the
estimation of the  magnitude, frequency, and duration at which a quantity of a toxicant is
available at certain exchange boundaries (i.e., lungs, gut, or skin) of a subject population over a
specified time interval. Exposure assessment is an element of the larger problems of risk
assessment and risk management, as demonstrated in Figure 1.1. The concentration estimates
generated during an exposure assessment are combined with demographic and toxicological
information to evaluate risk to a population-which can be used, in turn, to make policy
decisions regarding the use or disposal of the chemical.

Major components of risk assessment are indicated below. Of these, the first three constitute
the important steps for exposure assessment and  are discussed in detail here,
                                           1-2

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                             REGULATORY CONCERN
                                                                        SCIENTIFIC DATA
                                                                                     Population
                                                                                     Exposure
                                                                                     Product Ltf» Cycle
                                                         General Information Gathering
Preliminary Exposure
Assessement
      Hazard Identification TOxldty
      env. cone., etc.
                                                        Most Probable Areas of Exposure
                                                        Prallminaiy Exposur* Ass*«m«!it
                                                                     1
                                                           Preliminary FUak Analysis
                                                                  Decision
                                    Exposure A99*»smant
     In-Depth Exposure
                                                  I
                                                                           No N*»d lor Future
                                                                           Exposure Assessment

                                                                           Mum-Disciplinary
                                                                           Peer Review
                                                   Dontgn At««<«m«ot Stud/ Plan
                                                        Compt«h«nsV« Data Gaiharlng
                                                      Conduct Mlrwd Exposure Motteilng
                                                                Dvdstor
     Regulatory Rsspcnss
                                                                              HazarsJ Input
                                                           Formal Ptgk Asa*sorn»nt
                                                                  D^islon
                                         1
                            Regulatory Proposal
                                                                   Examined Exposure*
                                                                   Present No Umesenobto Risk
Figure  1.1 Decision path for risk assessment
                                                    1-3

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     o   Characterization and quantification of chemical sources
     o   Identification of exposure routes
     o   Quantification of contaminant movement through the exposure routes to the receptor
         population/location
     o   Characterization of the exposed population
     o   Integration of quantified environmental concentrations with the characteristics of the
         exposed populations to yield exposure profiles

Characterization of sources(s) requires in a broad sense the estimation of the loading of a
chemical into various environmental media. For the groundwater contamination problem, on a
regional scale, this requires data on chemical uses and distribution of those uses (spatially and
temporally). It also  requires information on the crops being grown, registered or proposed
chemical uses of those crops, and regional  management practices. For a specific field-scale
area, similar data would be needed to support an assessment; however, greater detail may be
necessary.

The identification of exposure pathways involves a qualitative (or semiquantitative) assessment
of how the chemical is thought to move from the source to the exposed population. Important
fate processes 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 groundwater
exposure, important pathways and processes are predefined to a large extent in the models to
be used. The quantification of concentrations in a medium, given the  source strength,
pathways, and attenuation mechanisms along each pathway, is the next step, and is the major
benefit of using models such as  PRZM-2. 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-2 produces time series of toxicant concentrations such as appears 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 exccedance 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, which is a cumulative frequency distribution
of the toxicant concentration. The cumulative frequency distribution shows 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" that y will be exceeded.

Thus far, only the concentration to which the organism will be exposed has been discussed and
nothing has been said concerning the duration of the event. If we take the same time series
and impose a window of length "t" on it at level j^ (Figure 1.5), and move it 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 shows the chance that the moving average of
duration  tQ will exceed the critical value of y, yc.
                                           1-4

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                                         Time  (t)
    Figure 1.2.
                     Time series plot of toxicant concentrations.
     100-
   a>
   d
   a
   •n
   o>
   JJ
   id
   n
   •H
   i
o
»
             Concentration (y)
                                                              Concentration  (y)
Figure 1.3.  Frequency distribution of toxi-
cant concentrations.
                                               Figure 1.4.  Cumulative frequency
                                               distribution of toxicant
                                               concentrations.
                                            1-5

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                                      Time  (t)
   Figure 1.5. Time series of toxicant concentrations with moving average window of duration
                                      PRZM-2
                                  (l-D Flow  and Transport)
                        VADOSE
                         ZONE
                        MODEL
     VADOFT
(1-D Flow  and Transport)
Figure 1.6. Linked modeling system configuration.
                                        1-6

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  The moving average window should be the same length as that specified for yc. For instance,
in the case of cancer risk, a 70-year (lifetime) window is normally used to average the data in
the simulated time series, The use of the moving window for averaging the time series allows
us to compare both the concentration and duration against the standard. The chance or
probability that the moving average concentration exceeds the standard is the essence of the
exposure assessment. This type of information provides a precursor to the estimates of risk
taken in using this chemical under the conditions of the model simulation. The use of models
like PRZM-2 that provide data in 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  concentration estimates and population characteristics makes possible the count-
ing 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 chemicals, their fate within the human body (e.g., pharmaco-
kinetics, toxicology),  and their effects on the exposed population are not considered. These,
however, are also elements of the risk assessment.

Although the concepts underlying an exposure assessment are relatively simple, the actual
application of  these concepts is complicated because of large variations in  source-specfic 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-2 in the evaluation  of exposure concentrations.

1.3 OVERVIEW OF PRZM-2

This section gives an overview of the PRZM-2 model highlighting the features and limitations
of the simulation package  as a whole, and the component models PRZM and VADOFT. The
PRZM-2 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 or parent/daughter
relationships.  The  model is also capable of estimating probabilities of concentrations or fluxes
in or from these various media for the purpose of performing exposure assessments.

To avoid writing an entirely new computer code, it was decided to make use of existing codes
and software to the extent possible. Thus, due  to its comprehensive treatment of important
processes, its dynamic nature, and its widespread use and acceptability to  the Agency and the
agricultural chemical industry, the Pesticide Root Zone model (PRZM) (Carsel et al. 1984) was
selected to simulate the crop root zone.

Having selected PRZM, two options were evaluated for developing the PRZM-2 linked 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
                                           1-7

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 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 is depicted in Figure 1.6. In
 this configuration, an enhanced version of PRZM is linked to a one-dimensional vadose zone
 flow and transport model. Both the vadose and PRZM models simulate water flow and solute
 transport. Subsequently, a new code  (VADOFT) was written to perform the flow and transport
 simulation in the vadose zone.
 1.3.1     Overview of PRZM

 1.3.1.1 Features—

The Pesticide Root Zone Model (PRZM) is a one-dimensional, dynamic, compartmental model
that can be used to simulate chemical movement in unsaturated soil systems within and
immediately below the plant root zone. It has two major components- hydrology (and
hydraulics) and chemical transport. The hydrologic component for calculating runoff and
erosion is based on the Soil Conservation Service curve number technique and the Universal
Soil Loss Equation. Evapotranspiration is estimated either directly from pan evaporation data,
or based on an empirical formula. Evapotranspiration is divided among evaporation from crop
interception, evaporation from soil, and transpiration by the crop. Water movement is
simulated by the use of generalized soil parameters, including field capacity, wilting point, and
saturation water content. The chemical transport component can simulate pesticide applica-
tion on the soil or on the plant foliage. With a newly added feature, biodegradation can also be
considered 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, erosion, decay, volatilization, foliar washoff, advection,  dispersion, and retarda-
tion. Two options are  available to solve the transport equations: (1) the original backwards-
difference implicit scheme that may be affected by excessive numerical dispersion at high
Peclet numbers, or (2) the method of characteristics algorithm that eliminates numerical
dispersion while slightly increasing 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 land segments joined together in  a horizontal manner. There are
three reasons a user may choose for implementing multiple zones:

         1)    to simulate heterogeneous PRZM  root zones with a homogeneous vadose zone

         2)    to simulate a homogeneous root zone with heterogeneous vadose zones

         3)    to simulate multiple homogeneous root zones with multiple homogeneous
              vadose zones

Weighing multiple zones together and their use are discussed in  detail in Section 5.

Another added feature is  the ability to simulate  as many as three chemicals simultaneously as
separate compounds or as a parent-daughter relationship. This gives the user the option to
                                           1-8

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 observe the effects 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.

 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 are
 broken into four categories:

         o    Hydrology
         o    Soil hydraulics
         o    Method of solution of the transport equation
         o    Deterministic nature of the model

 The Release II version of PRZM has been suitably modified to overcome many of these
 limitations.

 Hydrologic and hydraulic computations are still performed in PRZM on a daily time step even
 though, for some of the processes  involved (evaporation, runoff, erosion), finer time 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 hydrography. 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 not much evidence to suggest that it was utilized by model users to
 any great extent.) This had 1-day drainage assumption the effect, especially in  deeper soils,  of
 inducing a greater-than-anticipated movement of chemical  through the profile. While this
 representation of soil hydraulics has been retained in PRZM,  the user has the option of
 coupling PRZM to VADOFT. PRZM is then used to represent the root zone, while VADOFT,
with a more rigorous representation of unsaturated flow, is used to simulate the thicker vadose
 zone.  The 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 as 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

                                           1-9

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important one for simulating the effects of volatilization.  However, the process would seem
less likely to impact the movement of chemicals with high vapor pressures. For these
chemicals, vapor diffusion would be a major process for renewing the chemical concentration in
the surface soil.

Another limitation of the Release I model was the apparent inadequacy of the solution to the
transport equation in advection-dominated systems. The backward difference formulation of
the advection term tends to produce a high degree of numerical dispersion in such systems.
This results in overprediction of downward movement due to smearing of the peak and
subsequent overestimation of loadings to groundwater. In this new release, a new formulation
is available for advection-dominated systems. The advective terms are decoupled from the rest
of the transport equation and solved separately using the method of characteristics (MOC).
The remainder of the transport equation is then solved as before, using the fully implicit
scheme. This approach effectively eliminates numerical dispersion with 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 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-2 allows the user to output soil profile pesticide  concentra-
tions 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., the total concentration in each soil compartment, for any user-
specified day during the simulation period. In this way, the user can run PRZM-2 with only
monthly or annual output summaries and still obtain simulation results for selected days when
field  data were collected. There is no inherent limit to the number of SNAPSHOTS that can be
requested in a single run. When more than one chemical is being simulated, the concentration
profiles are provided by the order of the chemical number, i.e., NCHEM.

To better represent the expected behavior of the chemical being simulated, or the impacts of
tillage or other agricultural practices, the following parameters can be reset to new values at
any time during the simulation period:

        Solution Decay Rate (DWRATE)
        Sorbed Decay Rate (DSRATE)
        Partition Coefficient (KD)
        Bulk Density (BD)
        Curve Number (CN)
        USLE Cover Factor (USLEC)
                                          1-10

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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 appropri-
ate 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-2 model for predicting the
movement of pesticides within and below the plant root zone and assessing subsequent
groundwater contamination. The VADOFT code simulates one-dimensional, single-phase
moisture and solute transport in unconfined, variably saturated porous media. Transport
processes include hydrodynamic dispersion, advection, linear equilibrium sorption, and first-
order decay, The code predicts infiltration or recharge rate and solute mass flux entering the
saturated zone. The following description of VADOFT is adapted from Huyakorn et al.
(1988a).

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 concentra-
tion 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 properties are independent of contaminant concentrations.
Diffisive/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

                                          1-11

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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 transfor-
mations, exchanges these generated values for PRZM-2 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-2, through VADOFT the resolution of time scales is also straightfor-
ward. 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
                                          1-12

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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 "drain-
age 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 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 accommod-
ates all the water output by PRZM each day. This eliminates the problem of pending 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  groundwa-
ter, especially if chemical degradation rates are lower in the vadose zone than in the root zone.
1.3.5     Monte Carlo Processor

PRZM-2 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,
                                          1-13

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1,3.6     Overview Summary

A modeling system (PRZM-2) 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. A major objective was to keep the
model simple and efficient enough so that it could be operated on an IBM-PC or IBM-compati-
ble 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.
                                          1-14

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                                  SECTION 2

          MODEL DEVELOPMENT, DISTRIBUTION, AND SUPPORT
      NOTE: Refer to the README file for the latest supplemental information,
      changes, and/or additions to the PRZM-2 model documentation. A copy of
      the RE AD.ME file is included on each distribution diskette set or it can be
      down loaded from the Center for Exposure Assessment Modeling (CEAM)
      electronic bulletin board system (BBS). It can be installed on a hard disk
      using the INSTALL, (diskette) or INSTALP2 (BBS) program. It is an ASCII
      (non-binary) text file that can be displayed on the monitor screen by using
      the DOS TYPE command (e.g., TYPE READ.ME) or printed using the DOS
      PRINT command (e.g., PRINT READ.ME).

      The READ.ME file contains a section entitled File Name and Content that
      provides a brief functional description of each PRZM-2 file by name or file
      name extension type. Other sections in this  document contain further
      information about

          o system development took used to build the microcomputer release of the
           PRZM-2 model system
          o recommended hardware and software configuration for execution of the
           model and. all support programs
          o routine program execution
          o Minimum file  configuration
          o run time and performance
          o program modification
          o technical help contacts
2.1. DEVELOPMENT AND TESTING

The PRZW-2 model system was developed and tested on a Digital Equipment Corporation
(DEC) VAX 6310 running under version 5.4-2 of the VMS operating system (OS) and
version 5.5-98 of VAX VMS FORTRAN-77, and an Advanced Logic Research (ALR) 486/25
microcomputer running under version 4.00 of IBM PC DOS and version 2.51 of Salford
FORTRAN (FTN77/486).

The following FORTRAN tools also were used to perform static evaluations of the PRZM-2
FORTRAN code on an IBM PS/2 Model 8085-071 running under version 3.3 of IBM PC
DOS, MICRO EXPRESS (ME) 486/25 and 486/33 systems running under version 5.00 of
Microsoft (MS) DOS, and a Sun SPARCstation 1 +GX running version 4.1.1 of UNIX/Sun-
Os:

                                      2-1

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  o Lahey             - F77L, F77L-EM/32 versions 5.01,
                        4.02 (DOS, ext DOS)
  o Microsof          - MSFORT version 5.00 (OS/2, DOS)
  o Ryan-McFarland   - RMFORT versions 2.45, 3.10.01 (DOS)
  o Salford            - FTN77/386 version 2.50 (DOS,
                        extended DOS)
  o Silicon Valley
    Systems            - SVS-77/386 version 2.81 (DOS,
                        extended DOS)
  o Sun               - (UNIX/SunOS, version  1.4)
  oWaterloo           - Watcom-77/386 version 8.5E (DOS,
                        extended DOS)

In addition to the VAX and ALR systems, PRZM-2 has also been successfully executed on
a PRIME 50 Series minicomputer running under  PRIMOS, the Sun SPARCstation, and
the IBM PS/2 Model 8085-071.

The distribution version of the PRZM-2 model system is built with the Lahey FORTRAN
F77L-EM/32) extended (i.e., protected) mode FORTRAN compiler and link editor, version
5.01. Refer to section 2.4.2 for specific hardware and software run time requirements for
the host system for the PRZM-2 model system.
2.2 DISTRIBUTION

The PRZM-2 model system and all support files and programs are available on diskette
from CEAM, located at the U.S. EPA Environmental Research Laboratory, Athens,
Georgia, at no charge. The CEAM has an exchange diskette policy. It is preferred that
diskettes be received before sending a copy of the model system (refer to section 2.3,
Obtaining a Copy of the PRZM-2 Model Systemz).

Included in a distribution diskette set are

  o   PRZM-2 general execution and user support guide (READ.ME) file
  o   interactive installation program (refer to section 2.5, Installation)
  o   test input and output files for installation verification
  o   executable task image file for the PRZM-2 model system
  o   FORTRAN source code files
  o   command and/or "make" files to compile, link, and run the task image file (PRZM2-
      .EXE)

A FORTRAN compiler and link editor are NOT required to execute any portion of the
model. If the user wishes to modify the model, it will be up to the user to supply and/or
obtain

  o   an appropriate text editor that saves files in ASCII (non-binary) text format
  o   FORTRAN development tools to recompile and  link edit any portion of the model
                                       2-2

-------
CEAM cannot support, maintain, and/or be responsible for modifications that change the
function and/or operational characteristics of the executable task image, MAKE, or DOS
command files supplied with this model package.

The microcomputer release of the PRZM-2 model is a full implementation of the VAX/-
VMS version. The microcomputer implementation of this model performs the same
function as the U.S. EPA mainframe/minicomputer version.
2.3 OBTAINING A COPY OF THE PRZM-2 MODEL

         NOTE: k=l,024; m=l,048,576; b=l byte

2.3.1 Diskette

To obtain a copy of the PRZM-2 distribution model system on diskette, send

  o   the appropriate number of double-sided, double-density (DS/DD 360kb) 5.25 inch,
      or double-sided, high-density (DS/HD 1.44mb) 3.5 inch error-free diskettes

         NOTE:     To obtain the correct number of diskettes, contact CEAM at 706/-
                    546-3549.

  o   a cover letter, with a complete return address, requesting the PRZM-2 model to:

      Model Distribution Coordinator
      Center for Exposure Assessment Modeling
      Environmental Research Laboratory
      U.S.  Environmental Protection Agency
      960 College Station Road
      Athens, GA 30606-2720

Program and/or user documentation, or instructions on how to order documentation, will
accompany  each response.

2.3.2 Electronic Bulletin Board System (BBS)

To down load a copy of the PRZM-2 model system, or to check the status of the latest
release of this model or any other CEAM software product, call the CEAM BBS 24 hours
a day, 7 days a week. To access the BBS, a computer with a modem and communication
software is  needed. The phone number for the BBS is 706/546-3402. Communication
parameters for the BBS are

  o   300/1200/2400/9600 baud rate
  o   8 data bits
  o   no parity
  o   1 stop bit
                                       2-3

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2.4 GENERA1L/MINIMUM HARDWARE AND SOFTWARE INSTALLATION AND RUN
TIME REQUIREMENTS

NOTE: Refer to the READ.ME file for the latest supplemental and more complete
information, changes, and/or additions concerning specific hardware and software
installation and run time requirements.

2.4.1 Installation Requirements

  o   3.5 inch, 1.44mb diskette drive, or 5.25 inch, 360kb diskette drive
  o   hard disk drive
  o   approximately 4.5mb free hard disk storage
2.4.2 Run Time Requirements

  o   386 or 486 compatible microcomputer
  o   MS or PC DOS version 3.30 or higher
  o   640k base memory
  o   4mb of extended (XMS) memory
  o   4.5mb free hard disk storage

Refer to READ.ME file for suggested modification of the CONFIG.SYS and/or
AUTOEXEC.BAT DOS system configuration and start-up files.
2.5 INSTALLATION

To install the PRZM-2 model system and/or related support files on a hard disk, insert the
first distribution diskette in a compatible diskette drive (refer to section 2.4). Then type

                           A: \INSTALL or B: \INSTALL

at the DOS system prompt and press the  key. Then follow instructions and
respond to prompts presented on the monitor screen by the interactive installation
program. Complete installation instructions are also printed on each external diskette
label.

      NOTE: To install the PRZM-2 model system and/or related support files on a hard
      disk from an interactive, self-extracting installation program down loaded from the
      CEAM BBS or through Internet, type

                                   INSTALP2

      at the DOS system prompt then press the  key. This assumes that the
      current default drive and sub-directory is the same as the drive and sub-directory
      where the file INSTALP2.EXE is stored.  Then follow instructions and respond to
      prompts presented on the monitor screen by the interactive installation program.

                                       2-4

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The PRZM-2 distribution diskette sets and BBS files implement software product
installation standards to insure the most error-free, maintainable, and user-acceptable
distribution of CEAM products. It has a unique menu option, command, full-screen
(interactive), diagnostic, error-recovery, help, and selective installation capabilities using
state-of-the-art human-factors engineering practices and principles.

     NOTE: The contents of the distribution diskettes can be copied to another set
     of "backup" diskettes using the DOS DISKCOPY command. Refer to the DOS
     Reference Manual for command application and use. The "backup" diskettes
     must be the same size and storage density as the original source diskettes.
2.6 INSTALLATION VERIFICATION AND ROUTINE EXECUTION

Refer to the following sections in the RE AD.ME file for complete instructions concerning
installation verification and routine execution of the PRZM-2 model:

  o   File Name and Content
  o   Routine Execution
  o   Run Time and Performance
  o   Minimum File Configuration
2.7 CODE MODIFICATION

Included in the diskette set are

  o   an executable task image file for the PRZM-2 model system
  o   FORTRAN source code files
  o   command and/or "make" files to compile, link, and run the task image file (PRZM2-
      .EXE)

If the user wishes to modify the model or any other program, it will be up to him or her to
supply and/or obtain

  o   an appropriate text editor that saves files in ASCII (non-binary) text format
  o   FORTRAN development tools to recompile and link edit any portion of the model

CEAM cannot support, maintain, and/or be responsible for modifications that change the
function of any executable task image (*, EXE), DOS batch command (*.BAT), and/or
"make" utility file(s) supplied with this model package.
2.8 TECHNICAL HELP

For questions and/or information concerning
                                       2-5

-------
  o   installation and/or testing of the PRZM-2 model system and/or support programs or
      files, call 706/546-3590 for assistance
  o   PRZM-2 model and/or program content, application, and/or theory, call 706/546-
      3210 for assistance
  o   use of the CEAM electronic bulletin board system (BBS), contact the BBS system
      operator  (SYSOP) at 706/546-3590
  o   CEAM software and distribution Quality Assurance and Control, call 706/546-3125
  o   other environmental software and documentation distributed through  CEAM,
      contact the Model Distribution Coordinator at 706/546-3549
  o   other support available through CEAM, contact Mr. Dermont Bouchard, CEAM
      Manager
  o   by mail at the following address

         Center for Exposure Assessment Modeling (CEAM)
         Environmental Research Laboratory
         U.S. Environmental Protection Agency
         960 College Station Road
         Athens, Georgia 30605-2720

  o   by telephone at 706/546-3130
  o   by fax at  706/546-2018
  o   through the CEAM BBS message menu and commands. The CEAM  BBS commu-
      nication parameters and telephone number are listed above (section  2.3.2).

To help technical staff provide better assistance, write down a response to the following
topics before calling or writing. If calling, be at the computer, with the computer on, and
in the proper sub-directory (e.g., \PRZM2) when the call is placed.

  o   program information:

      - describe the problem, including  the exact wording of any error and/or warning
         message (s)
      - list the exact steps, command (s), and/or keyboard key sequence that will
         reproduce the problem machine information:

  o   machine information:

      - list computer brand and model
      - list available RAM (as reported  by DOS CHKDSK command)
      - list extended memory present and free (XMS)
      - list name and version of extended memory (XMS) manager (i.e., HIMEM,
         VDISK, RAMDRIVE, etc.)
      - list available hard disk space (as reported by DOS CHKDSK command)
      - list the brand and version of DOS (as reported by DOS VER  command)
      - list the name of any memory resident (TSR) program(s)  installed
         printer brand and model
         monitor brand and model
                                       2-6

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  NOTE: If contacting CEAM by mail, fax, or BBS, include responses to the above
  information in your correspondence.
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-2 model system, and moditfications to the DOS system configura-
tion 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.

CEAM software products are built using FORTRAN-77, assembler, and operating system
interface  command languages. The code structure and logic of these products  is designed
for single-user, single-tasking, non-LAN environment and operating platform for micro-
computer installations (i.e., single user on dedicated system).

A user will be on their own if he/she attempts to install a CEAM product on a multi-user,
multi-tasking, and/or LAN based system (i.e., Windows, DESQview, any LAN). CEAM
cannot provide installation, operation, and/or general user support under any combination
of these configurations.  Instructions and conditions for proper installation and testing are
provided with the  product in  a READ.ME file. While multiuser/multitasking/LAN
installations could work, none of the CEAM products have been thoroughly tested under
all possible conditions.  CEAM can provide scientific and/or application-support for
selected products if the user proves that a given product is installed and working
correctly.
2.10  TRADEMARKS

  o   F77L is a registered trademark of Lahey Computer Systems, Inc. All other Lahey
      products are trademarks of Lahey Computer Systems, Inc.
  o   IBM, Personal Computer/XT (PC/XT), Personal Computer/AT (PC/AT), PC DOS,
      VDISK, and Personal System/2 (PS/2) are registered trademarks of International
      Business Machines Corporation
  o   DESQview is a trademark of Quarterdeck Office Systems, Inc.
  o   Sun and SunOS are registered trademarks of Sun Microsystems, Inc.
  o   SPARC is a registered trademark of SPARC International, Inc.
  o   UNIX is a registered trademark of American Telephone and Telegraph
  o   SVS FORTRAN-77 is a trademark of Silicon Valley Software
  o   PRIME and PRIMOS are trademarks of Prime Computers, Inc.
  o   Microsoft, RAMDRIVE, HIMEM, MS, and MS-DOS  are registered trademarks of
      Microsoft Corporation
  o   Windows is a trademark  of Microsoft Corporation
  o   RM/FORTRAN is a trademark of Language Processors, Inc.
  o   DEC, VAX, VMS, and DCL are trademarks of Digital Equipment Corporation

                                       2-7

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o   386 is a trademark of Intel Corporation
o   US Robotics is a registered trademark and Courier HST is a trademark of U. S.Rob-
    otics, Inc.
                                     2-8

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                                   SECTION 3

                          MODULES AND LOGISTICS



The PRZM-2 model consists of four major modules. These are:

      o EXESUP, which controls the simulation

      o PRZM, which performs transport and transformation simulations for the root
         zone

      o VADOFT, which performs transport and transformation simulations for the
         vadose zone

      o 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 dimen-
sions. A brief description for each listing is also given.
                                       3-1

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TABLE 3-1.
LIST OF SUBROUTINES AND FUNCTIONS AND A BRIEF DESCRIP-
TION OF THEIR PURPOSE.
   MODULE
   CALLING
   ROUTINE
SUBROUTINE

FUNCTION PURPOSE
   EXESUP
   PRZM
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.
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.
PRZM2       controls model calling routines.
LSUFIX      performs  internal  reads.

BIODEG     perform time dependant solution for microbiodegradation.
SLPST1       set up coefficient matrix for the solution of pesticide
                                         3-2

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TABLE 3-1. (Continued)
   MODULE
   CALLING
   ROUTINE
SUBROUTINE

FUNCTION PURPOSE
                                  transport.
                    PRZMRD      reads PRZM input file.
                    HYDR2       perform soil hydraulic calculations.
                    PLGROW      determines plant growth parameters for use in other subrou-
                                  tines.
                    FARM
                                  insures pesticide application is applied during adequate
                                  moisture conditions.
                    INIDAT       provides common block CMISC.INC values.
                    TRDIAl       solves tridiagonal maxtrix.
                    HYDROL      calculates snowmelt, crop interception, runoff, and infiltra-
                                  tion.
                    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.
                    MASBAL      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.
                    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.
                                          3-3

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TABLE 3-1.  (Continued)

                   SUBROUTINE
MODULE
CALLING
ROUTINE
   VADOFT
  or
FUNCTION   PURPOSE

PRZEXM    creates input  file for  EXAMS  moldel.
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 horizonal 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.

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.
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.
                                         3-4

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TABLE 3-1. (Continued)
   MODULE        SUBROUTINE
   CALLING        or
   ROUTINE        FUNCTION PURPOSE
                   MCVAD      determines MONTE CARLO variables for VADOFT.
                   READVC      reads in vectors.
                   CONVER      computes the limiting values of water saturation for each
                                material.
   MONTE CARLO
                   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.
                   STOUT       initializes the amount of pesticide decay.
                   FRQTAB      prints tabular frequency output.
                   FRQPLT      plots cumulative distributions.
                   MCECHO     echoes MONTE CARLO input.
                   READM      reads in  MONTE CARLO  input.
                   MAXAVG     computes maximum daily  average output.
                   STATIS       performs summations for  MONTE CARLO.
                                        3-5

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TABLE 3-2.
LIST OF ALL PARAMETERS FILES, PARAMETER DIMENSIONS,
AND A BRIEF DESCRIPTION.
FILE
CTRACE.INC

PMXMAT.INC
PMXNLY.INC
PMXPRT.INC

PMXTIM.INC

PMXTMV.1NC

PMXVDT.INC

PCMPLR.INC
PARAMETER
MAXSUB=50
MAXLIN=10
MXMAT=5
MXNLAY=20
MXPRT=100

MXTIM=31

MXTMV-31

MXVDT=31

REALMX=1.0D+30
DESCRIPTION
maximum number of subroutines.
maximum number of lines for trace option.
maximum number of VADOFT materials.
maximum number of layers in VADOFT.
maximum number of VADOFT observation
nodes.
maximum number of VADOFT iterations
allowed.
maximum number of VADOFT time interpola
tion values.

maximum number of VADOFT time steps.
maximum real number.
  PMXNOD.INC
  PMXZON.NC
  PPARM.1NC
  PIOUNI.INC
  PMXNSZ.INC
  CMCRVR.INC
 REALMN=1.0D-30
 MAXINT=2147483647
 MAXREC=512
 EXNMX=-53.0
 EXPMN=REALMN
 EXPMX=53.0
 WINDOW=. TRUE.
 PCASCI=TRUE.
 NONPC=.FALSE.
 MXNOD=100
 MXZONE=10
 NCMPTS=100
 NAPP=50
 NC=5
 NPII=800
 NCMPP2=NCMPTS+2

 MXCPD=100

 KUOUT=6
 NMXF1L=99
 FILBAS=30
 MXNSZO=lo
 MCMAX=50
 NMAX=10

 NCMAX=10
 NRMAX=1000
 NEMP=20
 MCSUM=MCMAX+NMAX

 NPMAX=5
 minimum real number.
 maximum integer value.
 maximum record length.
 maximum 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 number of VADOFT nodes allowed.
 maximum number of PRZM zones.
 maximum number of compartments in PRZM.
 maximum number of applications in PRZM.
 maximum number of crops allowed in PRZM.
 maximum 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.
 maximum number of file units open.
 base file unit number.
 maximum number of VADOFT zones allowed.
 maximum number of random input variables.
 maximum number of summary output
 variables.
 maximum number of CDF'S.
 maximum number of MONTE CARLO runs.
 maximum number of empirical distributions.
maximum number of random  input and output
 variables.
 maximum length of MONTE CARLO averag-
 ing periods.
                                     3-6

-------
                                   SECTION 4

                       INPUT PARAMETERS FOR PRZM 2
This section describes the development of the input data files used in the Execution
Supervisor (PRZM2.RUN), PRZM, VADOFT and MONTE CARLO. All of these fries,
except for the meteorological file, nay "have embedded comment lines. A comment line is
any line beginning with three asterisks (***), These lines are ignored by the code during
execution. For best accuracy and process time, a text or line editor is recommended  for
inputing file records. To better understand record formats used in model input, an
example record format statement appears below:

     FORMAT     3I2,2X,F8.0,EI0.3,1X2(I5,1XF8.0)

     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 2 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 7 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, E 10.3, specifies there is one field often columns that may include an
exponential suffix. The format identifier, 2(I5,1X,F8.0), specifies that there are two
sequential sets of I5,1X,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.


4.1 INPUT FILE DESCRIPTIONS

The Execution Supervisor (PRZM2.RUN) is used to define: 1) which modules are chosen
for simulation; 2) the number of zones used in a simulation; 3) input, output, and scratch
file names with optional path statements; 4) the starting and ending date of a simulation;
5) the number of chemicals (either separate or daughter); 6) weighting parameters
between PRZM and VADOFT zones; 7) and global echo and trace levels during execution.

PRZM, VADOFT, and MONTE CARLO input files consist of various title and FORTRAN-
formatted  records. Each of these module files along with their examples are discussed in
the following pages. For further descriptions, see Section 5 on parameter estimation.
                                       4.1

-------
4.1.1 Meteorological File

The PRZM-2 model requires 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. These files are
from the National Oceanic and Atmospheric Administration (NOAA) and are available
from the Athens-ERL, An example file format is shown below:

RECORD  FORMAT     1X,3I2,6F10.0
READ STATEMENT:    MM, MD, MY, PRECIP, PEVP, TEMP, WIND, SOLRAD

     where

     MM       = meteorological month
     MD       = meteorological day
     MY       = meteorological year
     PRECIP   = precipitation (cm day:l)
     PEVP     = pan evaporation data (cm day:l)
     TEMP    = temperature (celsius)
     WIND    = wind speed (cm see-1)
     SOLRAD   = solar radiation (Langleys)
4.1.2 Execution Supervisor File (PRZM2.RUN)

The PRZM-2 model requires existence of a control file (PRZM2.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. On the following pages are examples
of the execution supervisor input file.
                                        4-2

-------
4.1.2.1 Example Execution Supervisor (PRZM2.RUN) input file
                      ONE ZONE
*** option records
PRZM
VADOFT
MONTE CARLO
TRANSPORT SIMULATION
*** zone records
PRZM ZONES
VADOFT ZONES
ENDRUN
*** Input file records
  PATH
  MCIN
  METEOROLOGY       1
  PRZM INPUT          1
  VADOFTINPUT       1
*** output file records
  PATH
  TIME SERIES         1
  PRZM OUTPUT        1
  VADOFT OUTPUT      1
  MCOUT
  MCOUT2
*** scratch file records
  PRZM RESTART       1
  VADOFT FLOW RS     1
  VADOFT TRANS RST   1
  VADOFT TAPE 10      1
ENDFILES
*** global records
  START DATE
  END DATE
  NUMBER OF CHEMICALS
  PARENT OF 2
  PARENT OF 3
ENDDATA
*** display records
ECHO
TRACE
ON
ON
OFF
ON

1
D:\PRZM2\INPUT\
MC.INP
MET.INP
PRZM3.INP
VADF3.INP

D:\PRZM2\OUTPUT\
TIMES. OUT
PRZM.OUT
VADF.OUT
MC. OUT
MC2.0UT

RESTART.PRZ
VFLOW.RST
VTRANS.RST
VADF.TAP
 010181
 311283
 3
 1
 2
 4
 OFF
 NOTE: Three asterisks (***) denote a comment line and are ignored by the program.
                                 4-3

-------
4.1.2.2 Example Execution Supervisor (PRZM2.RUN) input file
                     TWO ZONES WITH MONTE CARLO OPTION
***0ptions
                                ON
                                ON
                                ON
                                ON
                                2
                                2
PRZM
VADOFT
MONTE CARLO
TRANSPORT SIMULATION
PRZM ZONES
VADOFT ZONES
ENDRUN
***Input files
  MCIN
  METEOROLOGY       1
  METEOROLOGY       2
  PRZM INPUT         1
  PRZM INPUT         2
  VADOFTINPUT       1
  VADOFTINPUT       2
***0utput files
  TIME SERIES         1
  TIME SERIES         2
  PRZM OUTPUT        1
  PRZM OUTPUT        2
  VADOFT OUTPUT      1
  VADOFT OUTPUT      2
  MCOUT
  MCOUT2
***Scratch files
  PRZM RESTART       1
  PRZM RESTART       2
  VADOFT FLOW RST    1
  VADOFT FLOW RST    2
  VADOFT TRANS RST   1
  VADOFT TRANS RST   2
  VADOFT TAPE10      1
  VADOFT TAPE10      2
ENDFILES
  START DATE
  END DATE
  NUMBER OF CHEMICALS
  PARENT OF 2
  PARENT OF 3
  WEIGHTS
  1.0     0.0
  0.0      1.0
ENDDATA
ECHO
TRACE
                                MC.INP
                                MET.INP
                                METx.INP
                                PRZM.INP
                                PRZMx.INP
                                VADF.INP
                                VADFx.INP

                                TIMES. OUT
                                TIMESx.OUT
                                PRZM.OUT
                                PRZMx.OUT
                                VADF.OUT
                                VADFx.OUT
                                MC. OUT
                                MC2.0UT

                                RESTART.PRZ
                                RESTARTx.PRZ
                                VFLOW.RST
                                VFLOWx.RST
                                VTRANS.RST
                                VTRANSx.RST
                                VADF10.TAP
                                VADFlOx.TAP

                                010181
                                311281
                                3
                                1
                                2
                                            ON

                                            OFF
NOTE: Three asterisks (***) denote a comment line and are ignored by the program
                                4-4

-------
4.1.2.3 Execution Supervisor (PRZM2.RUN) Input Guide

RECORD 1 OPTIONS     FORMAT      A18,6X,A56

LABEL (Col. 1-18)          EXECUTION STATUS (Col. 25-78)

PRZM                    ON or OFF     (the root zone model execution)
VADOFT                  ON or OFF     (the vadose zone model execution)
MONTE CARLO            ON or OFF     (Monte Carlo execution)
TRANSPORT              ON or OFF     (vadose zone transport execution)

RECORD 2- ZONES       FORMAT      A18,6X,I2

LABEL (Col. 1-18)          ZONE NUMBER (Col. 25-78)

PRZM ZONES              1 to 10         (total number of PRZM land zones)
VADOFT ZONES           1 to 10         (total number of VADOFT land zones)
ENDRUN                 	           (specifies end of OPTIONS and ZONE
                                       records)

RECORD 3- INPUT FILES FORMAT      A18,1X,I2,3X,A56

LABEL (Col. 1-18)           ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)

PATH                    	                      directory (optional)
METEOROLOGY           1 to 10                    filename
PRZM INPUT              1 to 10                    filename
VADOFT INPUT           1 to 10                    filename
MCIN                    	                      filename

RECORD  4  OUTPUT FILES FORMAT A18,1X,I2,3X,A56

LABEL (Col. 1-18)          ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)

PATH                    	                      directory (optional)
TIME SERIES              1 to 10                    filename
PRZM OUTPUT            1 to 10                    filename
VADOFT OUTPUT          1 to 10                    filename
MCOUT                   1 to 10                    filename
MCOUT2                  1 to 10                    filename

RECORD 5  SCRATCH  FILES FORMAT A18,1X,I2,3X,A56

LABEL (Col. 1-18)          ZONE NUMBER (Col. 20-21) NAME (Col. 25-78)

PATH                    	                      directory (optional)
PRZM RESTART           1 to 10                    filename
VADOFT FLOW RESTART     1 to 10                    filename
VADOFT TRANS RESTART  1 to 10                    filename
VADOFT TAPE            1 to 10                    filename
ENDFILES                	(spectiles end of file name records)
                                   4-5

-------
RECORD 6  GLOBAL RECORDS FORMAT      A18,1X,3I2

LABEL (Col.  1-18)         VALUE (Col. 20-25)

START DATE            ddmmyy         (starting day, month, year)
END DATE               ddmmyy         (ending day, month, year)
NUMBER OF CHEMICALS 1 to 3           (number of chemicals)
PARENT OF 2            1               (parent of the second chemical if TRANS-
                                       PORT=ON and if more than one chemi-
                                       cal)
PARENT OF 3            1 or 2           (parent of third chemical if TRANS-
                                       PORT=ON and if more than one chemi-
                                       cal)
WEIGHTS                              (indicates next values are weights)

      NOTE: enter next lines only if PRZM or VADOFT have multiple zones.
             Enter a line for every increasing PRZM zone containing a frac-
             tional weight to each VADOFT zone. FORMAT 10(F8.2)

1.0   0.0     (PRZM zone 1 weight to VADOFT zone 1 and 2)
0.0   1.0     (PRZM zone 2 weight to VADOFT zone 1 and 2)
ENDDATA                	           (specifies end of GLOBAL data)

RECORD 7  DISPLAY RECORDS FORMAT     A18,6X,A56

LABEL (Col.  1-18)          VALUE (Col. 25-78)

ECHO                     1 to 9         (amount output increasingly displayed to
                                       the screen and to files)
TRACE                    ON or OFF    (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
Echo of line being read from
1 2
x X
X





data
input
3
X
X
X






4
X
X
X
X





5
X
X
X
X
X




6
X
X
X
X
X
X



7
X
X
X
X
X
X
X


8
X
X
X
X
X
X
X
X
X
Echo of image being read from input
9
X
X
X
X
X
X
X
X
X
X
                                   4-6

-------
  4.1.3 PRZM Input File

  The PRZM-2 model requires a PRZM input file if the PRZM option is specified "ON" in
  the execution supervisor file. The following page shows an example PRZM input file with
  various options implemented as a reference.


4.1.3.1 Example PRZM.INP input file for PRZM-2

3 CHEMICALS, 2 HORIZONS, EROSION, IRRIGATION, PRZM INPUT FOR ZONE 1
HYDROLOGY PARAMETERS (CROP DATA FROM USDA NO.283 HANDBOOK)
      0.72
      9.6
     15.1
      1
      0.15

      1
      1
 110582
     0.00
     9.7
    14.5

     0.14

     0.15
 2
12.2
12.5

 1.0
15.000
13.61
11.3

 2.0
                                       1
                                       5.4
                                       9.5

                                       5.8
3
1
9.0
5.5
15.0    80.000
                86  78  82  0.1  0.1  0.1  60.0
300982  151082
PESTICIDE TRANSPORT AND ^RS^(SffOiRMM36N AND APPLICATION PARAMETERS
ALDICARB
 120582         0
 120682         0
      1         1
SOILS PARAMETERS
     45.0       0.3
      4.3E3     O.OEOO
      3         0.25
              0
       ATRAZINE
              2.5
              2.5
            CARBOFURAN
        2.5
        2.5
          2.5
          2.5
                                              1.00
                                              1.00
                                    0
     1.00 1.00
     1.00 1.00
                             0
                                    5.5E-3
                          1     1
                          5.5E-7
                       000
                       2.5E-7  O.OEOO   5.5E-7
                       0.55     .78
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
                                              0.0  0.0 0.0
                                              0.014 0.0000.023
                                               .1  1.    .3
                                              0.0
                        0
2
1




2




0
VATR
2
,FLX1
INFL

15.0
0.020
0.5
8.3
0.000
30.0
0.020
2.5
8.3
0.000
0
YEAR
YEAR
TSER
TCUM

1.45 0.233
0.000 0.000
.233 .050
10.0 60.0
0.000 0.000
1.45 0.233
0.000 0.000
.233 .050
10.0 60.0
0.000 0.000


0.0
0.014
1.0
0.0

0.0
0.007
0.1
0.0


1 PEST YEAR

1.0E05
31



0.0
0.007
0.1
0.0
0.0 0.0 0.0
0.007 0.0000.023
0. .1 0.
0.0
                                                   CONC
                                                   0.2300.000
                                                             0.0230.000
                                                   YEAR
SPECIAL ACTIONS
 010782 SNAPSHOT
                                        4-7

-------
4.1.3.2 PRZM input guide for PRZM-2
RECORD 1
col: 1-78
RECORD 2
col: 1-78
RECORD 3
col: 1-8
col: 9-16
col: 17-24
coll: 25-32
col: 33-40
col: 41-48
RECORD 4

col: 1-48
RECORD 5
col: 1-48
RECORD 6
col: 1-8
RECORD 7

col: 1-8
col: 9-16
col.k 17-24
FORMAT A78
TITLE: label for simulation title.
FORMAT A78
HTITLE: label for hydrology information title.
FORMAT 2F8.0,I8,F8.0,2I8
PFAC: pan factor used to estimate daily evapotranspiration.
SFAC: snowmelt factor in cm/degrees Celsius above freezing.
IPEIND: pan factor flag. 0 = pan data read, 1 = temperature
data read, 2 = either available used.
ANETD: minimum depth of which evaporation is extracted
(cm).
INICRP: flag for initial crop if the simulation date is before
the emergence date, (see record 10). 1 = yes, 0 = no.
ISCOND: surface condition of initial crop if INICRP = 1.1 =
fallow, 2 = cropping, 3 = residue.
Only if IPEIND = 1 or 2 (see record 3).
FORMAT 6F8.0
DT: monthly daylight hours for January - June.
Only if IPEIND = 1 or 2 (see record 3).
FORMAT 6F8.0
DT: monthly daylight hours for July - December.
FORMAT 18
ERFLAG: flag to calculate erosion. 1 = yes, 0 = no.
Only if ERFLAG = 1 (see record 6).
FORMAT 5F8.0
USLEK universal soil loss equation (K) of soil erodibilty.
USLELS: universal soil loss equation (LS) topographic factor.
USLEP: universal soil loss equation (P) practice factor.
                                       4-8

-------
 col: 25-32        AFIELD:         area of field or plot in hectares,

 col: 33-40        TR:             average duration of rainfall produced by storms (hrs).

RECORD 8      FORMAT       18

 col: 1-8          NDC:            number of different crops in the simulation (1 to 5).

RECORD 9      Repeat this record up to NDC (see record 8).

                 FORMAT       I8,3F8.0,I8,3(1X,I3),3(1X,I3),2F8.0

                                 crop number of the different crop.

                                 maximum interception storage of the crop (cm).

                                 maximum rooting depth of the crop (cm).

                                 maximum areal coverage of the canopy (percent).

                                 surface condition of the crop after harvest date (see
                                 record 11). 1 =  fallow, 2 = cropping, 3 = residue.

                                 runoff curve numbers of antecedent moisture condi-
                                 tion 11 for fallow, cropping,  residue (3 values).
                                 universal soil loss cover management factors for
                                 fallow, cropping ,residue (C value).  Required if ERFL-
                                 AG = 1 (see record 6) else set to 0.0 (3 values).

                 WFMAX        maximum dry weight of the crop at full canopy  (kg
                                 mi). Required if FAM = 3 (see record 16) else set to
                                 0.0.

                 HTMAX:         maximum canopy height at maturation date (cm) (see
                                 record 11).

                 FORMAT       18

                 NCPDS:         number of cropping periods (sum of NDC for all
                                 cropping dates in record 11).
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 42-52
col: 54-64
ICNCN:
CINTCP:
AMXDR:
COVMAX:
1CNAH:
CN:
USLEC:
 col: 65-72



 col: 73-80


RECORD 10

 col: 1-8
RECORD 11     Repeat this record up to NCPDS (see record 10).
 col: 3-4

 col: 5-6

 col: 7-8

 col: 11-12
                 FORMAT        2X,3I2,2X,3I2,2X,3I2,I8

                 EMD:            integer day of crop emergence,

                 EMM:            integer month of crop emergence.

                 IYREM:          integer year of crop emergence.

                 MAD:            integer day of crop maturation.
                                        4-9

-------
 col: 13-14

 col: 15-16

 col: 19-20

 col: 21-22

 col: 23-24

 col: 25-32

RECORD 12

 col: 1-80

RECORD 13

 col: 1-8




 col: 9-16



 col: 17-24
RECORD 14
col: 1-60
RECORD 15

col: 3-4
col: 5-6
col: 7-8
col: 9-16
FORMAT
PSTNAM:
Repeat this
FORMAT
APD:
APM:
IAPYR:
WINDAY:
 col: variable
MAM:           integer month of crop maturation.

IYRMAT:        integer year of crop maturation.

HAD:            integer day of crop harvest.

HAM:           integer month of crop harvest.

IYRHAR:        integer year of crop harvest.

INCROP        crop number associated with NDC (see record 8).

FORMAT       A78

PTITLE:         label for pesticide title.

FORMAT       318

NAPS:           total number of pesticide applications occuring at
                 different dates (1 to 50). Note: if two or more pesti-
                 cides are applied on the same date then NAPS = 1
                 for that day.

NCHEM:         number of pesticide(s) in the simulation.  This value
                 should equal the number in the execution supervisor
                 file(l to 3).

FRMFLG:        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 =
                 yes,  0 = no.

                 3A20

                 names of pesticide (s) for output titles.

             record up to NAPS (see record 13).

                 2X,3I2,I8,6F8.0

                 integer target  application day.

                 integer target  application month.

                 integer target  application year.

                 number of days in which to check soil moisture val-
                 ues following the target date for ideal pesticide (s)
                 applications. Required if FRMFLG = 1 else set to 0.

DEPI:           depth of the pesticide(s) application (cm). Note:
                 DEPI should be entered in the same order as in
                 PSTNAM (record  14) if NCHEM is greater than one.
                                        4-10

-------
 col: variable



RECORD 16

 col: 1-8




 col: 9-16



 col: 17-24


RECORD 17



 col: 1-8


 col: 9-16

 col: 17-24


RECORD 18

 col: 1-78

RECORD 19

 col: 1-8



 col: 9-16




 col: 17-20


 col: 21-24
TAPP:           total application of the pesticide(s) (kg ha:l). Note:
                 TAPP should be entered in the same order as in
                 PSTNAM (record 14) if NCHEM is greater than one.

