x>EPA
United States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30605
EPA-600 .'3-78-080
'78
Researcfi and Development
User's Manual for
Agricultural
Runoff Management
(ARM) Model
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7 Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ECOLOGICAL RESEARCH series. This series
describes research on the effects of pollution on humans, plant and animal spe-
cies, and materials. Problems are assessed for their long- and short-term influ-
ences. Investigations include formation, transport, and pathway studies to deter-
mine the fate of pollutants and their effects. This work provides the technical basis
for setting standards to minimize undesirable changes in living organisms in the
aquatic, terrestrial, and atmospheric environments.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/3-78-080
August 1978
USER'S MANUAL
FOR
AGRICULTURAL RUNOFF MANAGEMENT (ARM) MODEL
by
Anthony S. Donigian, Jr.
Harley H. Davis, Jr.
Hydrocomp Incorporated
Palo Alto, California 94304
Grant No. R803722-01
Project Officer
Lee A. Mulkey
Technology Development and
Applications Branch
Environmental Research Laboratory
Athens, Georgia 30605
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30605
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DISCLAIMER
This report has been reviewed by the Environmental Research Laboratory, U.S.
Environmental Protection Agency, Athens, Georgia and approved for publication.
Approval does not signify that the contents necessarily reflect the views
and policies of the U.S. Environmental Protection Agency, nor does mention of
trade names or commercial products constitute endorsement or recommendation
for use.
11
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FOREWORD
As environmental controls become more costly to implement
and the penalties of judgment errors become more severe, envi-
ronmental quality management requires more efficient management
tools based on greater knowledge of the environmental phenomena
to be managed. As part of this Laboratory's research on the
occurrence, movement, transformation, impact, and control of
environmental contaminants, the Technology Development and
Applications Branch develops management or engineering tools
to help pollution control officials achieve water quality goals
through watershed management.
These efforts include a program to provide state-of-the-art
models for analyzing agricultural nonpoint pollution and evalu-
ating the impact and effectiveness of alternative land management
practices. A product of this research interest is the Agricultural
Runoff Management Model, which has undergone continuous development
since 1972. This document is designed to assist users in calibra-
ting and applying the model to their specific needs.
David W. Duttweiler
Director
Environmental Research Laboratory
Athens, Georgia
111
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ABSTRACT
This user manual provides detailed instructions and guidelines for using the
Agricultural Runoff Management (ARM) Model, Versions I and II. The manual
includes a brief general description of the ARM Model structure, operation,
and components, but the primary purpose of this document is to supply
information, or sources of information,' to assist potential users in using,
calibrating, and applying the ARM Model.
Data requirements and sources, model input and output, and model parameters
are described and discussed. Extensive guidelines are provided for
parameter evaluation and model calibration for runoff, sediment, pesticide,
and nutrient simulation. Sample input sequences and examples of model
output are included to clarify the tables describing model input and output.
The manual also discusses computer requirements and methods of analysis of
the continuous information provided by the model.
This manual, when used with an understanding of the simulated processes and
the model algorithms, can provide a sound basis for using the ARM Model in
the analysis of agricultural nonpoint pollution problems and management
practices.
This report was submitted in fulfillment of Grant No. R803722-01 by
Hydrocomp, Incorporated under the sponsorship of the U.S. Environmental
Protection Agency. This report covers the period 7/1/77 to 11/31/77 and
work was completed as of November 1977.