FORMAT       2I8,F8.0

FAM:            foliar application model flag. 1 = pest, application to
                 soil only, 2 = linear pesticide foliar application based
                 on crop canopy, 3 = pesticide foliar application using
                 nonlinear exponential filtration.

IPSCND:         condition for disposition of foliar pesticide after har-
                 vest. 1 = surface applied,  2 = complete removal, 3 =
                 left alone. Required if FAM=2 or 3.

FILTRA:         filtration parameter. Required if FAM = 3 else set to
                 0.0.

Only if FAM=2 or 3, repeat this record up to NCHEM.

FORMAT       3F8.0

PLVKRT        pesticide volatilization decay rate on plant foliage
                 (days-1).

PLDKRT:        pesticide decay rate on plant foliage (days-1).

FEXTRC:        foliar extraction coefficient for pesticide washoff per
                 centimeter of rainfall.

FORMAT       A78

STITLE:         label for soil properties title.

FORMAT       2F8.0,9I4

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).

UPTKF:          plant uptake factor. 1 = uptake is equal to transpira-
                 tion * dissolved phase concentration, 0 = no uptake is
                 simulated, .001 to .99 = uptake is a fraction of tran-
                 spiration * dissolved phase concentration.

BDFLAG        bulk density flag. 1 = mineral value  entered, 0 =
                 apparent bulk density known and entered in record
                 33.
THFLAG        field capacity and wilting point flag. 1 = calculated
                 by the model, 0 = water contents are entered.
                                        4-11

-------
 col: 25-28


 col: 29-32

 col: 33-36

 col: 37-40


 col: 41-44

 col: 45-48


 col: 49-52

RECORD 20
KDFLAG:


HSWZT:

MOC:

IRFLAG:


ITFLAG:

IDFLAG:


BIOFLG:

OnlyifBIOFLG

FORMAT
col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
RECORD 21

col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
AM:
AC:
AS:
AR:
KE:
OnlyifBIOFLG
FORMAT
KSM:
KCM:
KC:
MRS:
KR:
soil/pesticide adsorption coefficient. 1 = calculated by
the model, 0 = KD value entered in record 36.

drainage flag.  1 = restricted, 0 = free draining,

method of characteristics flag. 1 = yes, 0 = no.

irrigation flag. 0 = no, 1 = year round, 2 = during
cropping period only.

soil temperature simulation flag. 1 = yes, 0 = no.

thermal conductivity and heat capacity flag. 1 = yes,
0 = no.

biodegradation flag. 1 = yes, 0 = no.

= 1 (see record 19).

5F8.0

maintenance coefficient of the metabolizing X& popu-
lation (day-"]).

maintenance coefficient of the co-metabolizing X^
population (day:l).

maintenance coefficient of the sensitive X, population
(day:l).

maintenance coefficient of the non-sensitive Xj popu-
lation (day:l).

average enzyme content of the X;. population (dimen-
sionless).

= 1 (see record 19).

7F8.0

saturation constant of the metabolizing X^ popula-
tion with respect to pesticide concentration.

saturation constant of the metabolizing Xs popula-
tion with respect to carbon concentration.

saturation constant of the co-metabolizing X> popula-
tion.

saturation constant of the sensitive X^ population,

saturation constant of the non-sensitive Xj popula-
tion.
                                        4-12

-------
 col: 41-48

 col: 49-56

RECORD 22



 col: 1-8

 col: 9-16


 col: 17-24

 col: 25-32

 col: 33-40


 col: 41-48


RECORD 23



 col: 1-8


 col: 9-16
KIN:

KSK:

OnlyifBIOFLG

FORMAT

KLDM:

KLDC:


KLDS:

KLDR:

KL1:


KL2:


OnlyifBIOFLG

FORMAT

USM:


UCM:
 col: 17-24        MUC:


 col: 25-32        us:


 col: 33-40        UR:


RECORD 24
 col: 1-8
OnlyifBIOFLG

FORMAT

YSM:
inhibition constant (mg g:1 dry soil).

carbon solubilization constant (day-1).

=1 (see record 19).

6F8.0

death rate of the metabolizing Xg population (day-').

death rate of the co-metabolizing Xj population
(day:l).

death rate of the sensitive J^ population (day:l).

death rate of the non-sensitive ^ population (day-').

second order death rate of the X^ population (mg g:l
day-\).

dissociation constant of the enzyme substrate com-
plex (day-1),

= 1 (see record 19).

5F8.0

growth rate of the metabolizing Xg, population with
respect to pesticide concentration (day-1).

specific growth rate of the metabolizing X^ popula-
tion with respect to carbon concentration
(day-').

specific growth rate of the co-metabolizing Xj popula-
tion (day-1),

specific growth rate of the sensitive 2%, population
(day-1).

specific growth rate of the non-sensitive X^ popula-
tion.

= 1 (see record 19).

5F8.0

true growth yield of the metabolizing EQ population
with respect to pesticide concentration (rngfdry wt.)/-
mg).
                                        4-13

-------
col: 9-16
YCM:
true growth yield of the metabolizing X^ population
with respect to carbon concentration (mgfdry wt.)/m-
g).
col: 17-24
col: 25-32
col: 33-40
RECORD 25
col: 1-8
col: variable
col: variable
RECORD 26

col: 1-8
col: 9-16
col: 17-24
col: 25-32
RECORD 27

col: 1-8
col: 9-16
col: 17-24
YC:
YS:
YR:
FORMAT
DAIR:
HENRYK:
ENPY:
OnlyiflRFLAG
FORMAT
IRTYP:
PLEACH:
PCDEPL:
RATEAP:
OnlyiflRFLAG
FORMAT
QO:
BT:
ZRS:
true growth yield of the co-metabolizing X^ 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).
7F8.0
diffusion coefficient for the pesticide (s) in the air.
Only required if HENRYK is greater than 0 else set
to 0.0
henry's law constant of the pesticide (s) for each NCH-
EM.
enthalpy of vaporization of the pesticide (s) for each
NCHEM.
= 1 or 2.
I8,3F8.0
type of irrigation. 1 = flood irrigation, 2 = furrow, 3
= over canopy, 4 = under canopy sprinkler.
leaching factor as a fraction of irrigation water depth.
fraction of water capacity at which irrigation is ap-
plied.
maximum rate at which irrigation is applied (cm
hr-i),
= 1 or 2 and IRTYP = 2.
7F8.0
flow rate of water entering the heads of individual
furrows (ni? s%
bottom width of the furrows (m) .
slope of the furrow channel walls
                                  (horizontal/vertical).
                                        4-14

-------
 col: 25-32
SF:
col: 33-40
col: 41-48
col: 49-56
EN:
X2:
XFRAC:
RECORD 28



 col: 1-8


 col: 9-16

RECORD 29



 col: 1-8



 col: variable


RECORD 30



 col: 1-60

 col: 61-65


 col: 66-70


RECORD 31



 col: 1-60
slope of the furrow channel bottom (verti-
cal/horizontal) .

Manning's roughness coefficient for the furrow.

length of the furrow (m).

location in furrow where PRZM infiltration calcula-
tions are performed, as a fraction of the furrow
length (X2). If XFRAC = -1, average depths are used
in PRZM.
Only if IRFLAG = 1 or 2 and IRTYP = 2.

FORMAT       2F8.0
KS:
saturated hydraulic conductivity of the soil in which
furrows are dug (m s'-l).
HF:             green-amp infiltration suction parameter (m).

Only if KDFLAG = 1 (see record 19).

FORMAT       I8,3F8.0

PCMC:          flag for which model is used to estimate KD (see
                record 36). 1 = mole fraction, 2 = mg liter:l, 3 = micr-
                omoles liter-1, 4 = KOC entered (dimensionless).

SOL:            pesticide(s) volubility entered according to PCMC flag
                above for each NCHEM.

Only if ITFLAG = 1 (see record 19).

FORMAT       1415

ALBEDO:        monthly values of soil  surface albedo (12 values).

EMMISS:        reflectivity of soil surface to longwave radiation (frac-
                tion) .

ZWIND:         height of wind speed measurement above the soil
                surface (m).

Only if ITFLAG = 1 (see record 19).

FORMAT       1215
BBT:
average monthly values of bottom boundary soil
temperatures in degrees Celsius (12 values).
                                       4-15

-------
RECORD 32
col: 1-8
RECORD 33

col: 1-8
col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
RECORD 34

col: 9-16
col: 17-24
col: 25-32
col: 33-40
col: 41-48
col: 49-56
RECORD 35
col: variable
FORMAT
NHORIZ:
Repeat records
FORMAT
HORIZN:
THKNS:
BD:
THETO:
AD:
DISP:
OnlyifBIOFLG
FORMAT
Q:
CM1:
Yl:
Y2:
Y3:
Y4:
FORMAT
DWRATE:
18
total number of horizons (minimum of 1).
33 38 in data sets up to NHORIZ.
I8JF8.0
horizon number in relation to NHORIZ.
thickness of the horizon.
bulk density if BDFLAG = 0 or mineral density if
BDFLAG= 1.
initial soil water content in the horizon (cm? cmty.
soil drainage parameter if HSWZT = 1, else set to 0.0
(day:l).
pesticide (s) hydrodynamic solute dispersion coeffi-
cient for each NCHEM.
= 1 (see record 19).
8X,5F8.0
average carbon content of the population.
dimensionless.
mineralizable carbon (mg g;l).
concentration of metabolizing microbial population
concentration of co-metabolizing microbial population
(mg g:l).
concentration of sensitive microbial population (mg
concentration of non-sensitive microbial population
i -\\
(mg g-1).
8X,9F8.0
dissolved phase pesticide (s) hydrolysis decay rate for
col: variable
DSRATE:
each NCHEM (day-1).

adsorbed phase pesticide (s) hydrolysis decay rate for
each NCHEM (day-1).
                                      4-16

-------
 col: variable
DGRATE:
vapor phase pesticide(s) decay rate for each NCHEM
RECORD 36

 col: 9-16

 col: 17-24

 col: 25-32

 col: 33-40

 col: variable
 col: 9-16

 col: 17-24


 col: 25-32


 col: 33-40


 col: 41-48


RECORD 38
 col: 9-16

 col: 17-24


 col: 25-32
FORMAT

DPN:

THEFC:

THEWP:

OC:

KD:
Note: set DWRATE and DSRATE equal to simulate
lumped first-order degradation.

8X7F8.0

thickness of compartments in the horizon (cm).

field capacity in the horizon (cm? cm3),

wilting point in the horizon (cm? cna^),

organic carbon in the horizon (percent).

pesticide (s) partition coefficient for each NCHEM.
Required if KDFLAG = 0, else set to 0.0 (cm$ g-1).
RECORD 37    Only if ITFLAG  = 1 (see record 19).
FORMAT       8X,5F8.0

SPT:            initial temp, of the horizon (celsius).

SAND:          sand content in the horizon. Required if THFLAG =
                 1, else set to 0.0 (percent).

CLAY          clay content in the horizon. Required if THFLAG =
                 1, else set to 0.0 (percent).

THCOND:       thermal conductivity of the horizon (cm-"t day:l).
                Required if IDFLAG = 0, else set to 0.0.

VHTCAP:        heat capacity per unit volume of the soil horizon (cmn3
                celstafr j. Required if IDFLAG = 0, else set to 0.0.

Only if NCHEM greater than 1.  Note: this record is used for
parent/daughter-relationship. Set to zero for simulating inde-
pendent parent chemicals.

FORMAT       8X3F8.0

DKRT12:        transformation rate for chemical 1 to 2.

DKRT13:        transformation rate for chemical 1  to 3. If NCHEM =
                2, set to 0.0.

DKRT23:        transformation rate for chemical 2  to 3. If NCHEM =
                2, set to 0.0.
                                      4-17

-------
RECORD 39
col: 1-8
col: 9-16
RECORD 40

col: 1-80
RECORD 41
col: 5-8
col: 13-16
col: 17-24
col: 29-32
col: 37-40
col: 41-48
col: 53-56
col: 61-64
col: 65-72
col: 73-76
RECORD 42
col: 1-8
col: 13-16
FORMAT
ILP:
CFLAG:
Only if ILP = 1
SCO divided by
per line. Enter
FORMAT
PESTR:
FORMAT
ITEM1:
STEP1:
LFREQ1:
ITEM2:
STEP2:
LFREQ2:
ITEMS:
STEPS:
LFREQ3:
EXMFLG:
FORMAT
NPLOTS:
STEP4:
218
flag for initial pesticide (s) levels before simulation
start date. 1 = yes, 0 = no.
conversion flag for initial pesticide (s) levels, 0 =
mgAgl, 1 = kgfflatfh Leave blank if ILP = 0.
(see record 39). NOTE: number of lines = THKN-
DPN(I) where I = HORIZN. Maximum of 8 values
this record in data sets for each NCHEM.
8F8.0
initial pesticide(s) levels.
3(4X,A4,4X,A4,I8),I4
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 as LFREQ1.
pesticide concentration output flag. CONC is insert-
ed or leave blank.
same as STEP1.
same as LFREQ1.
flag for reporting output to file for EXAMS model. 1
= yes, 0 = no. If ERFLAG = 0, EXMFLG is automati-
cally set to 0.
I8,4X,A4
number of times series plots (max. of 7).
Time step of output. 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.
      4-18

-------
RECORD 43




 col: 5-8


 col: 9-9



 col: 13-16


 col: 17-24

 col: 25-32


RECORD 44



 col: 1-78

RECORD 45




 col: 3-4

 col: 5-6

 col: 7-8

 col: 10-17

 col: 19-21


 col: variable
Only if NPLOTS is greater than 0 and ECHOLV greater than 2.
NOTE: repeat this record up to NPLOTS.
                4X,A4,A1,3X,A4,I8,F8.0

                name of plotting variable (see Table 4-1 on page 4-
                23).

                index to identify which pesticide if applicable. 1 =
                first chemical, 2 = second chemical, 3 = third chemi-
                cal.

                plotting mode, enter TSER (daily) or TCUM (cumu-
                lative) to plot to times series file.

                argument value for PLNAME (see table 4-1),

                constant with which to multiply for unit conversion.
                Leave blank for default to 1.0.
FORMAT

PLNAME:


INDX:



MODE:


IARG:

CONST:


Only if special actions are desired (see record 45).

FORMAT       A78

ATITLE:         label for special actions title,

Only if special actions are desired. Repeat this record for each
special action required (up to 7).

FORMAT       2X,3I2,1X,A8,1X,I3,3F8.0

SADAY:         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 (see page 4-23),
                                      4-19

-------
 SPACT      NACTS                SPACTS
BD                                HORIZON NO.   NEW VALUE(S) (F8.0)
CN                                CROP NO.       NEW VALUES (318)
DSRATE                            HORIZON NO.   NEW VALUE(S) (3F8.0)
DWRATE                           HORIZON NO.   NEW VALUE (S) (3F8.0)
KD                                HORIZON NO.   NEW VALUE (S) (3F8.0)
SNAPSHOT*                        	         	
USLEC     CROP NO.                               NEW VALUE (S) (3F8.0)


* Used to display pesticide concentration profile.
                                 4-20

-------
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
Sediment Flux
ESLS
FORTRAN
Variable

CINT
SW
SNOW
THETN

PRECIP
SNOWFL
THRUFL
AINF

RUNOF
CEVAP
ET
TDET
SEDL
Description

Interception sto
rage on canopy
Soil water storage
Snow pack storage
Soil water content

Precipitation
Snowfall
Canopy throughfall
Percolation into

Runoff depth
Canopy evaporation
Actual evapotrans-
piration from each
compartment
Total daily actual
evapotranspiration
Event soil loss
Units

cm
cm
cm
cm cm:l


cm day:l
cm day:!
cm day:l
each soil com-
partment

cm day:l
cm day:l
cm day:l
Tonnes
day\
Arguments
Required
(IARG)

None
1-NCOM2
None
1-NCOM2

cm day:lNome
None
None
1-NCOM2

cm day:lNone
None
1-NCOM2
None
None
Pesticide Storages

 FPST         FOLPST
 TPST
              PESTR
Foliar pesticide
storage

Total soil pesticide
storage in each soil
compartment
                                              gcmf
None


1-NCOM2
                                   4-21

-------
TABLE 4-1. VARIABLE DESIGNATIONS FOR PLOTTING FILES (continued)
Variable
Designation
(PLNAME)
SPST
Pesticide Fluxes
TPAP
FPDL
WFLX
DFLX
AFLX

DKFX
UFLX
Pesticide Fluxes
RFLX
FORTRAN
Variable
SPESTR
TAPP
FPDLOS
WOFLUX
DFFLUX
ADFLUX

DKFLUX
UPFLUX

ROFLUX
Description
Dissolved pesticide
storage in 'each soil
compartment
Total pesticide
application
Foliar pesticide
decay loss
Foliar pesticide
washoff flux
Individual soil
compartment pesticide
net diffusive flux
Pesticide advective
flux from each soil
compartment
Pesticide decay flux
in each soil compart-
ment
Pesticide uptake
flux from each soil
compartment

Pesticide runoff flux
Units
g cmfi
g cm?
day:l
g cmf
day-'
gcmf
day-'
g cmi
dayl
gcnDh?
day:l

gcmi
day:l
g cmi
day:l

—4
g cmt£
day-'
Arguments
Required
(IARG)
1-NCOM2
None
None
None
1-NCOM2
1-NCOM2

1-NCOM2
1-NCOM2

None
 EFLX

 RZFX



 TUPX
ERFLUX     Pesticide erosion flux   g cm%

RZFLUX     Net pesticide flux      g cntf
            past the maximum root day:l
            depth

SUPFLX     Total pesticide uptake g cm-z
            flux from entire soil    day;l
            profile
None

None



None
                                    4-22

-------
TABLE 4-1. VARIABLE DESIGNATIONS FOR PLOTTING FILES (concluded)
Variable
Designation
(PLNAME)
TDKF
PCNC

VFLX
FPVL
FORTRAN
Variable
SDKFLX
TCNC

PVFLUX
FPVLOS
Description
Total pesticide decay
flux from entire profile
Pesticide concentration
in canopy
Soil pesticide
volatilization flux
Foliar pesticide
volatilization flux
Units
g cm,-z
gcm3

day:l
gcml?
day:l
Arguments
Required
(IARG)
None
None

None
None
Soil Temperature

 STMP         SPT


Canopy Height

 CHGT         HEIGHT
Soil temperature in    °C
each soil compartment
Canopy height
cm
                1-NCOM2
               None
                                  4-23

-------
4.1.4 VADOFT Input File

The PRZM-2 model requires a VADOFT flow input file if VADOFT is specified "ON" in
the execution supervisor (PRZM2.RUN) file. Also if TRANSPORT SIMULATION is
specified "ON", VADOFT transport input must follow.

4.1.4.1 Example of VADOFT FLOW and TRANSPORT input file for PRZM-2


************* **********************pkOW******************* *******************
3 CHEMICAL, 2 HORIZON, 1 MATERIAL, VADOSE ZONE FLOW SIMULATION FOR
ZONE 1
                   11    110
61 1
20 2
1 1
0.0
1
2
1 20
2 40
O.OEOO
0 1
7.12E02
0.045EOO
YEAR
1
1
1

0.0

1
1
0




                    0
                    .01
              1
              1.0
0
1
1.0
1
1.0
0
                   1.0

                  50.0
                  80.0

              0.0
               .43EOO
              -l.OEOO
    O.OEOO
    O.OEOO
    0.145EOO
        0000
        O.OEOO
        2.68EOO 0.626EOO
                                  5  10
************* *********************rj^^ysq-gpQjjrp ******* ***************************
3 CHEMICAL, 2 HORIZON, 1 MATERIAL, VADOSE TRANSPORT SIMULATION FOR
ZONE 1
61
0

1
2
1
2
0.
0
1
1
0.0
0.0

20
40
OEOO
0
1
1



1
1
0

1.30E01
l.OOEOO
1
1
1
5

1
0







1
0
1.0


50.0
80.0
0
0

1






.0



O.OEOO

0.0


1
1
1




0
0


.0





2






1
1.0




O.OEOO
.0
0
0







0
0
.43EOO
.01EOO
.0
2.000E-2
1
10





1
1
.OOEOO
.0
O.OOEOO








0.
0.
7.00E-3






OEOO
OEOO
0.0

O.OOEOO




                                        COO   O.OEOO

                                        2.30E-2  O.OEOO
 YEAR
                                    4-24

-------
4.1.4.2 VADOFT

RECORD 1

 col: 1-80

RECORD 2

 col: 1-5

 col: 6-10

 col: 11-15


 col: 16-20


 col: 21-25


 col: 26-30


 col: 31-35


 col: 36-40


 col: 41-45


 col: 46-50
Input Guide for Flow

 FORMAT       A80

 TITLE: label for flow simulation title.

          FORMAT        1015

 NP:      total number of Vadoft nodal points (max of 100).

 NMAT: total number of different porous materials (maximum of 5).

 NONU: flag to indicate if initial condition is non-uniform. 1 = yes, 0
          = no.

 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.

 IMODL: flag to indicate if running flow or transport model. 1 =  flow,
          0 = transport. Set to 1 here.

 IKALL: time stepping index. 1 = backward difference, 0 = central
          difference. This flag is automatically set to 1 in FLOW.

 IMBAL: flag to indicate if mass balance computation is required. 1 =
          yes, 0 = no.

 INTSPC:  flag to indicate initial conditions for head values. 1 = hydrau-
          lic head, 0 = pressure head.

 IHORIZ: flag to indicate if flow direction is horizontal. 1 = yes, 0 = no.
          Set to 0 if PRZM is ON.

 ICHAIN:  flag to indicate if daughter products are  used. 1  = yes,  0 =
          no. Automatically set to 0 for flow.
RECORD 3      FORMAT

 col: 1-5          NITMAX
 col: 6-10
 col: 11-15
 col: 16-25
 INEWT:
 IRESOL:
 HTOL:
3I5,E10.3

maximum number of iterations per time step. Sug-
gested value of 20.

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.

maximum number of refinements each time step if
solution does not converge. Suggested value  of 1.

head tolerance for the nonlinear solution (length).
Suggested value of 0.01.
                                       4-25

-------
RECORD 4

 col:: 1-5




 col: 6-10


 col: 11-15



 col: 16-20



 col: 21-25



 col: 26-30




 col: 31-35



 col: 36-40


RECORD 5



 col: 1-10
FORMAT

KPROP:




ITSGN:


ITMARK:



NSTEP:



NVPR:



IOBSND:




NOBSND:



IPRCHK:


OnlyiflTRANS

FORMAT

TIMA:
815
flag to indicate relationship between relative perme-
ability 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 com-
putational 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).

flag to indicate if values are  printed at certain obser-
vation nodes. 1 = yes, 0 = no. NOTE: Echo level
must be greater than or equal to 6 in PRZM2 .RUN
file.

number of observation node(s) to be printed. NOBS-
ND must not be greater than  NP (see record 2). If
IOBSND = 0 then set NOBSND = 0.

flag to indicate if detailed information is generated in
the flow matrix. 1 = yes, 0 = no,

= 1 (see record 2).

4E10.3

initial time value  (t). Suggested value if PRZM is
ON: 0.0
 col: 11-20


 col: 21-30


 col: 31-40
TIN:


TFAC:


TMAX:
initial time step value (t). Suggested value if PRZM
is ON: 1.0. Omit if ITSGN = 0.

time step multiplier. Suggested value if PRZM is ON:
1.0. Omit if ITSGN = 0.

maximum time step value  allowed (t). Suggested
value if PRZM is ON: 1.0 Omit if ITSGN = 0.
                                       4-26

-------
RECORD 6      Only if ITGSN = 0 (see record 4) and ITRANS = 1.

                FORMAT       8E10.3

 col: 1-80        m e ( I ) :       time values corresponding to the number of time
                               steps where I = 1...31 (t). Input up to 8 values per
                               line.

RECORD 7      Only if ITMARK = 1 and ITRANS = 1
 col: 1-5


 col: 6-15


 col: 16-25


RECORD 8



 col: 1-80



RECORD 9

 col: 1-5

RECORD 10



 col: 1-5

 col: 6-10

 col: 11-15


 col: 16-25

RECORD 11

 col: 1-10
FORMAT       I5,2E10.3

ITMGEN:        flag to indicate if backup file marker time values are
                used, 1 = yes, 0 = no.

STMARK:        starting marker time value (t). If PRZM and TRAN-
                SPORT are ON, set to 0.0.

DTMARK:        marker time value increment (t). If PRZM and TRA-
                NSPORT are ON, set to 1.0.

Only if ITRANS = 1, ITMARK = 1 and ITMGEN = 0.

FORMAT       8E10.3

TMFOMT:       output marker file time values (t) corresponding to
                TMVEC(I) (see record 6).  Input up to 8 values per
                line,

FORMAT       15

NLAYRG:        number of soil horizons to be discretized.

Repeat this record up to NLAYRG (see record 9).

FORMAT       3I5,E10.3

ILAYR:          horizon number in relation to NLAYRG,

NELM:          number of finite elements in ILAYR.

IMATL:          porous material number related to NMAT (see record
                2) in ILAYR.

THL:            thickness of the horizon (ILAYR).

FORMAT       EIO.3,15

CHINV:          default initial values of pressure (1) or hydraulic head
                (m 1s) for nodes in the matrix.
                                     4-27

-------
 col: 11-15



RECORD 12

 col: 1-5


 col: 6-10


 col: 11-20



 col: 21-30




 col: 31-35



 col: 36-40



 col: 41-50


 col: 51-60
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.

FORMAT        2I5,2E10.3,2I5,2E10.3

IBTND1 :         type of boundary condition for the first node. 1 =
                 pressure head, 0 = water flux.

IBTNDN:         type of boundary condition for the last node. 1 =
                 pressure head, 0 = water flux.

VALND1:         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.

VALNDN:        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.

ITCND1 :         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.

ITCNDN:         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.

FLX1:            fluid flux injected into the first node (I3t). Automati-
                 cally set to 0.0 for FLOW if PRZM is ON.

FLXN:           fluid flux injected into the last node (I3t). Automati-
                 cally set to 0.0 for FLOW if PRZM is ON.
RECORD 13     Repeat this record up to NMAT (see record 2).

                 FORMAT       4E10.3

 col: 1-10         PROP1:          saturated hydraulic conductivity of the material (use
                                 cm day! 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.
                                       4-28

-------
RECORD 14

RECORD 16



 col: 1-10


 col: 11-20



 col: 21-30


 col: 31-40


 col: 41-50



RECORD 16
Omit for FLOW simulation.

Repeat this record up to NMAT if KPROP = 1.

FORMAT       6E10.3

FVAL1:         residual water phase saturation of the material (re-
                sidual water content / saturated water content).

FVAL2:         parameter n of the relative permeability versus satu-
                ration relationship. Suggested value of 0.0 or nega-
                tive value.

FVAL3:         leading coefficient of the saturation versus capillary
                head relationship (alpha).

FVAL4:         power index of the saturation versus capillary head
                relationship (beta).

FVAL5:         power index of the saturation versus capillary head
                relationship (gamma). Suggested value of 1.- (l./FV-
                AL4).

Repeat records 16-19 in data sets up to NMAT if KPROP - 0.

FORMAT       15
col: 1-5
RECORD 17

col: 1-10
col: 11-20
col: 21-30
col: 31-40
NUMK
Only if KPROP
FORMAT
SMV1:
PKRW1:
SMV2:
PKRW2:
number of entry pairs of relative
saturation of the material,
= 0.
8E10.3
permeability and


value of water phase saturation for point 1 of the
entry pairs related to NUMK.
value of relative permeability (I2)
entry pairs related to NUMK.
etc.
etc.
for point 1 of the


RECORD 18
 col: 1-5
Only if KPROP = 0.

FORMAT       15
NUMP:
number of entry pairs of pressure head versus satu-
ration values for the material.
                                      4-29

-------
RECORD 19    Only if KPROP = 0.

col: 1-10
col: 11-20
FORMAT
SSWV1:
HCAP1:
 col: 21-30

 col: 31-40

RECORD 20
 col: 1-5


 col: 6-15


RECORD 21

RECORD 22

RECORD 23

RECORD 24
SSWV2:

HCAP2:
                                8E10.3

                                value of water phase saturation for point 1 of the
                                entry pairs related to NUMP.

                                value of the pressure head (1) for point 1 of the entry
                                pairs related to NUMP.
etc.

etc.
OnlyifNONU = l.
NOTE: enter next two variables sequentially for every non-
default node (CNPIN).

FORMAT       5(I5,E10.3)

N:              non-default node number relative to CNPIN (see
                record 11).

PINT:           non-default initial value of pressure head (1) or hy-
                draulic head (m I3) of the node number (n).

Omit for FLOW simulation.

Omit for FLOW simulation.

Omit for FLOW simulation.

Only if ITCND1 = 1 and PRZM is OFF.

FORMAT       15
 col: 1-5


RECORD 25



 col: 1-80
NTSNDH1:       number of selected time values of pressure head or
                water flux for transient simulation at first node.

Only if ITCND1 = 1 and PRZM is OFF.

FORMAT       8E10.3
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,
                                     4-30

-------
RECORD 26



 col: 1-80



RECORD 27

RECORD 28



 col: 1-5


RECORD 29



 col: 1-80



RECORD 30



 col: 1-80



RECORD 31

RECORD 32



 col: 1-80


RECORD 33

 col: 1-4
Only if ITCND1 = 1 and PRZM is OFF.

FORMAT       8E10.3
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.
Omit for FLOW simulation.

Only if ITCNDN =1 and PRZM is OFF.

FORMAT       15

NTSNDH2:       number of selected time values of pressure head or
                water flux for transient simulation at the last node.

Only if ITCNDN = 1 and PRZM is OFF.

FORMAT       8E10.3

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.

Only if ITCNDN = 1 and PRZM is OFF.

FORMAT       8E10.3
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.
Omit for FLOW simulation.

OnlyifIOBSND = l.

FORMAT       1615

NDOBS:         increasing sequential numbers of observation nodes.
                Enter up to 16 per line up to NOBSND (see record 4).

FORMAT       A4

OUTF:           output time step for printing. Enter DAY for daily,
                MNTH for monthly, YEAR for yearly.
                                    4-31

-------
4.1.4.3 VADOFT Input Guide for TRANSPORT


RECORD 1      FORMAT       A80

 col: 1-80         TITLE:          label for transport simulation title.

RECORD 2      FORMAT        1015

 col: 1-5          NP:             total number of Vadoft nodal points.

 col: 6-10         NM-AT:          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.

 col: 16-20        ITRANS:        flag to indicate if running in transient or steady-
                                 state. Must be set to 1 if PRZM is ON. 1 = tran-
                                 sient, 0 = steady-state.

 col: 21-25        IMODL:         flag to indicate if running flow or transport model. 1
                                 = flow, 0 = transport. Set to 0 here.

 col: 26-30        KALL:           time stepping index. 1 = backward difference, 0 =
                                 central difference. This flag is automatically set to 1
                                 for steady-state simulation.

 col: 31-35        IMBAL:         flag to indicate if mass balance computation is re-
                                 quired. 1 = yes, 0 = no.

 col: 36-40        INTSPC:        flag to indicate initial conditions for head values. 1 =
                                 hydraulic head, 0 = pressure  head. Automatically set
                                 to 0 for transport.

 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.
RECORD 3

RECORD 4

 col: 1-5



 col: 6-10
Omit for transport simulation.

FORMAT        815
KPROP:
ITSGN:
flag to indicate relationship between relative perme-
ability 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,
                                       4-32

-------
 col: 11-15       ITMARK:         flag to indicate if output time values differ from com-
                                 putational time values (see records 6 and 7). 1 = yes,
                                 0 = no.

 col: 16-20       NSTEP:          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).

 col: 21-25       NVPR:           value of which time step to output nodal velocities.
                                 When NVPR = n,  then output is printed.  Must be
                                 from 1 up to 31 (days).

 col: 26-30       IOBSND:         flag to indicate if values are printed at certain obser-
                                 vation nodes. 1 = yes, 0 = no. NOTE: Echo level
                                 must be greater than or equal to 6 in PRZM2.RUN
                                 file.

 col: 31-35       NOBSND:        number of observation node(s) to be printed. NOBS-
                                 ND 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.

RECORD 5     Only if ITRANS - 1 (see record 2).

                FORMAT        4E10.3
 col: 1-10


 col: 11-20


 col: 21-30


 col: 31-40


RECORD 6



 col: 1-80
TIMA


TIN:


TFAC:


TMAX:
initial time value (t). Suggested value if PRZM is
ON: 0.0

initial time step value (t). Suggested value if PRZM
is ON:  1.0. Omit if ITSGN = 0.

time step multiplier. Suggested value if PRZM is ON:
1.0. Omit if ITSGN = 0.

maximum time step value allowed (t). Suggested
value if PRZM is ON: 1.0 Omit if ITSGN = 0.
Only if ITGSN = 0 (see record 4) and ITRANS = 1.

FORMAT       8E10.3

TMVEC(I):       time values corresponding to the number of time
                steps where I = 1...31 (t). Input up  to 8 values per
                line.
                                      4-33

-------
RECORD 7      Only if ITMARK = 1 and ITRANS = 1

                FORMAT        I5,2E10.3

 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 TRAN-
                                SPORT are ON, set to 0.0.

 col: 16-25       DTMARK:        marker time value increment  (t). If PRZM and TRA-
                                NSPORT are ON, set to 1.0.

RECORD 8      Only if ITRANS = 1, ITM.ARK = 1 and ITMGEN = 0.

                FORMAT        8E10.3

 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

 col: 1-5          NLAYRG:        number of soil horizons to be discretized,


RECORD 10    Repeat this record up to NLAYRG (see record 9).

                FORMAT        3I5,E10.3

 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

 col: 1-10


 col: 11-15



RECORD 12

 col: 1-5
FORMAT       E10.3,I5 Repeat for each NCHEM.

CHINV:          default initial values of concentration (m I3) for nodes
                in the matrix.

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.

FORMAT       2I5,2E10.3,2I5,2E10.3

IBTND1:         type of boundary condition for the first node. 1 =
                concentration, 0 = solute flux.
                                     4-34

-------
 col: 6-10


 col: 11-20



 col: 21-30




 col: 31-35



 col: 36-40
 col: variable


RECORD 15

RECORD 16

RECORD 17

RECORD 18

RECORD 19
IBTNDN:
VALND1:
VALNDN:
ITCND1:
ITCNDN:
type of boundary condition for the last node. 1 =
concentration, 0 = solute flux.

value of the concentration or solute flux at the frost
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.
col: 41-50
col: 51-60
RECORD 13
col: 1-10
col: 11-20
RECORD 14
col: variable
FLX1:
FLXN:
Repeat records
FORMAT
CPROP1:
CPROP2:
FORMAT
CPROP3:
fluid flux injected into the first node (1?
cally set to 0.0 if PRZM is ON.
fluid flux injected into the last node (1?
cally set to 0.0 if PRZM is ON.
13-14 in data sets up to NMAT.
2E10.3
longitudinal dispersivity of the material
effective porosity of the material.
3(2E10.3)
retardation coefficient for the material.
t). Automati-
t). Automati-
Enter this
CPROP4:
value up to NCHEM.

molecular diffusion for the material. Enter this val-
ue up to NCHEM.
Omit for TRANSPORT

Omit for TRANSPORT

Omit for TRANSPORT

Omit for TRANSPORT

Omit for TRANSPORT
                                     4-35

-------
RECORD 20
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
RECORD 21

col: 1-5
col: 6-15
col: 16-25
col: 26-35
RECORD 22
col: 1-5
col: variable
FORMAT
N:
PINT:
Repeat records
FORMAT
I:
VDFI:
SWDFI:
UWFI:
FORMAT
I:
CLAMDI:
5(I5,E10.3)
non-default node number relative to CNPIN (see
record 11).
non-default initial value of concentration (m 1?) of the
node number (n).
21-22 in data sets up to NMAT.
I5,3E10.3
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.
I5,6E10.3
porous material number in relation to NMAT.
decay coefficient of the material. Enter this value up
 col: variable


RECORD 23

 col: 1-5




 col: 6-10




RECORD 24



 col: 1-5
                to NCHEM.

CRACMP:        transformation mass fraction of the material. Enter
                this value up to NCHEM.

FORMAT        215

NVREAD:        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.

IVSTED:         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.

Only if ITCND1 = 1 and PRZM is OFF.

FORMAT        15

NTSNDH1:       number of selected time values of concentration or
                solute flux for transient simulation at first node.
                                     4-36

-------
RECORD 25



 col: 1-80



RECORD 26



 col: 1-80



RECORD 27



 col: 1-80


RECORD 28



 col: 1-5


RECORD 29



 col: 1-80



RECORD 30



 col: 1-80



RECORD 31



 col: 1-80
OnlyiflTCNDl

FORMAT

TMHV1:
= 1 and PRZM is OFF.

8E10.3

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.
Only if ITCND1 = 1 and PRZM is OFF.

FORMAT      8E10.3

HVTM1:
OnlyiflBTNDl

FORMAT

QVTM1:
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.

= 0 and PRZM is OFF.

8E10.3

volumetric fluxes corresponding to TMHV1 at the
first node. Enter 8 values per line up to NTSNDH1.
Only if ITCNDN =1 and PRZM is OFF.

FORMAT       15

NTSNDH2:
Only if ITCNDN

FORMAT

TMHV2:
number of selected time values of concentration or
solute flux for transient simulation at the last node.

= 1 and PRZM is OFF.

8E10.3

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.
Only if ITCNDN = 1 and PRZM is OFF.

FORMAT       8E10.3

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.
Only if ITCNDN = 1 and PRZM is OFF.

FORMAT        8E10.3

QVTM2:
volumetric fluxes corresponding to TMHV2 at the
last node. Enter 8 values per lineup to NTSNDH2.
                                    4-37

-------
RECORD 32     Only if IOBSND = 1.

                FORMAT       1615

 col: 1-80         NDOBS:         increasing sequential numbers of observation nodes.
                               Enter up to 16 per lineup 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.
                                    4-38

-------
4.1.5 MONTE CARLO Input File

The PRZM-2 model 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.

4.1.5.1 Example MONTE CARLO input file for PRZM-2
  Title
MONTE CARLO TEST INPUT
H* H* ^IXTi im V^ar1 r\f r-iinc1 orirl r>r\r-»fir1 o
Number of runs and confidence level
**
    •  * r  i  •  9°;°
    onte Carlo inputs
KOC1                      1
FIELD CAPACITY            1
WILTING POINT            1
ORGANIC CARBON          1
FIELD CAPACITY            2
WILTING POINT            2
ORGANIC CARBON          2
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
                                     800.1400.
                                               10.10000.
1 .316
1 ,150
1 1.30
1 .288
1 .143
1 .110
1 50.0
.130 0.050.60
.066 0.030.30
.870 0.015.00
.110 0.04 .540
.076 0.03 .030
.070 0.011.00
15.0 10.090.0
&
5.
1.
5.
5.
1
7
                                           CDFWRITE
                                           CDFWRITE
                                  WILTING POINT 1
                                  ORGANIC CARBON 1
                                  WILTING POINT 2
                                  ORGANIC CARBON 2
                                                               0.757
                                                               0.609
                                                               0.757
                                                               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.
                                   4-39

-------
4.1.5.2 MONTE
RECORD 1
col: 1-80
RECORD 2
col: 1-5
col: 6-15
RECORD 3

col: 1-20
col: 21-25
col: 26-30
col: 31-40
col: 41-50
col: 51-60
col:61-70
col: 71-80




RECORD 4
col: 1-3
RECORD 5
CARLO Input
FORMAT
TITLE:
FORMAT
NRUN:
PALPH:
Repeat this
cords.
FORMAT
PNAME:
IND1:
INDZ:
VAR1 :
VAR2:
VAR3:
VAR4:
VAR5:




FORMAT
ENDIT:
Guide
A80
label for Monte Carlo simulation title.
I5,F10.0
number of Monte Carlo runs (1 to 1000).
confidence level for percentile confidence bounds.
Entered as a percent(%). Default of 90.
record for number of inputs desired up to 50 re-
A20,2I5,5F10.0
Monte Carlo input variable name (up to 20 charac-
ters). See Table 4-2 on page 4-47.
integer index for horizon, application, or material.
See Table 4-2 on page 4-47.
zone number (1 to 10).
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
A3
enter "END" to indicate end of record 3
only if VAR5 = 7 (see record 3).
col: 1-5
NDAT:
number of data pairs in empirical cumulative distri-
bution (1 to 20).
                                            4-40

-------
RECORD 6




 col: 1-10

 col: 11-20


RECORD 7




 col: 1-20


 col: 21-25


 col: 26-30

 col: 31-50


 col: 51-70


 col: 71-75
only if VAR5 = 7 (see record 3). Note: repeat record 5 for every
time VAR5 =7.

FORMAT        2F10.0

DIST1:           value of quantile for data pair I where I = 1... .NDAT,
DIST2:
   cumulative probability for data pair I where 1 = 1
   NDAT.
repeat this record for number of outputs desired up to 10 re-
cords.

FORMAT       A20,2I5,2(A20),I5

SNAME:         Monte Carlo output variable name, See Table 4-2 on
                page 4-47.

IND1:           integer index for horizon, application, or material
                number. See Table 4-2 on page 4-47.

INDZ:           zone number (1 to 10).

SNAME2:        enter "CDF" to indicate if cumulative  distributions
                are plotted.

SNAME3:        enter "WRITE" to indicate if values are written as
                output for each Monte Carlo run (NRUN).

NAVG:          length of the averaging period  (in days) for output
                variables (1 to  5).
RECORD 8

 col: 1-3

RECORD 9
 col: 1-20

 col: 21-25


 col: 26-30

 col: 31-50
FORMAT

ENDIT:

onlyifVAR5
to half of the
desired.

FORMAT

NAME1:

IND1:


INDZ:

NAME2:
   A3

   enter "END" to indicate end of output variables.

= 1, 2, 5, or 6 note: this record may be repeated up
number of inputs in record 3 if correlation is
   A20,2I5,A20,2I5,F10.0

   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.
                                      4-41

-------
 col: 51-55       IND1:           same as IND1 above.

 col: 56-60       INDZ:           same as INDZ above.

 col: 61-70       CORK:          the value of the correlation coefficient for NAME1
                                and NAME2.

RECORD 10    FORMAT       A3

 col: 1-3         ENDIT:          enter "END" to indicate end of correlation inputs.
                                     4-42

-------
TABLE 4-2. MONTE CARLO INPUT AND OUTPUT LABELS
Parameter
Monte Carlo Label
Index
Random PRZM Model Inputs
Soil Bulk Density
Wilting Point (crn^ems)
Field Capacity (cm37cm3)
Organic Carbon Content (%)
Application Mass, Chem 1 (kg/ha)
Application Mass, Chem 2 (kg/ha)
Application Mass, Chem 3 (kg/ha)
Dispersion Coeff., Chem l((oni?/day)
Dispersion Coeff., Chem 2(cmt/day)
Dispersion Coeff., Chem 3(cmt/day)
Decay Rate in Water, Chem l(days:l)
Decay Rate in Water, Chem 2(6fe$$s~1))
Decay Rate in Water, Chem 3(daps"f)
Decay Rate in Vapor, Chem Ifdays1!)
Decay Rate in Vapor, Chem 2(days:1)
Decay Rate in Vapor, Chem
Decay Rate of Sorbed, Chem
Decay Rate of Sorbed, Chem
Decay Rate of Sorbed, Chem
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 (cmS/cm?)
Total Soil Pesticide, Chem 1  (kg/ha)
Total Soil Pesticide, Chem 2(kg7ha)
Total Soil Pesticide, Chem 3(kg/ha)
Infiltration Depth (cm)
Runoff Depth (cm)
Precipitation (cm)
Evapotranspiration
Flood or Furrow Irrigation  Depth
Runoff Flux, Chem 1 (kg/ha/day)
Runoff Flux, Chem 2 (kg/ha/day)
Runoff Flux, Chem 3 (kg/ha/day)
Erosion Flux, Chem 1 (kg/ha/day)
Erosion Flux, Chem 2 (kg/ha/day)
Erosion Flux, Chem 3 (kg/ha/day)
Decay Flux, Chem 1 (kg/ha/day)
Decay Flux, Chem 2 (kg/halday)
Decay Flux, Chem 3 (kg/halday)
Volat. Flux, Chem 1 (kg/ha/day)
Volat. Flux, Chem 2 (kg/ha/day)
BULK DENSITY             Horizon
WILTING POINT            Horizon
FIELD  CAPACITY            Horizon
ORGANIC CARBON          Horizon
APPLICATION 1             App.
APPLICATION 2             App,
APPLICATION 3             App.
DISPERSION 1               Horizon
DISPERSION 2               Horizon
DISPERSION 3               Horizon
WATER DECAY 1            Horizon
WATER DECAY 2            Horizon
WATER DECAY 3            Horizon
VAPOR DECAY 1            Horizon
VAPOR DECAY 2            Horizon
VAPOR DECAY 3            Horizon
SORBED DECAY 1           Horizon
SORBED DECAY 2           Horizon
SORBED DECAY 3           Horizon
HENRY'S CONSTANT 1
HENRY'S CONSTANT 2
HENRY'S CONSTANT 3
IRRIG LEVEL
APP YEAR                  App.
APP DAY                   App.
THETA                     Comp.
SOIL PESTICIDE 1.           Comp.
SOIL PESTICIDE 2           Comp.
SOIL PESTICIDE 3           Comp.
INFILTRATION
RUNOFF
PRECIPITATION
EVAPOTRANSPIRATION      Comp.
IRREG  DEPTH
RUNOFF FLUX 1
RUNOFF FLUX 2
RUNOFF FLUX 3
EROSION FLUX 1
EROSION FLUX 2
EROSION FLUX 3
DECAY FLUX 1
DECAY FLUX 2
DECAY FLUX 3
VOLAT. FLUX 1
VOLAT, FLUX 2
                                    4-43

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TABLE 4-2. MONTE CARLO INPUT AND OUTPUT LABELS (conclude]
Parameter
Monte Carlo Label            Index
Random VADOFT Model Inputs

Volat. Flux, Chem 3 (kg/ha/day)
Plant Flux, Chem 1  (kg/ha/day)
Plant Flux, Chem 2  (kg/ha/day)
Plant Flux, Chem 3  (kg/ha/day)
Root Zone Flux, Chem 1 (kg/ha/day)
Root Zone Flux, Chem 2 (kg/ha/day)
Root Zone Flux, Chem 3 (kg/ha/day)
Hydraulic Conductivity
Residual Saturation
Van-Genuchten Alpha
Van- Genuchten N
Decay Rate Chemical 1
Decay Rate Chemical 2
Decay Rate Chemical 3
Dispersion Coefficient, Chemical 1
Dispersion Coefficient, Chemical 2
Dispersion Coefficient, Chemical 3
Retardation, Chemical 1
Retardation, Chemical 2
Retardation, Chemical 3

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
VOLAT. FLUX 3
PLANT FLUX 1               Comp.
PLANT FLUX 2               Comp.
PLANT FLUX 3               Comp.
ROOT FLUX 1
ROOT FLUX 2
ROOT FLUX 3
HYDRAULIC CONDUC        Material
RESIDUAL SATURATION      Material
V-G ALPHA                 Material
V-G POWER N               Material
VADOFT DECAY 1            Material
VADOFT DECAY 2            Material
VADOFT DECAY 3            Material
VAD DISPC 1               Material
VAD DISPC 2                Material
VAD DISPC 3                Material
VAD RETARD 1.              Material
VAD RETARD 2              Material
VAD RETARD 3              Material
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                Node
VAD CONC 2                Node
VAD CONC 3                Node
                                   4-44

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                                    SECTION 5

                           PARAMETER  ESTIMATION


This section describes estimation of the parameters established in Section 4 b provide the
user with an aid in inputing 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 of obscure definitions are defined.

ECHO -  This value can be entered as an integer value (1-9) to controlthe 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 ENDDAT-
E) 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 transforma-
ion 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.
                                        5-1

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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 of fluxes 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 X^ population, This value specifies
the amount of energy required to maintain co-metabolizing (inhibited growth) microorgan-
isms.

AD - Soil water drainage rate. This value is required if HSWZT = 1. It is an empirical
constant and dependent on both soil type and the number of compartments (DPN(I)/THK-
NS(I), where I = number of horizons) to be simulated. Although there is limited experi-
ence using this option, three soils were evaluated for testing AD.  The analysis was
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). 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 5.11.

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 IQ 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 measurement of root depth from the land surface. For ranges
on 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.

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
                                        5-2

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through evaporation.  For soils with limited drainage, set ANETD  to 10 cm. Values for
free drainage soils are shown in Figure 5.2.

AR - Maintenance coefficient of the non-sensitive X,. population.   This parameter specifies
the energy to sustain non-sensitive (indifferent) microorganisms.

AS - Maintenance coefficient of the sensitive X= population.  This parameter specifies the
value of 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 vilues. Two  methods are
provided for estimating BD if site data are not available.  Method one requires percent
sand, clay and organic matter. The procedure from Rawls (1983) is used, to estimate BD
in (5.1):

      Method 1
                                    100.0
             BD=     	__	.	                     (5.1)
                       %OM         +       100,0 - %OM
                       OMBD                  MBD
      where
             BD = soil bulk density, g ami
             OM = organic matter-content of the soil, %
             OMBD = organic matter bulk density of the soil, g cm3
             MBD = mineral bulk density, g cm:5-

             Step 1. Locate the percent sand along bottom of Figure 5.10.
             Step 2. Locate the percent clay along side of Figure 5.10.
             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.

BBT - Bottom boundary soil temperatures.  BBT values for each month must be specified.
The BBT soil temperature for shallow core depths may 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 5.7,  may be used to estimate BBT.

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
                                        5-3

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are very specific and may be difficult to obtain for soil conditions. As an alternative,
estimates of biological parameters can be found in literature on 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.

CFLAG - Conversion flag for initial pesticide levels. This flag is valid when ILP = 1.  If
CFLAG = 0, then initial pesticide levels (PESTR) are in units of mgbjg-1. If CFLAG = 1,
then initial pesticide levels (PESTR) are in units of kgha:l. Leave CFLAG blank if ILP  =
0.

CINTCP - The maximum 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
(1980). Values for several major crops are provided in Table 5-4.

CM - Mineralizable carbon (mg g:l). 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 of antecedent moisture condition II. The interaction of
hydrologic groups (Figure 5.4) and land use treatment (cover) is accounted for by assign-
ing a runoff curve number (CN) for average soil moisture condition (AMC II) 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.

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 areal crop coverage. PRZM estimates crop ground cover
to a maximum value, COVMAX, by linear interpolation between emergence and maturity
dates.  As a crop grows, its ground cover increases thus  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 percent.

DAIR - Vapor phase diffusion coefficient. When Henry's law  constant (HENRYK) is
greater than zero, vapor phase diffusion is used to calculate equilibrium between vapor
and solution phases. Pick's first law defines the diffusion coefficient as the proportionality
between the chemical flux and the spatial gradient in its concentration  (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. (1983)  has concluded that the diffusion coefficient
will not show significant variations for different pesticides at a given temperature; they
recommend using a constant value of 0.43 m?day:l for all pesticides. This value is
recommended unless other chemical-specific data are available. Note that DAIR is
entered in cm? day-'h  The user should be sure to convert the above recommended value.

DEP1 - The depth(s) of pesticide incorporation. This variable is only needed if soil
application of a chemical is specified  (FAM=1). Typical depths are 5 to 10 centimeters,
Representative values for several soil application methods are given in Table 5-15.
                                         5-4

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DGRATE - Vapor phase degradation rate constant(s). Pesticides are degraded by
different mechanisms, and at different rates, depending upon whether they are in vapor,
liquid or absorbed phase (Streile 1984). A lumped first-order rate is assumed for DGRAT-
E. In general, a zero value of DGRATE 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 may be rapid and substantial immediately following
application to the land surface, especially in the case of hydrophobic or cationic organics
that sorb  to soil particles (Miller et al. 1987).

DISP - Dispersion of pesticide(s). The dispersion or "smearing out" of the pesticide as it
moves down in the profile is attributed to a combination of molecular diffusion and
hydrodynamic dispersion.  Molecular diffusion, D£, in soils will be lower than free-water
diffusion and has been estimated by Bresler (1973)

             Dm - m ae*3                                                       (5.2)
       where
             TSU = molecular diffusion in free water, cm? day-1
             a = soil constants having a range of 0.001 to 0.005
             b = soil constants having an approximate value of 10
             6 = volumetric water content, ami? att3

Hydrodynamic dispersion is more difficult to estimate because of its site-soil  specificity
and its apparent strong dependence upon water velocity. Most investigators  have
established an effective diffusion or dispersion coefficient that combines molecular and
hydrodynamic terms. Most notable among these is

             D = 0.6 + 2.93 Vl'11                                                  (5.3)

       where
             D =  effective dispersion coefficient, CVB£ day:l
             v = pore water velocity, cm day:l

by Biggar and Nielsen (1976). Note in equation 5.3 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
5.5. For these runs, the DISP parameter was set to 0.0. The influence of DISP superim-
posed on the numerical dispersion created by the model at a AX value of 5.0 cm is shown
in Figure  5.6. 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.
                                         5-5

-------
             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 re-
                duced to 1.0 cm to minimize numerical dispersion.
             5) The Biggar and Nielsen (1976) equation previously noted can be used to
                bound the values only should the need arise to increase dispersion
                beyond that produced by the numerical scheme.

If the user chooses the MOC algorithm to simulate advection transport, then numerical
dispersion will be eliminated and a typical value for field-observed data dispersion should
be entered. Use of the MOC algorithm will result in increased model execution time.

DKRT12,DKRT13,DKRT23- Transformation rate from a parent chemical (1 or 2) to a
daughter chemical (2 and/or 3), When multiple chemicals are specified in PRZM2.RUN,
either a parent/daughter relationship exists or the chemicals are independent (chosen by
the user).  For a parent/daughter relationship, DKRTxx is the mass fraction degrading
from parent x to daughter x. By setting DKRTxx to 0.0, the user is specifying that the
multiple chemicals (xx) are independent parents.

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 of surface-
applied chemicals. For the surface horizon, DPN values in the range of 0.5 to 2.0 cm are
recommended; a 1.0 cm vallue  for DPN is commonly used. Smaller values down to 0.1 cm
can be used  for highly- volatile compounds where volatilization is a major loss mechanism.
For subsurface soil horizons, DPN values  in the range of 5.0 to 30.0 cm are recommended
depending on the spatial resolution needed at lower depths.

DSRATK - Absorbed phase degradation rate constant(s). See DWRATE for guidance.
                                        5-6

-------
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.

DWRATE - Solution phase degradation rate constant(s). This rate constant contributes
to the disappearance of pesticide(s) through decay. For most cases, the same values
should be used for solution (DWRATE) and adsorbed (DSRATE) phases for all depths.
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. This is a 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 molel for lindane and  20.640 kcal
mole-'J for napropamide (Streile 1984), Chemical-specific values are needed for ENPY, but
it appears that a value of 20 kcal mole-1 is a reasonable first guess.

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 (ERFLAG = 1).

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 PRZM2EXA.Dxx where xx is the year of
PRZM simulation.

FAM - Foliar application model  flag. This flag specifies how the pesticide is applied to
foliage (if FAM = 2 or 3).

FEXTRC - Foliar washoff extraction coefficient. Washoff from plant surfaces is modeled
using a  relationship among rainfall, foliar mass of pesticide, and an extraction coefficient.
The parameter (FEXTRC) is the  required input parameter to estimate the flux of pesticide
washoff. Exact values are  varied and depend upon the crop, pesticide properties, and
application method. Smith and Carsel (1984) suggest that a value of 0.10 is suitable for
most pesticides.
                                         5-7

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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 3.3m? kg-i. Miller (1979)
suggested a value of 2.8 m kg-'t for pasture grasses.  Most of the variation appears to be
due to the vegetation and not the aerosol. FILTRA only applies if FAM=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 percent 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 (WTNDAY) after the target application date (API))  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 condi-
tions, execution is halted.

HENRYK -  Henry's constant is a ratio of a chemical's vapor pressure to its volubility. It
represents the equilibrium between the vapor and solution phases. 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.

HORKZN -  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 drain  age 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 ID FLAG = 0, the user must enter thermal


                                         5-8

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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.

1NCROP - The crop number associated with the number of different crops (NDC).
IN CROP 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.

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 FAM = 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 JS^ population. See KSM and KCM  for
further explanation.

KCM - Saturation constant of the metabolizing X^ 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.

KE - Average enzyme content of the Xj 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).
                                         5-9

-------
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 3$ 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 Xj population.

KL2 - Dissociation constant of the enzyme substrate complex.

KLDC - Death rate of the co-iaae!ted]HdIiaii|g]!% population.

KLDM - Death rate of the  mettrfhodlzziqgg^ population.

KLDR - Death rate of the non-sensitive X^ population.

KLDS - Death rate of the sensitive X, population.

KR -  Saturation constant of the non-sensitive KT 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 Xa 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 X, population. See KSM and KCM for further
explanation.

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-metabolizing SQ 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.

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


                                         5-10

-------
a parent-daughter relationship or multiple separate chemicals (determined by transforma-
tion 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.

NDG - 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 grow-n (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 =  960M/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 (1985)  and  Nielsen et al.
(1983) 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.
                                        5-11

-------
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.

PCMC - Flag for estimating distribution coefficients (KD). PRZM allows the user to
estimate the KD by multiplying the organic carbon partition coefficient I|JKQ derived from
the volubility (SOL)- PCMC is the flag for using one of four different models for estimat-
ing EQ The four models  are:

             PCMC1 Log IQ = (-0.54 * Log SOL) + 0.44
                     K^. = organic carbon distribution coefficient
                     where SOL = water volubility, mole fraction

             PCMC2 LogKSc= 3.64- (0.55 * Log SOL)
                     where SOL = water volubility, mg 1"-J

             PCMC3 Logl^ = 4.40- (0.557 * Log SOL)
                     where SOL = water volubility, micromoles 1-1

             PCMC4EOSOL
                      where SOL = K~, dimensionless
                                    oc»
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 5.1 for specific regions of the United States.

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 volatiliza-
tion 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:

       o Organochlorine 5.0 ±4.6
       o Organophosphorus 3.0 4 2.7
       o Carbamate 2.4 4 2.0
       o 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.
                                        5-12

-------
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. (1970) has shown that the disappearance
rate from leaf surfaces can be estimated  by a first-order kinetic approach. Similar
observations for first-order kinetics were  found for volatilization of 2,4-D iso-octyl  ester
from leaf surfaces by Grover et al. (1985). Volatilization losses of toxaphene and DDT
from cotton plants decreased exponentially with time and were linearly related to  the
pesticide load  on these plants (Willis et al, 1983). Table 5-17 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 volatiliza-
tion.

PSTN AM - 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).

Q - Average carbon content of the Xj 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.

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 temperat-
ure. Snow is considered any precipitation that falls when the air temperature is  below 0
degrees Celsius.  In areas where climatology prevents snow fall, SFAC should be set to 0.0.
Typical ranges for SFAC are provided in  Table 5-1.

SOL - Pesticide water volubility. By specifying a water volubility (SOL)  for pesticides, the
model can calculate the IQ and KD by using one  of the models specified for PCMC. SOL
must be entered according to the PCMC model selected. Table 5-19 on page 5-45 provides
pertinent values for selected pesticides for obtaining SOL. Methods are also available to
calculate KJJ. (SOL if PCMC=4). The  octanol-water distribution coefficient can be used for
calculating KJJJ. with a relationship to organic carbon (OC). Karickhoff et al. (1979)
proposed a relationship between ]§&, and KJJJ given by
          Log Km- 1.00 (Log Kea) -0.21                                            (5.4)

      where
             = octanol-water distribution coefficient (cm? g1^


                                        5-13

-------
         K& = 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.

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 corre-
sponding approximately to the bottom  boundary temperature (BBTj.

TAPP - Total pesticide(s) application.  For each pesticide and each application date, the
amount of pesticide is entered in kg-active ingredient ha:h Typical rates are included on
the product's registration label.

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 ofcal cm-l9C:l
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 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 em^) of a specific soil. Method two utilizes a soil texture matrix for estimating
soil water content if only the sand (%)  and clay (96) contents are known. Method three
provides mean field capacity and wilting points if only soil texture is known.

      Method  1- Rawls and Brakensiek  (1982)                                   (5.5)

         ©== =         a + [b * SAND(%)] + [c * CLAY(%)] + [d  * ORGANIC MATTER(%)]
                      + [e *  BULK DENSITY (g cms)!

      where

         ©= =        water retention cnA csfi§ for a given matric potential (field capacity
                     = -0.33 bar and wilting point = -15.0 bar)

         a-e = regression coefficients
                                        5-14

-------
          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 poten-
                      tial.
       Method 2

          Use Figure 5.8 for estimating the field capacity and Figure 5.9 for estimating
          the wilting point, given the percent sand and clay.

       Method 3

          Use Table 5-25 to locate the  textural class of the soil of choice. After locatin
          the textural class, read the mean field capacity and wilting point potentials
              , 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.

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 5.3 provides regionalized values for different areas in
the United States.

UPTKF - Plant uptake efficiency factor. This value provides for removal of pesticides by
plants. It is also a  function of the crop root distribution and the interaction of soil, water,
and the pesticide. Several approaches to modeling  the uptake of nutrients/pesticides have
been proposed ranging from process models that  treat the root zone system as a distribu-
tion sink of known density or strength to empirical approaches that assume a relationship
to the transpiration rate. Dejonckheere et al. (1983) reported the mass of uptake into
sugarbeets for the  pesticides aldicarb and thiofanox for three soils (sandy loam, silt loam,
and sandy clay loam). Mass removal expressed as a percentage of applied  material for
aldicarb on sandy loam, silt loam, and clay loam  ranged from 0.46 to 7.14%, 0.68 to 2.32%,
and 0.15 to 0.74%, respectively. For thiofanox, 2.78 to 20.22%, 0.81 to 8.70%, and 0.24 to
2.42% removals were reported for the respective  soils. Other reviews have suggested
ranges from 4 to 20% for removal by plants. Sensitivity tests conducted with PRZM
indicate an increase in the uptake by plants as the crop root zone (AMXDR) increases and


                                        5-15

-------
the partition coefficient (KD) decreases.  For highly soluble pesticides and for crop root
zones of greater than 120 cm, values of greater than 20% were simulated. For initial
estimates, a value of 1.0 for UPTKF is recommended.

USLEC - The universal soil loss cover management factor (C value). Values for USLEC
are dimensionless and range from 0.001  (well managed) to 1.0 (fallow or tilled condition).
One value for each of the three growing periods (fallow, cropping, and residue) is required.
Specific values can be calculated by Wischmeier and Smith (1978) or obtained from a local
SCS office. Generalized values are provided in Table 5-7.

USLEK - The universal soil loss equation (K) of soil erodibilty. This is a soil-specific
parameter developed by the USDA. Specific values can be obtained from the  local SCS
office. Approximate values are listed in Table 5-3.

USLELS - The universal soil loss equation (LS) topographic factor. This is a slope length
and steepness parameter developed by the USDA. The value is dimensionless and can be
estimated from Table 5-5.

USLEP - The universal soil loss equation (P) practice  factor. This value  is developed by
the USDA to describe conservative agricultural practices. Values are dimensionless and
range from 0.10 (extensive practices) to 1.0 (no supporting practices). Specific values can
be estimated in Table 5-6.

UCM -  Specific growth rate  of the metabolizing& population with respect to carbon
concentration.

UR - Specific growth rate of the non-sensitive 1%. population.

US - Specific growth rate of the sensitive X$, population.

USM - Specific growth rate of the metabolizing Xg, population with respect to pesticide
concentration.

VHTCAP - See THCOND for guidance.

WINDAY - An integer number of days. This specifies the number of days after the target
date (APD) that the code checks for ideal moisture conditions. For this value  to be valid,
FRMFLG must equal 1. WINDAY 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 mtf) 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.


                                        5-16

-------
Yl - Metabolizing (XQ microbial population.

Y2 - Co-metabolizing (3Q microbial population.

Y3 - Sensitive (2Q) microbial population.

Y4 - Non-sensitive (JQ microbial population.

YC - True growth yield of the co-metabolizing JS^ population.

YCM - True growth yield of the metabolizing& population with respect to carbon
concentration.

YR - True growth yield of the non-sensitive 3%. population.

YS - True growth yield of the sensitive % population.

YSM  - True growth yield of the metabnliziog^^ 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 freed 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 meteorologi-
cal data reports for the station whose data are in the simulation.
                                        5-17

-------
5-18

-------
   figure 5.2
en
             |      | 10-15 cm
                    15-20 cm
                    20-30 cm
                    30-35 cm
   Figure 5.2. Diagram for estimating soil evaporation loss.

-------
Figure 5.3
                   I
           PERIOD
      MEAN STORM DURATION (hour*)

                 ZONE

234567
ANNUAL
MEAN
C.V.
SIMMER
MEAN
C.V,

5.8
1.05

4. 4
l.U

5.9
1.05

4.2
1.09

€.2
1.22

4.9
1.J3

7.3
1.17

5.2
1.Z9

4.0
1.07

3.2
1.08

3.6
1.02

2.6
1,01

20.0
1.23

11.4
1.20

4.5
0.92

2,8
0.90

4.4
1.20

3.1
1.14
           Maan - Man value
           C.V. - Coefficient  of variation

           Source:  Voodvard-Clyde Consultants.  "Methodology for Analyti* of Detention
                    kilns for Control of Urban  Runoff Quality, pr«p«rtd for U.S. EPA.
                    Offlea of  Uatar, Honpolnc Source Division,  Sapctsber, 1986.
 Figure 5.3. Representative regional mean storm duration (hours) values for the U.S.
                                              5-20

-------
Figure 5.4. Diagram for estimating Soil Conservation Service soil hydrologic groups, (from EPA Field Guide for Scientific
Support Activities Associated with Superfund Emergency Response. U.S. EPA, Corvallis, OR.

-------
figure 5.5
                        Pesticide concentration In total soil
                                  (Kr7xg.cnT3)
             0.0
  Depth
  (cm)
         100-
         150
 Figure 5.5. Numerical dispersion associated with space step (Dx).

                                   5-22

-------
figure 5.6
          50-
         100-
 Depih
  (cm)
         150
         200-
         250 -J
                        Pesticide concenlration In total  soil
                                  (I0~7xg cnT3)
                                                               0 = 0.0
                                                              AX = 5
 Figure 5.6. Physical dispersion (D) associated with advective transport. (Note: Numerical
dispersion included).
                                    5-23

-------
   \l
a

                                                                                             oi
                                                                                             I
                                                                                             o

                                                                                             O)
                                                                                             O!
                                                                                             a

                                                                                             s
 0)






t>




 0)
                                              5-24

-------
        100_
                               0.5%  Organic  matter
                      °'50      0.0%  Porosity change
                                   35
                                                    .10
           0   10   20    30   40    50   60   70  80   90   100
                          Sand  (%)
Figure 5.8. 1/3-bar soil moisture by volume, (provided by Dr. Walter J. Rawls, U.S.
Department of Agriculture, Agricultural Research Service, Beltsville, Maryland).
                               5-25

-------
      100—,
       9O—
                       0.40
                            O.35
                               0.5% Organic  matter

                               0.0% Porosity change
                                 0.30
                                     0.25
                         30    40    50   60    eo    80   90
                                                 0.15
                                                     0.10
                                                           0.05
10    20
                                                100
                              Sand  (%)
Figure 5.9. 15-bar soil moisture by volume, (provided by Dr. Walter J. Rawls, U.S.
Department of Agriculture, Agricultural Research Service, Beltsville, Maryland).
                                   5-26

-------
     100
                         1   I   I  I   '   I   '   I
                      20    30     40     50    60
                    Sand    (    %   )
Figure 5.10. Mineral bulk density (g cm-3). (provided by Dr. Walter J. Rawls, U.S.

Department of Agriculture, Agricultural Research Service, Beltsville Maryland).
                                 5-27

-------
      2.&
      1 . 2
        15
20
              Number  of  compartments
Figure 5.11. Estimation of drainage rate AD((tt^"f) versus number of compartments.
                        5-28

-------
TABLE 5-1. TYPICAL VALUES OF SNOWMELT (SFAC) AS RELATED TO FOREST
           COVER
Snowmelt Factor, (cm £
FOREST COVER
Coniferous - quite dense
Mixed forest - coniferous,
deciduous, open
Predominantly deciduous forest
Open areas
K^day-1)
MINIMUM
0.08-0.12
0.10-0.16
0.14-0.20
0.20-0.36
MAXIMUM
0.20-0.32
0.32-0.40
0.40-0.52
0.52-0.80
Source:  Anderson, E.A., "Initial Parameter Values for the Snow Accumulation and
        Ablation Model", Part IV.2.2.1, National Weather Service River Forecast System
        - User's Manual, NWS/NOAA, U.S. Dept. of Commerce, Silver Springs, MD.,
        March 31, 1978.


TABLE 5-2. MEAN DURATION (HOURS) OF SUNLIGHT FOR LATITUDES IN THE
           NORTHERN AND SOUTHERN HEMISPHERES*
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Ott
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
Latitude North
30 35
10.5
11.2
12.0
13.0
13.7
14.0
13.9
13.2
12.4
11.4
10.7
10.2
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
9 - Criddle, W.D. Methods of Computing Consumptive Use of Water, Proceedings ASCE.
84(IR 1). 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.
                                   5-29

-------
TABLE 5-3. INDICATIONS OF THE GENERAL MAGNITUDE OF THE
            SOIL/ERODIBILITY FACTOR, Ka
                                          Organic Matter Content
Texture Class
<0.5%
2%
4%
Sand
Fine sand
Very Fine Sand
Loamy Sand
Loamy Fine Sand
Loamy Very Fine Sand
Sandy Loam
Fine Sandy Loam
Very Fine Sandy Loam
Loam
Silt Loam
silt
Sandy Clay Loam
Clay Loam
Silty Clay Loam
Sandy Clay
Silty Clay
Clay
0.05
0.16
.42
.12
.24
.44
.27
.35
.47
.38
.48
.60
.27
.28
.37
.14
.25

0.03
0.14
.36
.10
.20
.38
.24
.30
.41
.34
.42
.52
.25
.25
.32
.13
.23
0.13-0.29
0.02
0.10
.28
.08
.16
.30
.19
.24
.33
.29
.33
.42
.21
.21
.26
.12
.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. (Control of Water Pollution from Cropland, Vol. I, A Manual for Guideline
Development. U.S. Environmental Protection Agency, Athens, GA. EPA-600/2-75-026a).
TABLE 5-4. INTERCEPTION STORAGE FOR MAJOR CROPS
Crop
Density
CINTCP (cm)
corn
Soybeans
Wheat
Oats
Barley
Potatoes
Peanuts
Cotton
Tobacco
Heavy
Moderate
Light
Light
Light
Light
Light
Moderate
Moderate
0.25-0.30
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
                                     5-30

-------
TABLE 5-5. VALUES OF THE EROSION EQUATION'S TOPOGRAPHIC FACTOR, LS,
           FOR SPECIFIED COMBINATIONS OF SLOPE LENGTH AND STEEP-
           NESS
           Slope Length (feet)
Slope
0.5
1
2
3
4
5
6
8
10
12
14
16
18
20
25
30
40
50
60
25
.07
.09
.13
.19
.23
.27
.34
.50
.69
.90
1.2
1.4
1.7
2.0
3.0
4.0
6.3
8.9
12.0
50
.08
.10
.16
.23
.30
.38
.48
.70
.97
1.3
1.6
2.0
2.4
2.9
4.2
5.6
9.0
13.0
16.0
75
.09
.12
.19
.26
.36
.46
.58
.86
1.2
1.6
2.0
2.5
3.0
3.5
5.1
6.9
11.0
15.0
20.0
100
.10
.13
.20
.29
.40
.54
.67
.99
1.4
1.8
2.3
2.8
3.4
4.1
5.9
8.0
13.0
18.0
23.0
150
.11
.15
.23
,33
.47
.66
.82
1.2
1.7
2.2
2.8
3.5
4.2
5.0
7.2
9.7
16.0
22.0
28.0
200
.12
.16
.25
.35
.53
.76
.95
1.4
1.9
2.6
3.3
4.0
4.9
5.8
8.3
11.0
18.0
25.0

300
.14
.18
.28
.40
.62
.93
1.2
1.7
2.4
3.1
4.0
4.9
5.7
7.0
10.0
14.0
22.0
31.0
--
400
.15
.20
.30
.44
.70
1.1
1.4
2.0
2.7
3.6
4.6
5.7
6.4
8.2
12.0
16.0
25.0
—
--
500
.16
.21
.33
.47
.76
1.2
1.5
2.2
3.1
4.0
5.1
6.4
7.0
9.1
13.0
18.0
28.0
—
--
600
.17
.22
.34
.49
.82
1.3
1.7
2.4
3.4
4.4
5.6
7.0
8.0
10.0
14.0
20.0
31.0
—
--
800
.19
.24
.38
.54
.92
1.4
1.9
2.8
3.9
5.1
6.5
8.0
9.0
12.0
17.0
23.0
-
~
--
     Values given for slopes longer than 300 feet or steeper than 18% are extrapolations
     beyond the range of the research data, and therefore, less certain than others.
     (Control of Water Pollution from Cropland, Vol. I, A Manual for Guideline Develop-
     ment. U.S. Environmental Protection Agency, Athens,  GA. EPA-600/2-75-026a).
                                   5-31

-------
TABLE 6-6. VALUES OF SUPPORT-PRACTICE FACTOR, P"
Practice
Land Slope (percent)
             1.1-2.0
                                    2.1-7.0
7.1-12.0
(Factor P)
                                                           12.1 -18.0    18.1 -24.0
Contouring (PJ

Contour Strip
   cropping
     R-R-M-M
     R-W-M-M
     R-R-W-M
     R-W
     R-0

Contour listing
   or ridge
   planting (P
-------
TABLE 5-7. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FAC-
         TOR, C, IN THE 37 STATES EAST OF THE ROCKY
Line Crop, Rotation, and Management0
No.
Base value: continuous fallow, tilled up and down
corn
1 C, RdR, fall TP, conv (1)
2 C, RdR, spring TP, conv (1)
3 C, RdL, fall TP, conv (1)
4 C, RdR, we seeding, spring TP, conv (1)
5 C, RdL, standing, spring TP, conv (1)
6 C, fall shred stalks, spring TP, conv (1)
7 C(silage)-W(RdL, fall TP) (2)
8 C,RdL, fall chisel, spring disk, 40-30%rc(l)
9 C (silage), W we seeding, no-till pi in c-k(l)
10 C(RdL)-w)RdL, spring TP) (2)
11 C, fall shred stalks, chisel pi, 40-30%rc(l)
12 C-C-C-W-M, RdL, TP for C, disk for W (6)
13 C, RdL, strip till row zones, 55-40% re (1)
14 C-C-C-W-M-M, RdL, TP for C, disk for W (6)
15 C-C-W-M, RdL, TP for C, disk for W (4)
16 C, fall shred, no-till pi, 70-50% re (1)
17 C-C-W-M-M, RdL, TP for C, disk for W (5)
18 C-C-C-W-M, RdL, no-till pi 2nd & 3rd C (5)
19 C-C-W-M, RdL, no-till pi 2nd C (4)
20 C, no-till pi in c-k wheat, 90-70% re (1)
21 C-C-C-W-M-M, no-till pi 2nd& 3rd C (6)
22 C-W-M, RdL, TP for C, disk for W (3)
23 C-C-W-M-M, RdL, no-till pi 2nd C (5)
24 C-W-M-M, RdL, TP for C, disk for W (4)
25 C-W-M-M-M, RdL, TP for C, disk for W (5)
26 C, no-till pi in c-k sod, 95-80% re (1)
Cotton6
27 Cot, conv (Western Plains) (1)
28 Cot, conv (South) (1)
Meadow
29 Grass & Legume mix
30 Alfalfa, lespedeza or Sericia
3 1 Sweet clover
Sorghum, grain (Western Plains)6
32 RdL, spring TP, conv (1)
33 No-till pi in shredded 70-50% re
Productivity
Level"
High Mod.
C Value
1.00 1

0.54 0
.50
.42
.40
.38
.35
.31
.24
.20
.20
.19
.17
.16
.14
.12
.11
.087
.076
.068
.062
.061
.055
.051
.039
.032
.017

0.42 0.
.34

.004 0.
.020
.025

0.43 0.
.11

.00

.62
.59
.52
.49
.48
.44
.35
.30
.24
.28
.26
.23
.24
.20
.17
.18
.14
.13
.11
.14
.11
.095
.094
.074
.061
.053

49
.40

01



53
,18
                            5-33

-------
TABLE 5-7. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FAC-
            TOR, C, IN THE 37 STATES EAST OF THE ROCKY MOETMMHSSftf

                                                             Productivity Level11
Line Crop, Rotation, and Management0                           High     Mod.
No.                                                              C Value
Base value: continuous fallow, tilled up and down                 1.00      1.00
Soybtesotrf
34 B, RdL, spring TP, conv (1)                                  0.48       0.54
35 C-B, TP annually, conv (2)                                    .43       .51
36 B, no-till pi                                                 .22       .28
37 C-B, no-till pi,  fall shred C stalks (2)                          .18       .22

Wheat
38 W-F, fall TP after W (2)                                     0.38
39 W-F, stubble mulch, 500 Ibs re (2)                             .32
40 W-F, stubble mulch, 1000 Ibs re (2)                            .21
41 Spring W, RdL, Sept TP, conv (N&S Dak) (1)                   .23
42 Winter W, RdL, Aug TP,  conv (Kansas) (1)                     .19
43 Spring W, stubble mulch, 750 Ibs re (1)                        .15
44 Spring W, stubble mulch, 1250 Ibs re (1)                       .12
45 Winter W, stubble mulch, 750 Ibs re (1)                        .11
46 Winter W, stubble mulch, 1250 Ibs re (1)                       .10
47 W-M,  conv (2)                                               .054
48 W-M-M, conv (3)                                            .026
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 vocationally derived C values should be used for conserva-
  tion planning at the field level. Tables of local values are available from the Soil
  Conservation  Service.
  Control of Water Pollution from Cropland, Vol. I, A Manual for Guideline Development,
  U.S. Environmental Protection Agency, Athens, GA. EPA-600/3-75-026a.
c Numbers in parentheses indicate number of years in the rotation cycle. No. (1) desig-
  nates a continuous one-crop system.
  High level is exemplified by long-term yield averages greater than 75  bu. corn or 3 tons
  grass-and-legume hay; or cotton management that regularly provides good stands and
  growth.
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.
                                       5-34

-------
TABLE 5-7. GENERALIZED VALUES OF THE COVER AND MANAGEMENT FAC-
            TOR, C, IN THE 37 STATES EAST OF THE ROCKY
Line Crop, Rotation, and Management'
No.
                                      Productivity Level
                                       High    Mod.
                                         C Value
Base value: continuous fallow, tilled up and down
                                      1.00
1.00
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 -


% re -

7-50% re -

RdR-

RdL-

TP-
pounds of crop residue per acre remaining on surface after new
crop seeding

percentage

70% cover for C values in first column; 50% for second column

residues (corn stover, straw, etc.) removed or burned

all residues left on field (on surface or incorporated)

turn plowed (upper 5 or more inches of soil inverted, covering
residues
                                     5-35

-------
TABLE 5-8. MEAN STORM DURATION* (TR) VALUES FOR SELECTED CITIES

Great Lakes
Champaign-UrbanaJL
Chicago, IL
Davenport, IA
Detroit, MI
Louisville, KY
Minneapolis, MN
Stubenville, OH
Toledo, OH
Zanesville, OH
Lansing, MI (30 Yr)
Lansing, MI (21 Yr)

Storm
Mean
Annual

6.1
5.7
6.6
4.4
6.7
6.0
7.0
5.0
6.1
5.6
6.2

Duration (hrs) Storm Duration (hrs)
Summer Summer
(June- Mean (June-
Sept) Location Annual Sept)

4.6
4.5
5.3
3.1
4.5
4.5
5.9
3.7
4.3
4.2
5.1

Lower Mississippi Vallev

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

Portland, OR (2 Syr)
Portland, OR (lOyr)
Eugene, OR
Seattle, WA

6.9
6.9
7.8
7.7


4.2
4.0
3.5
4.2
3.3
4.2
3.2



5.4
15.5
29.2
21.5

4.7
5.0
5.3
5.9


3.3
3.3
2.8
3.2
2.6
3.3
2.4



4.5
9.4
15.0
12.7
Southeast
Greensboro, NC
Columbia, SC
Atlanta, GA
Birmingham, AL
Gainesville, FL
Tampa, FL

Rocky Mountains

Denver, CO (8 Yr)
Denver, CO (25 Yr)
Denver, CO (24 Yr)
Rapid City, SD
Salt Lake City, UT
Salt Lake City, UT

California

Oakland, CA
San Francisco, CA
Northeast

Caribou, ME
Boston, MA
Lake George, NY
Kingston, NY
Poughkeepsie, NY
New York City, NY
Mineola, LI, NY (2)
Upton LI, NY
Wantagh, LI, NY (2)
Long Island, NY
Washington, DC
Baltimore, MD

5.0
4.5
8.0
7.2
7.6
3.6



4.3
4.8
9.1
8.0
4.5
7.8



4.3
5.9


5.8
6.1
5.4
7.0
6.9
6.7
5.6
6.3
5.6
4.2
5.9
6.0

3.6
3.5
6.2
5.0
6.6
3.1



3.2
3.2
4.4
6.1
2.8
6.8



2.9
11.2


4.4
4.2
4.5
5.0
4.9
4.8
4.0
4.6
4.0
3.4
4.1
4.2
Source: Woodward-Clyde Consultants, "Methodology for Analysis of Detention Basins for
        Control of Urban Runoff Quality", prepared for U.S. EPA, Office of Water,
        Nonpoint Source Division, 1986.

        These values may be misleading in arid regions or regions with pronounced
        seasonal rainfall patterns.
                                     5-36

-------
C6
IO

a
§
y

y)
on

uli
Ha

Mo

(Ju
I  -
!ll
1-1
s §1
sss

o
CO
     m
         o
         CO
     c?
          O
          t-
:i  ~j
             ^ 0
             ajiM
                    _
                  £ co
                          en
o o
CO CO
    o
    CO
                       o
                       CO
                                  o
                                TAB j]^ 5-9
IO IO
r-H r-H

10 ui
    in
    r-H

    in
                               a
                               IO
                                  m
                                  i— i
                                  in
            ~
          831
is
                         II
                          •SB'S

            sa
E
o
Soybeans
                                              O
                                              CO

                                              8
                                         O
                                         CO
                                               CN
                                               CO
                                SJ «
                                         13
                                         2
               o
               m
                                 g
in
i— i
ui
                                2
                                E
                                s
                                                 II
                                                • rH 
-------
TABLE 5-10. RUNOFF CURVE NUMBERS FOR HYDROLOGIC SOIL-COVER COMP-
            LEX^ (ANTECEDENT MOISTURE CONDITION H, AND \ = 0.2 S)


Land Use
Fallow
Row crops





Small
grain




Close-
seeded
legunnnsesB1
or rota-
tion
meadow
Pasture
or range




Meadow
Woods


Farmsteads
Roads
(dirt)*
(hard surface)'
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
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
30
45
36
25
59
72
74

B
86
78
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
58
66
60
55
74
82
84

C
91
85
85
84
82
80
78
84
83
83
81
79
78
85
81
83
78
80
76
86
79
74
81
75
70
71
77
73
70
82
87
90

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
78
83
79
77
86
89
92

   fMockus, 1972.
   ft Close-drilled or broadcast.
   f Including right-of-way.
                                5-38

-------
TABLE 5-11. METHOD FOR CONVERTING CROP YIELDS TO RESIDUE'
Crtffljpij
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
8 Crop residue = (straw/grain ratio) x (bushel weight in Ib/bu) x (crop yield in bu/acre).

15 Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion
 from Agricultural Management Systems. USDA, Conservation Research Report No. 26,
  1980.
TABLE 5-12. RESIDUE REMAINING FROM TILLAGE Operations
                                                           Residue
        Tillagefe                                            Remaining
        Operation                                          (%)

        Chisel Plow                                        65
        Rod weeder                                        90
        Light disk                                          70
        Heavy disk                                         30
        Moldboard plow                                     10
        Till plant                                          80
        Fluted coulter                                      90
        V Sweep                                           90
9 Crop residue remaining= (crop residue from Table 10) x (tillage factor(s),

6 Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff, and
  Erosion from Agricultural Management Systems. USDA, Conservation Research
  Report No. 26, 1980.

                                      5-39

-------
TABLE 5-13. REDUCTION IN RUNOFF CURVE NUMBERS CAUSED BY CONSERVA-
            TION TILLAGE AND RESIDUE Management
Large
Residue
Crapfr
(Ib/acre)
0
400
700
1,100
1,500
2,000
2,500
6,200
Medium
Residue
(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
Numteiirf
0
0
2
4
6
8
10
10
a Knisel, W.G. (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion
  from Agricultural Management Systems. USDA, Conservation Research Report No. 26,
  1980.

  Large-residue crop (corn).

c Medium residue crop (wheat, oats, barley, rye, sorghum, soybeans).

  Percent reduction in curve numbers can be interpolated linearly. Onlv apply 0 to 1/2 of
  these percent reductions to CNS for contouring and terracing practice 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
YielaK
(Bu/Ac)
110
62
35
40
BushdH
dry wt.
(Ibs/Bu)
56
56
60
60
Straw/Grain
Ratio
1.0
1.0
1.5
1.7
Units
Conversion
Factor
1.1214X10-4
1.1214X10-4
1.1214X10-4
1.1214X10-4
WFMAX
1.38
0.78
0.59
0.72
 10-year average
                                    5-40

-------
TABLE 5-15. PESTICIDE SOIL APPLICATION METHODS AND DISTRIBUTION
Method of
Application
Common Procedure
Distribution
DEPI
Broadcast
Disked-in
Chisel-plowed
Surface banded
Banded -
incorporated
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
Remains on the      0.0
soil surface
Assume uniform       10.0
distribution to
tillage depth
(10 cm)

Assume linear         15.0
distribution to
tillage depth
(15 cm)

Remains on soil      0.0
surface
Assume  uniform      5.0
distribution to
depth of incor-
poration (5 cm)
TABLE 5-16. MAXIMUM CANOPY HEIGHT AT CROP MATURATION
    Crop
References:
A. Szeicyetal. (1969)
B. Smith et al. (1978)
              Height (cm)
              Reference
Barley
Grain Sorghum
Alfalfa
corn
Potatoes
Soybeans
Sugarcane
20-50
90-110
10-50
80-300
30-60
90-110
100-400
A
B
A
A
A
B
A
                                      5-41

-------
TABLE 5-17. DEGRADATION RATE CONSTANTS OF SELECTED PESTICIDES ON
              FOLIAGE*
      Class
     Group
Decay Rate (days-1)
Organochlorine
Organophosphate
     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).
0.231-0.1386
                                                              0.1195-0.0510
0.2772-0.3013
                                                              0.1925-0.0541
Carbamate

Pyrethroid
Pyridine
Benzole acid
Fast
(carbofuran)
slow
(carbaryl)
(permethrin)
(pichloram)
(dicamba)
0.630
0.1260-0.0855
0.0196
0.0866
0.0745
* Knisel, W.G,  (Ed.). CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion
 from Agricultural Management Systems, USDA, Conservation Research Report No, 26,
  1980.
                                      5-42

-------
TABLE 5-18. ESTIMATED VALUES OF HENRYS CONSTANT FOR SELECTED
            PESTICIDES
Compound
Alachlor
Aldrin
Anthracene
Atrazine
Bentazon
Bromacil
Butylate
Carbaryl
Carbofuran
Chlorpyrifos
Chrysene
Cyanazine
DDT
Diazinon
Dicamba
Dieldrin
Diuron
Endrin
EPTC
Ethoprophos
Fenitrothion
Fonofos
Heptachlor
Lindane
Linuron
Malathion
Methomyl
Methyl Parathion
Metolachlor
Metribuzin
Monuron
Napropamide
Parathion
Permethrin
Picloram
Prometryne
Simazine
Terbufos
Toxaphene
Triallate
Trichlorfon
Trifluralin
2,4-D (acid)
2,4,5-T (acid)
Henry's Constant
(dimensionless)
1.3E-06
6.3E-04
4.4E-05
2.5E-07
2.0E-10
3.7E-08
3.3E-03
1.1E-05
1.4E-07
1.2E-03
4.7E-05
1.2E-10
2.0E-03
5.0E-05
3.3E-08
6.7E-04
5.4E-08
1.8E-05
5.9E-04
6.0E-06
6.0E-06
2.1E-04
1.7E-02
1.3E-04
2.7E-06
2.4E-06
4.3E-08
4.4E-06
3.8E-07
9.8E-08
7.6E-09
7.9E-07
6.1E-06
6.2E-05
1.9E-08
5.6E-07
1.3E-08
1.1E-03
2.3E+00
7.9E-04
1.5E-09
6.7E-03
5.6E-09
7.2E-09
References
A
D
D
A
A
c
A
A
A
A
D
A
C
C
A
C
C
D
C
C
B
A
D
B
A
B
A
A
A
A
C
C
C
A
B
C
A
A
A
C
B
A
A
B
References: A. Donigian et al. (1986) B, Spencer et al. (1984) C, Jury et al. (1984) D.
          Schnooretal. (1987)
                                 5-43

-------
    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 (Continued)
en
Chemical
Actellic
Alachlor
Antor
Aresin
Balan
Basalin
Baygon
Baygon Meb
Bayleton
Baythion
Baythion C
Betas an
Bromophos
Butachlor
Bux
Carbamult
Carbyne
Chlordimeform
Chlorfenvin-
phos
Chloro IPC
Chlorpyrifos
Co-Ral
Counter
DNOC
Dichlorprop
Dimetan
Dimethoate
Dinitramine
Dinoseb
Dazomet
Devrinol
Elocron
Evik
Far-Go
Fongarid
Common
Name
pirimiphosmethyl
alachlor
diethatyl ethyl
monol inuron
benefin
fluchloralin
propoxur
plifenate
triad! mefon
phoxim
chlorphoxim
bensulide
bromophos
butachlor
bufencarb
promecarb
barban
chlordimeform

chlorfenvinphos
chlorpropham
chlorpyrifos
coumaphos
terbufos
DNOC
dichlorprop
dimetan
dimethoate
dinitroamine
dinoseb
dazomet
napropamide
dioxacarb
ametryn
triallate
furalaxyl
Solubility
in water
20-25°C) Refer-
(mg/1) ence
5
220
105
735
70
0.7
2000
50
70
7
1.7
25
40
23
1
92
11
250

110
108
2
1.5
15
130
350
30000
X=25000
1
52
1200
73
6000
185
4
230
a
b
a
a
b
b
a
a
a
b
a
c
a
a
b
a
c
a

a
b
b
b
a
a
a
b
a
a
c
b
a
a
a
b
a
In-
sect-
icide
X





X
X

X
X

X

X
X

X

X

X
X
X
X

X
X




X



Mode of Action
Nema-
Herb- Fungi- to- Acar-
icide cide cide icide

X
X
X
X
X


X


X

X


X
X


X


X
X X
X


X
X
XXX
X

X
X
X
Mole-
cular
weight
(g)
274
269.9
311.5
214.6
335.3
355.7
209
336.2
267.45
298
301.45
397.5
366
312
221.3
207
258.1
196.7

359.5
213.7
350.5
362.8
288
198.1
235
197.3
229.1
322.2
240.2
162.3
271.36
223
227
304.6
301
Partitioning Model
Refer-
ence
b
b
c
b
b
b
b
d
d
b
d
b
b
e
b
d
b
b

b
b
b
b
d
b
b
b
b
c
b
b

b
b
b
d
PCMCl PMCM2 PCMC3
(mole (mg/1) ((im/l)
fraction)
3.28xlO'7
1.47x10°
6.07x10-"
6.17x10-'
3.76x10-"
3.55x10*
1.72x10^
2.68x10-"
4.72x10'"
4.23xlO'7
1.02xl07
1.13x10'"
1.97x10'"
1.33x10-"
8.14x10*
8.01x10^
7.70x1 Q-7
2.30x10'°

5.51x10-*
9.11x10-"
1.03xlO'7
7.45x10-"
9.38xlO'7
1.18x10-'
2.68x10-"
2.74xlO'3
1.97xl03
5.60x1 0'8
3.90x10-"
1.33x10^
4.85x10-'
4.85x10^
1.47xlO'5
2.37x1 0'7
1.38x10-°
5
220
105
735
70
0.7
2000
50
70
7
1.7
25
40
23
1.0
92
11
250

110
108
2.0
1.5
15
130
350
30000
25000
1
52
1200
73
6000
185
4
230
18
815
337
3430
209
2
9600
149
262
24
5.6
63
109
74
5
444
43
1270

306
505
6
4
52
656
1490
152000
109000
3
217
7390
269
26900
815
13
764
Degradation
Rate Constant
in Soil
Root Zone Refer-
(days"1) ence

.0384
.0099-.0173

0.3349
0.0169






.0198



.0347


.0055
.0058-.00267




.0578-.0866

.0057
.0193-.0856
.0462-.0231


.3465-.0248
.0231-.0077
.0231-.0713


f
g

f
f






f



g


f
g




f


f
g


f
g
g


-------
   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 (Continued)
en
Chemical
Fornothion
Fuji-one
Gardona

Gesaran
Goal
Guthion
Hoelon
TmiHnn
0>C
Linuron
Malathion
Mecoprop
MEMC
Merpelan AZ
Mesoranil
Mesurol

Methomyl
Methoxychlor
Meth-Para-
thion
Nemacur
Nortron
Orthene
Oxamyl
Parathion
Patoran
Phorate
Propachlor
Propanil
Prowl
Prynachlor
Quinalphos
Ronstar
Sancap
Semeron
Common
Name
fornothion
isoprothiolane
tetrachlorvin-
phos
methoprotryne
oxyfluorfen
azinphos-methyl
diclofop methyl
phosmet
propham
linuron
malathion
mecoprop
MEMC
isocarbamid
aziprotryn
mercaptodi-
methur
methomyl
methoxychlor
methyl Para-
thion
fenamiphos
ethofume&ate
acephate
oxamyl
parathion
metabromuron
phorate
propachlor
propanil
pendimethalin
prynachlor
quinalphos
oxadiazon
dipropetryn
desmetryn
Solubility
in water
20-25°C) Refer-
(mg/1) ence
2600
48

11
320
0.1
29
30
25
250
75
145
620
50000
13000
75

2.7xlOT
58000
0.1

X = 57.5
400
110
6.5x10"
2.8x10"
24
330
50
580
500
0.5
500
22
0.7
16
580
a
a

b
a
c
a
a
b
b
a
a
a
a
a
b

a
a
b

a
a
a
b
a
b
a
b
c
c
c
a
a
b
a
a
In-
sect-
icide
X
X

X


X

X


X





X
X
X

X


X
X
X

X




X



Mode of Action
Nema-
Herb- Fungi- to- Acar-
icide cide cide icide
X
X


X
X

X

X
X

X
X
X
X






X
X

X X

X

X
X
X
X
X
X
X
X
Mole-
cular
weight
(g)
257
290

366
271
361.7
317.3
340.9
317.3
179.2
249.1
330.4
214.6
295
185
225

225.3
162.2
345.7

263.2
300
286
183.2
219
291.3
258.9
260.4
211.7
218
281.3
221.7
298
345.23
255.4
213
Refer-
ence
b
d

b
b
c
b
d
b
b
b
b
b
d
d
b

b
b
b

b
b
d
b
b
b
d
b
b
b
c
b
d
b
b
b
Partitioning Model
PCMCl PMCM2 PCMC3
(mole (mg/1) ((im/l)
fraction)
1.82x1 0"4
2.98x10-"

5.42x10'
2.13x10'"
4.98xlO'9
1.65x10'"
1.59x10'"
1.42x10'"
2.51x10-'
5.42x10"
7.91x10-"
5.21x10'"
3.05x10"
1.27xlO-3
6.01x10'"

2.16
6.44xlO'3
5.21x10

3.94x10'"
2.38x10-'
6.93x10'°
0.06 6.5x10"
0.023 2.8x10"
1.48x10"
2.30x10'"
3.46x10-"
4.94x10-"
4.13x10-'
3.20xlO-8
4.06x10''
1.33x10'°
3.65x10*
1.13x10"
4.91x10'
2600
48

11
320
0.1
29
30
25
250
75
145
620
50000
13000
75

2.7xl07
58000
0.1

57.5
400
110
650000
280000
24
330
50
580
500
0.5
500
22
0.7
16
580
10100
166

30
1180
0.3
91
88
79
1400
300
439
2890
169000
70300
333

1.2xlOe
358000
0.3

219
1320
385
3550000
1280000
82
1280
192
2740
2290
1.8
2260
74
2.0
63
2720
Degradation
Rate Constant
in Soil
Root Zone Refer-
(days"1) ence



.1732-1386

.0231-.0173
.0533-.0014


.0347-.0116
.0280-.0039
2.91-.4152







.0046-.0033

.2207



.0354-.0646
.2962-.0046
.0234
.0363-.0040
.0231-.0139
.693- .231











c
f


g
f
f







f

f



f
f
f
f
g
g







-------
i

            '1U
             s*
            •e-s
            .2 "S
              •
                   co   to
                   O   iH
                   S   88S
                       8     8
                               0
                       00000
                     OlOlO'—I N C3 i-H 00
                     §
                       m m m   in

                        '
                   X   X
                       X
X
                             X   XX
                  05 EH EH EH

-------
TABLE 5-20. OCTANOL WATER DISTRIBUTION COEFFICIENTS (log Kg)) AND SOIL
          DEGRADATION RATE CONSTANTS FOR SELECTED CHEMICALS
Chemical Name
Alachlor
Aldicarb
Altosid
Atrazine
Benomyl
Bifenox
Bromacil
Captan
Carbaryl
Carbofuran
Chloramben
Chlordane
Chloroacetic Acid
Chloropropharn
Chloropyrifos
Cyanazine
Dalapon
Dialifor
Diazinon
Dicamba
Dichlobenil
Dichlorofenthion
2,4,-Dichloropheno-
acetic Acid
Dichloropropene
Dicofol
Dinoseb
Diuron
Endrin
Fenitrothion
Fluometuron
Linuron
Malathion
Methomyl
Methoxychlor
Methyl Parathion
Monolinuron
Monuron
MSMA
Nitrofen
Parathion
LogKgi
2.78
0.70
2.25
2.45
2.42
2.24
2.02
2.35
2.56
2.44
1.11
4.47
-0.39
3.06
4.97
2.24
0.76
4.69
3.02
0.48
2.90
5.14
2.81
1.73
3.54
2.30
2.81
3.21
3.36
1.34
2.19
2.89
0.69
5.08
3.32
1.60
2.12
-3.10
3.10
3.81
Degradation Rate
Constant (days:1)
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

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
Reference
A
A

A
A
A

A
A



D
C
D
A
A


D

D
D

A
C
A
A
A
A
D


A
                              5-47

-------
TABLE 5-20. OCTANOL WATER DISTRIBUTION COEFFICIENTS (log KM) AND SOIL
            DEGRADATION RATE CONSTANTS FOR SELECTED CHEMICALS
            (concluded)
Chemical Name
Degradation Rate
Constant (days-1)
Reference
Perrnethrin
Phorate
Phosalone
Phosmet
Picloram
Propachlor
Propanil
Propazine
Propoxur
Ronnel
Simazine
Terbacil
Terbufos
Toxaphene
Trifluralin
Zineb
2.88
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
4.75
1.78
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
0.0956-0.0026
0.0512
E
A


A
D
D
D
A


E
A
A
  Nash, R. G. 1980. Dissipation Rate of Pesticides from Soils. Chapter 17.
  IN CREAMS: A Field Scale Model for Chemicals, Runoff, and Erosion from Agricultur-
  al Management Systems. W.  G. Knisel, ed. USDA Conservation Research Report No.
  26. 643pp.
  Smith, C. N. Partition Coefficients (Log K$) for Selected Chemicals.
  Athens Environmental Research Laboratory, Athens, GA. Unpublished report, 1981.

  Herbicide Handbook of the Weed Science Society of America, 4th ed. 1979.

  Control of Water Pollution from Cropland, Vol. I, a manual for guideline development,
  EPA-600/2-75-026a.

  Smith, C. N. and R. F. Carsel. Foliar Washoff of Pesticides (FWOP) Model:
  Development and Evaluation. Accepted for publishing in Journal of Environmental
  Science and Health - Part B. Pesticides, Food Contaminants, and Agricultural Wastes,
  B 19(3), 1984.
                                     5-48

-------
TABLE 5-21. ALBEDO FACTORS OF NATURAL SURFACES FOR SOLAR RADIA-
             TION*
             Surface                                        Reflectivity
             Fresh Dry Snow                                 0.80-0.90
             Clean, Stable Snow Cover                         0.60-0.75
             Old and Dirty Snow Cover                        0.30-0.65
             Dry Salt Cover                                   0.50
             Lime                                            0.45
             White Sand, Lime                                0.30-0.40
             Quartz Sand                                    0.35
             Granite                                         0.15
             Dark Clay, Wet                                  0.02-0.08
             Dark Clay, Dry                                  0.16
             Sand, Wet                                       0.09
             Sand, Dry                                       0.18
             Sand, Yellow                                    0.35
             Bare Fields                                     0.12-0.25
             Wet Plowed Field                                0.05-0.14
             Newly Plowed Field                              0.17
             Grass, Green                                    0.16-0.27
             Grass, Dried                                    0.16-0.19
             Grass, High Dense                               0.18-0.20
             Prairie, Wet                                     0.22
             Prairie, Dry                                     0.32
             Stubble Fields                                   0.15-0.17
             Grain Crops                                     0.10-0.25
             Alfalfa, Lettuce, Beets, Potatoes                   0.18-0.32
             Coniferous  Forest                                0.10-0.15
             Deciduous Forest                                0.15-0.25
             Forest with Melting Snow                         0.20-0.30
             Yellow Leaves (fall)                              0.33-0.36
             Desert, Dry Soils                                 0.20-0.35
             Desert, Midday                                  0.15
             Desert, Low Solar Altitude                        0.35
             Water (0 to 300)"                                 0.02
             Water (600)"                                     0.06
             Water (850)"                                     0.58
* References:

Van Wijk, W.R. 1963. Physics of Plant Environment, p. 87. North-Holland Publishing
  Co.,  Amsterdam.

Brutsaert, W. 1982. Evaporation into the Atmopshere: Theory, History, and
  Applications.  D. Reidel Publishing Co., Dordrecht,  Holland.

a angle of solar  incidence.


                                       5-49

-------
TABLE 5-22. EMISSIVITY VALUES FOR NATURAL SURFACES AT NORMAL TEM-
            PERATURES*
       Surface                                         Emissivity

       Sand (dry-wet)                                   0.95-0.98
       Mineral Soil (dry-wet)                             0.95-0.97
       Peat (dry-wet)                                    0.97-0.98
       Firs                                            0.97
       Tree Vegetation                                  0.96-0.97
       Grassy Vegetation                                0.96-0.98
       Leaves                                          0.94-0.98
       Water                                           0.95
       Snow (old)                                       0.97
       Snow (fresh)                                     0.99


       References

Van Wijk, W.R. 1963. Physics of Plant Environment, p. 87. North-Holland Publishing
       Co.,  Amsterdam.

Brutsaert, W.  1982. Evaporation into the Atmosphere: Theory, History, and Applica-
tions, D. Reidel Publishing Co., Dordrecht, Holland.
Table 5-23.  COEFFICIENTS FOR LINEAR REGRESSION EQUATIONS FOR PRE-
            DICTION OF SOIL WATER CONTENTS AT SPECIFIC MATRIC POTEN-
            TML&
Sand
Matric Intercept (%)
Coefficient a b
-0.20
-0.33
-0.60
-1.0
-2.0
-4.0
-7.0
-10.0
-15.0
0.4180
0.3486
0.2819
0.2352
0.1837
0.1426
0.1155
0.1005
0.0854
-0.0021
-0.0018
-0.0014
-0.0012
-0.0009
-0.0007
-0.0005
-0.0004
-0.0004
Clay
(%)
c
0.0035
0.0039
0.0042
0.0043
0.0044
0.0045
0.0045
0.0044
0.0044
Organic
Matter
('%)
d
0.0232
0.0228
0.0216
0.0202
0.0181
0.0160
0.0143
0.0133
0.0122
Bulk
Density
(g cm-*)
e
-0.0859
-0.0738
-0.0612
-0.0517
-0.0407
-0.0315
-0.0253
-0.0218
-0.0182
R2
KT
0.75
0.78
0.78
0.76
0.74
0.71
0.69
0.67
0.66
 Rawls, W. J., U.S. Department of Agriculture, Agricultural Research
          Service, Beltsville, MD. Personal Communication.
                                    5-50

-------
TABLE 5-24. THERMAL PROPERTIES OF SOME SOIL AND REFERENCE MATERI-
Material
Clay
Light Soil w/Roots
Wet Sandy Soil
Dead Air
Hudson River Sand

Podunk Fine Sandy Loam

Leonardtown Silt Loam

Muck Soil

Yolo Clay

Granite Sandy Loam

Fine Calcareous Loam

Granitic Sand

Barns Loam

Chester Loam

Herman Sandy Loam

Kalkaska Loamy Sand

Northway Silt Loam

Fairbanks Silty Clay Loam

Dakota Sandy Loam

Black Cotton Soil
Water Content
(%)




4.5
18.1
6.6
20.2
9.0
18.4
23.0
59.0
0.0
29.0
0.0
22.7
0.0
24.4
0.0
13.1
5.1
26.0
2.0
13.4
1.3
13.4
0.8
5.7
6.6
22.5
12.3
25.4
1.9
4.9

Heat Capacity
(cal cm*1)
1.44
0.09
0.64
0.000312
0.2
0.336
0.221
0.371
0.316
0.338
0.251
0.321
0.236
0.72
0.291
0.706
0.175
0.430
0.269
0.636
0.29
0.35
0.32
0.37
0.30
0.37
0.32
0.37
0.384
0.636
0.436
0.625
0.269
0.483
0.336
Thermal Cond.
(cal cm^1 T-l see"1)
0.00288
0.00027
0.0064
0.00005
0.0091
0.03
0.0012
0.0026
0.0018
0.0021
0.00076
0.00108
0.0014
0.0083
0.0017
0.0071
0.00079
0.0048
0.00137
0.0108
0.00041
0.00086
0.00045
0.00087
0.00049
0.00087
0.0006
0.00124
0.0013
0.0025
0.002
0.0028
0.00059
0.0054
0.00037
* References

Rosenberg, N.J. 1974. Microclimate: The Biological Environment, p. 105. Wiley -
  Interscience, New York.

Kilmer, V.J. 1982.  Handbook of Soils and Climate in Agriculture. CRC Press, Inc. Boca
  Raton, Florida.
                                    5-51

-------
TABLE 5-25. HYDROLOGIC PROPERTIES BY SOIL TEXTURE!
Texture
Class
Sand
Loamy
Sand
Sandy
Loam
Loam
Silt Loam
Sandy Clay
Loam
Clay Loam
Silty Clay
Loam
Sandy Clay
Silty Clay
Clay
IRawls, W I
Range of
Textural Properties
(Percent)
Sand silt Clay
85-100
70-90
45-85
25-50
0-50
45-80
20-45
0-20
45-65
0-20
0-45
0-15
0-30
0-50
28-50
50-100
0-28
15-55
40-73
0-20
40-60
0-40
, D. L. Brakensiek, and
0-10
0-15
0-20
8-28
8-28
20-35
28-50
28-40
35-55
40-60
40-100
K. E. Saxton.
Water Retained at Water Retained at
-0.33 Bar Tension -15.0 Bar Tension
cmf cunt j cnw cast's
0.091"
(0.018-0.164)°
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)
Estimation of Soil
0.033*
(0.007-0.059)°
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)
Water Properties,
  Transactions ASAE Paper No. 81-2510, pp. 1316-1320. 1982.



15 Mean value.



E One standard deviation about the mean,
                                  5-52

-------
TABLE 5-26. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR FIELD
             CAPACITY (PERCENT BY VOLUME)
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9- 1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.6-0.9
0.9- 1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9- 1.2
Sample
Size

52
50
42
39

456
454
435
373

371
362
336
290

230
208
178
146
Mean

11.8
9.6
7.3
7.1

19.5
18.8
18.7
17.5

22.4
22.8
22.7
22.2

24.1
26.1
25.0
24.1
Original
Median

9.4
8.1
5.9
5.8

19.1
18.8
18.7
17.5

22.5
23.2
22.9
21.3

24.2
26.3
25.6
24.4
Data
s.d.

9.2
7.9
5.8
5.0

8.3
7.4
7.1
7.6

7.8
7.8
8.6
8.9

9.1
9.3
8.2
8.1
CV
Distribution Model
(%) Transform Mean

78
82
79
70

42
39
39
43

35
34
38
40

38
36
33
33

In
In
In
In

ty
STJ

£

Su
su
Su
Su

&u
§y
§y
§y

2.25
1.99
1.73
1.73

0.316
0.311
0.298
0.288

0.363
0.369
0.368
0.359

0.387
0.419
0.403
0.390
s.d.

0.65
0.73
0.73
0.71

0.13
0.12
0.11
0.12

0.12
0.12
0.13
0.13

0.14
0.14
0.13
0.12
CV = coefficient of variation
s.d. = standard deviation

Source: Carsel et al. (1988)
                                  5-53

-------
TABLE 6-27. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR WILT-
            ING POINT (PERCENT BY VOLUME)
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9- 1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.6- 1.2
Class C
0.3-0.3
0.3-0.6
0.6-0.9
0.9- 1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size

118
119
113
105

880
883
866
866

678
677
652
582

495
485
437
401
Mean

4.1
3.2
2.9
2.6

9.0
9.4
9.1
8.6

10.8
12.2
12.2
11.8

14.6
16.9
16.6
15.7
Original
Median

3.1
2.3
2.1
1.9

8.7
9.3
8.9
8.4

10.4
12.1
11.9
11.5

13.8
17.0
16.3
15.1
Data
s.d.

3.4
2.4
2.3
2.3

4.0
4.3
4.4
4.6

5.1
5.6
6.0
5.7

7.6
7.3
7.4
7.6
CV
(%)

82
75
81
87

45
46
48
53

48
46
49
48

52
43
45
48
Distribution
Transform

In
In
SB
SB

Su
Su
Su
Su

Su
Su
Su
Su

Su
Su
Su
Su
Model
Mean

1.83
0.915
3.32
3.43

0.150
0.156
0.151
0.143

1.63
0.202
0.201
0.194

1.26
0.277
0.271
0.257
s.d.

0.64
0.71
0.88
0.92

0.066
0.071
0.072
0.076

0.62
0.091
0.096
0.092

0.76
0.12
0.12
0.12
CV = coefficient of variation
s.d. = standard deviation

Source: Carsel et al. (1988)
                                  5-54

-------
TABLE 5-28. CORRELATIONS AMONG TRANSFORMED VARIABLES OF ORGANIC
              MATTER, FIELD CAPACITY, AND WILTING POINT
Stratum
(m)
 OM+WP             FC + OM
N         Corr.       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
118
119
111
98
877
870
844
780
673
664
627
543
0.738
0.630
0.487
0.456
0.545
0.372
0.375
0.392
0.495
0.473
0.457
0.434
52
49
42
38
459
446
419
347
369
355
321
264
0.624
0.404
0.427
0.170
0.609
0.384
0.336
0.412
0.577
0.409
0.434
0.456
51
49
42
39
455
450
429
370
370
361
334
289
0.757
0.759
0.811
0.761
0.675
0.639
0.714
0.762
0.745
0.775
0.784
0.751
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
488
472
420
384
0.538
0.434
0.456
0.415
228
201
171
137
0.496
0.454
0.369
0.106
226
204
174
145
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.
                                      5-55

-------
TABLE 5-29. MEAN BULK DENSITY (g cnt$ FOR FIVE SOIL TEXTURAL CLASSIFI-
            CATION
Soil Texture
Mean Value
Range Reported
Silt Loams
Clay and Clay Loams
Sandy Loams
Gravelly Silt Loams
Loams
All soils
1.32
1.30
1.49
1.22
1.42
1.35
0.86-1.67
0.94-1.54
1.25-1.76
1.02-1.58
1.16-1.58
0.86-1.76
t Baes, C. F., Ill and R.D. Sharp. 1983. A Proposal for Estimation of Soil Leaching
Constants for Use in Assessment Models, J. Environ. Qual.    12(1): 17-28.
TABLE 5-30. DESCRIPTIVE STATISTICS FOR BULK DENSITY (g
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size

40
44
38
34

459
457
438
384

398
395
371
326

259
244
214
180
Mean

1.45
1.50
1.57
1.58

1.44
1.51
1.56
1.60

1.46
1.58
1.64
1.67

1.52
1.63
1.67
1.65
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
1.72
1.72
s.d.

0.24
0.23
0.16
0.13

0.19
0.19
0.19
0.21

0.22
0.23
0.23
0.23

0.24
0.26
0.27
0.28
CV
(%)

16.2
15.6
10.5
8.4

13.5
12.2
12.3
12.9

15.0
14.5
14.2
14.0

15.9
16.0
16.3
17.0
CV = coefficient of variation
s.d. = standard deviation
Source: Carsel et al. (1988)
                                    5-56

-------
TABLE 5-31. DESCRIPTIVE STATISTICS AND DISTRIBUTION MODEL FOR OR-
            GANIC MATTER (PERCENT BY VOLUME)
Stratum
(m)
Class A
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class B
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Class C
0.0-0.3
0.3-0.6
0.3-0.9
0.9-1.2
Class D
0.0-0.3
0.3-0.6
0.6-0.9
0.9-1.2
Sample
Size

162
162
151
134

1135
1120
1090
1001

838
822
780
672

638
617
558
493
Mean

0.86
0.29
0.15
0.11

1.3
0.50
0.27
0.18

1.45
0.53
0.28
0.20

1.34
0.65
0.41
0.29
Original
Median

0.62
0.19
0.10
0.07

1.1
0.40
0.22
0.14

1.15
0.39
0.22
0.15

1.15
0.53
0.32
0.22
Data
CV
s.d. (%)

0.79 92
0.34 114
0.14 94
0.11 104

0.87 68
0.40 83
0.23 84
0.16 87

1.12 77
0.61 114
0.27 96
0.21 104

0.87 66
0.52 80
0.34 84
0.31 105
Distribution
Mean

-4.53
-5.72
-6.33
-6.72

-4.02
-5.04
-5.65
-6.10

-3.95
-5.08
-5.67
-6.03

-4.01
-4.79
-5.29
-5.65
Model
s.d.

0.96
0.91
0.83
0.87

0.76
0.77
0.75
0.78

0.79
0.84
0.83
0.88

0.73
0.78
0.82
0.86
CV = coefficient of variation
s.d. = standard deviation
Source: Carsel et al. (1988)

"Johnson sfi transformation is used for all cases in this table.
                                   5-57

-------
TABLE 5-32. ADAPTATIONS AND LIMITATIONS OF COMMON IRRIGATION METH-
             ODS
Irrigation
Method
Adaptations
Limitations
Furrow               Light, medium-and fine-
textured soils; row crops.
crops; 10 percent cross
Sprinklers

and hot climate.

Flood

than 2 percent.
All slopes; soils; crops.
Light, medium, and heavy
soils.
Slopes up to 3 percent in
direction of irrigation; row
slope.

High initial equipment cost;
lowered efficiency in wind
Deep soils; high cost of land
preparation; slopes less
Source: Adapted from Todd (1970).
TABLE 5-33. WATER REQUIREMENTS FOR VARIOUS IRRIGATION AND SOIL
             TYPES
Typical Application Rate (Inches/Hour) bv Sprinklers
Slope
(%)
Sprinkling 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: Adapted from Todd (1970).
                                     5-58

-------
   TABLE 5-34. REPRESENTATIVE FURROW PARAMETERS DESCRIBED IN THE LITERATURE
01

61
CO
Reference Location
Elliott et al. Colorado
(1982)
Hall (1956)
Fangmeier and Arizona
Ramsey (1978)
Karmeli et al. Colorado
(1978)
TABLE 5-35. FURROW
Soil Texture
Soil

Clay loam
Clay loam
Loamy sand
Medium
Fine
loam
Clay
sandy
loam
Crop
Corn
Corn
Corn
Corn
Manning's
Channel Flow Furrow Bottom Roughness
Slope Rate(m3/s) Length(m) WidtMcm) Coefficient
0044
.0092-.0095
.0023-.0025
.005
None
(test
furrows)
None
IRRIGATION RELATIONSHIPS FOR

Max allowable
nonerosive
Slope furrow stream
(percent)
.25
.50
.75
1.00
1.50
2.00
3.00
5.00
(gpm)
40
20
13
10
7
5
3
2


2
Coarse

4

VARIOUS
.01
.0045


.001-.003
.00085-.00096
.003-.005
.0004-.0018
.0011
625
425-450
350
200
9
625
--
10-20
.02-.03
.02-.03
.02-.03
.035
.02-.04
.01-.048
SOILS, SLOPES, AND DEPTHS OF APPLICATION
Medium

6

8
Depth
2
of irrigation
4
application (inches)
6 8

2
Fine

4 6


8
Maximum allowable length of run (feet)
500
345
270
235
190
160
125
95
720
480
380
330
265
225
180
135
875
600
480
400
330
275
220
165
1,000
680
550
470
375
320
250
190
820
560
450
380
310
260
210
160
1,150
800
630
540
430
370
295
225
1,450 1,650
975 1,120
775 900
650 760
530 620
450 530
360 420
270 320
1,050
730
580
500
400
345
270
210
1,500 1,750
1,020 1,250
820 1,000
750 850
570 700
480 600
385 470
290 350
2,140
1,460
1,150
990
800
675
550
410

-------
TABLE 5-36. SUITABLE SIDE SLOPES FOR CHANNELS BUILT IN VARIOUS KINDS
             OF MATERIALS
Material                                                      Side slope

Rock                                                         Nearly vertical

Muck and peat soils                                            !4:1

Stiff clay  or earth with concrete lining                            l/2\\ to 1:1

Earth with stone lining, or earth for large channels                 1:1

Firm clay or earth for small ditches                              IVz'.l

Loose sandy earth                                             2:1

Sandy loam or porous clay                                      3:1


Source: Adapted from Chow (1959).
TABLE 5-37. VALUE OF "N" FOR DRAINAGE DITCH DESIGN



Hydraulic radius (ft)                                            EN

less than 2.50.040-0.045

2.5 to 4.0                                                     .035-.040

4.0 to 5.0                                                     .030-.035

more than 5.0                                                 .025 -.030


Source: Adapted from U.S. Dept. of Agric. Soil Conservation Service.
                                     5-60

-------
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
Sandy loam
Hydraulic
Conductivity
(In/s)
J0--H ; jftps
l&f .-lfe«
IP ,i$)6
10$ .-1$)6
Idff .lfb»
m> ,w
Material
Very fine sand
Find sand
Medium sand
Coarse sand
Gravel and sand
Gravel
Sandstone
Limestone*
Shale
Hydraulic
Conductivity
(m/s)
ifoe8 -HBO?
103.- m?
105-1D02
^ .-ifb-3
^ .-1^4
^"W
* Excluding cavernous limestone.

Source: Adapted from Todd (1970).

* See also Table 5-40.
TABLE 5-39. VALUES OF GREEN-AMPT PARAMETERS FOR SCS HYDROLOGIC
            SOIL GROUPS
SCS
Hydrologic
Soil Group
A
B
C
D
Saturated Hydraulic^
Conductivity KS
(cm hr-1)
1.0 -10.0
.60 -1.0
.20 -0.60
.005 -0.20
Suction
Parameter HF
(cm)
10
10-20
15-10
20-150
Source: Adapted from Brakensiek and Rawls (1983).

"Also see Table 5-30.
                                 5-61

-------
5.3 VADOFT PARAMETERS

Input data for variably saturated flow simulations include the following:

   (1) System Geometry

      •  Soil column dimensions (L)

   (2) Porous Medium Properties

          •  Saturated hydraulic conductivity, KB (LT1)

          •  Specific storage, S, (Lrt)

          •  Effective porosity, $

   (3) Constitutive Relationships for Variably Saturated Flow

          •  Tabulated data of kpj versus SW, or values of parameters of analytic expres-
            sions for kj^ versus SW

          •  Tabulated data of SW versus ty, or values of parameters of analytic expres-
            sions for SW versus \p,

   (4) Initial and Boundary Conditions

          •  Prescribed values of pressure head, f (L)

          •  Prescribed values of nodal fluid flux
             (infiltration rate), I (LT1)

Input data for the transport model include the following:

   (1) System Geometry

          •   Soil column dimensions (L)

   (2) Porous Medium Properties

          •  Longitudinal dispersivity «£, (L)

          •  Molecular diffusion coefficients, D* (LlT1)

          •  Effective porosity, f

   (3) Properties of Solute Species

          • Decay coefficient, A (T-l)

                                         5-62

-------
          •  i Retardation coefficient, R

   (4) Darcy Velocity, V (LT)

   (5) Water Saturation, SW

   (6) Initial  and Boundary Conditions

          •   Prescribed value of concentration, CO

          •   Prescribed value of solute flux,

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 lognorm-
al.

Soil-Water Characteristic Data - The user is allowed two options: either to input these
data as a set of paired functions (water saturation |S^I versus relative conductivity l]Kj4]
and pressure head [vf] versus water saturation |S$J or to input parameters of the analytic
expressions for these functions in the  code. The parameterization of the latter functions
is discussed here.
                                        5-63

-------
TABLE 5-40. DESCRIPTIVE STATISTICS FOR SAT. HYDRAULIC CONDUCTIVITY
              (cm hr-1)

                                    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
Sandy Loam
X
0.20
0.26
1.04
14.59
0.25
0.45
0.02
0.07
29.70
0.12
1.31
4.42
s
0.42
0.70
1.82
11.36
0.33
1.23
0.11
0.19
15.60
0.28
2.74
5.63
CV
210.3
267.2
174.6
77.9
129.9
275.1
453.3
288.7
52.4
234.1
208.6
127.0
n
114
345
735
315
88
1093
126
592
246
46
214
1183
* n = Sample size, i = Mean, s = Standard deviation, CV = Coefficient of variation
   (percent)
** Agricultural soil, less than 60 percent clay

Source: Carsel and Parrish (1988).
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, (5, and f of the van
Genuchten model (see Section 7). 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 (\^g) and the residual water phase saturation (SW. 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 0,

Note that y is related to (3 by the relationship:
In addition, Table 5-44 gives the correlations between these parameters by soil textural
classification.

                                       5-64

-------
Specific Storage - For unsaturated zone flow, set the specific storage to 0.

Effective Porosity - Mean values of saturated water content (Q) and residual water
content (6^ shown in Table 5-42 can be used to estimate effective porosity. The satura-
tion water content ($J) is equal to the total porosity of the soil. The effective porosity can
be roughly approximated as the difference of 6, and f)r 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 in Section 5.2 of
the dispersion coefficient having units of era? day:l.) Dispersion coefficients are calculated
by the model as the product of the seepage velocity and the dispersivity input by the user.
In the absence of site-specific values it is recommended that the dispersivity be chosen as
one-tenth of the distance of the flow path or:

                                     a  = 0.1 x,

   where
                         x^ = the thickness of the vadose zone.

Molecular Diffusion - See the discussion in Section 5.2.
                                        5-65

-------
    TABLE 5-41. DESCRIPTIVE STATISTICS FOR VAN GENUCHTEN WATER RETENTION MODEL PARAMETERS, a, p, y
                 (Carsel and Parrish 1988)
                            Parameter a, cm'1            Parameter 6                Parameter y
    Soil Type                X      SD     CV    N      X      SD      CV    N     X     CV     N

    Clayfl                  0.008   0.012  160.3   400    E09    O09     7^9   400    O08   O0782?7400~

    Clay Loam             0.019   0.015   77.9   363    1.31    0.09     7.2   364    0.24   0.06   23.5   364

    Loam                  0.036   0.021   57.1   735    1.56    0.11     7.3   735    0.36   0.05   13.5   735

    Loamy Sand            0.124   0.043   35.2   315    2.28    0.27    12.0   315    0.56   0.04    7.7   315

    Silt                    0.016   0.007   45.0   88     1.37    0.05     3.3     88    0.27   0.02    8.6     88

    Silt Loam              0.020   0.012   64.7   1093   1.41    0.12     8.5  1093    0.29   0.06   19.9   1093
en
§   Silty Clay              0.005   0.005  113.6   126    1.09    0.06     5.0   374    0.09   0.05   51.7   374

    Silty Clay Loam        0.010   0.006   61.5   641    1.23    0.06     5.0   641    0.19   0.04   21.5   641

    Sand                  0.145   0.029   20.3   246    2.68    0.29    20.3   246    0.62   0.04    6.3   246

    Sandy Clay             0.027   0.017   61.7   46     1.23    0.10     7.9     46    0.18   0.06   34.7     46

    Sandy Clay Loam       0.059   0.038   64.6   214    1.48    0.13     8.7   214    0.32   0.06   53.0   214

    Sandy Loam            0.075   0.037   49.4   1183   1.89    0.17     9.2  1183    0.47   0.05   10.1   1183


    x = Mean, SD = Standard Deviation, CV = Coefficient of Variation, N  = Sample size

    "Agricultural Soil, Clay 60 percent

-------
TABLE 5-42. DESCRIPTIVE STATISTICS FOR SATURATION WATER CONTENT
           AND RESIDUAL WATER CONTENT
Saturation Water Content (6)) Residual Water Content (6|)
Statistic*
Soil Type
Clay**
Clay Loam
Loam
Loamy Sand
silt
Silt Loam
Silty Clay
Silty Clay Loam
Sand
Sandy Clay
Sandy Clay Loam
Sandy Loam
X
0.38
0.41
0.43
0.41
0.46
0.45
0.36
0.43
0.43
0.38
0.39
0.41
s
0.09
0.09
0.10
0.09
0.11
0.08
0.07
0.07
0.06
0.05
0.07
0.09
CV
24,
22
22.
21
17
18,
19.
17,
15.
13.
17.
21.
.1
.4
1
.6
.4
.7
,6
.2
1
,7
,5
0
n
400
364
735
315
82
1093
374
641
246
46
214
1183
X
0
0
0
0,
0,
0,
0,
0.
0.
0.
0.
0.

.068
.095
.078
.057
.034
.067
.070
,089
,045
,100
,100
,065
s
0
0
0
0
0
0
0,
0,
0,
0,
0.
0,

.034
.010
.013
.015
.010
.015
.023
.009
.010
.013
,006
.017
CV
49.9
10.1
16.5
25.7
29.8
21.6
33.5
10.6
22.3
12.9
6.0
26.6
n
353
363
735
315
82
1093
371
641
246
46
214
1183
   n = Sample size, x-^Vlean, s = standard deviation,
   CV = coefficient of variation (percent)

  Agricultural soil, less than 60 percent clay.
                                  5-67

-------
TABLE 5-43. STATISTICAL PARAMETERS USED FOR DISTRIBUTION APPROXIMA-
           TION
Soil
Tex
ture**
s
s
s
s
SL
SL
SL
SL
LS
LS
LS
LS
SIL
SIL
SIL
SIL
SI
SI
SI
SI
c
c
c
c
SIC
SIC
SIC
SIC
sc
sc
sc
sc
SICL
SICL
SICL
SICL
CL
CL
CL
CL
Hydrau- Trans-
lie forma-
Variable tion
R
Qt
a
P
K.
9,
a
P
K.
er
a
P
K.
er
a
P
K,
er
a
P
K,
er
a
P
K.
er
a
P
K.
er
a
P
K.
er
a
P
K.
o.
a
P
SB
LN
SB
LN
SB
SB
SB
LN
SB
SB
NO
SB
LN
SB
LN
SB
LN***
ND***
NO
NO
SB
su**
SB**
LN**
LN
NO
LN
SB
LN
SB
LN
LN
SB
NO
SB
NO
gB***
SU
LN
SB
Limits of
Variation
A B
0.0 70.0
0.0 0.1
0.0 0.25
1.5 4.0
0.0 30.0
0.00 0.11
0.00 0.25
1.35 3.00
0.0 51.0
0.0 0.11
0.0 0.25
1.35 5.00
0.0 15.0
0.0 0.11
0.0 0.15
1.0 2.0
0.0 2.0
0.0 0.09
0.0 0.1
1.2 1.6
0.0 5.0
0.0 0.15
0.0 0.15
0.9 1.4
0.0 1.0
0.0 0.14
0.0 0.15
1.0 1.4
0.0 1.5
0.0 0.12
0.0 0.15
1.0 1.5
0.0 3.5
0.0 0.115
0.0 0.15
1.0 1.5
0.0 7.5
0.0 0.13
0.0 0.15
1.0 1.6
Mean
-0.39387
-3.11765
0.37768
0.97813
-2.49047
0.38411
-0.93655
0.63390
-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
-2.75043
1.23640
-5.87171
0.67937
-4.21897
0.13248
Estimated*
Standard
Deviation
1.15472
0.22369
0.43895
0.10046
1.52854
0.70011
0.76383
0.08162
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
0.60529
0.06130
2.92220
0.06005
0.71389
0.72498
Truncation Limits
on Transformed
D*** Variable
0.045
0.053
0.050
0.063
0.029
0.034
0.044
0.039
0.036
0.043
0.027
0.070
0.046
0.073
0.083
0.104
0.168
0.089
0.252
0.184
0.122
0.058
0.189
0.131
0.205
0.058
0.164
0.069
0.130
0.078
0.127
0.100
0.049
0.056
0.082
0.082
0.058
0.061
0.052
0.035
















-2.564 -0.337
0.013 0.049



0.0065 0.834
-5.01 0.912
0.00 0.315












-8.92 2.98



                                5-68

-------
TABLE 5-43. STATISTICAL PARAMETERS USED FOR DISTRIBUTION APPROXIMA-
             TION  (continued)
Soil
Tex
ture**
SCL
SCL
SCL
SCL
L
L
L
L
Hydrau- Trans-
lie forma-
Variable tion
Of-
a
0
K.
a
B
SB
SB***
SB
LN
SB
SB
SB
su
Limits of
Variation
A
0.0
0.0
0.0
1.0
0.0
0.0
0.0
1.0
B
20.0
0.12
0.25
2.0
15.0
0.12
0.15
2.0
Mean
-4.
1
-1.
0.
-3,
0.
-1.
0.
.03718
.65387
,37920
38772
,71390
63872
,27456
53169
Estimated*
Standard
Deviation
1
0,
0.
0,
1
0,
0.
0.
.84976
,43934
82327
,08645
.77920
,48709
78608
09948
Truncation Limits
on Transformed
D*** Variable
0,
0
0.
0,
0.
0.
0.
0.
,047
.077 0.928 2.94
048
,043
019
,064
039
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.
                                     5-69

-------
TABLE 5-44. CORRELATIONS AMONG TRANSFORMED VARIABLES PRESENTED
          WITH THE FACTORED COVARIANCE MATRIX*

Silt **(n = 61)
K.
er
a
0
Clay (n= 95)
K,
er
a
§
Silty Clay (n =
K,
9r
oc
0
Sandy Clay (n
K,
er
a
0
Sand (n- 237)
K,
9r
a
6
K.

0.5349258
-0.204
0.984
0.466

1.9614077
0.972
0.948
0.908
123)
1.2512845
0.949
0.974
0.908
= 46)
2.0172105
0.939
0.957
0.972

1.0370702
-0.515
0.743
0.843
Of

-0.0015813
0.0075771
-0.200
-0.610

0.0701669
0.0170159
0.890
0.819

0.0082067
0.0027392
0.964
0.794

0.8827527
0.3241979
0.937
0.928

-0.1092256
0.1816914
0.119
-0.858
a

0.0030541
0.0000021
0.0005522
0.551

0.5645309
-0.0798488
0.1716520
0.910

0.3143268
0.0404171
0.0608834
0.889

0.5391195
0.0634106
0.1501651
0.932

0.3276629
0.2583835
0.1429585
0.298
P

0.0128700
-0.0145118
0.0144376
0.0133233

0.0475514
-0.0142394
0.0021973
0.0164640

0.3674505
-0.0858769
0.0660396
0.1305065

0.0756103
0.0035688
0.0010668
0.0178225

0.0805436
-0.0471785
-0.0013674
0.0167064
SandyLoani(n=l 145)
K,
er
a
5
1.6026856
-0.273
0.856
0.686
-0.1529235
0.5378436
0.151
-0.796
0.0372713
0.0174500
0.0142626
0.354
0.2108253
-0.1943369
0.0193794
0.1084945
Loamy Sand(n= 313)
K,
0r
a
i
Silt Loam (n=
K.
9r
a
0
1.4754063
-0.359
0.986
0.730
1072)
1.4754063
-0.359
0.986
0.730
-0.2005639
0.5215473
-0.301
-0.590

-0.02005639
0.5215473
-0.301
-0.590
0.0372713
0.0174500
0.0142626
0.354

0.5245489
0.0300399
0.0820163
0.775
0.2108253
-0.1943369
0.0193794
0.1084945

0.3525548
-0.1696100
0.2341768
0.1583593
                             5-70

-------
TABLE 5-44. CORRELATIONS AMONG TRANSFORMED VARIABLES PRESENTED
             WITH THE FACTORED COVARIANCE MATRIX* (continued)
Silty Clay Loam (n= 591)
K,                1.6177521
6r                0.724
a                 0.986
B                 0.918
Clay Loam (n= 328)
R
9,
a
1.9200165
0.790
0.979
0.936
Sandy Clay Loam (n= 212)
K,                 1.8497610
6r                0.261
a                 0.952
|                 0.909

Loam (n= 664)
                 0.0056509
                 0.0053780
                 0.777
                 0.549
0.0395603
0.0307122
0.836
0.577
                 0.1020156
                 0.3775754
                 0.392
                 -0.113
                 0.5116521
                 0.0475299
                 0.0731704
                 0.911
 0.5886263
-0.0619715
 0.1060875
 0.909
                 0.7838769
                 0.1223451
                 0.2198684
                 0.787
                0.0486478
               -0.0089569
                0.0080399
                0.0171716
 0.5417671
-0.1536351
 0.0653030
 0.1159401
                0.0766289
               -0.0305588
               -0.0078559
                0.0155766
K.
9r
a
P
1.4083953
0.204
0.982
0.632
-0.0995016
0.4775039
-0.086
-0.748
0.6110671
0.0727710
0.0926351
0.591
0.0545016
-.0545793
0.0256843
0.0288861
      Entries in the lower triangular portion of the matrix are sample Pearson product-
      moment correlations given to three decimal places. The diagonal and upper
      triangular entries form the triangular Cholesky decomposition of the sample
      covariance matrix.

**    n = Sample size.

Source:  Carsel and Parrish,  1988.


Pesticide Decay Coefficients - See the discussion in Section 5.2.

Retardation Factors - In VADOFT, in contrast to PRZM, the user inputs the retarda-
tion factor R instead of the distribution coefficient,^ (cm? g:l). The retardation factor is
defined for saturated conditions in the input:
                                                                            (5.5)
                                      5-71

-------
and is adjusted internally for values of 6K 9§, In the above equation, p is the soil bulk
density (g cm?) and 0, is the saturation water content (cm? cutl). In making this calcula-
tion, the user should directly use the value for p, if known. If necessary, p can be
approximated according to:


                     p= 2.65(1-®                                            (5.6)

The CV of the retardation factor, R, can be computed knowing the uncertainties in K^ p
and 8, (Taylor 1982).  The fractional uncertainties add to give an upper bound error on R
    nS? or are combined as a root mean square for  independent  random errors.  Thus,
                     c«aaa = (eve. + ew^ + cvp>                                (5.7)

or

                     CV = 100
The uncertainty in the value of Kg will depend upon whether it is measured, calculated as
the product of Jty and % organic carbon, and whether the IQ is calculated from a
surrogate parameter such as octanol water partition coefficient (JKg^ or volubility (s).
Directly measured values would obviously have lower CVs. Assuming that Kj is calculat-
ed from a measured soluble concentration, then it is possible that the CV would be on the
order of 60 to 130% (Jury 1985). For Kg derived from KJQ or volubility, the CV could be on
the order of 1000%.
                                       5-72

-------
                                    SECTION 6

                    PESTICIDE ROOT ZONE MODEL (PRZM)
                               CODE AND THEORY
6.1 INTRODUCTION AND BACKGROUND (PRZM)

This section describes the theoretical background for a mathematical simulation model
(PRZM) that has been developed and partially tested to evaluate pesticide leaching from
the crop root zone under field crop conditions.

Following this short 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.
This section 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 groundwa-
ter 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, areal
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 volubility 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 groundwa-
ter contamination and is the problem that the model is designed to investigate,
                                        6-1

-------
Numerical models for the movement of solutes in porous media for steady-state, transient,
homogeneous, and multi-layered conditions have been previously developed. Included in
such studies have been linear and nonlinear sorption, ion exchange, and other chemical-
specific reactions. These investigations have proven valuable in interpreting laboratory
data, investigating basic transport processes, and identifying controlling factors in
transport and transformation. As  noted in a recent review of models for simulating the
movement of contaminants through groundwater flow systems, however, the successful
use of such models requires a great deal of detailed field data. This unfortunate conclu-
sion arises from the scaling problems associated with laboratory experiments 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 problems in modeling pesticide leaching with  existing procedures are discouraging
when one considers the need to  evaluate future problems arising from pesticides not yet
widely distributed or used. Models used  to perform such 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 for leaching with reason-
able estimates of water movement through soil systems. Data input requirements must
be reasonable in spatial and temporal requirements and generally available from existing
data bases. This model attempts to meet these objectives.

6.1.2 Background

The Pesticide Root Zone Model (PRZM) (Camel et al. 1984, Carsel et al. 1985) was
selected as the code to provide the capability to simulate the transport and transformation
of agriculturally applied pesticides in the crop root zone. PRZM was initially designed for
this purpose and has 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 H is a one-dimensional, dynamic, compartmental model for use in simulat-
ing 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 simulations allow the consideration of pulse loads, the prediction of
peak events, and the estimation of time-varying mass emission or concentration profiles,
                                        6-2

-------
Figure 6.1
 Figure 6.1. Pesticide Root Zone Model.
                                           6-3

-------
thus overcoming limitations of 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 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 may also be considered.

Dissolved, adsorbed, and vapor-phase  concentrations in the soil are estimated by simulta-
neously Considering the processes of pesticide uptake by plants, surface runoff, erosion,
decay, volatilization, foliar washoff, advection, dispersion, and retardation. The user may
elect to solve the transport equations using one of two finite-difference numerical
solutions, 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 pesticide transport equations (i.e., 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 period. A daily time series value for
various fluxes or storages  can be written to sequential files during program execution.

6.2.2 Limitations

There were severe limitations of the PRZM Release I Code, some that were obvious to the
developers and some that  were pointed out subsequently by model users. These can  be
broken into four categories:

       •  Hydrology
       •  Soil hydraulics
       9  Method of solution  of the transport equation
       »  Deterministic nature of the model

In Release II, many of these limitations to an extent, have been overcome.

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 freer time  step 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 hydrography. This depends to some extent
upon 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


                                         6-4

-------
meteorological data. A portion of this limitation has been mitigated, we hope, by en-
hanced parameter guidance.

The method of computing potential evapotranspiration using Hamon's formula, in the
absence of some evaporation data, has also been retained. Evapotranspiration from
irrigated citrus in Florida was found to be substantially underpredicted when using this
method to estimate potential  evapotranspiration (Dean and Atwood 1985). Users should
check the model's hydrologic simulation carefully when using this option.

The capability to simulate soil temperature has been added to PRZM-2 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 transpira-
tion 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 differentia-
tion is made between evaporation and transpiration in PRZM at this time.

In PRZM Release I, the soil hydraulics were simple-all drainage to field capacity water
content was assumed  to occur within 1 day. (An option  to  make drainage time dependent
was also included, but there is not much evidence to suggest that it was utilized by model
users to any great extent). This had the  effect, especially in larger soil cores, of inducing
a greater-than-anticipated movement of chemical through the profile.  While this  repre-
sentation of soil hydraulics has been retained in PRZM-2, the user has the option, with
the linked modeling system, of coupling PRZM to VADOFT. PRZM-2 is then used to
represent the root zone, while VADOFT,  with a more rigorous representation of unsatu-
rated flow, is used to simulate the thicker vadose zone. The difficulties in parameterizing
the Richards equation for unsaturated flow in VADOFT is overcome by using the  tech-
nique of van Genuchten  to generate soil water characteristic curves using soil textural
information. For short soil cores, PRZM can obviously 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-2 simulates only advective,
downward movement of water and does not account for diffusive movement due to soil
water gradients. This  means that PRZM-2 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 volatiliza-
tion. However, the process would seem less likely to affect 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 inadequacy of the solution to the
transport equation in  advection-dominated systems. The backward difference  formulation
of the advection term tends to produce a  high degree of numerical dispersion in such
systems. This results in  overprediction of downward movement due to smearing of the
peak and subsequent overestimation of loadings to  groundwater. In  this new release, a
new formulation is available for advection-dominated systems. The advective terms are
decoupled from the rest of the transport equation and solved separately using a Method of


                                        6-5

-------
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 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 to run PRZM 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).

6.3 DESCRIPTION OF THE EQUATIONS

The mathematical description of the processes simulated by PRZM are broken down in
the following discussion into five categories:

       • Transport in Soil
       • Water Movement
       • Soil Erosion
       • Volatilization
       • Irrigation

The first three categories were simulation options previously available in PRZM Release I.
Since the capability to simulate pending is new, the mathematical basis of the pending
algorithms  is described in detail. The final process, volatilization, was not available in
the previous release of PRZM, and its theoretical basis is also described in detail.

6.3.1 Transport in Soil

The PRZM-2 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
write mass balance equations for both the surface zone and the subsurface zones.
Addition of the vapor phase and ponded water compartments in PRZM-2 require the
consideration of additional terms.  The surface zone expressions for each of the dissolved,
adsorbed, and vapor phases can be written as:


              A A
                                        6-6

-------
(Surface Layer:
Runoff)
(Surface L
JQR
(Wro )
Diflusion
fcK
ft \rf*r* ^ ----- 1
over, "*^
Erosion)








SOLIDS
c
P
S
s
Adsorption/
Desof]

?wm


(JDS )













j
I

I Diffusion /?„,*
Leaching ,
\ i
i'v
WATER
C
w
0
(J,
•DM )
Ml

-------
                                             =_J   _J
                                            =j   - j
where
        A     =   cross-sectional area of soil column (cm?)
        Az    ~   depth dimension of compartment (cm)
        (%    =   dissolved concentration of pesticide (g cm$)
        6,    ~   sorbed concentration of pesticide (g g:l)
        Qg    =   gaseous concentration of pesticide (g  cnml)
        0     =   volumetric water content of soil (cm? cm$)
        a     =   volumetric air content of the soil (cm! ctm^j)
        (%     ~   soil bulk density (g cnm^
        t      =   time (d)
        JD    =   represents the effect of dispersion and diffusion of dissolved phase (g
                  day1)
        Jy    =   represents the effect of advection of dissolved phase (g day!)
        Jgg    =   represents the effect of dispersion and diffusion in vapor phase (g day!)
        J0W    =   mass loss due to degradation in the dissolved phase (g day-i)
        Jgg    =   mass loss due to degradation in the vapor  phase (g day-1)
        jy    =   mass loss by plant uptake of dissolved phase (g day-1)
              =   mass loss by removal in runoff (g day-!)
              =   mass gain due to pesticide deposition on the soil surface (g day-"!)
              =   mass gain due to washoff from plants to soil (g day:l)
        Jg§    =   mass loss due to degradation of sorbed phase chemical (g day-!)
        Jgg    =   mass loss by removal on eroded sediments (g day-!)
              =   mass gain or loss due to parent/daughter transformations
Equations for the subsurface zones are identical to Equations 6-1, 6-2, and 6-3 except that
J^g, Jjpjjp, and JEE are not included. J^Jp applies to subsurface zones only when pesticides
are incorporated into the soil. For subsurface layers below the root zone, the term Jl^J is
also not utilized.

                                          6-8

-------
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 below).

Each term in Equations 6-1 through 6-3 are now further defined. Dispersion and
diffusion in the dissolved phase are combined and are described using  Pick's law as
                                                                                 (6_4)
where
       Eli^    =  diffusion-dispersion coefficient for the dissolved phase, assumed constant
                 (on£ day:1)
       C}     =  dissolved concentration of pesticide (g 
-------
       4>      = total porosity (cm? cmf*)

       Da    = molecular diffusivity of the chemical in air, assumed constant (cm? day!)


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 describ-
ing volatilization.

The advective term for the dissolved phase, JV, describes the movement of pesticide in the
bulk flow  field and is written as
                                       /LAz W , A Az                            (6-9)


                                        Kg Cg a  Az                            (6-10)
                                        6-10

-------
where
       |^    = lumped, first-order decay constant for solid and dissolved phases (day"r)
       ^    = lumped, first-order decay constant for vapor phase (day-1)
       68    = solid-phase concentration of pesticide (g g:l)
Plant uptake of pesticides is modeled by assuming that uptake of a pesticide by a plant is
directly related to transpiration rate. The uptake is given by:

                                     *=f CyJSoe AAz                            (6-11)
where
       sKJ     = uptake of pesticide (g day:l)
       f      = the fraction of total water in the zone used for transpiration (day:l)
       &      = 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
                                                                                (6-12)
                                            w
in which
       J^S    = pesticide loss due to runoff (g day:l)
       Q     = the daily runoff volume (cm& day:l)
       Aw,    = watershed area
and the loss of pesticide due to erosion is

                                  Jfe-
where
       JER    = the pesticide loss due to erosion (g day;l)
       Xe     = the erosion sediment loss (metric tons day:l)
                                         6-11

-------
        r^    = the enrichment ratio for organic matter (g g-i)

        p     = a units conversion factor (g tons-1)


Soil erosion is discussed in more detail in Section 6.3.3.

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 7.1. It is partitioned between the  plant canopy and the soil surface, and the
rate at which it reaches the soil surface is designated
Pesticides applied to the plant canopy can be transported to the soil surface as a result of
rainfall washoff. This term, JBQEu is defined as:


                                               MA                             (6-14)
where

        E     = foliar extraction coefficient (cm:l)

        Pr     = daily rainfall depth (cm day:l)

        M     = mass of the pesticide on the plant surface projected area basis (g cnaf)


The foliar pesticide mass, M, is further subject to degradation and losses through
volatilization. Its rate of change is given by
                              dt
                                                                                (6- 1 5)
where
       Kf     = lumped first-order foliar degradation constant (day1!)

       Aj.     = application rate to the plant (gha-"I day:l)

       It> =3= a units conversion factor (ha)

Adsorption and resorption 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:


                                      Q - K& OK                                (6-16)
                                         6-12

-------
where
       ^g     = partition coefficient between the dissolved and solid phases (cm? g:l)


A similar expression can be developed to express the vapor phase concentration in terms
of the dissolved-phase concentration as follows


                                     Cg = K$3fy                                (6-17)
where
       KH    = Henry's constant, i.e., distribution-coefficient between liquid phase and

                vapor phase (cm? cmi'3)

The transformation of parent to daughter is assumed to be first order and takes place
according to


                                            C^vAAz 6                         (6-18)
where
            = the transformation rate constant (day:1)
When simulating an end-of-chain daughter, J^ may also be a source term equal to the
sum of the first-order transfers from any and all parents.


                               JIM = £ K^ C*A Az 6                         (6-19)
in which the superscript k denotes a parent compound. For intermediate products, the
solute transport equation may 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. The section includes a description of the equations used to simulate this
process.

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:
                                        6-13

-------
                                                                a
                                                                             (6-20)
Equation 6-20 is solved in PRZM-2 for the surface layer with ffl = 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 diffu-
sion, out of the bottom of the soil profile.

6.3.2 Water Movement

Because V and 0 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):
                                                                              (6-21)
where
and
       K(6)   = hydraulic conductivity at various heads (cm see-ff
       §      = soil water content (cm? cro3)
                                                                              (6-22)
or, in simpler terms
                                       i_    a*?
                                                                             (6-23)
                                        6-14

-------
where

        6 = soil water content (cna? cmf)

        V = soil water velocity (cm day:1)


Writing Equation 6-23 in an integrated backwards finite difference form yields


                                       Az (BW1                                (6-24)


or


                                  fe * ft - KMt + 0'Az                       (6-25)
In these equations, t and t+1 denote the beginning and end of time step values, respec-
tively, and i is the soil layer index. These equations can be further simplified by substi-
tuting the nomenclature SW for $£z so that


                                                       £                        (6-26)
where

       SW= soil water content (cm)

The velocities in Equation 6-26 are a function of inputs to the soil (precipitation, infiltra-
tion) and outflows from the soil (evapotranspiration, runoff).

Water balance equations are separately developed for (a) the surface zone, (b) horizons
comprising the active root zones, and (c) the remaining lower horizons within the
unsaturated zone, The equations are:

       Surface Zone


                                               ^1- E[-U(                   (6-27)
                                        6-15

-------
        Root Zone
        Below Root Zone
                                                                               (6-29)
where

        ($W)f = soil water in layer "i" on day "t" (cm)
        EI     = evaporation (cm day-1)
        Uj     = transpiration (cm day-1)

        Ij     = percolation out of zone i (cm day:l)
        INF   = infiltration into layer  1 (cm day:l)

Daily updating of soil moisture in the soil profile using the above equations requires the
additional calculations for infiltration, evaporation, transpiration, and percolation.

        Infiltration is calculated as


                                                <@-K                         (6-30)
where, assuming a unit area of 1 cm?,
       P     = precipitation as rainfall, minus crop interception (cm day:l)
       SM    = snowmelt (cm day-"l)

       Q     = runoff (cm day:l)
       E     = evaporation (cm day-1)

The calculations of precipitation, snowmelt, and runoff on a daily time step are described
below. The disaggregation of these values and the calculation of the change in the depth
of pending on a finer time step is included in Sections 6.3.5.4 and 6.4.4 describing the
simulation of furrow irrigation and ponded surface water.
                                         6-16

-------
Input precipitation is read in and pan evaporation and/or air temperature are inputs from
which potential evapotranspiration (PET) is estimated, Incoming precipitation is first
partitioned between snow or rain, depending upon temperature. Air temperatures below
0°C produce snow and may result in  the accumulation of a snowpack. Precipitation first
encounters the plant canopy and once the interception storage is depleted, the remaining
depth is available for the runoff or infiltration.

The runoff calculation partitions the precipitation between infiltrating water and surface
runoff. Infiltrating water may be ponded 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 USDA Soil Conservation Service curve number
approach (Haith et al. 1979).  Snowmelt is estimated on days in which a snowpack exists
and above freezing temperatures occur as


                                     SM = CT                               (6-31)
where

       Cjyi= degree-day snowmelt factor (cm 8C:1 day:1)
       T =  average daily temperature PQ

The precipitation and/or snowmelt are inputs to the SCS runoff equation written as
where S, the watershed retention parameter, is estimated by


                                 s = ioeo/Rew  -10                           (6-33)


where

       RCN = SCS runoff curve number

Curve numbers 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 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).
                                        6-17

-------
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 transpi-
ration, 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:
                                                                             (6-34)
where
       El\    = the actual evapotranspiration from layer T (cm)

       fgj     = depth factor for layer T
       WHj   = wilting point water content in layer T (cm)
       ETp    = potential evapotranspiration (cm)


This equation states that the transpiration from any layer  T is the minimum of the
available water in layer T or the demand remaining after extracting available water from
layers above T in the profile.

The depth factor, &ij 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 may also be limited by soil moisture availability. The potential rate
may not be met if sufficient soil water is not available to meet the demand. In that case,
PRZM-2 modifies the potential rate by the following equations.


                        Elg = E2p         tf SW k .06 FC

                        EBg = SMFAC Elfytfm* SW< 0.6 FC                  (6-35)

                              0;          if mi^ W
where

                                        6-18

-------
       FC      = soil moisture content at field capacity (cm)
       WP      = soil moisture content at wilting point (cm)
       SMFAC = soil moisture factor

The SMFAC concept has been used in other similar water balance models (Haith et al,
1979, Stewart et al. 1976) and is internally set in the code to linearly reduce ETUjJ when
soil water becomes limited. Finally, if pan evaporation input data are available, ETC^ is
related to the input values as

                                         • Ch PE                              (6-36)
where
       PE    = pan evaporation (cm day:l)
       6p     = 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, EUtj, is estimated by

                                      14000 l (SRZD)                          (6-37)
where
       L%     = possible hours of sunshine per day, in 12-hour units
       SVD = saturated vapor density at the mean air temperature (g cm:l)
       SVD = 0.622  SVP/(Rg T^
where
       SVP = saturated vapor pressure at the mean absolute air
              temperature  (rob)
       R§     = dry-air gas constant
              = absolute mean air temperature
The final term in the water balance equations that must be defined is the percolation
value, 1. Because the Richards equation is not solved in PRZM-2 utilizing soil water
characteristic curves to predict water movement, PRZM-2 resorts to "drainage rules"
keyed to soil moisture storages and the time available for drainage. Two options are

                                        6-19

-------
included. Although these options are admittedly simplistic representations of soil
moisture redistribution, they are consistent with the objectives of PRZM-2 and its
intended uses.

6.3.2.1 Option 1-

Percolation, I, 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 theoreti-
cal 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
(Haith et al.  1979, Stewart et al. 1976). Wilting point is a function of both the soil and
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, is calculated to be  in
excess of field capacity, then percolation is allowed to remove the excess water to a lower
zone. 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
will displace the water within the lower soil layer simulated, and so  on. 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 is
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 may occur. First, conditions may prevail that raise the
soil moisture levels above field capacity for periods of time because the water is "backed
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 conditions, two additional
parameters are needed. Maximum soil moisture storage, 0,, 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
                                        6-20

-------
                                   dt
                                                                               (6-38)
which has the solution


                                                                               (6-39)
where
       §      = soil layer water content (cml crof)
       0£.     = water content at field capacity (cm! cm/3)
       a      = drainage rate parameter (day"1)


In this equation, t and t+1 denote beginning and end of time step values, respectively, and
i is the soil layer index. The value t* denotes a value of time between the beginning and
the end of the time step. The variable 6fr here denotes current storage plus any percola-
tion from the next layer above, before the occurrence of any drainage from the current
layer. Because Equation 6-39 is solved independently for each layer in the profile, there
is a possibility of exceeding the storage capability (saturation water content, @^> 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 §i >GBL. 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 6-39
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 one of two
factors limits percolation from a 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 §i represented by Equation (6-39). 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 Soil Erosion

Removal of sorbed pesticides  on eroded sediments requires estimates for soil erosion. The
Modified Universal Soil Loss Equation (MUSLE) as developed by Williams (1975) is used
to calculate soil loss:
                                         6-21

-------
                               Xe=a (Vr qJ>*K LS C P                        (6-40)
where
       2j^     = the event soil loss (metric tons day:l)

       Vr     = volume of event (daily) runoff (n$)
       eh     = peak storm runoff (ml see:J)

       K     = soil erodability factor
       LS     = length-slope factor

       C     = soil cover factor
       P     = conservation practice factor
       a     = units conversion factor

Most of the parameters in Equation 6-40 are easily determined from other calculations
within PRZM (e.g., V,J, and others are familiar terms readily available from handbooks.
However, the peak storm runoff value, qg, can vary widely depending upon rainfall and
runoff characteristics. A trapezoidal hydrography is assumed in PRZM-2. From the
assumed  hydrography shape and the storm duration, a peak runoff rate is calculated.
The enrichment ratio, rjji, is the remaining term that needs to be defined to estimate the
removal of sorbed pesticides by erosion, 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 used in PRZM-2, leading to the adoption of an
empirical approach (Mockus 1972). The enrichment ratio for organic matter is calculated
from


                                     • 2 t  0.2 Iitfi                          (6-41)
6.3.4 Volatilization

As volatilization was not available in the previous release of PRZM, its theoretical basis is
discussed in detail here.  The following key processes have been identified as being
important in volatilization algorithms to simulate vapor-phase pesticide transport within
the soil/plant compartments:
                                        6-22

-------
       •   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 6.3  is a schematic of the pesticide vapor and volatilization processes considered in
soil and plant compartments.

6.3.4.1 Soil Vapor Phase and Volatilization Flux-

The governing equations for chemical transport in the vapor phase were introduced
previously  in the description of transport in the soil. Fluxes from the soil colunm in the
vapor phase are summarized in that discussion by Equations 6-3, 6-5, and 6-9. The terms
in these equations are summed with the other flux terms to produce the transport
Equation 6-20. In addition to these enhancements, the upper boundary of PRZM-2 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 below.

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, volatilization may 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 only allows water to pass
through and keeps the solute behind, Experimental results show that, within the top
centimeter of the soil  surface, the pesticide concentration 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 volati-
lization is molecular diffusion through the 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 volatiliza-
tion flux will not depend strongly on the  thickness of the boundary layer. Conversely, if


                                        6-23

-------
                  Plant Compartment
                  Volatilization Flax
      HORIZON 1
      HOPJZON2]
      HOREON 3-
                       Rom
Reference
Height
                                             -j Canopy Height
                                            Plant
                                           Compartment
                                                       Ail/Soil
                                                       Boundary Layer
                              1 Vapor DifFosioo-
   Sdl
   Layer
Figure 6.3 Schematic of pesticide vapor and volatilization processes,
                                  6-24

-------
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:
                                                                                 (6'42)
where
        JL    = volatilization flux from soil (g day-lj)

        Da   = molecular diffusivity of the chemical in air (ctmi? day-"J;D
        A    = cross-sectional area of soil column (cnn$)
        d    = thickness of stagnant air boundary layer (cm)
        6g^  = vapor-phase concentration in the surface soil layer (g ennui)

             = vapor-phase concentration above the stagnant air boundary layer (g
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 G-*^
can take on a value of zero if the soil surface is bare or can be positive if a plant canopy
exists,

6.3.4.2 Volatilization Flux Through the Plant Canopy —

In pioneering work on this topic, Parmele et al.  (1972) discuss a number of micrometeoro-
logical techniques for calculating pesticide volatilization flux from observed aerial
pesticide concentrations. Their procedures are based on the assumption that the vertical
diffusivity coefficient (IQ 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.
                                          6-25

-------
                                              (dPfd®                          (6-43)

where
                = pesticide flux at height Z (g m$ s:l)

       (dP/dZ) = pesticide concentration gradient (g m:2)
       KJ!Z)    = the vertical diffusivity at the height Z (m£ s:l)
The value of K± depends on the turbulent flow of the atmosphere into which the pesticide
vapor is dissipated. Therefore, it is a function of the prevailing meteorological conditions
and not of any physical or chemical property of the pesticide.

In order to apply these concepts, 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. All of
these data requirements  pose signficant limitations for a predictive modeling approach.

In developing this PRZM-2 module, the following approaches are proposed to circumvent
the intensive data requirements.  First, a relationship for K^ is derived as a function  of
height within the canopy. Then one need only consider the pesticide concentration
gradient (or a suitable surrogate) in order to compute  the pesticide volatilization flux.

Estimation of Kt(Z) --Mehlenbacher and Whitfield (1977) present the following formula to
compute Kq, at various heights within the plant canopy.
                               * Kgfife) exp  lO  ~ - 1.0                      (6-44)
                                     » I/* Jk (Iffl - OM*                       (6-45)


                                                                              (6-46)
where

                 = thermal eddy diffusivity at height Z (m? s-\)

                 = thermal eddy diffusivity at canopy height(m? s:l)

                 = canopy height (m)

       Z0        = roughness length (m)

       jQ        = zero plane displacement height (m)

                                        6-26

-------
                    von Karman's constant, 0.41
       U*       =  friction velocity (m s-1)

               — =  stability function for sensible heat
                 =  integrated momentum stability parameter as a function of

                    stability function for momentum
               " "^  wind velocity at the canopy height (m S-1)
For agricultural applications, the canopy height is used as a reference height for calculat-
ing U*. The user is required to input the wind speed and the height where the measure-
ment was made. The wind speed at the canopy height (UQfl) is computed based on the
logarithm law. The relationship is:
                                                                              (6-47)
                               measured
The friction velocity U* can be visualized as a characteristic of the flow regime in the
plant canopy compartment in which the logarithmic velocity distribution law holds. As
shown in Equation 6-44, U* is calculated as a function of U£H, Zgg, ZQ, D> and JJfjg.
Rosenberg  (1974) describes ZQ + I) as the total height at which the velocity profile above
the canopy extrapolates to zero wind velocity. The values for both ZQ and D can be
estimated with the following equations presented by Thibodeaux (1979). For very short
crops (lawns, for example), ZQ adequately describes the total roughness length, and little
adjustment of the zero plane is necessary (i.e., Q = 0). Q is assumed to be zero in the
current code when Z^J is less than 5 cm. For tall crops, ZQ is related to canopy height
                             fag Ze = 0.997 % 2£$ -OC8B3                      (6-48)
In tall crops, ZQ is no longer adequate to describe the total roughness length, and a value
of Dj the zero plane displacement, is needed. For a wide range of crops and heights, 0.02
m 
-------
Strictly speaking, both Z^knndlBsfchniiDid be evaluated from experimental observations. In
the calculation of K,, the module uses these two equations for estimation of ZQ and Bj
since there is no method available to justify any variations for crop type, row spacing, or
canopy density.

With estimates of ZQ and D> U* (friction velocity) can be estimated if the values of the
stability parameters (^ and $$ are known. These two variables are closely related to Ri,
the Richardson number, which is the measure of the rate of conversion of convective
turbulence to mechanical turbulence. It is defined as follows (Wark and Warner  1976).
                                  Ri =                                        (6_5Q)
                                         (dU/dZ)2
where

       g     = acceleration of gravity (m see?)
       T     = potential temperature (°K)

       Z     = elevation (m)
       U     = wind velocity (m s:l)

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 the model, the measured temperature is used in the
Richardson number estimation 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 Ri to the atmospheric
stability parameters; for these purposes, the sign of Ri is of greater concern than its
magnitude. When Ri is larger than 0.003, the atmosphere exhibits little vertical mixing,
reflecting stable conditions: when the absolute value of Ri, |Ri|l, is less than 0.003,
neutral stability conditions exist (Oliver 197 1); and when Ri is less than -0.003, convective
mixing becomes dominant and atmospheric conditions are unstable.

To relate the atmospheric stability parameters to the Richardson number, Thorn et  al.
(1975) proposed the following formulas based on the work by Dyer (1974) and Dyer  and
Hicks (1970).

       For stable conditions -
       For unstable conditions -
                                        6-28

-------
        For neutral conditions -

                                               l                              (6-53)
The integrated momentum stability parameter, q?m<) can be evaluated based on the
following equation as derived by Lo (1977).


                                ln(8) + $m + 3 IK( based on the stability condition and associated Equations
           6-51, 6-52, or 6-53.

        4) Calculate tp~, from Equation 6-54.

        5) Calculate ZQ and B. from canopy height using Equations 6-48 and 6-49.

        6) Estimate K$Z) by applying Equations 6-46, 6-45, and 6-44.

The resistance approach for the estimation of volatilization flux from soil- The calculation
of the volatilization flux from the soil is based on a resistance-type approach. For pre-
plant pesticides, and time periods just after emergence and post-harvest, transport by
volatilization from plant surfaces is much less than vapor phase transport by other
mechanisms. For those conditions in which the plant leaves do not act as significant
sources or sinks for pesticide vapor, the resistances of the air for the whole plant compart-
ment can be estimated as follows (Mehlenbacher and Whitfield 1977).
                                        6-29

-------
                                                                               (6-55)
                                       Ru = 4                                (6-56)
*** I
                                              
-------
 approach is possible that requires the user to input the first-order rate constant for
 volatilization. The plant leaf volatilization flux can be estimated as follows.


                                       Jpl= M Kf                                (6-59)

 where
        JIjJ = volatilization flux from the leaf (g cm? day:1)

        M = foliar pesticide mass (g cm'ty

        K| = first-order volatilization rate (day1*)

 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 volatiliza-
 tion flux from plant leaf surfaces described above.  This approach, which requires the user
 to specify the first-order rates constant 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
will exist only after pesticide application to the plant foliage has been specified in the
model input. When a plant canopy exists, the average concentration  in the air within the
plant canopy can be estimated as follows.


                                 c;  = (Jpc «• Jj £ ^                          (6-60)

where

        6^   = average concentration in the air between the ground surface and the  plant

               canopy height (g cnd'3)
            = canopy resistance from half canopy height to the top of the canopy
                                            _*_                               (6-61)

                                         'CH  *
Equation 6-60 then calculates the mean plant compartment pesticide concentration as the
concentration at one-half of the canopy height. This approach assumes a linear concentra-
tion gradient from ground surface to canopy height.

6.3.4.4 Soil Temperature Simulation--

Soil temperature is modeled in order to correct the Henry's law constant, E^, for tempera-
ture effects. 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


                                         6-31

-------
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 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 bound-
ary 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.

Table 6-1  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 (1975, 1976) 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,  1Q, 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 temperatures were then estimated (Wierenga
and de Wit 1970)  by using these estimates of TQ 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.
                                        6-32

-------
   TABLE 6-1.  SUMMARY OF SOIL TEMPERATURE MODEL CHARACTERISTICS
 o
CO
Model/
Author(s)
(1975)
1) Type of Model:
a) Process-Oriented
b) Semi-Process-Oriented
c) Non-Process-Oriented
2) Heat Flow Process
a) Conduction
b) Convection
c) Radiation
3) Upper Boundary Temperature
a) Est. by Energy Partitioning
b) Est. by Empirical Relationship
4) Soil Temperature Profile:
(Solving 1-D Heat Flow Eqn.
Using the Procedure of:)
a) Hanks et al. (1971)
b) Wierenga and de Wit (1970)
c) Curve Fitting
5) Input Data Required
a) Daily Max and Min Air Temp.
b) Daily Max and Min Soil
Surface Temperature
c) Hourly Air Temperature
d) Hourly Solar Radiation
e) Surface Albedo
f) Wind Velocity
g) Humidity /Dewpoint Temp.
h) Canopy Shadow/Ht. of Veg.
i) Soil Water Content
j) Soil Bulk Density
k) Soil Mineral Composition
1) Percentage Organic Matter
6) Soil Surface Condition
a) Residue Cover
b) Tillage Condition
c) Crop Canopy
7) Time Step
a) Hourly
b) Daily
* - Horton et al. (1984) used
Van Bavel Thibodeaux Gupta et al.
and Hillel


X



X

X

X





X*





X
X
X
X
X
X
X
X
X
X

X

X

X

a 2-D heat flow equation
** - Regression equation is fitted for soil temp at 5-cm
(1979)
'82, '83)

X





X

X











X
X
X
X
X
X







X

X

(1981,



X


X




X



X



X
X

X





X
X
X
X

X
X
X

X

Parton
(1984)



X


X




X



EX



X



XX











X

X

Cruse et al. Hasfurther Williams Wagenet
(1980) and Burman
(1974) (1983)



X X



X


X





X" X

X X



XX
X
X


at 5 cm
X
X
X

X
X



X X
AVE • "Average" measured soil surface temperatures
depth.
DD - Damping depth parameter is used to predict soil
temperature at different
ME - Simplified mathematical
radiation, surface albedo,
depths.

etal.
(1987)

X



X

X


ME





DD

X



XX
X



X
X




X
X


X
and Hutson
(1983)

X



X




AVE



X












X
X
X
X

X



X
X
Chen
etal.


X



X
X
AT













X





X
X
X
X

100%



X
X
are used.
AT - Ambient air temperature is used as upper boundary
XX - Total daily solar
EX - Explicit
radiation.

temperature.



Finite Difference Scheme.
relationship involving solar
, and daily min and max air temperatures.

-------
For modeling soil profile temperatures, Hanks et al. (1971) used a numerical approxima-
tion 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 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 meteorolog-
ic 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, which may be estimated from soil mineral composition, organic
matter percentage, bulk density, and soil water content. The upper boundary tempera-
tures  are 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 tempera-
ture 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 soil temperature model in PRZM-2 is derived from a combination of the work by van
Bavel and Hillel  (1976) and Thibodeaux (1979) for estimating the soil surface/upper
boundary temperature. The soil profile temperature procedures were developed by Hanks
et al.  (1971)  and  applied by Gupta et al. (1981, 1982, 1983) and Wagenet and Hutson
(1987),

Estimating upper boundary temperature-An energy balance procedure is used in PRZM-2
to estimate soil surface temperature (Thibodeaux 1979, van Bavel and Hillel 1976). The
same  procedure is used in the POSSM model (Brown and Boutwell 1986), which employs
PRZM-2 as a framework for PCB fate simulation.

The basic energy-balance equation with terms having units of cal cnri? day-\ at the air/soil
interface may be described as:


                              Rn - Hs - i, - G3 = A7#                        (6-62)
where

       Rn   = net radiation (positive downward)

       H,   = sensible air heat flux (positive upward)
       LE,  = latent heat flux (positive upward)

       G,   = soil heat flux (positive downward)

       ATH = change in thermal energy storage in the thin soil layer (cal

              cmf day'})
                                       6-34

-------
The term ATH can be evaluated as:

                               ATH  -   fe$ s     - 1                          (6-63)
where
        Pb      = bulk density of soil (g cnm3)
        d      = thickness of a thin, surface soil layer (cm)
        s       = the specific heat capacity of soil (cal g:l *CM)
        T\jH,<4  = the representative temperature for the surface layer at two consecutive
               time steps and can be represented as the average of temperatures at the
               top and bottom of the soil layers.

For evaluating the heat exchange across the air/soil interface,  the thickness, d, can be set
to a small value so that ATH may be neglected. As a result,  the right side of Equation
6-62 is set equal to zero.
Net radiation flux at any surface  can be represented as:

                                                                               (6-64)
where
       Rj,    = the net radiation flux (cal cnmf day:l)
       R8    = incident short-wave solar radiation (cal cm
       K^   = reflected short-wave solar radiation (cal cmi day-l)
       Rjj   = incident long-wave atmospheric radiation (cal cmi day-!)
       Rjaf   = reflected long-wave atmospheric radiation  (cal cmf dffljp-)
       Rj,    = long-wave radiation emitted by the soil (cal cum/? day:l)

The terms R, and E5$ include both the direct and diffuse short-wave radiation, and are
related as follows.
                                                                               (6-65)
where
       a     = the albedo of the surface (dimensionless)
Therefore, the short-wave radiation component of the energy balance is

                                         6-35

-------
                                               - a)                           (6-66)
The incident short-wave radiation can either be measured directly using pyranometers or
else calculated using a variety of available empirical relationships or nomography. The
model requires input of a radiation time series, whether measured or calculated, in order
to simulate soil temperature,

The albedo of a canopy-covered land surface can be estimated as:


                             a(t) = og C(t) + «3 (1 - C(t))                      (6-67)


where

       a(t) = albedo on day t
       a£   = albedo of canopy cover (0.23 for vegetation)

       C(t) = canopy cover on day t  (fraction)

       a,   = albedo of soil surface (dimensionless)


Since the albedo of soil surface changes with the soil surface condition, it is 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 6-67, 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, RJj, is represented as
where

       e^j   = emissivity of the atmosphere [dimensionless]

       a    = the Stefan-Boltzmann constant (11.7 *1GD$ cal om£ 8K4 day:ij

       TE   = the air temperature (°K)


Wunderlich (1972) has proposed a correction to Equation 6-68 for the effects of cloud
cover, which could increase Rjj, 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
timeseries, 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 ej have been proposed (Salhotra 1986). A simple
reliable method is the use of Swinbank's formula:
                                        6-36

-------
                                       0.936 *103 ^                            (6-69)
The reflected long-wave radiation, R,ar, can be expressed as:
                                 /ciflF

                                               t-y)                             (6-70)
where

       ¥     = the reflectivity of the surface for long-wave radiation [dimensionless]

The resulting net atmospheric long-wave radiation component becomes:
                                    - y) = 0.936 *103 2~ a (1 - y)               (671)
The long-wave radiation component emitted by the soil surface is represented in an
analogous equation to the atmospheric component, as follows.
where

       e,    = infrared emissivity of soil (dimensionless)

       T,    = soil surface temperature (~K)

Since the soil emissivity and reflectivity are related as e,=l-5y, we can replace (1 - y) in
Equation 6-71 with e,..

Combining the radiation components from Equations 6-66, 6-71, and 6-72, the net
radiation flux is calculated as follows.
                        Ify = (1 - a) + 0.936 *10..§ a T% e, a T?                  (6-73)


The evaporative heat flux, LE^ is estimated by:


                                                                              (6-74)
                                        6-37

-------
where
        #    = latent heat of vaporization/unit quantity of water

                (580.0 cal g-1)
        E    = evaporation rate (cm day:i)

        Pw   = density of water (1.0 g cmi)

The evaporation rate 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, H,, is given by:


                                X, = Pi % ft (T, - %)                         (6-75)
where

        Pa    = air density (g

             = (-0.0042 Tfc * 1.2WJHKD3
        Cjj£   = specific heat of air at constant pressure

               (0.2402 Sd g-1  °K*)
        h    = heat transfer coefficient at air-soil interface (cm day-1)
        Ta    = the air temperature PC)

The air density is computed based on the daily air temperature using a simple linear
correlation Equation 6-73 developed from  data in Thibodeaux (1979). The heat transfer
coefficient is given by:
                               h=Kl2V(  in \-HL-Z\\                        (6-76)
where
       K!    = Von Karman's number (0.41)
       $%    = wind velocity (cm day:l)

       ZM   = reference height at which V^ is measured (m)
       iQ    = zero plane displacement (m)
                                        6-38

-------
       Z0    = roughness height (m)


Equation 6-76 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 D and ZQ to the canopy height as
described previously in  this section by Equations 6-48 and 6-49.

From the fundamental equation of heat conduction, the soil heat flux, G,, is given by:


                                 Gs = (Ts - 7\) yz>t                           (6-77)


where
       T!    = temperature of the soil at  bottom of layer 1 (SK)

       T,    = soil surface temperature (°K)
       Aj.    = thermal conductivity of layer 1 (cal cm'l dajy ~ °K-1)

       H\    = thickness of layer 1 (cm)


Substituting Equations 6-71, 6-72, 6-73, and 6-75 into Equation6-68£ the flollowing fourth-
order equation in terms of T, results.


                                      , - [(1 - a)*. + 0.936*10-'  « if e,
                                                                              ID-to)
                                                     - o
The value of T, 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 measured air temperature, and R~s LE,,, Hs» and Gft
are calculated as explained above. 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 convergence criteria (set to 0, 1 fjl(f)i

Simulation of heat flow through soil profile- The soil profile temperature model is based
on the one-dimensional partial differential equation describing heat flow in soils:
                                                                              (6-79)
where

       d     = the thermal diffusivity.

                                        6-39

-------
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 = W                                (6-80)
where
       d     = thermal diffusivity of the soil layer (cnni? day:l)
       A     = thermal conductivity of the soil layer

               (cal cm^1 day-1 °C-\)
       c     = heat capacity per unit volume of the soil layer

               (cal cm$ °C-\)
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):


                               - *„., exp          1 - A                     (6-81)
where
             = Henry's constant at the reference temperature T[

             = patial molar enthalpy of vaporization from solution
                 (J mole!)

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 imple-
mented in the code due to the general lack of information required to use it.

6.3.5 Irrigation Equations

PRZM-2 irrigation algorithms determine depths of irrigation water to be applied at the
soil surface. These depths are computed from the soil water deficit and are added as
infiltration to the frost PRZM soil compartment. Above- and below-canopy sprinklers,

                                        6-40

-------
flooding, and furrow irrigation can be simulated. Methods for computing water applica-
tion depths for each type of irrigation are described in the following paragraphs.
6.3.5.1 Soil Moisture Deficit--
Irrigation is triggered when the average root-zone soil moisture volume falls below a level
fe defined by the user as a fraction of the available water capacity. The soil moisture
deficit, D, is then given by:

                                   D =     - S  Z                              (6"82)
where
       D    = soil moisture deficit (cm)
       6k    = average root-zone soil moisture content
       Q;    = average root-zone soil moisture content at field capacity (cm?om3)
       Zr    = root zone depth (cm)

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.
6.3.5.2 Sprinkler Irrigation-
Irrigation water from sprinklers may be applied either above or below the crop canopy.
When applied above the crop canopy, irrigation water is intercepted by the  canopy and
may run off when it reaches the soil surface. The depth of water applied during a daily
PRZM-2  time step by overcanopy sprinklers is estimated from the soil moisture deficit:

                                                  -zi                           (6-83)
where
       Da   = depth of irrigation water applied to the field (cm)
       If    = crop canopy interception capacity (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)
The water depth Da is applied as precipitation above the crop canopy, and canopy
interception is computed for the current crop in the PRZM-2 crop growth subroutines.
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 precipita-
tion. Water that does not run off infiltrates into  the first PRZM-2 soil compartment.
                                         6-41

-------
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:

                                                                              (6-84)
The irrigation water depth APDEP is applied as throughfall to the soil surface, and
sprinkler runoff is estimated using the SCS curve number approach.

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 E^ (cm hr-~) for the sprinkler system.
6.3.5.2 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 :


                                      = Dflh-LEf)                            M5)
Since the field is assumed to be diked around the edges, no water is allowed to run off
from the field.

6.3.5.4 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 infiltrat-
ing at the dowstrem end (Figure 6.4), Hydraulic characteristics  of the furrow deter-
mine  how quickly water moves down the channel, while soil characteristics determine the
rate of infiltration once water reaches a location in the furrow.

The PRZM-2 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 (Wilke and Smerdon 1965, Fok and Bishop 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 computa-
tionally intensive for this application, while simpler empirical models involve infiltration
parameters  that are not easily related to physical soil characteristics.
                                        6-42

-------
                        DISTANCE ALONG THE FURROW
            Inflow
            End
Outflow
End
  Figure 6.4 Variability of iniltration depths within an irrigation furrow.


The PRZM-2 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 inertial accelerations and assumes
that the water surface slope is equal to the ground slope. The equations of motion then
reduce to:
                                      6-43

-------
                                   dz    dt    dt


where

       Q     = flow rate in the channel (m? s4)

       A     = cross-sectional area of flow (m?J

       x      = distance down the fin-row (m)

       q      = volume infiltrated per unit length of channel (mf m:l)

The flow area A is related to the flow rate Q by Manning's equation:
                                   Q = i AR2* S1/2                             (6-87)
                                       n
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 to PRZM-2.


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.
                                        6-44

-------
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
                                                                               (6-88)
                                        Az
in which the subscripts "i" 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 C[i,j]-C[i-l,j]). 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, (C[i,jl-C[i,j-l]). The equations are then made implicit by writing each
concentration for the (j+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 solution was added as an option to PRZM-2. 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

                                         6-45

-------
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. M. 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-2 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 simula-
tion 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 compart-
ment. 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 concentra-
tion 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.

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 (1983a, 1983b) has been incorporated into the
PRZM-2 code. The  model states that the controlling mechanism for pesticide volatiliza-
tion is  molecular diffusion through the stagnant surface boundary layer. The volatiliza-
tion flux from soil profile can be estimated by:


                                        6-46

-------
                                                                              (6"89)
where

       $\     = volatilization flux from soil (g day:l)

       Dj    = molecular diffusivity of the chemical in air

       QjT} — ~ vapor-phase concentration in the surface soil layer

                (gorfS)
             = vapor-phase concentration above the stagnant air boundary layer (= 0,

                for the no-canoy field condition) (g cms)
       d~ === thickness of stagnant air boundary layer (cm)


This equation defines the new flux-type boundary condition for the volatilization simula-
tion. In order to incorporate the new flux-type boundary condition into the
PRZM-2 code, new mass balance equations were derived for the surface soil and stagnant
air layers.  Figure 6. 5 (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 0^a under
the no-canopy field  condition.

A mass balance equation for the uppermost soil compartment is



                                                                              ***
where

       Dg     = molecular diffusivity of pesticide in air filled pore space

       V     = volume of the compartment (cm?)

       A     = area of the compartment (cm?)

       a      = volumetric air content (cnoi? cnm3)

       1^     = first-order reaction rate  constant (day-1)


The first term of the right side of Equation 6-90 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.
                                        6-47

-------
                              -
                                      9,3
                   (a)  without plant  canopy
                               c* —^

                     (b) with plant  canopy
 Figure 6.5. Schematic of the top two soil compartments and the overlaying surface
compartment (a) without plant canopy, (b) with plant canopy.
                                 6-48

-------
                          -l] KB CJM-1] = - -    Dg[2ji] KH
                                                                               (6-91)
where

       n      = time index

By substituting Equation 6-91 into the overall (i.e., all phases) mass balance equation for
the uppermost soil layer, a flux-type upper boundary condition is obtained. Figure 6.5(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.
                                                                               (6'92)
where

       J^    = volatilization flux through the plant canopy (g cm? day:l)
       ZR    = vertical transfer resistance (day cm-1, described in

                Section 6.3.4.3)
       C*     = concentration above the plant canopy (assumed to be zero)

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 6.3.4.4 describes the theoretical basis
for the simulation of soil temperature. The distribution of temperature within the soil
profile is summarized by Equation 6-79.  This  equation is solved numerically for soil
temperature, T, as a function of depth, Z, and  time, t, based on the input thermal
diffusivity, d, for each soil compartment,  and the following initial and boundary condi-
tions.

Initial Condition:
                                         6-49

-------
                                      rv = TXz)                               (6-93)


Boundary Conditions:
                                      Tpj = r,(t»                               (6-94)


                                                                             (6-95)
where

       T(z)   = initial soil temperature in each soil compartment (°C)

       T,(t)   = calculated soil surface temperature for each time step (8C)

       Tr(t) = lower boundary temperature condition at the bottom of the soil core (!€)


The lower boundary temperature is defined by the user as 1 2 monthly values correspond-
ing 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).
                          At
                                                                             (6_96)
Equation 6-96 is solved using a modified numerical solution procedure of Hanks et al.
(1971), which involves 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  6-86), Manning's
equation is approximated as follows.


                                     A  = a  Qm                               (6-97)
a and m are constants that are estimated by the model from the parameters of Manning's
equation as follows.
where


                                        6-50

-------
                                 md.                                          (6-98)
                                      \n(Qj ^ w$)

                                                                              (6-99)
       A[, j8§ = cross-sectional areas (iri?) at depths y\ and y£

       Qi; Q2 = flow rates (m3 s"-1) computed from Manning's equation [(Equation 6-75)] at
                depths y\ and y$
       yi     = 1 cm

       y2     = 10 cm


The depths yj. and y£ were chosen to represent the range of depths likely to occur in
furrows.

Substituting Equation 6-97 into Equation 6-86 produces:
                                dQ  + d«tQM) =  _  dq                        (6_100)
                                dz       dt         dt
No closed-form solution to the above equation is known when infiltration is time-variable.
Equation 6-88 therefore, is, solved for Q using the backwards-space, backwards-time
finite-diHerence solution described by Li et al, (1975). Writing Equation 6-100 in finite-
difference form producers:
                                                                             (6-101)
                       Az               At                 At
where
       Q^     = flow rate at time k, station i

       A2     = spatial step
       At     = time step


Infiltration volumes are computed using the Green-Ampt model:
                                at
                                        6-51

-------
where
       I*     = infiltration depth at time k (m), station i
            —= saturated hydraulic conductivity of the soil (m s:l)
              3= ponded water depth (m)

       HB    = suction parameter (m)

               = available porosity (fraction)
       11 =s= total volume of infiltrated water (m)


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 I* is an average infiltra-
tion depth for the channel at location i:


                                     ak = Wk!k                             (6-103)
where
              = volume infiltrated at location i (m? mf)

              = current flow width at location i (m)


Furrow channels are assumed to be trapezoidal in shape. E uation 6-87 is solved at each
station at the end of each time step for teh new flow rateQ^+i • 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 6-90).

The PRZM-2 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-2 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 upon options selected by the user. This depth of water then
infiltrates through the root zone as determined by the PRZM-2 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 volatization rates. A test  comparison 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) (Carsel et al., 1985;
Jones 1983; Jones et al., 1983). The results of these tests demonstrate that PRZM is a

                                        6-52

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usefiul tool for evaluating groundwater threats from pesticide use. Please refer to these
references for information regarding the further testing of PRZM-2 under field conditions.

6.5.1 Transport Equation Solution Options

Currently, two numerical solution options are available to the PRZM-2 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 character-
istics 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 6.6 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 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
(mgfara^)
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 logged 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.

6.5.1.2 Low Peclet Number--

Figure 6.7 illustrates the results of a SLPSTO and MOC/SLPST1 simulation 8 days after
an incorporation of 69 mgfanri? 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

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6.5.2 Testing Results of Volatilization Subroutines

To test and validate the operation of the volatization 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 volatization 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 volatization flux at the soil surface:

       1) 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

       2) Linear equilibrium adsorption isotherm

       3) Linear equilibrium liquid-vapor partitioning (Henry's law)

       4) Uniform incorporation of a quantity of chemical to a specified depth below the
          surface

       5) Pesticide loss by volatilization  through a stagnant air boundary layer at the
          soil surface

       6) Infinite  depth of uniform soil below the depth of incorporation

Assumptions 2 to 5 are satisfied by the current PRZM-2 code. Assumption 6 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
volatization were designed to satisfy this requirement. In order to comply with  assump-
tion 1, the hydrological computation subroutines in PRZM were bypassed and replaced
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-2 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 6-2 and were obtained from  Jury et al. (1983a). The test
run results for daily volatilization flux are presented in Figures 2.8(a), 2.8(b), 2.9(a), and
2.9(b), corresponding to the four test cases listed at the  bottom of Table 6-2. Two different
                                         6-54

-------
                       5    7   9   11    13   15
                       COMPARTMENT NUMBER
17    19
Figure 6.6 Comparison of simulation results at high Peclet number.
                               6-55

-------
                             COMPARTMENT NUMBER
  Figure 6.7 Comparison of simulation results at low Peclet number.
Velocity = 1.82 cm/day
Diffcoef=4.0cm!/day
Retardation Coef = 11.74
Decay = 0.1/day
Delta x = 1 cm
Delta t = 1 day
Core Length =20 cm
Peclet = 0.46
                                     6-56

-------
soil compartment depths (DE LX) 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 6.8(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:l are
shown in  Figure 6.8(b). Because of the leaching influence, the predicted daily flux is
smaller than the corresponding daily value shown in Figure 6.8(a), The differences
between the analytical solution and the PRZM-2 predictions are due to the finite differ-
ence 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 6.9 shows the simulation results under evaporating conditions with the upward
advective  velocity at 0.01  (Figure 6.9(a)) and 0.25 (Figure 6.9(b))  cm day-"h The "wick
effect" phenomenon (described in Section  6.3.4) 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
6.9(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 6.9(b). The smaller 0.1-cm DELX
showed good agreement with the analytical solution for  both test cases shown in Figure
6.9.

Based on these test cases, it appears that a freer  DELX, in the  range of 0.1 to 0.5 cm, is
needed for top soil layers when volatilization processes are simulated with PRZM-2.
However,  this finer DELX requirements poses an additional computational burden for
PRZM-2 applications due to the increase in the number of soil compartments. To
circumvent this burden, the PRZM-2 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.

6.5.2.2 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 experi-
mental watershed site in  north-central Georgia was selected because of its use of a
                                        6-57

-------
TABLE 6-2. INPUT PARAMETERS FOR THE TEST CASES - ANALYTICAL SOLU-
             TION
T

%

8

a

M

L

KH
           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-!)

4.3xlO"§(m2day-})

0.5

1.35 (kgm§)

25°C

0.0125

0.3

0.2

1 (kghtl)

0.1 m

5.5 x 10-9


0.02(m?kg-1)

4.62 x 10"i (day-1)

0.3 m

30 days
       Test case #1: no evaporation and no leaching ($j[ = E = 0)

       Test case #2: with leaching (A| = 0.01 cm day"-1?

       Test case #3: with evaporation (E = 0.01 cmday:l)

       Test case #4: with evaporation (E = 0.25 cmday:1)
                                       6-58

-------
          ( E - 6 Kg/ ha-day )
       20
    3   15
    I
    jj   10
    5J
    "c
         5
                                     —  Analytic Sol'n
                                         PRZM Results
                                      «   DBLX - 0.1 cm
                                      *  DBLX - 1 cm
                                  i   i    i   i
            2  4   6  8  10  12  14  16  18  20  22 24  26 28  30
                                                            (days)
                   No Evaporation & No Leaching
          ( E - 6 Kg/ ha-day )
       20
I
 R
 o
>
        10
                                           —  Analytic Sol'u
                                               PRZM Results
                                            «   DELX - 0.1 cm
                                           *   DELX - 1 cm
                  i    i   i _ i
                                    i _ i   i    i
                                                      i    i   i
              2   46   8  10  12  14  16  18  20  22 24  26  28  30
                                                              (days)
                       Leaching Rate - 0.01 cm/ day
Figure 6.8 Comparison of volatilization flux predicted by PRZM and
Jury's analytical solution: Test cases #1 and #2.
                                   6-59

-------
           ( 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  20 22  24 26 28    30
                     Evaporation Rate • 0.01 cm/ day
      so
          ( E - 6 Kj/ hft-day )
  I
      40
      20
                                            — Analytic Sol'n
                                               PRZM Results
                                             o  DELX - 0.1 cm
                                               DELX - 1 cai
            2   46   8  10  12  14 16  18  20 22  24  26 28   30
                                                            (day)
                   Evaporation Rate - 0.25 cm/ day
Figure 6.9 Comparison of volatilization flux predicted by PRZM and
Jury's analytical solution: Test cases #3 and #4.
                                  6-60

-------
volatile pesticide (trifluralin), surface-applied to a major crop (soybeans), with a compre-
hensive 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 equip-
ment, 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 (EOT) on  15 June 1973.

The field results shown in Table 6-3 were obtained from White et al. (1977). The values
in columns 2, 4 and 5 of Table 6-3 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  6-3; 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-2 input parameters for trifluralin and the Watkinsville site are listed in Table
6-4. 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 6-3, assuming that decay accounts for all
losses from the soil other  than volatilization. A value of 0.0206 per day for the frost-order
decay rate constant obtained from these data points is consistent with the value of 0.0198
per day used by Donigian et al. (1986)  after reviewing the literature. An initial value for
Kg was obtained from the  organic  carbon content of 0.55% and an organic-carbon partition
coefficient (Koc) value of 13,700, resulting in a  Kg of  75 ml/g. Figure 6.10 shows the
results of sensitivity analyses runs for Kg and the decay rate; the observed data for
trifluralin from Table 6-3  are also included for  comparison. Figure 6.10(a) shows a good
representation of the observed cumulative volatilization curve. Figure 6.10(b) shows that
a value of 40 for K$, 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 6.11  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 6-5 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 6-5. 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 6-5. 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 6-5 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.


                                        6-61

-------
TABLE 6-3. 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
% of Total
Applied
3.5
3.8
5.3
10.9
20.5
23.4
24.4
25.1
25.4
25.9
Volatilized
% 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- to 7.5-cm depth as compared with an
       initial 1.0 pg/g level at application (rate was 1.12 kg/ha).
TABLE 6-4. 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
             Incorporation depth
14 June 1973
31 December 1973
6.7 X103
0.43m?day:l
15 June 1973
1.12kgha-l
5 cm
Horizon
Content
1
2
3
4
Thickness
(cm)
5
10
15
60
DELX
(cm)
0.1
5.0
5.0
5.0
Field
Capacity
.207
.207
.339
.320
Wilting
Point
.095
.095
.239
.239
Initial
Water
0.166
0.217
0.318
0.394
                                   6-62

-------
     1
     1
          40
           30
      20
           10
                 12   24   36    4*   60   72   84   96  108   120
                                                            (day)
                          Sensitivity of KD
I
    o
        "
    M  20
    §•
              of applied)
                                                    Field Data
                                                  PRZM Results
                                            	K - 0.01
                                            	K-0.02
                                            	K • 0.03
                          sfc    4s   fc    ii    to
                          Sensitivity of Decay Rate
                                                            120
                                                            (day)
Figure 6.10 Sensitivity of cumulative volatilization flux to Kjj and decay rate.

-------
         40
         30
         20
         10
           (% of applied)
         Field Data
         PRZM Results
	DELX - 0.1 * 5 cm
	DELX - 0.25 & 5 cm
	DELX - 0.5 & 5 cm
	DELX = 1.0 ft 5 cm
                12    24    36   48    60    72   84    96
                    Effect of DELX on Volatilization Flux
                      10B
 120
(day)
           (% of applied)
                                            Field Data
                                            PRZM Results
                                   	DELX - 0.1 & 5 cm
                                   	DELX - 0.25 A S cm
                                   	DELX - 0.5 & 5 cm
                                   	DELX - 1X)& 5 cm
               12    24    36  48    60   72   84    96   108
                    Effect of DELX on Pesticide Decay
                            120
                          (day)
Figure 6.11 Effects of DELX on volatilization flux and pesticide decay.
                                    6-64

-------
TABLE 6-5. SIMULATION RESULTS OF USING DIFFERENT COMPARTMENT
              DEPTH (DELX)
Constant DELX
Horizon Depth DELX
(cm) (cm)
1 5
2 10
3 15
4 60
Total
Volatilization
Flux (kg/ha)
1.0
1.0
1.0
1.0
0.393


DELX
(cm)
1.0
5.0
5.0
5.0
0.398


Variable
DELX
(cm)
0.5
5.0
5.0
5.0
0.338


DELX
DELX
(cm)
0.25
5.0
5.0
5.0
0.317


DELX
(cm)
0.1
5.0
5.0
5.0
0.316





Field
Value
0.290


CPU (See)
129
39
46
67
106
Figure 6.12(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 6.12, indicate a much better
agreement with  the observed field values in Figure 6.12(b). The impact of the two-step
decay on both cumulative decay and volatilization flux is shown in Figure 6.13. The
cumulative pesticide decay shown in Figure 6.13(a) improves considerably (compared to
Figure 6.11(b)), while the results for cumulative volatilization flux (Figure 2.13(b)) are
slightly better than those in Figure 2. n(a).

6.5.2.3 Conclusions from Volatilization Model Testing-

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.
                                        6-65

-------
        90

        80

        70

        60

        50
        40

        30

        20

        10
          (% of applied)
              	 FWdData
                     PRZM Results
              	DELX - 0.1 & 5 cm
              	 DELX - 0.25 A 5 cm
              	 DELX - 0.5 & 5 cm
              	DELX - 1.0 A- 5 cm
              12   24   36   48   60   72    84   96   108  1
                 Simulations with Constant Decay Rate
        90
        80

        70

        60

        50

        40

        30

        20

        10
           (% of applied)
         (b)
                       Field Data
                       PRZM-2 Results
                       DELX - 0.1 & 5 cm
                       DELX - 0.25 & 5 cm
                       DELX - 0.5 & 5 cm
                       DELX - 1.0 & 5 cm
              12
24
36   48   (50   72   84   96   108
                  Simulations with Two-Step Decay Rates
Figure 6.12 Comparison of constant and two-step decay rates.
                                  6-66

-------
    i
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
               12    25    3e   48    GO   728455"
                    Simulation with Two-Step Decay Rate
                                                       (day)
          40
     I   30
          20
          10
             (% of applied)
                                   Field Data
                               PRZM Results
                           	DELX -0.1* 5 cm.
                           	DELX - 0.25 & 5 cm
                           	DELX - 0.5 & 5 cm
                           	  DELX 1.0 ft 5 cm
                12    24   36   48   60   72   84   96   108  120
                                                            (day)
                   Simulations with Two-Step Decay Rates
Figure 6.13 Effects of two-step decay rates on volatilization flux and pesticide
decay.
                                   6-67

-------
     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, 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 6.3.4.4) 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 cnoi? day lf
     Upper-boundary temperature,  I^J = 30°C
     Lower-boundary temperature, T^ = 20eC
     Initial  temperature, f^jj          ' = 20°C
Figures 6.14 and 6.15 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 6.14 the finite difference solution is obtained by using a i^
hour time step, while in Figure 6.15 a 1-day time step is used. The following observations
are evident from these testing results.
                                        6-68

-------
     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 in Figure 6.14.

     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 6.15). 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 300(2 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 (Figures 6.14(b) and 6.15(b)).

Table 6-6 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-2, under expected environmental conditions, with a non-uniform initial tempera-
ture 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.

6.5.4 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-2 to simulate parent/daughter relationships. An
analytical solution to the decay and transformation model was derived to check the
numerical model.
                                        6-69

-------
       30T
       24..
       21"
       18-
       15
                                             Profile Initial Temp - 20 C
                                             Upper Boundary Temp - 30 C
                                             Lower Boundary Temp = 20 C
                                             Time Step - 1 hr
                                      Analytical Sola.  	
                                      Finite Diff. Sdbi. ~™ • ~~ • —
         0     iC   20    30    40   50    60    70   80    90   100

                           Depth of Soil Profile in cm
27--


24=-



21--


iS--


IJ
                                             Piofite Initial Ttenp - 20 C
                                             Upper Boundary Temp - 30 C
                                             Lower Boundary Temp - 20 C
                                             Time Step - 1 hr

                                             Analytical Serin. 	
                                             Finite Diff. Soln.	
                                     -J-
               1020304050607080
                            Dep
-------
    30
    27
    24
    21
    IS

                  \  X
Profile Initial Temp.   - 20 C
Upper Boundary Temp. - 30 C
Lcw«r Boundary Twnp- - 20 C
Time Step          • 1 day

 Analytical Scdn.    _  _  _

 Ptoto Diff. Soln,	
      0      10    20     30     40    50    60    70     80     90     100
                         Depth, of Soil Profile in cm
     30
     27--
     24--
     21.-
     18--
     15
 Profile Initial Temp   = 20 C
 Upper Boundary Temp • 30 C
 Lower Boundary Temp - 20 C
 Time Step           - 1 day

 Analytic! Soln.  —  —  —
 Finite Diff. Sota.	
               ffj     2\)    lo     Jo    ^0     GO    70
                          Depth of Soil Profile in cm
Figure 6.15 Comparison of soil temperature profiles predicted by
analytical and finite difference solutions (Time Step=l day).
                                      6-71

-------
TABLE 6-6. 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
The system that was modeled is shown in Figure 6.16. The Q are dissolved concen-
trations and the C[ are adsorbed concentrations. The K| are adsorption partition coeffi-
cients, the kj are decay and transformation rates in the dissolved species, the  k[ are
adsorbed phase decay coefficients and 0 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:
                           &

                            d
                                                                        (6-104)
                               &
                                                                        (6-105)
                                                                        (6-106)
                                      6-72

-------
                   k *
                     1
k  *
  2
          C1
C2
     C3
                        k *
                         3
                                                    ADSORBED PHASE
DISSOLVED PHASE
  Figure 6.16 Schematic of a system of parent and daughter pesticide.
                                                                    (6-107)
                                                                    (6-108)
                               dt
                                                                    (6-109)
                               dt
Making use of C\ K| = Q* we can reduce the six equations above to three equations in

three unknowns, namely:
                                   6-73

-------
in which
                                          e +
                                    aA =
                                                                             (6-HO)
                                    l=a4C2+a5C3                         (6-H2)
                                  dt     4253
                                                                             (6-114)
(6-116)
                                     4   e + A:SP


                                         7    1 * V
                                         *» " *»   3p                          (6-117)
                                          6 + K, p
These ordinary differential equations with constant coefficients can be solved analytically
for C|, G§ and C% using the initial conditions Cjl= C^ when t = 0 and C/£ = C$ = Oatt = 0.
The solutions as given in Dean and Atwood (1985)  are:


                                     C{ = Credit                             (6-H8)
and
                                         6-74

-------
In PRZM-2, 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 Ql-o C$ -> C$ may be modeled or the system can be configured for $\ -4
Cj and G! —> C% or for independent C\, Cg and Cg simply by selecting zero or positive
values for the appropriate transformation rate constants.

Figures 6.17 through 6.18 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 represents the approximate numerical
solution. In Figure 6.17, there was no decay of the dissolved phase chemicals and no
adsorption of any species. The rate of transformation from C\ to Q, was 0.2 day:l and that
from  C$ to G| was 0.5 day:l.  After 20 days nearly all the chemical is in formCj. 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
6.18 shows the same system with a decay rate of 0.01 day:l in the dissolved phase.

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 (KJ = 0.55, Kj = 0.16 and K% = 0.185) and decay
and transformation rate constants (kj = 0.07, lq = 0.55, fcg = 0.01, fcj = 0.031 and fc§ =
0.0152) were taken from Bromilow et al. (1980). A soil bulk density of 1.45, a water
content of 0.27 cm? cm ? 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 6.19.

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.
                                        6-75

-------
                            I   I   I   I   I   I   I   I   I    I   I   I   I
                                                                  18   20
                                flME,INDAYS
Figure 6.17 Conversion of Q to C| to Cg with no adsorption and no decay.

-------
    100.
  8
 I
  u
  §
  u
                                                  I    I    I    I    I   I   I
                           I    I    I    I    I   I
II
           2       4       6       8       10       12       14      16      18    20
                                   TIME, IN DAYS
Figure 6.18 Conversion of C\ to C^ to Q with decay but no adsorption,
                                      6-77

-------
                                             X PARENT ALDICARB
                                             • SULFOXDE
                                             OSULFONE
                                             0 TOTAL
                                I   I	L   I    I       I   I    I ..... I
                            30      40     50     60
                              TIME, IN DAYS
90
Figure 6.19 Conversion of aldicarB to al'dicarB sulfbxide to aldicarb sulfone.
                                   6-78

-------
6.6 Biodegradation Theory and Assumptions

The biodegradation model is based on the: Mathematical Model for Microbial Degradation
of Pesticides in the Soil. Soil Biol. Biochem. V.I4 pp. 107-1 15 (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 (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
                                »», r

was chosen where

              X; = concentration of the $i population in the moist soil
              e
              Xj = concentration of the X[ population in the soil solution

and
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
                                        6-79

-------
                                                                             (6.122)
This represents growth at the expense of both the pesticide (S) and the carbon (C) in the
soil solution. The population decreases as a result of a first-order death process with a
death rate constant kj~.
For the co-metabolizing population,

                                            Xc
                                                      ~ k* *c                  (6-123)
                                PCCWK   '^-y 'k&xc
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,

                          — =  u C  ^-kWSX-k X                  (6-124)
This includes a supplementary death term following second-order kinetics.
For the non-sensitive, non-degrading population,
This is the basic relation of growth term and death term.


The equation concerning the pesticide concentration,
                                                                              (6-126)
                                         6-80

-------
has two parts. The first term concerns the degradation due to the metabolizing popula-
tion, while the second deals with the action of the co-metabolizing population. The
equation for the concentration of carbon in the moist soil,
                        Cm
                                                                            (6-127)
-is derived from the basis that the concentration is 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.

N.B. There are some minor differences between the equations as developed by Soulas and
as reported in his Appendix 3. In addition, some slight changes were made to the
equations to correct what were assumed to be some typographical  errors. These changes
include:

Definitions:

       Xj = Concentration of the X| population in the moist soil (i = m, c, s, r)*

       St= Pesticide concentration in  the moist soil

       Cty  = Carbon concentration in the moist soil

       PJ   = Maximum specific growth rate of the \ population (i = sm, cm, c, s, r)*

       K|j = Saturation constant of the Xj population (i= sm, cm, c, s, r)*

       fcaj  = Death rate of the Xj population (i= m, c, s, r)*

       YI   = True growth yield of the X| population (i = sm, cm, c, s, r)*

       k|   = Second-order death rate of the X^ population

       kj, = Dissociation constant of the enzyme-substrate complex

          =  Inhibition  constant
                                        6-81

-------
In addition,


                                          T+W
                                             -^                            (6-128)
       where

          Kj = distribution coefficient

       and


                                      Wv~£                               (6-129)
                                           P


       with

          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 that is degraded biologically over the timestep interval.

These equations are solved in PRZM-2 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-2 time step. Also, the changes to the organism populations are calculated and
saved for use in the subsequent timestep.
                                        6-82

-------
                                   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 Iwo-component PRZM-2 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 transforma-
tions 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 concentra-
tion or  prescribed solute mass flux per  unit area. All boundary conditions may be time
dependent.

7.2.1.3 Assumptions—

                                       7-1

-------
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.

        •  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,
                                          7-2

-------
        •  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:
                                                "f
                                                             (7-D
where
      z

      t

      n
the pressure head (L)

the saturated hydraulic conductivity (LTJ)

the relative permeability

the vertical coordinate pointing in the downward direction (L)

time (T)

an effective water storage capacity (Li) defined as:
                                                                              (7-2a)
where
                  specific storage (L'f), SW is water saturation

                  the effective porosity.
                                         7-3

-------
 Specific storage is defined by

       8i~pgftcf=Ml-4»cJ                                                     (7-2b)

 where

       cf     =     the fluid compressibility 
-------
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 relation-
ship of relative permeability versus water saturation. These functions are given by
Brooks and Corey (1966) and by van Genuchten (1976):
      k™ = Se"                                                                 (7-6a)

and

            S^fl-d-S/n2                                                  (7-6b)
where

      manbdly= empirical parameters

      §i    =     the effective water saturation defined as Se= (SJ[ - S^)/(l - S$), with
S$ being referred to as the residual water saturation.

The relationship of pressure head versus water saturation is described by the following
function (van Genuchten 1976, Mualum 1976):
                                                     for * < +
                                                     y   V   Va                (7-7)
                                 1        for !|r fc i|»a


where

      a and £ = empirical parameters

      y^    =     the air entry pressure head value (L)

            =     the residual water phase saturation.
                                         7-5

-------
The parameters P and y are related by y = 1- 1//0.

Descriptive statistical values for a, [?, and y have been determined by Carsel and Parrish
(1987) 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 Figures 7.1 through 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 present-
ed in  Figures 7.4 through 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 7-3 through 7-5. After the distributions of tp and
% have been determined, the Darcy velocity is computed from
                     I)                                                          (7-8)
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 i in the finite-element grid (see Figure 7.7), the equation may be expressed as
                                         7-6

-------
 figure 7.1
                   to-"
                                   CAPILLARY HEAD, cm
                                          (a)
                                       SATURATION
                                           (b)
  Figure 7.1. Logarithmic plot of constitutive relations for clay, clay loam, and loamy sand:
(a) saturation vs. capillary head and (b) relative permeability vs. saturation.
                                         7-7

-------
(Figure 7.2
                I
                     I.D
                  I 0


                 Ifl-l


                 IB'*





                 II '*


                 .«*-
              p .c-»
              w
                  10'
                                   SfLTY CLAY
                             SILTY CLAY LOAM
                                           SILT
                                    SILT LOAM
                                                    10]
                                CAPILLARY HEAD, cm
                                        (a)
!«•*
                                          I*"*
                                      SATURATION

                                          (b)
                                 t 0
Figure 7.2. Logarithmic plot of constitutive relations for silt, silty clay loam, silty clay, and
silty loam.
                                        7-8

-------
figure 7.a
                 1 C
                i.e
                               CAPILLARY HEAD, cm
                                       (a)
  Figure 7.3. Logarithmic plot of constitutive relations for sandy clay, sandy clay loam, sandy
loam, and sand.
                                        7-9

-------
'igure 7.4
   I
   t
   UJ
   5
   oc
   UJ
   LU
   oc
                                    0.4         0,6


                                     SATURATION
0.8
1.0
Figure 7.4 Standard plot of relative permeability vs. saturation for clay, clay loam, loam and
loamy sand.
                                        7-10

-------
 'igure 7.5
      1.0
      0.8 •-

  3   0.6 4-

  3

  UJ
  5
  QC
  UJ
  a   0.4--
  UJ
  UJ
  QC
      0.2-
      0.0
          SILT  LOAM

                   SILT
SILTY CLAY LOAM
         SILTY  CLAY
        0.0
0.2
 0.4         0.6

 SATURATION
  Figure 7.5 Standard plot of relative permeability vs. saturation for silt, silt clay loam, silty

clay and silty loam.
                                  7-11

-------
ITgure 7.6
     1.0
     0.8

 S   0.6.
 IU
 ae
 UJ
 *•   0.4
 UJ
 (X
     0.2"
0.0 ^
  0.0
                SAND
       SANDY LOAM
SANDY  CLAY LOAM
        SANDY CLAY
            0.4        0.6
            SATURATION
                 0.2
 Figure 7.6 Standard plot of relative permeability vs. saturation for sandy clay, sandy clay
loam, sandy loam and sand.
                               7-12

-------
figure 7.7
                                               z - 0
                                      NP  « Total  number of nodes
                                      NE  « Total  number of elements
                           1-1
                                                   '1-1
                          1+1
                                               z - L
Figure 7.7 Finite element discretization of soil column showing node and element numbers.
                                    7-13

-------
                                                                              (7-9)


where k+1 is the current time level, and 8j, Pi) Yij and dj are given by


                                                                           (7-10a)
                                          AVi

                                                                           (7_10b)
                                                                           (7-100
and &4 and A% are the spatial and time increments, respectively. Note that the braces
({}) are used in the equations above (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 scheme, and
the third is a Newton-Raphson scheme modified by Huyakorn (1988, personal communica-
tion) .

In the Picard scheme, the matrix coefficients, % &« ¥}) and d}, are first evaluated using an
initial estimate of pressure head values, ^. The resulting system of linearized
equations is then solved for yffi 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, pp.  159-162) to Equation
7-9. This leads to the following system of linearized algebraic equations.
                                                                             (7-11)
                                       7-14

-------
where superscript r is used to denote the r-th iterate; &\, Pi? Yi? and di are as defined
previously; and Q.*it li, yi, and dj are given by
where Ij =
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

The governing equation for one-dimensional transport of a nonconservative solute species
in a variably saturated soil takes the form
where D is the apparent dispersion coefficient (L^F'f), c is the solute concentration (ML"
§), 6 is the volumetric water content (@=<$§w), R is the retardation coefficient, and A is the
frost-order decay constant (T:l). Note that the apparent dispersion coefficient is defined as
                                        7-15

-------
D • aEV +  Yi} and dj are given by
      o, = TCC* + {OR},.! AZiV(6Atk)                                                (7- 1 8a)

      Pi = T0* + [{0R}i Azf + {ORJi.! AZiJ/OAtg                                     (7- 1 8b)

      Yi = tyl + {6R}; Az/(6Atk)                                                   (7- 1 8c)
                                         7-16

-------
                                            *+1 + 2c*)AZj                        (7-18d)

                                         X{6R}U Az,!
                              m.i  ^
                                                                              (7_18e)
with T and «J 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 1/2. This
corresponds to using the Crank-Nicholson central difference time stepping scheme. The
upstream weighting factor w 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 (7-
13) is modified by adding a source term accounting for transformation of parent compo-
nents. This source term is given by
      m = -L $ Sw e, X, R, c(                                                     (7-19)
where

      subscript { =     the parent species

          _rr     the number of parent species
      §t     =     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 previously for a single species. The source term from Equation
7-19 is incorporated into the finite element matrix equation by adding d^ to the right side.
The term d; is given by
                                        7-17

-------
                                                                                (7-20)
              **1
In performing the solute transport analysis, the selection of nodal spacing
(Az) and time step value (At) should follow the so-called Peclet number and Courant
number criteria where possible. These two criteria are given as follows.
           4                                                                    (7-21)


          AtfAzSl                                                              (7-22)

          = VZBR                                                                (7-23)


where

      «£    =     the longitudinal dispersivity

            »     the solute velocity

           =     Darcy velocity

           =^=     water content

           =     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 Ifl, the weighing factor, is determined by using the
following formulae:
      u - 1 - 4a^/f,    \ > 4ax                                                    (7-24)

      W^(£|,         | < 4at                                                     (7-25)
                                         7-18

-------
where

      04    =     the longitudinal dispersivity

      fi     =     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-2.

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 is hydrostatic:  (t = 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 oft = 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 Figures 7.8 and 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 ($&= Sgn)
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 7.11, respectively. These results
of the VADOFT code are virtually identical to corresponding results obtained using the
SATURN code.
                                       7-19

-------
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 parame-
ters and discretization data used in the numerical simulation are given in Table 3-4. 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 t = 25 hours and t =  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-

n this problem, downward  vertical transport of dissolved contaminants in a soil column
above the water table of an unconfined 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:l 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:l. 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 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 approxi-
mate solutions by Shamir and Harleman (1967) and Hadermann (1980) 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 discretiza-
tion, the VADOFT code could  be  used to determine the validity of the analytical solutions
at large dispersivity values.
                                        7-20

-------
TABLE 7-1. SOIL PROPERTIES AND DISCRETIZATION DATA USED IN SIMULAT-
             ING TRANSIENT FLOW IN A SOIL COLUMN
       Parameter                                                   Value


Length of soil column, L                                              20 cm
Saturated hydraulic conductivity, K                                 10 cm d:l
Porosity, $                                                          0.45
Residual water phase saturation, S$                                   0.333
Air entry value, V.                                                 0-0 cm
Constitutive relations:
                   )= (1 - SJOL -
where > = -100 era.
Az = 0.5 cm
At = 0.01 d
TABLE 7-2. SOIL PROPERTIES USED IN SIMULATING STEADY-STATE
             INFILTRATION

       Parameter                                                   Value


Length of soil column, L                                             550 cm
Saturated hydraulic conductivity, K                                 25 cm dl
Porosity, $                                                         0.331
Residual water saturation, S\^                                           0.0
Air entry value, vpa                                                 0.0 cm
Constitutive relations:
               [1

where Se = (M - S^)/(l - S=j>, a ^ 0.014 cm-1, ^ » 0 cm,

       M 1.51, y= 0.338
                                    7-21

-------
Figure V.8
      ie
     UNSAT2
O    VADOFT
X    SATURN
     12
                   -20
            -40
           PRESS WE HEAD, em
-to
-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).
                                     7-22

-------
b'igure 7.9
                                                      IWSAT2
                                                  O   VADOFT
                                                  X   SATURN
                                  0.4          o.e
                              WATtft PHASE SATURATION
  Figure 7.9 Simulated profile of water saturation for the problem of transient upward flow
in a soil column.
                                      7-23

-------
TABLE 7-3. ITERATIVE PROCEDURE PERFORMANCE COMPARISON
Number of Nonlinear Iterations


Case
n
n
n
n
n
= 3
= 4
= 6
= 8
= 10
Newton-
Raphson
12
13
19
27
31

Picard
33
56
n.c.*
n.c.
n.c.
 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 hr4
Porosity, $                                                0.25
Longitudinal dispersivity, e^                                    5 cm
Concentration at the source, CO                                  1

Az = 10 cm
                                  7-24

-------
 'igure 7.10
                -to
                          -!•
                                     »»•        -«»
                                        HEAD tern)
 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).
                                       7-25

-------
Figure 7.11
          t»o
* n
•+ n
x n
 • 10
 - •
 - e
 * 4
- 3
       v
      tu
      o
         too
        400
        too
          ft.o
                       .t
                .4           .•

                  SATURATION
                                                                          1.D
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).
                                        7-26

-------
''igure 7.12
                                               — Analytic Soln.
                                                 ©  VADOFT
                                   158.     200.     250.

                                     Distance,  cm
Figure 7.12 Simulated concentration profiles for the problem of solute transport in a semi
infinite soil column.
                                        7-27

-------
TABLE 7-5. CONCENTRATION PROFILE CURVES AT t = 25 hr AND t = 50 hr
         SHOWING COMPARISON OF THE ANALYTICAL SOLUTION AND
         RESULTS FROM VADOFT
Concentration Values
Z
Distance
(cm)
00.0
10.0
20.0
30.0
40.0
50.0
60.0
7040
80.0
90.0
100.0
110.0
120.0
130.0
140.0
150.0
160.0
170.0
180.0
190.0
200.0
210.0
220.0
230.0
240.0
250.0
260.0
270.0
280.0
290.0
300.0
310.0
320.0
330.0
t = 25
Analytical
1.0000
0.9997
0.9983
0.9945
0.9854
0.9662
0.9313
0.8745
0.7924
0.6858
0.5619
0.4321
0.3099
0.2060
0.1264
0.0713
0.0369
0.0175
0.0075
0.0030
0.0011
0.0003
0.0000











hr
VADOFT
1.0000
0.9998
0.9987
0.9954
0.9870
0.9688
0.9346
0.8781
0.7956
0.6889
0.5660
0.4394
0.3222
0.2235
0.1474
0.0928
0.0560
0.0327
0.0184
0.0101
0.0054
0.0029
0.0015











t = 50 hr
Analytical
1.0000
1.0000
1.0000
1.0000
0.9999
0.9999
0.9996
0.9991
0.9981
0.9960
0.9921
0.9854
0.9743
0.9570
0.9313
0.8953
0.8475
0.7872
0.7151
0.6331
0.5447
0.4541
0.3660
0.2845
0.2129
0.1532
0.1058
0.0701
0.0444
0.0270
0.0157
0.0087
0.0046
0.0000

VADOFT
1.0000
1.0000
1.0000
1.0000
1.0000
0.9999
0.9997
0.9994
0.9985
0.9967
0.9933
0.9871
0.9767
0.9599
0.9348
0.8991
0.8513
0.7908
0.7186
0.6368
0.5491
0.4598
0.3736
0.2942
0.2246
0.1662
0.1193
0.0831
0.0563
0.0371
0.0239
0.0150
0.0092
0.0055
                             7-28

-------
TABLE 7-6. VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA
            USED IN SIMULATING ONE-DIMENSIONAL TRANSPORT IN A
            FINITE SOIL COLUMN

    Parameter                                         Value

Thickness of soil column, L                                  20m
Darcy velocity, V                                        0.25 m d:l
Water content, 0                                        0.25
Retardation coefficient, R                                   1
Longitudinal dispersivity, %                                4m
Source leachate concentration, 6}                             1
Case 1:
Decay constant, A                                            a
Case 2:
Decay constant, A                                        0.25 d:f
Az= 1.0m
Att=0.5 d
                                    7-29

-------
 figure 7.13
                                                — Analytic  So1n
                                                   VADOFT
                                                  12.      16.      X
                                        (»)  X  » 0 d'1
                                                  Analytic Soln.
                                               C VAMFT
                                         0.       12.
                                       Distance, m
                                      (b) ^ • 0.25 d"1
Figure 7.13 Simulated concentration profiles for two cases of the problem of solute transport
in a soil column of finite length, (a) X = 0 d:l, and (b) X = 0.25 d:L
                                          7-30

-------
TABLE 7-7. CONCENTRATION PROFILE CURVES SHOWING COMPARISON OF
            THE ANALYTICAL SOLUTION AND VADOFT
Distance
z, (m)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
t =
Analytical
0.764
0.638
0.502
0.371
0.256
0.164
0.097
0053
0.027
0.013
0.009

5d
VADOFT
0.751
0.624
0.489
0.360
0.247
0.158
0.094
0.052
0.027
0.014
0.009

t
Analytical
0.884
0.820
0.742
0.655
0.561
0.466
0.375
0.293
0.224
0.176
0.157
Case 1: h =
= 10d
VADOFT
0.878
0.812
0.733
0.645
0.552
0.457
0.367
0.286
0.219
0.171
0.152
Od^1
t =
Analytical
0.963
0.942
0.914
0.881
0.841
0.796
0.748
0.698
0.652
0.617
0.602

20 d
VADOFT
0.961
0.939
0.911
0.877
0.837
0.791
0.742
0.692
0.646
0.610
0.595
Distance
z, (m)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
20.0
t =
Analytical
0.593
0.416
0.283
0.186
0.116
0.069
0.038
0.020
0.009
0.004
0.002

5d
VADOFT
0.588
0.411
0.279
0.182
0.113
0.067
0.037
0.019
0.009
0.004
0.002

t
Analytical
0.615
0.449
0.326
0.236
0.169
0.119
0.083
0.057
0.039
0.028
0.024
Case 1: h =
= 10d
VADOFT
0.613
0.447
0.325
0.234
0.167
0.118
0.083
0.057
0.039
0.028
0.024
Od^1
t =
Analytical
0.618
0.453
0.333
0.244
0.179
0.131
0.096
0.071
0.053
0.042
0.038

20 d
VADOFT
0.617
0.452
0.332
0.243
0.178
0.131
0.096
0.071
0.053
0.042
0.038
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 Figures 7.14 and 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.
                                      7-31

-------
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 conserva-
tive solute species has a uniform initial concentration and moisture content. The initial
con-centration is assumed to be zero, and the inlet concentration CO is assumed to be 1
ppm. The solute is transported by dispersion and advection. Note that the solute front
and the wetting front advance at different rates. The solute velocity,  Yy., was previously
defined as Equa-tion 7-23. 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 per-
formed 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 con-centration 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.
                                        7-32

-------
Figure 7.14
   c
   o
   c
   o*
   u
   c
   o
   QJ
   oc
CASE  1

—Analytic  Soln

   VADOFT
                                 700.        750.


                                   Time,  s
                         850.
 Figure 7.14 Simulated outflow breakthrough curve for case 1 of the problem of solute

transport in a layered soil column.
                                     7-33

-------
  ''igure 7.15
        1.6
        .6  ••
    e
    o
    CJ
    u
   O)
   o:
        .4  •-
.2  -
        0.0
                                                 CASE 2

                                                 —A Analytic Solution

                                                    O VADOFT
                                                  \	\	1
                          S6Z.
£02.       700.

 Time, s
                                                eac.
1B00
Figure 7.15 Simulated outflow breakthrough curve for case 2 of the problem of solute

transport in a layered soil column,
                                        7-34

-------
TABLE 7-8. VALUES OF PHYSICAL PARAMETERS USED IN THE SIMULATION OF
            TRANSPORT IN A LAYERED SOIL COLUMN

                           	Value for Layer i	
Parameter                   Layer 1         Layer 2           Layer 3

Layer thickness, (|              25.48           30.31            30.31 cm
Seepage velocity, Bi             0.127           0.123            0.121 cm si
Retardation coeff., Rj            1.0              1.0             1.0
Decay constant, hi                0               0              ° §
Source concentration, 6Q          1.0
Case 1:
Dispersivity, ay                0.076           0.174            0.436 cm
Case 2:
Dispersivity, eft                0.76             1.74            4.36 cm
Az = 0.6888 cm
                                    7-35

-------
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
720
730
740
750
760
770
780
790
800
810
820
830
840
850
Concentration
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
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
Values for Case 1
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
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
                             7-36

-------
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
680
690
700
710
720
730
740
750
760
770
780
790
800
810
820
830
840
850
900
Concentration
Analytical Solution
0.303
0.330
0.357
0.384
0.412
0.439
0.466
0.493
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
Values for Case 2
Numerical VADOFT
0.310
0.337
0.365
0.394
0.422
0.450
0.478
0.505
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
                              7-37

-------
TABLE 7-11. VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA
             USED IN SIMULATING TRANSPORT IN A VARIABLY SATURATED
             SOIL TUBE

       Parameter                                       Value


Length of soil column, L                                  20 cm
Saturated hydraulic conductivity, K                          1 cm d"t
Initial pressure head, vpj                                -83.33 cm
Remaining flow parameters                                See Table 3-2
Initial concentration, 6j                                     0 ppm
Longitudinal dispersivity, a^                                0 cm
Molecular diffusion, D*                                     \ qrp? d-1
Decay constant, A
Retardation coefficient,  R                                   1

Az = 0.25 cm
Alt-0.0025 d
TABLE 7-12. VALUES OF PHYSICAL PARAMETERS AND DISCRETIZATION DATA
             USED IN SIMULATING TRANSIENT INFILTRATION AND
             CONTAMINANT TRANSPORT IN THE VADOSE ZONE

                                     Material 1               Material 2
   Property                             (Sand)                (clay loam)

Saturated conductivity, K                  713                      6.24 cm d:l
Porosity, f                                0.43                    0.41
Residual Water Saturation, %               0.105                   0.232
Air entry value, ipa                         0.0                     0.0 cm
Soil moisture parameter, a                   0.145                   O.OlBonntl1
Soil moisture parameter, |3                   2.68                    1.31
Soil moisture parameter, y                   0.63                    0.24
Longitudinal dispersivity, a^                 1.0                     1.0 cm
Retardation coefficient, R                    1.1                     1.5
Decay coefficient, A                         0.00274                 0.00274 d-1
                                    7-38

-------
ir'igure 7.Id
               SOLUTE       WETTING
               FRONT |     ]  FRONT
J    I
                   / y y  X
                                   /   s  s  s s  s
«
1
!
FLOW
                                                         ••83.33 cm
                                                     d*
                  }-	20  em
          c or g
                             SU.tJ
  Figure 7.16 One-dimensional solute transport during absorption of water in a soil tube.
 (Adapted from Huyakorn et al.,  1985).
                                      7-39

-------
figure 7.17
            i.o
                          VADOFT
                          UMSATX
                          • EMI-ANALYTIC SOLUTION
                                                                              II
   Figure 7.17 Simulated profiles of water saturation during absorption of water in a soil tube.
   (Adapted from Huyakorn et al.,  1984a).

                                          7-40

-------
figure 7.1b
                                                       0   VADOFT

                                                     — »•— FEMWASTE
                                                           SEMI-ANALYTIC
                                                           SOLUTION
 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).
                                        7-41

-------
b'igure 7.19
     20 cm
    120 cm
   280 cm
     l,cm/d

 I  I  II  1  i  I
SAND Ksat - 713 cm/d


  CLAY  LOAM
                          6,24 cm/d
    SAND


  Ksat" 713  cm/d
               WATER TAB!
 Figure 7.19 Problem description for transient infiltration and transport in the vadose zone.


                      7-42

-------
''igure 7.20
        5
        4 ••
        3-
  E
  o
 JO
 ^5
  n
         1
                     ll
                                  8          12

                                  time , days
20
 Figure 7.20 Infiltration rate vs. time relationship used in numerical simulation.



                                    7-43

-------
'igure 7.21
      604-
     120-
  I  «0T
  0-
  O  240T
     300"
     360+
     420
                     0.2
0.4          0.6
  SATURATION
10
Figure 7.21  Simulated water saturation profiles.

                                     7-44

-------
[Figure 7.22
     420
                     -100         -200         -300
                               PRESSURE HEAD,  cm
-400
 Figure 7.22 Simulated pressure head profiles.
                                    7-45

-------
[Figure 7.23
     420
                                    2             3
                                    VELOCITY,  cm/d
  Figure 7.23 Simulated vertical Darcy velocity profiles.

                                       7-46

-------
b'igure 7.24
  I
                                  0.4           0.6
                                  CONCENTRATION
0.8
1,0
  Figure 7.24"Simulated solute concentration profiles.
                                       7-47

-------

-------
                                    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-2 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 uncertain-
ty 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 G|
represents the concentration at the receptor, then
                                                                              (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 Fgw(C^) given a probabilistic characterization of X, Note that F£W(£$ is defined
as:
                                        8-1

-------
                                                      ) = Probability (Cl * Q     (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, Xl, $3? . . . ^, the
composite model computes the output variable (e.g., a downgradient receptor well
concentration G$) as:
                                                                                 (8-3)
Application of the Monte Carlo simulation procedure requires that at least one of the
input variables, XI ... X^ 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 C%. 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 simula-
tion technique involve:

 i) Selection of representative cumulative probability distribution functions for describing
uncertainty in the relevant input variables.

 ii) Generation of pseudo-random numbers from the distributions selected in (i). These
values represent a possible set of values for the input variables.

iii) Application of the model to compute the derived inputs and output(s).

 iv) Repeated application of steps (ii) and (iii) .

v) Presentation of the series of output (random) values generated in step (iii) as a cumula-
tive probability distribution function (CDF).

vi) Analysis and application of the cumulative probability distribution of the output as a
tool for decision making.
                                         8-2

-------
8.2.2 Uncertainty in the Input Variables

The parameters required by a transport and transformation model can be broadly
classified into two different sets that exhibit different uncertainty characteristics. These
are:

• '  Chemical parameters. Examples of these variables include the octanol-water partition
coefficient, acid, neutral, and base catalyzed hydrolysis rate, soil-adsorption coefficient,
Henry's Law Constant, etc.

•  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 quantified by the user. This implies that for each input parame-
ter 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.
                                         8-3

-------
         i)      Uniform
        ii)      Normal
       iii)      Log-normal
        iv)      Exponential
         v)      Johnson SB distribution
        vi)      Johnson SU distribution
       vii)      Empirical
       viii)      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 piece-wise  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 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 maxi-
mum value (upper bound) B. The equation for the uniform probability density distribu-
tion of variable x is given by:

                                         8-4

-------
       fu(x)       - 1/(B - A)                                                    (8-4)


where

       fu(x)       = the value of the probability density function for x

The cumulative distribution F(x) is obtained by integrating Equation (8-4). This yields
the probability distribution:


       Fu(x)      = (x - A)/(B - A)                                               (8-5)


where

       F(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 distribu -
tion. Normal distributions are symmetrical about the mean value and are unbounded,
although values further from the mean occur less frequently. The spread of the distribu-
tion is generally described by the standard deviation.  The normal distribution has only
two parameters) -the mean and the standard deviation. The probability density function
of x is  given by:
                           /,(*) = — ~ «SP
-0.5
(8-6)
where

       S,         = the standard deviation

       ni         = the mean  of x
                                         8-5

-------
The cumulative distribution is the integral of the probability density function:


       Fn(x) = J fn(x)dx                                                           (8-7)
The above integration must be performed numerically, but tables of numerically-integrat-
ed values of Fn(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, if y is the natural log of x, then the probability distribution
of y is normal with mean ir^, and standard deviation Sy and a probability density function
similar to Equation 8-10. The mean and standard deviation of x (ir^ and Sx) are related to
the log-normal parameters niy and Sy as follows.


                   + 0.5(Sy)2]                                                     (8-8)


                         ll                                                      (8-9)
To preserve the observed mean and standard deviation of x, the parameters of the log-
normal distribution (rtiy and Sy) are selected such that the above relationships are
satisfied. Note that niy and Sy do not equal the natural logs of m^ 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 described by an
exponential equation:
                 expC-x/m,)
            =   	                                                    (8-10)
                   m,


where m, is the mean of x. The cumulative distribution is given by:


      F.OO = 1-  exp(-x/nO                                                      (8-11)
The exponential distribution is bounded by zero; the probability density function peaks at
zero and decreases exponentially as x increases in magnitude.
                                         8-6

-------
8.3.5 The Johnson System of Distributions

The Johnson system involves two main distribution types-SB (log-ratio or bounded) and
SU (unbounded or hyperbolic arcsine). These two distribution types basically represent
two different transformations applied to the random variable such that the transformed
variable is  normally distributed. The specific transformations are:


                                                                              (8-12)
                           SB: Y = In
                                      fl-x
                                                                             (8-13)
where
       in         =  natural logarithm transformation

       x         =  untransformed variable with limits of variation from. A to 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 most frequent value (i.e., the mode). Figure
8.1 shows an example triangular probability density function. The cumulative distribu-
tion for values of x less than the most frequent value, xm, is given by:
                    (x - Xl)2
where

      Xi         = the minimum value

and
                                         8-7

-------
       Xg         = the maximum value

For values of x greater than the most frequent value, the cumulative distribution is:
                                                                              (8-15)
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 probabi-
lity 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.
                                         8-8

-------
Kigure 8.i
     f(X)
t
    1.0  -1
    0.0 ->•
                                     m
  Figure 8.1. Triangular probability distribution.



                                           8-9

-------
The correlation coefficient is a measure of the linear dependence between two random
variables and is defined as:
       Vxy        D n                                                           \y >•»>
                 H* Py


where

       piy            = the correlation coefficient between random variables x any y

       cov(x,y)        = the covariance of x and y as defined below

       p,, py          = the standard deviation for x and y.


The covariance of x and y is defined as:


       cov(x,y)        =               E

                       +00
                     =  JI (x-mj (y-my) fxy(X)y) dx dy                             (8-17)
                        -OO


where

       E             = the  expected value

       m,, iriy        = the mean of the random variables x and y

       f^ (x,y)        = the joint probability distribution of x and y.

Note that the linear correlation coefficient between x and y  can be computed using



                                       £ x, y, - nxy
                                       '=1                                      (8-18)
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:
         = Be                                                                 (8-19)
                                        8-10

-------
where
       e          = the vector of uncorrelated, normally distributed random numbers.

       B         = and 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 = B BT                                                                (8-20)


where BT is  the transpose of the B matrix. Since the normal variables V 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 8-20 by using a
Choleski decomposition algorithm. This algorithm will decomposes a symmetric positive
definite matrix, such as S, into a triangular matrix such as B (de Marsily 1986, p. 381).

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 Xj that have a normal distribution, the Y' numbers are transformed
as follows.

       X, = m, + ox(YJ)                                                          (8-21)

For parameters that follow the lognormal distribution, the following transformation
applies.
      Xi = exp[(Yp (a^) + ^,1                                                  (8-22)


where

      M-inj        = the log mean of the 1th parameter

      ain.i        = the log standard deviation of the 1th parameter


For parameters with Johnson SB and SU distributions, the Y are first transformed to
normally distributed  variables Y with mean My and standard deviation ay:


      Y, = My + ay YI                                                          (8-23)


Johnson SB numbers are then computed from Yj as follows.


                                       8-11

-------
       X, - (B expCYj) - A)/(l + expC^))                                           (8-24)


Johnson SU numbers are computed by:


       X; = A + (B-A) [exp(Y.) . expC-Yi)]^                                         (8-25)
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 (xg) and the other with a log
normal distribution (xl), the following equation is used to transform correlations to normal
space (Meija and Rodriguez-Iturbe, 1974).
                                                                               (8-26)
where

       av  v           = the correlation coefficient between the two variables in the
        ylJ2
                        normal space

       crx  f           = the correlation coefficient between the two variables in the
        1 &
                        arithmetic  space
        o
       ay             = the variance of y1 derived from Equation (8-9)


If both x1 and x2 are log-normally distributed then the correlation coefficient is trans-
formed using Meija and Rodriguez-Iturbe (1974):
                                       ln
(8-27)
                                        8-12

-------
where the relationships between S, (SIf>) and Syi(Syo) are given by Equations (8-8)
   i o r\                          \  £t       L  &
and 8-9.

Thus, for log-normal variables, the user enters the values of the correlation coefficients in
log-normal space; Equations 8-26 and 8-27 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 coeffi-
cients between normal-transformed numbers. This may be accomplished by first trans-
forming Johnson SB and SU data to normal data using Equations 8-12  and 8-13. The
covariance matrix S is then derived using only normal,  log-normal, and normal-trans-
formed 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 pro-
prietary 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 1988).

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 compo-
nents, 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 occurence.
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
                                       8-13

-------
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 Ouantiles

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-2 code,  four such
methods can be used. Among these are distribution-free or nonparametric techniques as
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^,,.... x(n),
where n represents the sample size. The empirical cdf can be defined simply as

             f 0,  if  x(1) <  x,
      g(x) = \ 1/n, if x(i) < x < xa+1), for i=l,..., n-1                                (8-28)
             I  1, if     x > x^.
Mathematically, g(x) is a step function, discontinuous at each value x(i).

By definition, the lOOp-th percentile (i.e., the p-level quantile) is given by up where


       p = Pr{X
-------
      p = F(Up) and up = F'Hp)                                                  (8-30)


When only sample information is available, UP is unknown, but it can be estimated by
forming an appropriate function of the observations.

Nonparametric point estimates of Up can be constructed as linear combinations of the
order statistics. In particular, each of Yt through Y3 below is an estimator of Up. Let [z]
denote the largest integer less than or equal to z. Define


      j = [np],    g = np-J                                                   (8-31)

      i = [np + 0.5], r = (np + 0.5) - i                                            (8-32)

      k  = Kn+l)p], h = (n+l)p - k                                              (8-33)

Then,

      Y! = (1-h) X^ + h X^u                                                   (8-34)
      Y2=   - ,ifg=0                                       (8-35)
                      2


        = XO+D  ,   if gX)


      Y3 = (0.5+i-np) Xa) + (0.5 -i+np) X(i+1)                                       (8-36)

        = (1 - r) X
-------
in which i is the rank of the outcome in the sample. The specific quantile of interest is
then determined by interpolation.

8.4.2 Confidence of u^

Approximate confidence statements can be placed on up by selecting appropriate order
statistics to serve as the upper and lower confidence bounds. The rationale is given as
follows.

For a given distribution, the value up is such that exactly 100p% of all values of this
distribution are less than up, and 100(l-p)% exceed this value. An  individual value
selected randomly from the distribution has probability p of being less than up. In a
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 (X^, X^y) will contain up is equivalent to the probability that
exactly i of the n elements of the sample will be less than up. Hence, this probability is
                                         ' (1 -Prl                              (8-38)
which is a simple binomial probability.

This expression can be calculated for each pair of consecutive order statistics X^,, X^, for
i=l, . .., n-1. However,  it is more convenient to deal with these several intervals by
calculating cumulative probabilities of the form
                                                                               (8-39)
For practical convenience, the normal approximation

       F {[(i+0.5)-np]/V [np(l-p)]}                                                (8-40)

can be used, where F represents the cdf of the standard normal distribution.

All of this is utilized for determining two order statistics, denoted below with subscripts i
and j, with the property


       PriX,,, < up < Xg)} = 1 - a                                                   (8-41)


where 1-a is the predetermined confidence  coefficient; typically, 1-cc = 0.95. Computation-
ally, i and j can be determined by solving the equations
                                         8-16

-------
       a/2 = F{[(i+0.5)-np]A/ [np ( 1-p) ]}                                           (8-42)

and

       l-a/2= F{[(j+0.5)-np]/\/ [np(l-p)]}                                        (8-43)


This results in


       i = (np-0.5) + V[np(l=p)Tp-1
-------

-------
                                   SECTION 9

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                                  SECTION 10

                                  APPENDICES
 10.1 ERROR MESSAGES AND WARNINGS

The PRZM-2 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 10-1. Along with  each
message, troubleshooting approaches are described. Error or warning messages originat-
ing in PRZM-2 (the main code) are numbered beginning with 1000; PRZM, 2000;
VADOFT, 3000; 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-2 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 used 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 10-1. The message will be something such as:  "Error in attempting to open file
 []"  or "Error in input....".  Again, check the compiler's run time error list for
the exact cause.

When errors and warnings are reported in the output echo file, three lines of information
are provided. The first line reports the number and whether the condition was an error
(fatal)  or warning (non-fatal). The second line supplies the associated message. The third
line supplies the subroutine trace of where the error occurred. For example, the third line
might be: 'PRZM2>INPREA>VADINP'. This implies that the  error occurred in the
subroutine VADINP (the VADOFT input routine), which was called from subroutine
INPREA, which was called from the PRZM-2 main program. This third line will not
appear if an error occurs in the routine INITEM, which is the  routine to read the
PRZM2.RUN file and initialize the simulation.

 10.2 VARIABLE  GLOSSARY

This section presents the major variables used in the PRZM-2 code. Table 10-2 presents
variables used  in the EXESUP module, Table 10-3 presents PRZM variables, Table  10-4
presents VADOFT variables, and Table 10-5 presents variables used in the Monte Carlo
module.
                                       10-1

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES
      Error or
      Warning
                   Troubleshooting Approach/Explanation
1010 Water table is  above
      vadose zone
1020 Water table is  above
      root zone.
1050 Zero or negative mass in
      VADOFT/PRZM nodes below
      the water table
1070 Error in  the file name
      input, line with ....

1090 Bad value [nnnn] for
      number of chemicals
1092 Bad index  [nnnn]  of
      chemical
1100 Bad value  [nnnn] for
      chemical parent species
1190 Bad identifier reading
      global data []
1200 End  date is before start
      date
                   The water table has accumulated to above
                   the top of the vadose zone. Use higher
                   conductivities or increase the thickness
                   of the vadose zone.

                   The water table is above the top of the
                   root zone. Use higher conductivities or
                   increase the thickness of the root zone.

                   This is a warning only, the
                   concentration values
                   in the VADOFT or PRZM nodes below the
                   the water table will not be adjusted for
                   the current timestep.  If this warning
                   appears repeatedly, the VADOFT or PRZM
                   geometry might have to be adjusted.

                   An incorrect  (or misspelled) identifier
                   was supplied for a file.

                   The number of chemicals  must be between
                   1 and 3,inclusive. Change the number in
                   the global data group of PRZM-2 input
                   file.

                   An invalid index was provided for input
                   record EXESUP3 with ANAME =
                   'PARENT OF'. Values less than 1 or
                   greater than  NCHEM are not valid.

                   Check input values. Chemical 1 can have
                   a parent of 0 only. Chemical 2 can have
                   a parent of 0 or 1. Chemical 3 can have
                   a parent of 0,  1, or 2.

                   An invalid label appears in the global
                   data section (EXESUP) of the PRZM2.RUN
                   input file.

                   Check the 'START DATE' and 'END DATE'
                   records of PRZM2.RUN input file.
                                       10-2

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
      Error or
      warning
                   Troubleshooting Approach/Explanation
1190  Bad identifier  reading
      global data []
1190 Bad identifier reading
      global data []
1200 End date is before start
      date

1202 End date and start date
      are the same

1210 Unrecognized label
      [

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
       Error or
       Warning
                   Troubleshooting Approach/Explanation
1270   Too many files requested
       to be  open at once
1250   Error reading PRZM-2
       run file ....
1260   File type ['nn'] has
       already been specified
                   The maximum number of files allowed
                   (defied in the include file IOUNITS.PAR)
                   is too small a number for the (recently
                   modified) version of PRZM-2. This error
                   should not appear in the current version
                   of PRZM-2.

                   Error in reading PRZM-2 input data, most
                   likely there are inappropriate characters
                   in a data field which is attempting to be
                   interpreted as integer data.

                   A file with the same unit number has been
                   open while PRZM-2 is running. Should
                   never occur in current version of PRZM-2.
1270  Too many files requested
      to be open at once
1280    ENDFILE statement present
      before file [nn] was
      opened
1290   Request to close file
       [nn] which was not open
1300 Unknown unit number to
      open file
                   The maximum number of files allowed
                   (defined in the include file IOUNITS.PAR)
                   is too small a number for the (recently
                   modified) version of PRZM-2.  This error
                   should not appear in the current version
                   of PRZM-2.

                   An input file, which is required for the
                   current PRZM-2 simulation configuration,
                   has not been identified in the file group
                   of the PRZM-2 input file.

                   Should never occur in current version of
                   PRZM-2. 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.
                                        10-4

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES  (Continued)
      Error or
      Warning
                   Troubleshooting Approach/Explanation
1310  Too many lines required
      for Trace option
1320  Argument [] too
      large for EXP

1330  Negative or zero argument
      []

1350  Single precision overflow
1360  Negative argument
      [] to SQRT
1390  Invalid index [nnnn]
      in reading record
      []

1400  Error reading PRZM data
1500  ENDDATA before starting
      end day was provided
1510  ENDDATA before end day was
      provided
                   Should never occur in current version of
                   PRZM-2. 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
                   PRZM2.RUN file before the record was
                   provided.

                   The label 'ENDDATA appears in the global
                   parameters section of PRZM2.RUN file
                   before the 'END DATE' record was provided.
                                        10-5

-------
TABLE 10-1.
             PRZM 2 ERROR MESSAGES, WARNINGS, AND
             TROUBLESHOOTING APPROACHES (Continued)
       Error or
       Warning
                                Troubleshooting Approach/Explanation
 1530
1540
1550
ENDDATA before number of
chemicals was provided
 ENDDATA before the parent
of chemical n
was provided
 dd/mm/yy - Invalid START
(or END) DATE
The label 'ENDDATA' appears in the global
parameters section of PRZM2.RUN file (with
TRNSIM = 'ON') before the 'NUMBER OF
CHEMICALS record was provided.  The
'NUMBER OF CHEMICALS' record is required
for a transport simulation.

The label 'ENDDATA appears in the global
parameters section of PRZM2.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 PRZM2.RUN
input file. Check to see whether the month
being specified had the number of days
which is being implied (e.g., 31/02/88 is
not valid).
1560  End of file [] encountered

1570    Monte Carlo simulation -
      Level reset to 1
2000    Simulation date (dd/mm/yy),
      meteorological date
      (ddhmdyy) do not  match
                                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 PRZM2.RUN do not
                                correspond to the dates in the
                                meteorological data file.
                                        10-6

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
      Error or
      warning
                   Troubleshooting Approach/Explanation
2010  Number of chemicals in
      PRZM [NN] <> number of
      chemicals  in EXESUP [nn]
2040  NPI [nnnn] + NEW [nnnn] is
      greater than NPII [nnnn]
2050  Solution for tridiagonal
      matrix not found, previous
      day's values used

2060  NDC [nnnn] is greater than
      NC [nnnn]
2070  NCPDS [nnnn] is greater
      than NC [nnnn]
2080  NAPS [nnnn] is greater
      than NAPP [nnnn]
2090  NHORIZ [nnnn] is greater
      than NCMPTS [nnnn]
2100  NCOM2+1 [nnnn] is greater
      than NCMPTS [nnnn]
                   The value supplied to the PRZM input file
                   for the number of chemicals being
                   simulated does not agree with the number
                   supplied to the PRZM2.RUN input file.

                   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 transport
                   solution 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.

                   Change PRZM problem definition geometry so
                   that the input value of NCPDS is less than
                   or equal to the parameter NC or change the
                   value of NC and recompile.

                   Change PRZM problem definition geometry so
                   that the input value of NAPS is less than
                   or equal to the parameter NAPP or change
                   the value of NAPP and recompile.

                   Change PRZM problem definition geometry so
                   that the input value of NHORIZ is less
                   than or equal to the parameter NCMPTS or
                   change the value of NCMPTS and recompile.

                   Change PRZM problem definition geometry so
                   that the input value of NCOM2 is less than
                   the parameter NCMPTS or change the value
                   of NCMPTS and recompile.
                                       10-7

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
       Error or
       Warning
                   Troubleshooting Approach/Explanation
2110   NPLOTS [nnnn] is greater
             than 7

2120   Sum of horizon thicknesses
       exceeds depth
2130  Soil profile description
      is incomplete, data
      available for xx.xx of
      xx.xx cm

2140  Calculated field capacity
      water content exceeds the
      saturation value
2150    Application [nn] failed
      to meet ideal soil
      conditions
2160  WIND AY [nn] for
      application [nn]
      is too large
3000   Fatal error in HFINTP,
      interpolation failed
                   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
                   WINDAY value expired. Currently this
                   error will halt execution.

                   The value for WINDAY, specified in the
                   PRZM input sequence, causes overlap on a
                   proceeding application date. Reduce  the
                   value for WINDAY to a value lesser than
                   the difference of application dates.

                   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.
                                        10-8

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
      Error or
      Warning
                   Troubleshooting Approach/Explanation
3010  VARCAL - timestep nnn
      solution fails to converge
      after nnn reductions
3020  Attempt to run VADOFT
      w/PRZM on and ITRANS
      .ne.l
3030  Incorrect value for IMODL
3040  in VADOFT input
                   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; IMODL = O for transport,
                   IMODL = 1 for flow.
3050  Requested value of NOBSND
      [nnnnl greater than
      MXPRT [nnnn]
3060  Transport simulation,
      NVREAD reset to 1
3070  PRZM is on; IVSTED reset
      to 1

3080  PRZM is on; flow boundary
      conditions will be over-
      written
                   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.

                   If PRZM is on and linked to VADOFT, a
                   prescribed flux b.c. w-ill 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 O; no action required.
                                       10-9

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
       Error or
       Warning
                   Troubleshooting Approach/Explanation
3090   PRZM is on; transient data
       at top node ignored
3120  PRZM is on; transport
      boundary conditions will
3130  PRZM is on; transient
      data at top node ignored
3170   Invalid index [nnn] in
       reading PINT
3190  ITMGENol  in linked mode,
      results may be unpredict-
      able
3210  End of file reading
      VADOFT  Darcy velocities
5000  Format error in reading
      Monte Carlo input file
5010   Premature end of
       Monte Carlo input file
                   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 3190 occurred
                   prior to this fatal error. Make necessary
                   changes to VADOFT input file.

                   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.
                                       10-10

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES (Continued)
      Error or
      warning
                   Troubleshooting Approach/Explanation
5020  Uniform random number
      could not be generated for
      exponential distribution
5030  Cannot have a negative
      mean for a log normal
      distribution.  Mean
      equals 

5040  Subroutine DECOMP
      terminated, matrix BBT
      is not positive definite

5050  The number of [MONTE CARLO
      RUNS] is greater than
      maximum of 
5060  The number of [MONTE CARLO
      VARIABLES] is greater than
      maximum of 
                   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.
5070  The number of [EMPIRICAL
      DIST. DATA POINTS] is
      greater than maximum of
      
                  Reduce number in input file or change NEMP
                  and  recompile.
5080  The number of [MONTE CARLO
      OUTPUT VARIABLES] is
      greater than maximum of
      
                  Reduce number in input file or change NMAX
                  and  recompile.
5090  The number of [DAYS IN
      OUTPUT AVG. PERIOD] is
      greater than maximum of
      
                  Reduce number in input file or change
                  NPMAX and recompile.
                                      10-11

-------
TABLE 10-1.
PRZM 2 ERROR MESSAGES, WARNINGS, AND
TROUBLESHOOTING APPROACHES  (Continued)
      Error or
      Warning
                  Troubleshooting Approach/Explanation
5100 The number of  [REQUESTED
      OUTPUT CDFS] is greater
      than maximum of 

5110 First element for horizon
      [] not found
                  Reduce number in input file or change
                  NCMAX and recompile.
                  The PRZM horizon value provided for a
                  variable defined in the Monte Carlo input
                  file is probably invalid (does not match
                  the PRZM horizon/element number
                  description provided in the PRZM input
                  file).
                                     10-12

-------
TABLE 10-2.
EXESUP PROGRAM VARIABLES
Variable
BASEND
BOTFLX
DAFLUX
DAVFLX
DISUNS
EDAT
FLOSIM
ICHEM
IDAY&
ILDLT
IMONa
IPRZM

IPZONE
IYRO-
LLSTS
NCHEM
Units Type
scalar
cm day4 Array
q cm"2 Array
day-'
ppm cm Array
day-'
ppm Array
(q cm"3)
Array
Logical
scalar
scalar
scalar
scalar
scalar

scalar
scalar
days scalar
scalar
Description
Number of bottom PRZM 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.
bub-
routine
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP
EXESUP

EXESUP
EXESUP
EXESUP
INITEM
EXESUP
INPREA
INITEM
Common
Block I,M,0
M
VADSTO M
PRZSTO M
VADSTO M
M
M
M
M
M
M
M
M

M
M
I
0
I
I
0
                              10-13

-------
TABLE 10-2.
 EXESUP PROGRAM VARIABLES (Continued)
Variable Units
NDAYS days
NLDLT
NP
NPNARY --
NPRZM
NPV

NPZONE
NPZ
PINT L
M/L**3
PRZMON --
Type
scalar
scalar
scalar
Array
Scalar
Scalar

scalar
scalar
Array
Logical
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.
Sub- Common
routine Block
EXESUP
EXESUP
INPREA
INITEM
EXESUP CONTR2
EXESUP
EXESUP
INPREA
INITEM
EXESUP

EXESUP
INPREA
INITEM
EXESUP
EXESUP VADSTO
EXESUP
INPREA
INITEM
I,M,0
M
I
I
0
I
M
I
I
0
I

I
I
0
M
M
I
I
0
PRZMPF   q cm-2    Array
          day1

PRZMWF   cm day4   Array
P2VWHT
REDAT
Array
Array
Daily chemical flux from
the base of PRZM.

Daily water flux from the
base of PRZM.

Weighting factors for trans-
fer of water or chemical
flux from PRZM to VADOFT.

Ending day, month, year of
PRZM simulation within a
timestep.
                                   EXESUP
                                   EXESUP
           PRZSTO


           PRZSTO
EXESUP    ZONWHT
EXESUP
M


M


M



M
                                        10-14

-------
TABLE 10-2.
          EXESUP PROGRAM VARIABLES (Continued)
Variable    Units     Type      Description
                                               Sub-        Common
                                               routine       Block        I,M,0
RSDAT
RSTFG
SAVHED   cm
SDAT
TOPFLX
TOWFLX
TRNSIM
VADFON
VD2TC
WHGT
         Array
         scalar
SAVCNC    ppm      Array
         Array
         Array


cm day-'  Array
(gem4*

day1)

cm day'1  Array


         Logical
         Array
         scalar
ZPESTR    g cm'2     Array
           day1
Starting day, month, year
of PRZM simulation within
timestep.

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 pesti-

cide) flux leaving the
base of PRZM.

Water flux from PRZM to top
of VADOFT for each timestep.

Indicator for flow and
transport simulation.
         Logical    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.
EXESUP
EXESUP
                                               EXESUP     VADSTO
EXESUP


EXESUP


EXESUP
                                               EXESUP
VADSTO
VADSTO
EXESUP     VADSTO
EXESUP     VADSTO
EXESUP
M



M



M



M


M


M



M
EXESUP
INPREA
INITEM
EXESUP
INPREA
INITEM
1
1
0
1
I
o
            PRZSTO
               M
               M
               M
                                           10-15

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION

Variable
A
AAA

ABSOIL
AD


ADFLUZ


ADS

AFIELD

AINF
AIRDEN
AIRLMD
AKAY
ALAMDA
Units
day-'
cm-1

fraction
day1


gem"2
day"


mg kg'1

ha

cm
gm cm
cal cm'1
day1 °C -1

cal cm'1
day1 « CJ
Type
Array
scalar

scalar
Array


Array


Array

scalar

Array
scalar
scalar
of Air
Array
Array
Description
Lower Diagonal Element
of Solution Matrix (1-1)
A Variable Used to
Calculate the Average
Temperature Gradient
in the Top Compartment
Daily Value of Soil
Surface Albedo
Soil Horizon Drainage
Parameter


Advective Flux of
Pesticide


Adsorbed Portion of
Pesticide in Each
Compartment
Area of Field

Percolation Into Each
Soil Compartment
Density of Air at
Ambient Temperature
Thermal Conductivity
K-Factor in the Soil
Thermal Conductivity
Equation
Thermal Conductivity
of Soil Constituent
Sub-
routine
SLPEST
TRDIAG
SLTEMP

SLTEMP
READ
ECHO
INITL
HYDR2
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC

READ
EROSN
HYDROL
HYDR1
HYDR2
SLTEMP
SLTEMP
SLTEMP
SLTEMP
Common
Block I,M,0
PEST 0
I
M

M
HYDR 0
I
I
I
PEST 0
I
I
I


HYDR 0

HYDR 0
I
I
M

M

                                10-16

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
ALBEDO
AMXDR
ANETD
ANUM
APD
APDEP
APM
ATEMP
AVSTOR
AW
B
BBB
BBT
BD
BDFLAG
units Type
fraction Array
cm scalar
cm scalar
cm scalar
scalar
cm scalar
scalar
°C Array
cm3 cm^ scalar
scalar
day"1 Array
°K cm"1 scalar
°C Array
g cm"3 Array
scalar
Description
Soil Surface Albedo at
Start of Each Month
Maximum Rooting Depth
of Each Crop
Minimum Depth from Which
ET is Extracted Year
Around
Total Available Water
in Profile
Day of Month of Pesticide
Application
Depth of irrigation water
applied to soil
Month of Pesticide
Application
Air Temperature
Available Water Storage
Fraction of Soil Voids
Occupied by Water
Diagonal Element of
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
Sub-
routine
READ
SLTEMP
READ
INITL
PLGROW
READ
INITL
EVPOTR
READ
IRRIG
READ
Main
HYDR2
EVPOTR
SLPEST
SLTEMP
READ
SLTEMP
SLTEMP
READ
ECHO
INITL
Common
Block
MET
CROP
CROP







PEST

MET
HYDR

I.M.O
0
I
0
I
I
0
I


0




0
M
o
I
I
o
I
I
                         Entered)
                                  10-17

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
BT m

c day"1
CB kg ha'1
CC a
o
CELLBG

CEVAP cm

CFLAG
CHANGE g

CINT cm



CINTB cm
CINTCP cm
Type
scalar

Array
scalar
Array
Scalar

Scalar

scalar
Array

scalar



scalar
Array
Description
Bottom width of furrows

Upper Diagonal Element of
Solution Matrix (1+1)
Cumulative Pesticide
Balance Error
Total mass associated
with a moving point
First location in a
compartment
Current Daily Canopy
Evaporation Depth

Conversion Flag for
Initial Pesticide Input
Change in total pesticide
mass per compartment
Current Crop Interception
Storage



Crop Interception From
Previous Time Step
Maximum Interception
Storage of Each Crop
Sub-
routine
FURROW
IRREAD
SLPEST
TRDIAG
OUTPST
MOC1
INITL
INITL

EVPOTR
MASBAL
OUTHYD
OUTTSR
READ
INITL
MOC1

INITL
HYDROL
EVPOTR
MASBAL
OUTHYD
OUTTSR
PMAIN
MASBAL
OUTHYD
READ
ECHO
PLGROW
Common
Block
IRGT

PEST

PEST


HYDR

MISC


HYDR



HYDR
CROP
I.M.O
I
0
0
I

M
M

0
I
I
I
0
I
M

0
I
I
I
I
I
0
I
I
0
I
I
CLAY       percent     Array     Percent Clay in Each Soil
                             Horizon

CONC     --         Alpha-    Flag for Output of Soil
                     numeric   Pesticide Concentration
                             Profile
                                   SLTEMP
                                   PMAIN
HYDR
                                       10-18

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
CONDUC cm day1


CONST


CORED cm


COVER fraction

COUNT

COVMAX fraction



CN


CNCPOND g cm*


CNDBDY cm day4

CNDM


CNDMO

CPBAL g cm*


CRC day m'1
Type
scalar


scalar


scalar


scalar

Array

Array



Array


scalar


scalar

Array


Array

scalar


Array
Description
Canopy Conductance
Including Boundary
Layer's Conductance
Constant Values Used to
Multiply Each Time Series
output
Total Depth of Soil
Profile

Current Areal Cover of
crop canopy
Number of moving points
in a compartment
Maximum Areal Coverage
of Each Crop at Full
Canopy Development

Runoff Curve Numbers for
Antecedent Soil Moisture
Condition II
Concentration of
pesticide in inflowing
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
Sub-
routine
MAIN
SLPSTO
SLPST1
READ
ECHO
OUTTSR
READ
ECHO
INITL
SLTEMP

MOC1

READ
ECHO
INITL
PLGROW
READ
ECHO
HYDROL
MOC1
INITL

MAIN

PMAIN


SLTEMP

MASBAL

OUTPST
CANOPY
Common
Block
PEST





HYDR


CROP



CROP



HYDR


PEST







MISC

PEST



I.M.O
0
I
I
0
I
I
0
I
I
I

M

0
I
I
I
0
1
I
1


0




I

M

I
0
                                 10-19

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
CRCNC day m4

CTOT g

CURVN

CWBAL cm

D m

DAIR cm2 day1




DAY


DELT day




DELTA °K

DELX cm
DELXSQ cm-2

DEN
Type
Array

Scalar

Scalar

Scalar

Scalar

Scalar




Alpha-
numeric

Scalar




Scalar

Array
Scalar

Array
Description
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
Compartment Thickness
Squared
Point density. The
Sub-
routine
MAIN
OUTPST
MOC

HYDROL

MASBAL
OUTHYD
CANOPY
SLTEMP
ECHO
MAIN
READ
SLPSTO
SLPST1
PMAIN


INITL
HYDR2
PLPEST
SLPEST
MASBAL
SLTEMP

SLTEMP
INITL
SLPEST
INITL
Common
Block I.M.O
PEST I
0
M



HYDR M
I
0
M
PEST I
I
0
I
I



MISC 0
I
I
I
I
M

HYDR I
HKYDR 0

HYDR M
DENOM     cm
Scalar
number of points in the
horizon divided by the
depth of the horizon.

Total Voids in the Soil
Profile
EVPOTR
                                       10-20

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
DENOM

DEPI
DFFLUX
DGAIR
DGRATE


DIFFCH
DIFFCO
DIFK
DIN
DISP

DISS

DKFLUX

Units
cm hr'1

cm
gem'2
day1
cm2 day"1
day1


m2 day1
cm2 day"1
ma day1
cm
__2
cm
day1

mgl"1

gem"2

Type
Scalar

Array
Array
Array
Array


Scalar
Array
Scalar
Scalar
Array

Array

Array

Description
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

Sub-
routine
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


PEST
PEST

PEST





HYDR
PEST



PEST

I.M.O


0
I
0
I
I
I
I
I
I
0
I
I
0
M
0
0
I
I
0
I
I
I


0
I
I
I
                                 10-21

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
DKRATE
INITL

DKRT12

DKRT13

DKRT23
DOM
DPN
DT
DVF
DW
DX
EF
ELTERM

EMD
Units
day1


day1

day1

day1
-
cm
hr
kg ha'1
day4
Fraction
m
kg ha'1
day1

-
Type
Array
I

Array

Array

Array
Scalar
Array
Array
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
Sub-
routine
READ
ECHO

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
Common
Block
PEST


ffEST

ffEST

PEST
MISC
HYDR
MET

IRGT
IRGT

PEST


I.M.O
0
I

I
I
0
0
I
I
0
0
I
I
0
0
I
I
I
0
0
I
I
0
0
M
I

0
I

                                 10-22

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
EMM
EMMISS
EN
ENP
ENPY

ENRICH

ERFLAG
ERFLUX

EVAP

EXTRA
F
Units Type
Scalar
fraction Scalar
Scalar
Kcal Scalar
mole"1
Kcal Array
mole'1

Scalar

Scalar
g cm"2 Scalar

cm day"1 Scalar

cm3 cm"3 Scalar
g cm"2 Array
day"1
Description
Month of Crop Emergence
Infrared Emissivity of
Soil Surface
Manning's roughness
coefficient for furrows
Enthalpy of Vaporization
Enthalpy of Vaporization

Enrichment Ratio for
Organic Matter
Erosion Flag (0= Not
Calculated, 1= Calculated)
Erosion Flux of Pesticide
From Soil Surface

Daily Evaporation from the
Top 5 cm of Soil After
Adjusting for Crop
evapotranspiration
Extra Water Occurring in
a Compartment Over the
Allowed Saturation Amount
Vector of Source Terms
for Each Compartment
Sub-
routine
READ
ECHO
READ
SLTEMP
FURROW
IRREAD
KHCORR
ECHO
MAIN
READ
EROSN

READ
PMAIN
SLPEST
MASBAL
OUTPST
SLTEMP

OUTTSR
HYDR2
SLPEST
TRDIAG
Common
Block

MET
IRGT

PEST



HYDR
PEST




PEST
I,M,0

0
I
I
0
I
I
I
0


0
I
0
I
I
M

I
0
I
                              (Tri-diagonal
FO/         kg ha"1      Scalar    Current Foliar Pesticide
                              Storage

FAIH       "          Scalar    Stability Function for
                              Sensible Heat

FAIM       "          Scalar    Stability Function for
                              Momentum
                                    OUTPST


                                    CANOPY


                                    CANOPY
0


0
                                         10-23

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
FAM
FC
FCV
FDAY
FEXTRC
FILTRA
FIRST
FL
PLEACH
FOLPO/
FP
FPDLOS
Units Type
Scalar
cm Array
Array
Scalar
cm"1 Scalar
m2 kg"1 Scalar
Scalar
kg ha"1 Scalar
Fraction Scalar
g cm"2 Scalar
kg ha"1 Scalar
g cm"2 Scalar
Description
Pesticide Application
Flag (1= Soil, 2= Linear
Foliar, 3= Exponential
Foliar)
Field Capacity Water
Depth in Soil Compartment
Regression Coefficients
for Prediction of Field
Capacity Soil Water
Content
Loop Limit, First Day
Foliar Extraction Coef-
ficient 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
Sub-
routine
READ
ECHO
PESTAP
INITL
EVPOTR
THCALLC
PMAIN
READ
ECHO
PLPEST
READ
ECHO
PESTAP
MOC
OUTPST
IRRIG
IRREAD
PLPEST
MASBAL
OUTPST
PMAIN
OUTPST
PLPEST
MASBAL
OUTPST
OUTTSR
Common
Block
PEST
HYDR


PEST
PEST
HYDR

IRGT
PEST

PEST
I,M,0
0
I
I
0


0
I
I
0
I
I
M

I
0
0
I
I
I

0
I
I
I
                                 10-24

-------
TABLE 10-3.
           PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
           DESIGNATION (Continued)


Variable
FPVLOS
FPWLOS
FRAC

Units
gem'2
day1
gem-8
-

Type
Array
Scalar
Scalar

Description
Daily Foliage Pesticide
Volatilization Flux
Current Daily Pesticide
Washoff Loss
Fraction of the Distance
Sub-
routine
MASBAL
OUTPST
PLPEST
PLPEST
READ
Common
Block I.M.O
PEST I
I
0


FRAC



FRAC



FRACOM



FS


FX1




FX2




GAMMA


GEE



GFLD



GRADT
m
Fraction
Fraction
"Cm4
         a Curve Number is Between
         Increments of Ten

Scalar    Fraction of the Current
         Crop Growing Season
         Completed

Array     Number of Compartments
         Available to Extraction
         ofET

Scalar    Fraction of Layer Attri-
         buted to the Current
         Horizon

Array     Infiltration depth at each
         station in furrow

Scalar    Fourth Order Energy
         Balance Equation in
         Terms of Soil Surface
         Temperature

Scalar    Derivative of Energy
         Balance Equation in
         Terms of Soil Surface
         Temperature

Array     Pesticide  Uptake Effi-
         ciency by Plant

Array     Depolarization Factors of
         Soil Constituent in Three
         Dimensions

Scalar    Depolarization Factor of
         Entrapped Air at Field
         Capacity  Water Content

Scalar    Temperature Gradient
                                                    PLGROW
                                                    EVPOTR
                                                    INITL
FURROW
IRRIG

SLTEMP
                                                    SLTEMP
PLGROW
SLPEST

SLTEMP
SLTEMP
CANOPY
                                                                  IRGT
                                                                  PEST
0
I

M
                         M
0
I

M
M
                                             10-25

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
GRADW day1
HAD
HAM
HEIGHT cm
HENRY cm3 cm"3
HENRYK cm3 cm-3
HF m
HGT m
HORIZN
HSWZT
HTEMP °C
HTITLE
HTMAX cm
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Array
Scalar
Scalar
Alpha-
numeric
Array
Description
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
Soil Horizon Number
Hydraulics Flag (0= Free
Draining Soils, 1= Res-
tricted Drainage)
Average Air Temperature
Comment Line to Enter
Information about Hydro-
logy Parameters
Maximum Canopy Height
Sub-
routine
CANOPY
READ
ECHO
READ
ECHO
MAIN
OUTPST
PLGROW
SLTEMP
KHCORR
ECHO
MAIN
READ
FURROW
INFIL
IRREAD
CANOPY
READ
ECHO
INITL
OUTHYD
OUTPST
OUTCNC
READ
ECHO
INITL
PMAIN
CANOPY
READ
ECHO
ECHO
PLGROW
READ
Common
Block I.M.O
0


CROP I
I
0
I
I
PEST I
I
0
IRGT I
I
0
0
MISC 0
I
I
I
I
I
0
I
I
I
0

CROP I
M
0
                                 10-26

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
I

IAPDY
LAPYR
IARG
IARG1
IB
1BM1
ICNAH

ICNCN

ICROSS


IDEL
IDFLAG
IEDAY

Type
Scalar

Array
Array
Array
Scalar
Scalar
Scalar
Array

Array

Scalar


Scalar
Scalar
Scalar

Description
Loop Counter

Julian Day of Pesticide
Application
Year of Pesticide
Application
Argument of Variable
Identified by 'PLNAME'
Argument of Variable
Identified by 'PLNAME'
Backward Loop Index
Counter
Soil Surface Condition
After Harvest

Crop Number

Number of horizon inter-
faces where points need
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

Sub-
routine
SLTEMP
KHCORR
CANOPY
READ
ECHO
PMAIN
READ
ECHO
PMAIN
READ
ECHO
OUTTSR
OUTTSR
INITL
HYDR2
INITL
READ
ECHO
PLGROW
READ
ECHO
INITL
INITL,
MOC

MOC
ECHO
READ
SLTEMP
OUTCNC
READ
PMAIN
ECHO
Common
Block


MISC
MISC
MISC



HYDR

CROP

HYDR



MET
MISC

I,M,0


0
I
I
0
I
I
0
I
I



0
I
I
0
I
I
M


M
I
0
I
I
0
I
I
                                 10-27

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
IEDY
IEMER

IEMON


IEND
IERROR

IEYR

IFIRST
IHAR


II
IJ
ILP

INABS cm

Type
Scalar
Array

Scalar


Scalar
Scalar

Scalar

Scalar
Array


Scalar
Scalar
Scalar

Scalar

Description
Counter
Julian Day of Crop
Emergence

Ending Month of Simula-
tion ECHO

Index of point at which
consolidation ends
Error Flag if Tri-
Diagonal Matrix Cannot
be Saved
Ending Year of Simulation

Flag to Print Output
Heading and Initialize
Output Array
Julian Day of Crop Harvest


Loop Counter
Loop Counter
Initial Level of Pesti-
cide Flag (0= No Pesti-
cide, 1= Initial Pesticide)
Initial Abstraction of
Water from Potential
Sub- Common
routine Block
INITL
READ CROP
ECHO
INITL
PLGROW
READ MISC
I
PMAIN
MOC
SLPEST
TRDIAG
READ MISC
ECHO
PMAIN
OUTTSR
READ CROP
ECHO
INITL
PLGROW
OUTPST
PMAIN
READ MISC
ECHO
HYDROL HYDR
EROSN
I,M,0

0
I
I
I
0

I
M


0
I
I

0
I
I
I


0
I
0
I
                         Surface Runoff
                                  10-28

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION  (Continued)

Variable Units
INCROP --





INICRP



INTFC

IOUT

IPEIND


IPSCND




IRTYPE --

Type
Array





Scalar



Scalar

Scalar

Scalar


Scalar




Scalar

Description
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 Indica-
tor Flag (0= Data Read
In, 1= Calculated)
Foliage Pesticide
Condition after Harvest:
1. Surface Applied
2. Removed
9. Surface Residue
Irrigation type flag
0=NO irrigation
Sub-
routine
READ
ECHO
INITL
PLGROW
OUTHYD
OUTPST
READ
ECHO
INITL

INITL

MOC1

READ
ECHO

ECHO
PLGROW
READ


IRRIG
IRREAD
Common
Block I.M.O
CROP 0
I
I
I
I

CROP 0
I
I



M

MET 0
I

CROP I
M
0


IRGT I
0
ISCOND
ISDAY
ISDY
        l=Flood irrigation
        2=Furrow irrigation
        3=0ver-canopy sprinklers
        4=Under-canopy sprinklers

Scalar    Surface Condition After
        Harvest Corresponding to
        'INICRP'
Scalar    Starting Day of Simula-
        tion
Scalar    Counter
READ
ECHO
PLGROW
HYDROL
EROSN

READ
ECHO
INITL
PMAIN

INITL
                                                 HYDR
MISC
           0
           I
           I
           I
           I
                                         10-29

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
ISMON



ISTART

ISTYR



ITEM1


ITEM2


ITEMS


ITEMP °C


ITFLAG



ITMP


IY





Type
Scalar



Scalar

Scalar



Alpha-
numeric

Alpha-
numeric

Alpha-
numeric

Scalar


Scalar



Scalar








Description
Starting Month of Simu-
lation


Index of point at which
consolidation starts
Starting Year of Simula-
tion


Hydrology Output Summary
Indicator

Pesticide Output Summary
Indicator

Soil Pesticide Concentra-
tion Profile Output
Indicator
Mean Daily Temperature
Rounded to Next Lowest
Whole Number
Soil Temperature Flag



Number of Compartments
Pesticide is Applied to
When Incorporated
Annual Loop Counter





Sub-
routine
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
Common
Block
MISC





MISC



MISC


MISC


MISC


MISC


MET












I.M.O
0
1
1
1
M

0
1
1
1
0
1
1
0
1
I
0
1
1
0















                                 10-30

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
IYREM



IYRHAR



IYRMAT



J






JJ
JP1
JP1TIO
JTIO
JULDAY -



K
KD cm3 gj









Type
Array



Array



Array



Scalar






Scalar
Scalar
Scalar
Scalar
Scalar



Scalar
Array









Description
Year of Crop Emergence



Year of Crop Harvest



Year of Crop Maturation



Loop Counter






Loop Counter
Counter (J+l)
Counter (JP 1*10)
Counter (J*10)
Julian Day



Loop Counter
Adsorption/partition
Coefficient for Soil
Compartment







Sub-
routine
READ
ECHO
INITL
PLGROW
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
Common
Block I.M.O
CROP 0
I
I
I
CROP 0
I
I
I
CROP 0
I
I
I











MISC 0
I
I
I

PEST 0
I
I
0
I
I
I
I
I
I
                                 10-31

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
KDFLAG
KH
KK
KOC
KS
L
LAYERS
LBTEMP
LDAY
LEAP
LFREQ1
LFREQ2
LFREQ3
LL
LOGO
Units Type
Scalar
cm3 cm"3 Array
Scalar
cm3 gj Scalar
-oc
m/s Scalar
Scalar
Scalar
°C Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Partition Coefficient
Flag (0= Kd Read In,
1= Kd Calculated)
Henry's Constant at
Current Time
Loop Counter
Organic Carbon Partition
Coefficient
Saturated hydraulic
conductivity of soil
Loop Counter
Number of Layers in Canopy
Daily Value of Bottom
Boundary Temperature
Loop Limit (Last Day)
Additional Day Flag for
Leap Year
Frequency of Soil Com-
partment Reporting in
Water Output Summary
Frequency of Soil Com-
partment Reporting in
Pesticide Output Summary
Frequency of Soil Com-
partment Reporting in
Concentration Profile
Output Summary
Loop counter
Logarithm of Zero
Sub-
routine
READ
ECHO
PMAIN
MAIN
SLPSTO
SLPST1
READ
KDCALC
FURROW
INFIL
IRREAD
SLTEMP
CANOPY
SLTEMP
PMAIN
SLTEMP
READ
OUTHYD
READ
OUTPST
READ
OUTCNC
MOC1
CANOPY
Common
Block I.M.O
0
I
I
PEST 0
I
I
I

IRGT I
I
0

0
M

I
MISC 0
I
MISC 0
I
MISC 0
I

0
LOGKOC
       Displacement Height

Scalar   Natural Log of Koc
KDCALC
                                    10-32

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
LOGZO
M
MAD
MAM
MASS g
o

MASSO g
o

MAT


MCFLAG
MD
MDOUT kg ha-1
MEOUTW cm
MINPP kg haul

MINPP1 kg ha-1
MINPP2 kg ha-1
MINPW cm
Type
Scalar
Scalar
Scalar
Scalar
Array

Array

Array


Scalar
Scalar
Array
Array
Array

Scalar
Scalar
Array
Description
Logarithm of Roughness
Length
Loop counter
Day of Month of Crop
Maturation
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/Disper-
sion Flux from Each
Compartment
Monthly Foliar Applied
Pesticide
Monthly Soil Applied
Pesticide
Monthly Infiltration into
Sub-
routine
CANOPY
MOC1
READ
ECHO
READ
ECHO
MOC1

MOC1
INITL

READ
ECHO
INITL
PLGROW
ECHO
READ
PMAIN
PMAIN
OUTPST
OUTHYD
OUTPST

OUTPST
OUTPST
OUTHYD
Common
Block






PEST

MISC


PEST

ACCUM
ACCUM
ACCUM

ACCUM
ACCUM
ACCUM
I.M.O
0



M

M

0
I
I
I
I

M,
M
M

M
M
M
                         Each Soil Compartment
                                  10-33

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
MINPW1
MINPW2
MINTH
MM

MNTHP1

MODFC
MONTH

MOUTP
MOUTP1
MOUTP2
MOUTP3
MOUTP4
MOUTP5
MOUTT6
MOUTW
MOUTW1

MOUTW2
Units
cm
cm





-
-

kg ha'1
kg ha'1
kg ha'1
kg ha'1
kg haj
kg ha'1
kg ha'1
cm
cm

cm
Type
Scalar
Scalar
Alpha-
numeric
Scalar

Scalar

Scalar
Scalar

Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar

Scalar
Description
Monthly Precipitation
Monthly Snowfall
Flag for Monthly Output
Summary (for Either Water
or Pesticide)
Number of Month Read from
Meteorologic File
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
Flux from Profile
Monthly Exfiltration from
Each Compartment
Monthly Canopy Evapo-
ration
Monthly Thrufall
Sub-
routine
OUTHYD
OUTHYD
PMAIN
PMAIN

OUTHYD

INITL
SLTEMP

OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD

OUTHYD
Common
Block
ACCUM
ACCUM






MISC

ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM

ACCUM
I.M.O
M
M






I

M
M
M
M
M
M
M
M
M

M
                                 10-34

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
MOUTW3 cm
MOUTW4 cm
MOUTW5 cm
MOUTW6 MTonne
MSTART
MSTR cm

MSTR1 cm

MSTR2 cm
MSTRP kg ha'1

MSTRP1 kg ha-1
MY

N
NAPPC
NAPS


NBYR
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array

Scalar

Scalar
Arr

Scalar
Scalar

Scalar
Scalar
Scalar


Scalar
Description
Monthly Runoff
Monthly Snowmelt
Monthly Evapotrans-
piration
Total Monthly Sediment
Loss
Flag for Positioning
Meteorologic File
Previous Month Storage
of Water in Each Soil
Compartment
Monthly Canopy Inter-
ception
Monthly Accumulation of
Snow
Storage of Pesticide from
Previous Month in Each
Soil Compartment
Storage of Foliar Pesti-
cide from Previous Month
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
Sub-
routine
OUTHYD
OUTHYD
OUTHYD
OUTHYD
PMAIN
OUTHYD

OUTHYD

OUTHYD
OUTPST

OUTPST
PMAIN

CANOPY
SLTEMP
PMAIN
PESTAP
READ
ECHO
INITL
PMAIN
INITL
PLGROW
Common
Block
ACCUM
ACCUM
ACCUM
ACCUM

ACCUM

ACCUM

ACCUM
ACCUM

ACCUM



PEST
PEST



I.M.O
M
M
M


M

M

M
M

M



0
I
0
1
I
I

                         (Loop Limit)
                                  10-35

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
NCELL
NCOMO/
NCOM1
NCOM2
NCOM2M -
NCOMRZ -
NCP
NCPDS
NCROP
NDC
NDCNT
NDYRS
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Compartment number in
which a point is located
Number of Compartments
from Which ET is Extracted
Year Round
Current Number of Com-
partments, that ET is
Extracted From
Number of Compartments
in Soil Profile
Number of Compartments
in Soil Profile Minus 1
(NCOM2 = 1)
Number of Compartments
in the Root Zone
Number of Current Crop-
ping Period
Number of Cropping
Periods in the Simulation
Number of Current Crop
Number of Different Crops
in Simulation
Number of Days Since Crop
Emergence for Current
crop
Number of Years Between
Emergence and Maturation
Sub-
routine
MOC1
INITL
INITL
PLGROW
PLGROW
EVPOTR
OUTHYD
SLTEMP
INITL
SLPEST
INITL
SLPEST
OUTHYD
OUTPST
LNITL
PLGROW
READ
ECHO
INITL
PLGROW
INITL
PLGROW
HYDROL
EROSN
READ
ECHO
INITL
PLGROW
INITL
PLGROW
INITL
PLGROW
Common
Block

HYDR
HYDR
HYDR
HYDR
CROP
CROP
CROP
CROP
CROP
MISC

I,M,0
M
0
I
0
I
I
I
0
1
0
I
I
I
0
1
0
I
I
I
0
I
I
I
0
I
I
I
0
I

                        of a Crop
                                  10-36

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
NET g
o
NEW
NEWK cm3 cm-3
NEXDAY -
NEYR
NHORIZ
NLINES
NM1
NOPRT
NPI
NPLOTS
NRZCOM
NSPACE
Type
Array
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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
Number of furrow stations
for finite difference
Sub-
routine
MOC1
MOC1
KHCORR
PLGROW
INITL
PLGROW
READ
ECHO
INITL
KDCALC
ECHO
TRDIAG
OUTHYD
OUTPST
MOC1
INITL
READ
ECHO
PMAIN
OUTTSR
PLGROW
FURROW
IRRIG
Common
Block I.M.O
M
M
0


MISC 0
I
I
I



HYDR M
MISC 0
I
I
I

IRGT M
I
NSUM
Scalar   Cumulative Sum of Com-
       partment Numbers
EVPOTR
                                    10-37

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
NSUMM
NUM
NUM
NUMDYS -
OC percent
OKH cm3 cm-3
ORGM percent
OSNOW cm
OUTPUT -
PA kg ha'1
PB kg ha'1
PBAL g cm'2
PCDEPL Fraction
PCMC
Type
Scalar
Scalar
Scalar
Scalar
Array
Array
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Description
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 MASBAL
Output Array for Time
Series
Daily Foliar Pesticide
Application
Pesticide Balance
Current Pesticide Balance
Error
Fraction of available water
capacity where irrigation
is triggered
Partition Coefficient
Model Flag (1= Karick-
Sub- Common
routine Block I,M,0
OUTHYD
OUTPST
KHCORR I
MOC1 HYDR I
INITL
SLTEMP M
SLTEMP PEST I
INITL PEST 0
MAIN I
SLPSTO I
SLPST1 I
INITL
PMAIN HYDR
HYDROL
I
OUTTSR
OUTPST
OUTPST
MASBAL PEST
OUTPST
IRRIG IRGT
IRREAD
READ MISC
KDCALC
                         hoff, 2= Kenega,
                         9= Chiou)
                                   10-38

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable units
PCOUNT

PESTR g cm'2




PET cm
PETP cm
PEVP cm
PFAC


PI
PLDKRT day-'

PLNAME
PLNTAP g cm-2
PLVKRT day1

Type
Array

Array




Scalar
Scalar
Scalar
Scalar


Scalar
Array

Alpha-
numeric
Scalar
Array

Description
Number of points crossing
an interface with Ratio
greater than 2.
Total Pesticide in Each
Soil Compartment




Total Daily Potential
Evapotranspiration
Running Total of Avail-
able Evapotranspiration
Pan Evaporation
Pan Factor for ET


3.1415926
Foliar Pesticide Decay
Rate ECHO

Time Series Output Iden-
tifier (Options Listed
in User's Guide)
Pesticide Applied to Crop
canopy
Foliage Pesticide
Volatilization Rate

Sub-
routine
INITL,
MOC

READ
ECHO
INITL
PMAIN
PESTAP
MASBAL
OUTPST
EVPOTR
EVPOTR
PMAIN
EVPOTR
READ
ECHO
EVPOTR
CANOPY
READ
PLPEST
READ
OUTTSR
PESTAP
OUTPST
OUTTSR
ECHO
PLPEST
READ
Common
Block
HYDR

PEST






MET
MET



PEST
I

MISC
PEST
PEST

I.M.O
M

0
I
I
I
I
I
I


0
I
0
I
I

0
I
0
I
0
I
I
I
1
0
PNBRN
Array   Output Array for Time
       Series
OUTTSR
                                    10-39

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
PRECIP




PTEMP
PTITLE

PVFLUX



PWIND
Q
QC1
QENF
QGHF
QLW1
QLW2
QO
Units
cm




gcmj
--

gem-2
day1



m day4
m'
cal cm'2
day1 °K4
cal cm'2
day'1
cal cm'1
day'1 °K 4
cal cm'2
day1 "K4
cal cm 2
day1 "K'1
m3/s
Type
Scalar




Array
Alpha-
numeric

Array



Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Description
Precipitation




Temporary storage of
total pesticide mass
per cc water after
advection step
Comment Line to Input
Information About Pesti-
cide 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
Sub-
routine
PMAIN
HYDROL
EROSN
MASBAL
OUTHYD
OUTTSR
MOC1
READ
ECHO

MASBAL
OUTPST
OUTRPT
OUTTSR
SLPSTO
SLPST1
MAIN
EROSN
SLTEMP
SLTEMP
SLTEMP
SLTEMP
SLTEMP
FURROW
IRREAD
Common
Block I.M.O
MET 0
I
I
I
I
I
M
MISC 0
I

PEST I
I
I
I
0
0
0

M
M
M
M
M
IRGT I
0
QQP
Scalar   Runoff Energy Factor
EROSN
                                   10-40

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable Units
QS m3/s
QSWR cal cm'2
day1
RATIO

RETEAP cm/hr

RF kg ha'1
RINUM
RMULT
RMULT1
RMULT3
RNSUM

RNUM ha cm'2

RODPTH
ROFLUX g cm-2
day1


RTR day1
Type
Array
Scalar
Array

Scalar

Scalar
Scalar
Scalar
Scalar
Scalar
Scalar

Scalar

Scalar
Scalar


Array
Description
Flow rate in furrow at each
downstream station
Net Shortwave Radiation
Flux Term
The ratio of point
densities between
adjacent horizons.
Maximum rate of water that
sprinklers can deliver
Pesticide Runoff Flux
Richardson Number
Multiplication Factor for
Time Series Output
Multiplication Factor for
Curve Number AMC I
Multiplication Factor for
Curve Number AMC III
Converts NSUM to a Real
Number
Numerator of Peak Runoff
Rate
Number of Soil Compart-
ments that Affect Runoff
Runoff Flux of Pesticide
From Land Surface


Transformation Term
from Daughter Product
Consideration
Sub- Common
routine Block
FURROW IRGT
SLTEMP
INITL, HYDR
MOC

IRRIG IRGT
IRREAD
OUTPST
CANOPY
OUTTSR
READ
READ
EVPOTR

EROSN

HYDROL
SLPEST PEST
MASBAL
OUTHYD
OUTTSR
PSTLNK PEST
SLPSTO
SLPST1
I.M.O
M
M
M

I
0










0
I
I
I
0
I
I
                                 10-41

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
RUNOF






RVEL
RZD

RZFLUX



RZI

SA

SAIM

SAND

SD


SDKFLX


SEDL


SF

SFAC


Units Type
cm Scalar






Arr
cm Scalar

g cm"2 Scalar



Scalar

kg haul Scalar

Scalar

percent Array

kg ha4 Scalar


g cm"2 Scalar
day"1

MTonne Scalar
day1

Fraction Scalar

cm °C4 Scalar


Description
Current Runoff Depth






Retarded solute velocity
Maximum Root Zone Depth
for All Crops
Dispersive/Advective Flux
of Pesticide Past the
Bottom Root Zone Com-
partment
Active Root Zone Flag

Application of Pesticide
to the Soil
Integrated Momentum
Stability Parameter
Percent Sand in Each Soil
Horizon
Sum of the Decay Fluxes
From All Compartments
in Soil Profile
Sum of the Decay fluxes
From All Compartments in
Soil Profile
Erosion Sediment Loss


Slope of furrow channel
(vertical/horizontal)
Snowmelt Factor


Sub-
routine
HYDROL
PMAIN
EROSN
SLPEST
MASBAL
OUTHYD
OUTTSR
MOC1
INITL
OUTHYD
SLPEST
OUTTSR


INITL
PLGROW
OUTPST

CANOPY

SLTEMP

OUTPST


SLPEST
OUTPST

PMAIN
EROSN
OUTHYD
FURROW
IRREAD
READ
ECHO
HYDROL
Common
Block
HYDR









PEST



MISC





HYDR




PEST


HYDR


IRGT

MET


I.M.O
0
I
I
I
I
I
I
M


0
1


0
I


0

I




0
I

0
M
0
I
0
0
I
I
                                 10-42

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
SIGMAO
SIGMA1
SIGMA2
SJDAY
SLKGHA
SMDEF
SMELT
SNOW
SNOWFL
SOILAP
SOL
SOLRAD
Units
~~
cal cm"1
°C day1


kg ha'1
day1
cm
cm
cm
cm
gem'2
mole
fraction
mglj
umoles I'1
Cal cm-2
day1
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Description
Summation Variable Used to
Calculate K Factor in the
Soil Thermal Conductivity
Equation
Total Numerator Value in
the Soil Thermal Conduc-
tivity 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
Pesticide Applied to the
Soil
Pesticide Solubility -
Karickhoff Model
Kenaga Model
Chiou Model
Shortwave Solar Radiation
Sub-
routine
SLTEMP
SLTEMP
SLTEMP
INITL
EROSN
IRRIG
HYDROL
EROSN
OUTHYD
SLTEMP
HYDROL
MASBAL
OUTHYD
OUTTSR
PESTAP
PMAIN
OUTPST
OUTTSR
READ
KDCALC
READ
SLTEMP
Common
Block I.M.O
M
M
M


IRGT 0
HYDR 0
HYDR I
MET 0
1
I
I
PEST 0
I
I
I
0
I
MET 0
I
                                 10-43

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
SPESTR

SPT
SPTEMP
SRC
SRCFLX
STEMP
STEP1
STEP2
STEPS
STITLE

STK
STTDET
Units Type
g cmj Array

°C Array
g cm4 Array
g cm'3 Array
day1
g cm'2 Array
day1
°C Array
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric
Alpha-
numeric

°K Scalar
cm day1 Scalar
Description
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
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
Soil Surface Temperature
in Kelvin Scale
Daily Evaporation from the
Top 5cm of Soil
Sub-
routine
INITL
PMAIN
PESTAP
SLPEST
SLTEMP
MAIN
MOC1
SLPST1
INITL
PSTLNK
SLPSTO
SLPST1
SLPSTO
SLPST1
OUTPST
KHCORR
READ
ECHO
OUTHYD
READ
ECHO
OUTPST
READ
ECHO
OUTCNC
READ
ECHO

SLTEMP
SLTEMP
EVPOTR
Common
Block
PEST

MET
PEST
PEST
PEST

MISC
MISC
MISC
MISC


MET
I.M.O
0
I
I
0
I
M
0
I
I
0
0
I
1
0
I
1
0
I
I
0
I
I
0
I

M
I
0
                                 10-44

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
Su


SUMC

SUMXP

SUPFLX


Sv

sw






T

TA

TAPP



TB

TC

TCNC

TCORR

units
kg ha'1


g

kg haj

gem'2
day1

kg ha'1
day1
cm






-

day-'

gem"2



day1

day1

gem"3

mole
car1
Type
Scalar


Array

Scalar

Scalar


Scalar

Array






Scalar

Array

Army



Array

Array

Array

Scalar

Description
Sum of the Uptake Fluxes
From All Soil Compart-
ments
Sum of mass in a
compartment
Sum of Soluble Pesticide
in Profile
Sum of the Uptake Fluxes
From All Soil Compart-
ments
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
Sub-
routine
OUTPST


MOC1

OUTPST

SLPEST
OUTPST
OUTTSR
OUTPST

INITL
HYDROL
EVPOTR
HYDR1
HYDR2
SLPEST
OUTTSR
INITL

SLTEMP

READ
ECHO
INITL
PESTAP
SLTEMP

SLTEMP

OUTPST

KHCORR

Common
Block I.M.O



M



PEST 0
I
I
0

HYDR 0
I
I
I
I
I
I


M

PEST 0
I
I
I
M

M

0

M

                                 10-45

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
TEMP
TEMPK

TEND

TERM
TERM1
TERM2

TF
TFRAC

THAIR
THCOND
THEFC

THETAS
THETH

Units Type
°C Scalar
°K Scalar

day Scalar

Scalar
Scalar
Scalar

°C Array
Scalar

cm3 cm"3 Array
cal cm'1 Array
day1 'C'1
cm3 cm'3 Array

cm3 cm'3 Array
cm3 cm'3 Scalar

Description
Ambient Air Temperature
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
Total Fraction of Com-
partments Available for
Evapotranspiration
Extraction
Volumetric Air Content
Thermal Conductivity of
Soil Compartment
Field Capacity Water
Content for Each Soil
Horizon
Soil Compartment Water
Content at Saturation
Soil Moisture Content Half
Way Between Wilting Point
Sub-
routine
SLTEMP
SLTEMP

MOC1

PLPEST
PLPEST
PLPEST

SLTEMP
EVPOTR

SLPSTO
SLPST1
SLTEMP
SLTEMP

SLTEMP
INITL
HYDROL
Common
Block I.M.O
MET I
M

M





M


0
M
HYDR I

HYDR I
HYDR 0
I
                          and Field Capacity in the
                          Top Soil Compartments
                                    10-46

-------
TABLE 10-3.
TLEFT
         PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
         DESIGNATION (Continued)

Variable units
THETN cm3 cm"3





THETO cm3 cmj
THEWP cm3 cm-3
THFLAG -
THKLYI cm
THKNS cm


THRUFL cm
THZERO cal cm'1
day10 C'1
TITLE
Type
Array





Array
Array
Scalar
Scalar
Array


Scalar
Array

Alpha-
numeric
Description
Soil Water Content at the
End of the Current Day
for Each Soil Compartment





Soil Water Content at the
End of the Previous Day
for Each Soil Compartment
Wilting Point Water Content
for Each Soil Horizon
Soil Water Content Flag
(0= Field Capacity and
Wilting Point are Input,
1= Field Capacity and
Wilting Point are
Calculated)
Thickness of Top
Compartment
Soil Horizon Thickness


Precipitation that Falls
Past the Crop Canopy to
the Soil Surface
Thermal Conductivity of
Soil at Water Content
and Wilting Point
Title of the Simulation
(User Supplied)
Sub-
routine
HYDR1
HYDR2
PMAIN
SLPEST
MASBAL
OUTHYD
OUTPST
OUTTSR
OUTCNC
SLTEMP
SLTEMP
READ
ECHO
PMAIN
SLTEMP
READ
ECHO
INITL
HYDROL
HYDROL
OUTHYD
OUTTSR
SLTEMP

READ
ECHO
Common
Block
HYDR





HYDR
HYDR
MISC

MISC


MET


MISC
I.M.O
0
0
I
I
1
I
I
1
I
I
I
0
I
I

0
I
I
I
0
I
I
M

0
I
day
Scalar   Travel time left in
       current time step
MOC1
M
                                    10-47

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
TMPK
TNDGS
TOL
TOP
TOT
TOTAL
TOTR
TR
TRFLUX
TS
TSRCFX
TSW
TTHKNS
TTRFLX
units
°K
day
-

day m'1
mg kg"1
day m"1
hr
gem"2
day"1
cm3 cm"3
gem"2
day"1
cm
cm
gem"2
day"1
Type
Scalar
Array
Scalar
Array
Scalar
Array
Scalar
Scalar
Array
Array
Array
Scalar
Scalar
Array
Description
Soil Temperature
Total Number of Days in
Each Growing Season
Fraction Compartment
Check
Location of top compart-
ment in horizon where
points are consolidated
Canopy Resistance
Total Pesticide in Each
Compartment
Total Canopy Resistance
Duration of Average
Erosive Storm Event
Transformation Flux of
Pesticide from Each Soil
Compartment
Previous Soil Compartment
Water Content Minus
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 Computa-
tional Check)
Sum of the Transformation
Flux from All Compartments
in Soil Profile
Sub-
routine
KHCORR
INITL
PLGROW
INITL
INITL,
MOC
CANOPY
OUTCNC
CANOPY
READ
ECHO
EROSN
SLPSTO
SLPST1
OUTPST
HYDR2
SLPSTO
SLPST1
OUTPST
EVPOTR
INITL
SLPSTO
SLPST1
OUTPST
Common
Block I,M,0
M
CROP o
I

HYDR M
0

0
MET 0
I
I
PEST 0
0
I

PEST 0
0
I


PEST 0
0
I
                                 10-48

-------
TABLE 10-3.
            PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
            DESIGNATION (Continued)
Variable      units
            Type      Description
                                          Sub-          Common
                                          routine        Block       I,M,0
TWLVL       cm cm"'
TWP
u

UBT


UPF
URH


USLEC



USLEK



USLELS



USLEP



USTAR

UTEMP
             cm
kg ha'1
UPFLUX      g cm-'
UPTKF
m day'1
m day'1

°C
Scalar     Fraction of Water to Soil
          Depth for Runoff
          Calculation

Scalar     Total Wilting Point Depth
          in Compartments Available
          for Evapotranspiration
          Extraction

Array     Upper Decomposed  Matrix

Scalar     Upper Boundary or Soil
          Surface  Temperature

Scalar     Daily Pesticide  Uptake
          Flux in Profile

Array     Uptake Flux of Pesticide
          From Each Soil Compartment

Scalar     Plant Pesticide  Uptake
          Efficiency Factor
Scalar    Wind Velocity at Reference
         Height

Array    Universal Soil Loss
         Equation 'C' Factor
Scalar    Universal Soil Loss
         Equation 'K' Factor


Scalar    Universal Soil Loss
         Equation 'Ls' Factor


Scalar    Universal Soil Loss
         Equation 'P' Factor


Scalar    Friction Velocity

Array    Air Temperature
                                                      HYDROL
                                                      EVPOTR
TRDIAG
SLTEMP
OUTPST
SLPEST PEST
OUTPST
READ PEST
ECHO
PLGROW
CANOPY
MAIN
READ HYDR
ECHO
EROSN
READ HYDR
ECHO
EROSN
READ HYDR
ECHO
EROSN
READ HYDR
ECHO
EROSN
CANOPY
CANOPY

M

0
I
0
I
I
I
0
0
I
I
0
I
I
0
I
I
0
I
I
0
I
                                               10-49

-------
TABLE 10-3.
           PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
           DESIGNATION (Continued)
Variable
Units
Type     Description
Sub-         Common
routine         Block      I,M,0
UWIND       mday1     Array    Wind Velocity

VAPLMD     cal cm'1     Scalar    Thermal Conductivity of

             day10 C°             Vapor in the Soil Pores

VAR1         kg ha"1      Scalar    Daily Advection/Disper-
                                 sion Flux of Pesticide
                                 Into a Compartment

VAR2         kg ha"1      Scalar    Daily Advection/Disper-
                                 sion Flux of Pesticide
                                 Out of a Compartment

VAR2D       cm         Scalar    Water Storage in a Single
                                 Compartment for the
                                 Previous Day

VAR2M       cm         Scalar    Water Storage in a Single
                                 Compartment for the
                                 Previous Month

VAR2RZ      kg ha"'      Scalar    Daily Advection/Disper-
                                 sion Flux of Pesticide
                                 Out of the Root Zone

VAR2Y       cm         Scalar    Water Storage in a Single
                                 Compartment for the
                                 Previous Year

VAR3         kg ha"1      Scalar    Pesticide Storage in a
                                 Single Compartment for
                                 the Previous Day

VEL          cm day"1     Array    Water Velocity in Each
                                 Soil Compartment
VHTCAP      cal cm"3      Array    Heat Capacity Per Unit
             "C"1                  Volume of Soil
                                                    CANOPY

                                                    SLTEMP



                                                    OUTPST



                                                    OUTPST



                                                    OUTHYD



                                                    OUTHYD



                                                    OUTPST



                                                    OUTHYD



                                                    OUTPST
                                                    HYDR1
                                                    HYDR2
                                                    SLPEST

                                                    SLTEMP
                                                      HYDR
                         0
                         0
                         I

                         M
                                              10-50

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable units
VLFLAG
VOLCOR
WBAL cm
WEIGHT kg m-2
WF kg ha4
WFMAX kg m-2
WIND cm sec'1
WLVL cm
WOFLUX g cm-2
day1
WP cm
WPV
WTERM g cm-2
Type
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Array
Array
Scalar
Description
Advection flux flag
(0= all soil water
velocities are zero,
1 = soil water velocity
is nonzero)
A Variable Used to Convert
Weight Percents of Soil
Constituents to Volume
Fractions of Bulk Volume
Current Water Balance
Error
Current Plant Dry Foliage
Weight
Daily Pesticide Washoff
Flux
Maximum Plant Dry Foliage
Weight at Full Canopy
Wind Speed
Total Soil Water in the
Compartments that Affect
Runoff
Washoff Flux of Pesticide
From Plant Foliage
Wilting Point Water Depth
in a Soil Compartment
Regression Coefficients
for Prediction of Wilting
Point Soil Water Content
Current Daily Pesticide
Washoff Loss
Sub-
routine
HYDR1
PMAIN
HYDR2
SLTEMP
MASBAL
OUTHYD
PLGROW
PESTAP
OUTPST
READ
ECHO
INITL
READ
SLTEMP
MAIN
HYDROL
SLPEST
OUTPST
EVPOTR
THCALC
PLPEST
SLPEST
Common
Block
HYDR

HYDR
CROP

CROP
MET

PEST
HYDR

PEST
I.M.O
I

0
I
0
I

0
I
I
0
I
I

0
1
0

0
I
                                 10-51

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable units
x g cm"3
XFRAC Fraction
XL m
XP g cm-3
XVOL fraction
Y
YDOUT kg ha"'
YEAR
YEOUTW cm
YINPP kg ha-1
YINPP1 kg ha'1
YINPP2 kg ha-1
YINPW cm
Type
Array
Scalar
Scalar
Array
Array
Array
Array
Alpha-
numeric
Array
Array
Scalar
Scalar
Array
Description
Dissolved Pesticide in
Each Soil Compartment
Location in furrow where
infiltration is to be used
in PRZM transport
calculations (as fraction
of total furrow length)
Length of furrows
Total Pesticide in
Each Soil Compartment
Volume Fraction of Soil
Constituent
Intermediate Matrix Solu-
tion Array
Annual Pesticide Decay
From Each Soil Compartment
Flag for Annual Water and
Pesticide Summary Output
Annual Evapotranspiration
From Each Soil Compartment
Annual Advective/Disper-
sive Flux Into Each Soil
Compartment
Annual Pesticide Applied
to Foliage
Annual Pesticide Applied
to Soil
Annual Infiltration Into
Sub-
routine
TRDIAG
SLPEST
MASBAL
OUTPST
OUTTSR
OUTCNC
PMAIN
IRRIG
IRREAD
IRRIG
FURROW
IRREAD
MASBAL
SLTEMP
TRDIAG
OUTPST
PMAIN
OUTHYD
OUTPST
OUTPST
OUTPST
OUTHYD
Common
Block
PEST
PEST
IRGT
IRGT



ACCUM

ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I.M.O
0
I
I
I
I
I
I
0
I
I
0



M

M
M
M
M
M
                         Each Soil Compartment
                                   10-52

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
YINPW1
YINPW2
YOUTP
YOUTP1
YOUTP2
YOUTP3
YOUTP4
YOUTP5
YOUTP6
YOUTW
YOUTW1
YOUTW2
YOUTW3
YOUTW4
YOUTW5
YOUTW6
YSTR
units
cm
cm
kg ha'1
kg ha4
kg ha'1
kg ha'1
kg ha'1
kg ha'1
kg ha'1
cm
cm
cm
cm
cm
cm
MTonne
cm
Type
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Scalar
Scalar
Scalar
Scalar
Scalar
Scalar
Array
Description
Annual Precipitation
Annual Snowfall
Annual Pesticide Uptake
From Each Soil Compartment
Annual Pesticide Washoff
Flux
Annual Pesticide Runoff
Flux
Annual Pesticide Erosion
Flux
Annual Foliar Pesticide
Decay Flux
Total Annual Pesticide
Uptake Flux
Total Annual Pesticide
Soil Decay Flux
Annual Exfiltration From
Compartment
Annual Canopy Evaporation
Annual Trufall
Annual Runoff
Annual Snowmelt
Total Annual Evapotrans-
piration
Total Annual Sediment
Loss
Previous Year Storage of
Water in Each Soil Corn-
Sub-
routine
OUTHYD
OUTHYD
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTPST
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
OUTHYD
Common
Block
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
ACCUM
I.M.O
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
YSTR1
          cm
       partment

Scalar   Annual Canopy Interception
OUTHYD    ACCUM
M
                                    10-53

-------
TABLE 10-3.
PRZM PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE
DESIGNATION (Continued)

Variable
YSTR2
YSTRP
YSTRP1
Z
Z
zc
ZCH
ZCTOT
ZIN
zo
ZRH
ZTOT
ZWIND
Units Type
cm Scalar
kg ha"1 Array
kg ha4 Scalar
Fraction Scalar
Array
Array
m Scalar
Scalar
Array
m Scalar
m Scalar
Scalar
m Scalar
Description
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
Reference Height
Location of consolidated
Points
Distance Above the Ground
Where Wind Speed was
Measured
Sub-
routine
OUTHYD
OUTPST
OUTPST
FURROW
IRREAD
MOC1
INITL
MOC1
INITL
CANOPY
MAIN
SLTEMP
MOC
MOC1
CANOPY
SLTEMP
CANOPY
MAIN
MOC
READ
MAIN
SLTEMP
Common
Block I,M,0
ACCUM M
ACCUM M
ACCUM M
IRGT I
0
HYDR M
HYDR M
I
0
M
M
M
0
M
I
0
M
0
0
I
                                 10-54

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS

Variable Units
A
ASTORN -
B
BALSTO
BSTOR1
BSTORN -
c
CORD L
CSTOR1
CTRFAC
D
DETAND -
DIS L
M/L**3
Type
ARRAY
SCALAR
ARRAY
ARRAY
SCALAR
SCALAR
ARRAY
ARRAY
SCALAR
ARRAY
ARRAY
ARRAY
ARRAY
Description
Left Diagonal of a
Tridiagonal Matrix
Value of A(NP) Where
NP=Number of Nodes
Main Diagonal of a
Tridiagonal Matrix
Array Containing Mass
Balance Information
Value of B(l)
Value of B(NP) Where
NP=Number of Nodes
Right Diagonal of a
Tridiagonal Matrix
Nodal Coordinates
Value of C(l)
Coordinate Transform-
ation Factors for
Different Soil Materials
Right-Hand-Side Vector
of a Tridiagonal Matrix
Nodal Storage Factor
Current Nodal Value of
Head of Concentration
Sub-
routine
ASSEMF
ASSEMT
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
MAIN
BALCHK
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
MAIN
VSWCOM
ASSEMF
ASSEMT
BALCHK
CONVER
DSWFUN
MAIN
ASSEMF
ASSEMT
ASSEMF
MAIN
ASSEMF
BALCHK
VARCAL
VSWCOM
Common
Block
ASOLV
WORKA
ASOLV

WORKA
WORKA
ASOLV
CORD
WORKA
WORKN
ASOLV
WELEM
BSOLV
I.M.O
M
M
M
M
0
M
M
M
I
M
M
M
M
M
0
                                10-55

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
DLAMDA 1/t


DLAMND 1/t



DPKND lit

DPKRAV L"2


DSTOR1


DSTORN -


DTEPS

DTMARK -
DX
EL L




ETAND


FLX1 L**3/t



FLXN L**3/t



Type
SCALAR


SCALAR



ARRAY

SCALAR


SCALAR


SCALAR


SCALAR

SCALAR
SCALAR
SCALAR




ARRAY


SCALAR



SCALAR



Description
Value of Decay Constant
for the Node Currently
Being Evaluated
Nodal Value of Decay
Constant


Nodal Values of Hyd.
Conductivity Increment
Value of Rel. Perm, for
Node Currently Being
Solved
The Value of D(l)


The Value of D(NP)
Where NP = Number of
Nodes
Time Step Tolerance
Parameter
Marker Time Increment
DX = THL(I) NEL
Elemental Values for
Finite-Element Element
Length Formulation


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)

Sub-
routine
MAIN
ASSEMT
VARCAL
MAIN
ASSEMT
BALCHK
VARCAL
ASSEMF

ASSEMF
PKWFUN

ASSEMF
ASSEMT
BALCHK
ASSEMF
ASSEMT
BALCHK
MAIN

MAIN
MAIN
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
ASSEMF
ASSEMT
BALCHK
MAIN
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMT
HFINTP
VARCAL
Common
Block I.M.O
CONTR M


I



WELEM M

M


WORKA M


WORKA M


M

M
M
M




WELEM M


CONTR M



CONTR M



                                 10-56

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)
Variable Units
FVAL





HAVE L


HCAP L


HCRIT L


HDOBS L
M/L**3

HINV L
M/L**3
HTOL L



HVTM L


IBTND1



IBTNDN



ICONVG
Type Description
ARRAY Functional Coefficient
Values for the Soil
Moisture Relationship



SCALAR Average Head Value


ARRAY Value of Pressure
Head on Press. Head
vs. Sat. Curve
SCALAR Critical Head Value


ARRAY Head or Concentration
of Observation Node
for Current Time
SCALAR Default Value of Initial
Head or Concentration
SCALAR Head Tolerance Allowed
for Nonlinear Solution


ARRAY Value of function
corresponding to
Time Values(TMHV)
SCALAR Last Node Boundary
Condition Code (l=lst
type, 0=3rd type)

SCALAR Last Node Boundary
Condition Code (l=lst
Type, 0=3rd type)

SCALAR Convergence Flag
(l=Converged, 0=Not
Sub- Common
routine Block I,M,0
MAIN MDATA M
ASSEMT
HFINTP
SWFUN
CONVER
DSWFUN
ASSEMF M
SWFUN
DSWFUN
MAIN SWHDA M
ASSEMF
INTERP
ASSEMF I
SWFUN
DSWFUN
MAIN DAOBS M
0

MAIN I

MAIN CONTR I
ASSEMF
VARCAL
DSWFUN
MAIN M
HFINTP

MAIN
ASSEMF
ASSEMT
VARCAL
ASSEMF
MAIN
ASSEMT
VARCAL
MAIN
VARCAL
                         Converged)
                                  10-57

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE  DESIGNATIONS (Continued)
Variable      Units
Type     Description
Sub-
routine
Common
 Block
I.M.O
IHORIZ



IKALL



ILAYR

IMAT
IMATL


IMBAL


IMOD



IMODL



INEWT



INOCTS




INPFL
SCALAR  Simulation Orientation
         Indicator  (0=Vertical,
         l=Horizontal)

SCALAR  Time Stepping Scheme
         Indicator  (l=Backward,
         0=Central)

SCALAR  Current Layer Number

SCALAR  Counter Used in Looping
         with Respect to Materials
ARRAY   Material Identifying
         Number for Current Layer

SCALAR Mass Balance  Computation
         Indicating Parameter

SCALAR For Modified Newton
         Raphson Solution
         Procedure

SCALAR Simulation Identifier
         (Flow or Transport)
SCALAR Nonlinear Iterative
         Procedure Flag
         (l=Newton, 0=Picard)

SCALAR Number of Computation
         Time Steps Required to
         Simulate This Target
         Time Step

SCALAR Unit Number for Input
         File
MAIN
MAIN
MAIN

MAIN
ASSEMF
ASSEMT
INTERP
PKWFUN
SWFUN
CONVER
DSWFUN
MAIN
MAIN CONTR
MAIN CONTR 1
DSWFUN
MAIN CONTR
BALCHK
VARCAL
MAIN CONTR
ASSEMF
VARCAL
MAIN
VARCAL
I
I
I
I
I
I
MAIN
                                           10-58

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
INTSPC



IOBSND
IPRCHK


IPROP
IREP
IREPMX

IRESOL
IRLTYP
ITCND1
ITCNDN
Type Description
SCALAR Initial Condition
Specifier for Head
Conversion Convert
Initial Head Values
(l=Yes, 0=No)
SCALAR Observation Node Index
SCALAR Print Check Flag
(Triggers Additional
Diagnostic Output)


ARRAY Generated Material
Property Identifiers
SCALAR Time Step Refinement
Counter
SCALAR Maximum Number of
Nonlinear Solution
Cycles
SCALAR Maximum Number of
Time Step Refinements
SCALAR Flag for the Type of
Relative Function Being
Evaluated
SCALAR Node 1 Boundary
Condition Flag
(1 = Transient,
0 = Steady State)
SCALAR Node 1 Boundary
Condition Flag
Sub-
routine
MAIN



WORKA
MAIN
ASSEMF
ASSEMT
BALCHK
VARCAL
CONVER
MAIN
ASSEMF
ASSEMT
MAIN
VARCAL
MAIN
VARCAL

MAIN
VARCAL
ASSEMF
INTERP
MAIN
HFINTP
MAIN
HFINTP
Common
Block I,M,0
I



I
I


MDATA I
M
I

I
I
I
I
                          (1 = Transient,
                          0 = Steady State)
                                   10-59

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
ITER



ITMARK
ITMFC
ITMGEN

ITRANS
ITSGN
ITSTH


IVSTED
KPROP


MARK
Type Description
SCALAR Iterative Counter
(Current Iteration
Number)


SCALAR Backup File Output
Indicator
SCALAR Marker Time Increasing
Parameter
SCALAR Marker Time Value
Generation Indicator
SCALAR Transient Steady-State
Flag (1=TR, 0=SS)
SCALAR Time Step Generation
Indicator
ARRAY Identifies Location of
Previous Time Value of
Time Graph
SCALAR Steady-State Velocity
Field Indicator
SCALAR Flag for Perm-Saturation
and Pressure Head-
Saturation Curves
(l=Functional,
0==abulated)
SCALAR Flow Direction Flag
(l=Vertical,
0=Horizontal)
Sub-
routine
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
Common
Block I.M.O
M



M
M
I

CONTR I
I
I


I
CONTR I


CONTR
MM
SCALAR Place Holder for Loop
       Incremented
M
                                    10-60

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
MXMAT
MXNODE -
MXTMV t
NDCOUN -
NDM1
NDOBS
NE
NEL
NELM
NITMAX -
NLAYRG -
NMAT
Type
SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
ARRAY
SCALAR
SCALAR
ARRAY
SCALAR
SCALAR
SCALAR
Description
Maximum Number of
Materials Allowed
(Due to the Size of
Arrays)
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 Descritized
Number of Soil Materials
Sub- Common
routine Block I,M,0
MAIN 'I
ASSEMF
ASSEMT
INTERP
SWFUN
DSWFUN
MAIN I
ASSEMF
ASSEMT
BALCHK
TRIDIA
VARCAL
VSWCOM
MAIN I
HFINTP
MAIN M
MAIN M
MAIN DAOBS I
MAIN CONTR I
VSWCOM
MAIN M
MAIN I
MAIN CONTR I
VARCAL
MAIN I
MAIN I
CONVER
                                 10-61

-------
TABLE 10-4. VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND VARIABLE DESIGNATIONS
           (Continued)

Common
Variable units
NOBSND -

NONU

NOWRIT -
NP






NPIN

NPROB

NSTEP

NTN1
NTNP
NTOMT
NTS
NTSNDH -

NUMK

Type Description
SCALAR Number of Observation
Nodes in the Simulation
SCALAR Nonuniform Initial
Condition Indicator
SCALAR Restart Data Writing
Indicator
SCALAR Total Number of Nodal
Points





SCALAR Number of Nondefault
Initial Values
SCALAR Number of Simulations
to be Made
SCALAR Nodal Value Printout
Control Parameter
SCALAR Storage Location for
NTSNDH(l)
SCALAR Storage Location for
NTSNDH(NP)
SCALAR Number of Backup File
Output Marker Time
Values
SCALAR Number of Time Steps
in This Simulation
ARRAY Number of Time Values
on the Time Graph
([1]=CONC, [2]=HEAD)
ARRAY Values of Permeability
from the Permeability
vs Saturation Table

routine Block
MAIN

MAIN

MAIN
MAIN CONTR
ASSEMF
ASSEMT
BALCHK
TRIDIA
VARCAL
VSWCOM
MAIN

MAIN

MAIN CONTR
BALCHK
MAIN
MAIN
MAIN
VSWCOM
MAIN
MAIN
HFINTP
MAIN SWHDA
ASSEMF
INTERP
Sub-
I.M.O
I

I

I
I






I

I

I

M
M
I
M
I

I
                             for Each Material
                                        10-62

-------
TABLE 10-4.
PKWOUT
          L**2
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
NUMP
NUMT
NVPR
NVREAD -
OUTFL
PCUR L
M/L**3
PINT L
M/L**3
PKND lit
PKRW L«2
Type
ARMY
SCAM
SCALAR
SCALAR
SCALAR
ARRAY
ARRAY
ARRAY
ARRAY
Description
Number of Pressure
Head vs. Saturation
Values for Each Material
Time Step Incrementor
Velocity Printout Control
Parameter
Velocity Reading
Indicator
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)
Sub- Common
routine Block
MAIN SWHDA
ASSEMF
INTERP
MAIN
MAIN CONTR
VSWCOM
MAIN
MAIN
ASSEMF
ASSEMT
BALCHK
INTERP
VARCAL
VSWCOM
ASSEMF BSOLV
VARCAL
MAIN BSOLV
ASSEMF
ASSEMT
BALCHK
VARCAL
MAIN WELEM
ASSEMF
VSWCOM
MAIN SWHDA
ASSEMF
INTERP
I.M.O
1
I
1
I
I
M
I
M
M
SCALAR Relative Permeability
       Computed Using Function
       Then Passed Back
PKWFUN
M
                                     10-63

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable units
PROP








QVTM L"3/t


SLOPE


SSWV


STMARK t

SWAVE

SWDFI


SWND




SWNDPT -


SWRKP
swv


Type
ARRAY








ARRAY


SCALAR


ARRAY


SCALAR

SCALAR

ARRAY


ARRAY




ARRAY


ARRAY
ARRAY


Description
Saturated Materiel
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)
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)
Sub- Common
routine Block
MAIN MDATA
ASSEMF
ASSEMT






MAIN
HFINTP

HFINTP
INTERP

ASSEMF SWHDA
INTERP

MAIN

ASSEMF
PKWFUN
MAIN


MAIN
ASSEMF
ASSEMT
VARCAL
VSWCOM
MAIN
VSWCOM

CONVER WORKN
MAIN SWHDA
ASSEMF
INTERP
I,M,0
I








M


M


M


M

M

I


M




M


M
M


                                 10-64

-------
TABLE 10-4.
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable units
TAPS
TAP 10
TDIFF t
TERIFL
TEROFL
TFAC
THETA
THETM1 -
THL L
TIN t
TIMA t
Type
SCALAR
SCALAR

SCALAR
SCALAR
SCALAR
SCALAR
SCALAR
Description
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
ARRAY Thickness of Current
Layer
SCALAR
SCALAR
Value of Initial Time
Step
Initial Time Value of
the Simulation
Sub- Common
routine Block
MAIN
MAIN MDATA
VSWCOM
MAIN
MAIN
MAIN
MAIN
MAIN
ASSEMT
BALCHK
VARCAL
MAIN
ASSEMT
BALCHK
VARCAL
MAIN
MAIN CONTR
ASSEMF
ASSEMT
BALCHK
VARCAL
MAIN CONTR
VSWCOM
I.M.O
I
I
M
I
I
I
M
M
M
I
I
                                  10-65

-------
TABLE 10-4.
UWFI
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)

Variable Units
TIMAKP t
TITLE
TMACCU L"3
m
TMAX t
TMCUR t
TMDCAY m
TMFOMT t
TMHV t
TMVEC t
TMVECX t
UWF
Type
SCALAR
Description
Storage Location for
the Value of Time Where
Iteration Computation
is Taking Place
ALPHA- Title of Simulation
NUMERIC
ARRAY
SCALAR
SCALAR
SCALAR
SCALAR
ARRAY
ARRAY
ARRAY
SCALAR
SCALAR
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
Sub-
routine
MAIN
MAIN
MAIN
MAIN
MAIN
VSWCOM
MAIN
BALCHK
MAIN
VSWCOM
MAIN
HFINTP
MAIN
BALCHK
MAIN
BALCHK
HFINTP
VARCAL
MAIN
ASSEMT
VARCAL
Common
Block I,M,0
M
I
I
CONTR M
BALCHK
I
M
CONTR M
I
M
I
M
M
CONTR M
        Being Evaluated

ARRAY   Value of Upstream-
        Weighting Factor for
        the Current Material
MAIN
TPDEF
M
                                      10-66

-------
TABLE 10-4.
XX
YY
VADOFT PROGRAM VARIABLES, UNITS, LOCATION, AND
VARIABLE DESIGNATIONS (Continued)
Variable units
VALND1


VALNDN


VDAR lit



VDARPT lit
VDFI lit
Type Description
SCALAR Value of First Node
(Depending on: Type of
Run & Type of Boundary

SCALAR Value of Last Node
(Depending on: Type of
Run & Type of Boundary

ARRAY Darcy Velocity for Each
Node



ARRAY Nodal Darcy Velocities
at Previous Time
ARRAY Default Value of Darcy
Sub- Common
routine Block
MAIN
ASSEMF
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMF
ASSEMT
HFINTP
VARCAL
MAIN
ASSEMF
BALCHK
VARCAL
VSWCOM
MAIN
VSWCOM
MAIN
I.M.O
M


M


M
o



M
I
        Velocity for Current
        Material

SCALAR The X value Passed in
        INTERP (to be Used in
        the Interpolation)

SCALAR The Y Value Passed in
        INTERP (to be Used in
        the Interpolation
                                                        INTERP
INTERP
                      M
M
                                       10-67

-------
TABLE 10-5.
           MONTE CARLO PROGRAM VARIABLES

Variable
BBT
CORK
DECOM
DIST
IN2
IOUT
IOUT2
IRUN
IVAR
LARR
MCMAX
MCVAR
units
Double
Precision
Double
Precision
Array
Integer
Real
Array
Integer
Integer
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.
Sub-
routine
Main program.
READM, INITMC
Main Program,
STATIS, OUTPUT
Main Program,
INITMC, RANDOM
Main Program,
READM, Random
Main Program,
READM
Main Program,
READM, OUTPUT
Main Program,
STATIS
Main Program,
STATIS
Main Program
Main Program,
READM, INITMC
Main Program
Main Program,
r>T?Ar\i/r TMTTi/rr1
NCMAX     Integer
NDAT
NEMP
Integer
Array

Integer
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.
                                                                          RANDOM
                                                            Main Program
  Main Program,
READM, RANDOM

  Main Program,
READM, RANDOM
                                         10-68

-------
TABLE 10-6.
MONTE-CARLO PROGRAM VARIABLES (Continued)

Variable
NMAX


NRMAX

NRUNS

NVAR

PNAME

RMC

SNAME

STAT


VAR


XCDF


XMC

Units
Integer


Integer

Integer



Character
Array
Real
Array
Character
Array
Double
Precision
Array
Real
Array

Real
Array

Real
Array
Description
Maximum possible number of
variables for which summary
statistics can be printed.
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.
sub-
routine
Main Program


Main Program

Main Program
READM, OUTPUT
Main Program

Main Program,
READM, INITMC
Main Program,
RANDOM
Main Program,
READM, OUTPUT
Main Program,
STATIS, OUTPUT

Main Program,
READM, INITMC,
RANDOM
Main Program,
STATIS.OUTPUT

Main Program,
STATIS
                                10-69

-------
  10.3 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 N0.283 HANDBOOK)
      0.72      0.03     0      15.000    1    1
      0
      1
      1         0.00    20.0    80.000   1     86  78  82  0.0  0.0 0.0 60.0
      1
 110582    300982  151082
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATION PARAMETERS
               1        0
ALDICARB
 120582         0         1.0      1.00
      1         1
SOILS PARAMETERS
     20.0       0.3      0      0       0      0    0   01    1   0
      4.3E03    l.OE-7    5.5 E-3
 0.150.150.150.150.15  0.150.150.150.150.15  0.150.150 .9710.0
 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    0.330   0.0    0.0
               0.012    0.011  0.000
               1.0       .330    .133   1.0    0.3
               8.3     10.0    60.0      0.0    0.0
      1         1
      0.000     0.000    0.000  0.000    0.000 0.000       0.000        0.000
      0.000     0.000    0.010  0.020    0.030 0.040       0.050        1.000
      0.050     0.040    0.030  0.020
 WATR  YEAR         1  PEST  YEAR      1    CONC  YEAR  1
     5    YEAR
TUPX1    TSER         1.0E05
RZFX1    TSER         1.0E05
 CHGT    TSER
 PRCP    TSER
VFLX1    TCUM         1.0E05
SPECIAL ACTIONS
 120682       KD               0.5
 170682        SNAPSHOT


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.
                                      10-70

-------
 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
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATIN PARAMETERS
              1       0
ALDICRB
 120582        0       2.5    1.00
     1        1
SOILS PARAMETERS
    20.0                     0       0
     O.OEO     ::8EOO 8.0EOO
     1
     1      20.0
              0.012
              2.5
     0        0
 WATR  YEAR
            1.45   0.233
            0.012  0.000
             .233    .050
RFLX1
 RUNF
  INFL
YEAR
TSER
TSER
TSER
               0.0

               1.0

 1   PEST   YEAR

 1.0E05

12
0    0    00    0   0


0.0

1
                                1    GONG YEAR 1
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.
An example of a basic sequence without any options.
                                   10-71

-------
3 CHEMICALS, 2 HORIZONS, EROSION, IRRIGATION, PRZM INPUT FOR ZONE 1
HYDROLOGY PARAMETERS (CROP DATA FROM USDA NO.283 HANDBOOK)
      0.72
      9.6
     15.7
      1
      0.15
 0.00
 9.7
14.5

 0.14
 2
12.2
12.5

 1.0
 0.000
13.6
11.3

 2.0
 1
15.4
 9.5

 5.8
15.5
9.0
      1         0.15    30.0    80.000    3    86   78  82  0.1  0.1  0.1  60.0
      1
 110582    300982  151082
PESTICIDE TRANSPORT AND TRANSFORMATION AND APPLICATON PARAMETERS
               3
         0
ALDICARB
120582 0
120682 0
1 1
SOILS PARAMETERS
45.0 0.3
4.3E3 l.OE-7
3 0.25
0.150.150.150.150.15 0
8.3 8.3
2
1




2




0
WATR
2
RFLX1
RUNF
8.3 8.3 8.3 8.3

15,
0
0.
8.
0
30,
0
2.
8.
0
0
YEAR
YEAR
TSER
TSER

.0
.012
,5
,3
.000
.0
.012
5
,3
.000





2
2
0
2
0
.5
.5
.5E-7
.55
ATRAZINE
1.0 2.5
1.0 2.5
0 0
1.4E-7
.78
.150.150.150.150
8

1
0

10
0
1
0

10
0

1

1

3 83

.45
.000
.233
.0
.000
.45
.000
.233
.0
.000

8

0
0.

60
0.
0
0.

60
0.

.3 8.3

.233
000
.050
.0
000
.233
000
.050
.0
000

2.00
1.00
0
CARBOFURAN
1.002.00
0.001.00
0111
5.5 E-5 5.5 E-3
.15 0.150 .150.9710.0
8.3 8.3

0.0
0.010
1.0
0.0

0.0
0.005
0.5
0.0


PEST YEAR

.OE05












0.0
0.0100
.1
0.0

0.0
0.0050
.1
0.0


1





0.0 0
.0000.
1.


0.0 0
.0000.
.5





.0
015 0.0150.
.3


.0
015 0.0150.
.1






,000




,000




CONG YEAR 1









                                                                     0
                                                                      5.5E-5
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.
                                      10-72

-------
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
    210
ALDICARB
  120281    0  0.5  0.00
  120581    0  0.5  0.00
    1    1
SOILS PARAMETERS
  45.0   0.0000000111
   .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
0.150.150.150.150.15 0.150.150.150.150.15 0.150.150  .9710.0
8.3 8.3 8.3 8.3 8.3 8.3 8.3  8.3 8.3 8.3 8.3 8.3

                                       0.0

                                         0.000  0.000  0.000


                                       0.0

                                         0.000  0.000  0.000
            .350   .150  0.06   1.
            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  CONG   DAY
    3  YEAR
  RFLX1 TSER      1.0E05
  THET TSER    2
  INFL   TSER    2
2
1

15.0

1.50

. v.
0.350
0.5.000001



2




O.%0
2.5
8.3
30.0
0.5
0.000
2.5
8.3
0.000
.350
10.0
1.50
0.000
.150
60.0

0.0
.00001

0.0
0.05
0.000 0.000

0.
0.
0.

0 1
05
,000
0.06

0.350
0$5.000001
0.000
.350
10.0
0.000
.150
60.0



0.0 0
0.0
.00001
0.000
0.06
0.0 0
.0
0.0
0.05
0.000
1.
.0

0.
0,
0



0 0
.05
.000


                                    10-73

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This PRZM input file represents a scenerio 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
                ^
 1 CHEMICAL, 3 MATERIAL, VADOSE ZONE FLOW SIMULATION FOR ZONE 1
6
20
 1
    1
1
0.0
 1
 3
 1
 2
 3
   0
 1
 1    1
          0.0
 1
 .01

1.0
              1
11100
                   0
                        1.0
                                1.0
           0
             1.0
     20
     20
     20
O.OOEOO
0     1
7.12E02
24.96EOO
 1.06E02
0.045EOO-1.0EOO
0.078EOO-1.0EOO
0.065EOO-1.0EOO
  5  10
* *S|< **************
    1    40.0
    2    40.0
    3    40.0
    0
    0.0   O.OEOO   0
     .43EOO  O.OEOO
     .43EOO  O.OEOO
     .41EOO  O.OEOO
                             0
                             O.OEOO
                             O.OEOO
                             O.OEOO
                           0  0
                   0.145EOO 2.68EOO 0.626EOO
                   0.036EOO 1.56E000.358EOO
                   0.075EOO 1.89E000.470EOO
                              TRANSPORT
1 CHEMICAL, 3 MATERIAL, VADOSE TRANSPORT SIMULATION FOR ZONE 1
61
 0

 1
 3
 1
 2
 3
      1
      0.0
          1
          0.0
         1
         0
         1.0
          0
          0

          1.0
        1
        1
        1.0
        1
        1.0
     20
     20
     20
        40.0
        40.0
        40.0
O.OEOO
      0
0.12E02
1.480EOO
0.12E02
1.480EOO
0.12E02
1.480EOO
1
2
3
1
0.0
 .43EOO
O.OEOO
 .43EOO
O.OEOO
 .41EOO
O.OEOO
                   0.0 0
                       000
 1
 1
 1
 2
 2
 3
 3
 1
 5
      1.0
      0.0
         1.0  O.OEOO
      O.OOIEOO O.OEOO
               1.0  O.OEOO
      ~:805EOO O.OEOO
      0.0       1.0  O.OEOO
      0.004EOO O.OEOO
      1
     10
YEAR

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.
                                      10-74

-------
 ***********************************
                             FLOW
 1 CHEMICAL, 3 MATERIAL, 91 NODES,VADOSE ZONE FLOW SIMULATION FOR ZONE
 1
                           1100
91 3
20 2
1 1
0.0
1
3
1 20
2 20
3 50
O.OOEOO
0
7.12E01
24.96EOO
1.06E02
0 1
1
1 1
1.0
0.0

1
2
3
0
0.0
.43EOO
.43EOO
.41EOO
1 1
.01
0 1
1.0
1.0

40.0
40.0
120.0

O.OEOO
O.OEOO
O.OEOO
O.OEOO
                                  0
                               1.0
 0.045EOO-1.0EOO
 0.078EOO-1.0EOO
 0.065EOO-1.0EOO
 5   10
         0000
         O.OEOO
         O.OEOO
         O.OEOO
0.145EOO 2.68E000.626EOO
0.036EOO 1.56E000.358EOO
0.075EOO 1.89E000.470EOO
     i< *************************** *rpj^J\]\f5p QRT*

1 CHEMICAL, 3 MATERIAL, 91 NODES, VADOSE TRANSPORT SIMULATION FOR ZONE
                               1.0
91 3 1 1
0110
0.0 1.0
1 0.0
3
1 20 1 40.0
2 20 2 40.0
3 50 3 120.0
O.OEOO 1
0 0 0.0
1.20EOO .43EOO
l.OOOEOO O.OEOO
1.20EOO .43EOO
1.500EOO O.OEOO
1.20EOO .41EOO
l.OOOEOO O.OEOO
1 0.0
1 0.0
1 O.OOIEOO
2 0.0
2 0.005EOO
3 0.0
3 0.004EOO
1 1
5 10
0 1
0 1 2
1.0
1.0





0.0 0







1.0 O.OEOO
O.OEOO
1.0 O.OEOO
O.OEOO
1.0 O.OEOO
O.OEOO


                               000
YEAR
ThisVADOFTinput file represents 91 nodes and 90 elements at a depth of
200 cm. Dispersion, retardation, and degradation are simulated.
                                    10-75

-------
                                    ************************************
                               FLOW
3 CHEMICAL, 2 HORIZON, 1 MATERIAL, VADOSE ZONE FLOW SIMULATION FOR ZONE
1
6
20
1

1
2
1
2
1 1
2
1
0.0


20
40
O.OOEOO
0
1
1
1
1

0.0

1
1
0

1 1

1 0
1.0
1

50
80

0.0 0.

.01


.0

.0
.0

1

1
1.0





OEOO
1

2






0
                                 1
                                 1.0
                                    0

                                    0
                              0
                                000
7.12EO;    .43EOO  O.OEOO    O.OEOO
0.045EOO-1.0EOO    0.145EOO  2.68E000.626EOO
 5   10
     ****************************
                                         **********************************
                              TRANSPORT
3 CHEMICAL. 2 HORIZON, 1 MATERIAL,VADOSE TRANSPORT SIMULATION FOR
ZONE 1
61
 0
      1    1
      1
      0.0
1
1
0
1.0
 2
 1
 2
     20
     40
OzOEO;0
      0
OIOOEOO
l.OOOEOO
 1
 1
 1
 1
0.0

1
1
2
     0.0
 .43EOO
l.OOOEOO
0.1   2
0.1   2
0.1   2
0.0  1.0
0
0

1.0
1
1
1.0
   50.0
   80.0
    O.OEOO
                    l.OOOEOO
                   0.1
                   0.1
                   0.1
                  O.OEOO
1
 1.0
                             2  O.OEOO
                             0.00    0
                     2
                     0
                             0
              O.OEOO O.OEOO  O.OEOO
 1     O.OOOEOO O.OOOEOO O.OOOEOO O.OOEOOO.OOEOO O.OEOO
      1
 5    10
YEAR

This VADOFT input file represents 3 chemicals having initial concentrations at the top two
nodes. Dispersion, degradation, and dispersion are simulated over 2 horizons with a total
depth of 130 cm. 21 nodes are placed at 2.5 cm distances from 20 elements and the remaining
40 nodes are placed at 2 cm distances from the remaining 40 elements.
                        •U.S. GOVERNMENT PRININGING OFFICE:  1993-750-002 60159
                                      10-76

-------

-------

-------

-------
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Environmental Protection Agency
Center for Environmental Research Information
Cincinnati, OH 45268

Official Business
Penalty for Private Use
$300
EPA/600/R-93/046
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-------