IV
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CONTENTS
Abstract iv
Figures vii
Tables viii
Acknowledgments ix
1. Introduction 1
2. The Agricultural Runoff Management (ARM) Model 2
2.1 Model Structure 2
2.2 Model Operation and Components 6
2.2.1 LANDS 6
2.2.2 SECT 8
2.2.3 ADSRB 8
2.2.4 DEGRAD 8
2.2.5 NUTRNT 8
2.3 Computer Requirements 11
3. Data Requirements and Sources 13
3.1 Model Execution Data 13
3.2 Parameter Evaluation Data 14
3.3 Calibration and Verification Data 14
3.3.1 Data for Hydrologic Calibration 15
3.3.2 Data for Sediment, Pesticides, and Nutrient
Calibration 15
3.4 Data Sources 16
4. Model Input and Output (I/O) 20
4.1 Model Input Sequence 20
4.1.1 Meteorologic Data Input Format and Sequence ... 20
4.2 Model Output 23
4.2.1 Calibration Output 23
4.2.2 Production Output 26
4.2.3 Disk Output 26
5. Model Parameters and Parameter Evaluation 30
5.1 ARM Model Parameters 30
5.1.1 Control Parameters 30
5.2 Parameter Input Sequence 38
5.2.1 Nutrient Parameter Input Sequence 43
5.3 Parameter Evaluation Guidelines 54
v
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5.3.1 Hydrology Parameters 54
5.3.2 Snow Parameters 64
5.3.3 Sediment Parameters 65
5.3.4 Soil Parameters 72
5.3.5 Pesticide Parameters 73
5.3.6 Nutrient Parameters 76
6. Calibration Procedures and Guidelines 85
6.1 ARM Model Calibration Process 85
6.2 Hydrologic Calibration 87
6.2.1 Annual Water Balance 87
6.2.2 Seasonal or Monthly Distribution of Runoff. ... 88
6.2.3 Initial Soil Moisture Conditions 88
6.2.4 Storm Event Simulation. 89
6.3 Sediment Calibration 90
6.3.1 Sediment Balance 90
6.3.2 Primary Calibration Parameters 91
6.3.3 Sediment Fines Storage 91
6.3.4 Transport Limiting vs. Sediment Limiting 91
6.3.5 Tillage Operations 92
6.3.6 Soil Splash and Transport Exponents 92
6.3.7 Concentration vs. Mass Removal 92
6.4 Pesticide Calibration 92
6.4.1 Pesticide Degradation or Persistence 93
6.4.2 Vertical Distribution and Leaching 93
6.4.3 Pesticide Adsorption/Desorption 94
6.4.4 Pesticide Runoff Calibration . 96
6.4.5 Monthly and Storm Comparisons 96
6.5 Nutrient Calibration 97
6.5.1 Nutrient Percolation 97
6.5.2 Plant Uptake of Nutrients 98
6.5.3 Soil Nutrient Reaction Rates 98
6.5.4 Nutrient Runoff 99
6.6 How Much Calibration? 99
6.6.1 Data Problems 100
6.6.2 Problems Analyzed vs. Model Capabilities 100
6.6.3 Guidelines 101
6.7 Conclusion 102
7. Simulation Analysis and Applications 103
7.1 Methods of Analysis 103
7.2 Applications 107
References 110
Appendices
A. Sample Input Sequences for the ARM Model 113
B. Sample Output from the ARM Model 133
C. Formatted Input Sequence for the ARM Model 156
VI
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FIGURES
Number Page
2.1 ARM Model structure and operation 3
2.2 Model soil layers for pesticide and nutrient storage .... 5
2.3 Pesticide (P) and nutrient (N) movement in the ARM Model. . . 7
2.4 Pesticide adsorption/desorption algorithms 9
2.5 Nutrient transformations in the ARM Model 10
4.1 Format of compressed records 29
5.1 Nominal lower zone soil moisture (LZSN) parameter map .... 56
5.2 Watershed locations for calibrated IANDS parameters 57
5.3 Interflow (INTER) parameter map 62
5.4 Soil credibility nomograph 68
5.5 Theoretical degradation curve for applied pesticides 75
5.6 Corn growth and nutrient uptake 80
6.1 Example of the response to the INTER parameter 89
6.2 Relationships of pesticide adsorption/desorption parameters . 95
7.1 Sediment frequency analysis 105
7.2 Pesticide frequency analysis 106
Vll
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TABLES
Number Page
2.1 ARM Model Components 4
2.2 Expected Compilation and Execution Run Times for the
ARM Model 12
3.1 Meteorologic Data Requirements for the ARM Model 13
3.2 Selected Meteorologic Data Published by the Environmental
Data Service 17
3.3 Selected Federal Agencies as Passible Data Sources for
the ARM Model 18
4.1 Input Sequence of Parameters and Meteorologic Data. 20
4.2 Sample Input and Format for Daily Meteorologic Data 21
4.3 Meteorologic Data Input Sequence and Attributes 22
4.4 ARM Model Precipitation Input Data Format 24
4.5 Information Provided in Monthly and Yearly Summaries of
Calibration and Production Runs 25
4.6 File Label Format 28
5.1 ARM Model Input Parameter Description 31
5.2 ARM Model (Versions I and II) Input Sequence and Parameter
Attributes 39
5.3 ARM Model (Versions I and II) Nutrient Parameter Input
Sequence and Attributes 44
5.4 Watersheds with Calibrated Lands Parameters 58
5.5 Indications of the General Magnitude of the Soil-Eredibility
Factor, K 67
5.6 Values of Support-Practice Factor, P 69
5.7 C Values for Permanent Pasture, Rangeland, and Idle Land. ... 71
5.8 C Factors for Woodland 71
5.9 Persistence of Agricultural Chemicals in Soil 77
5.10 Nutrient Reaction Rates and Temperature Coefficients Used
for the P2 and P6 Watersheds 78
5.11 Approximate Yields and Nutrient Contents of Selected Crops. . . 81
5.12 Past Management, Surface Soil Nitrogen Properties, and Net
Mineralization Rate'of Mineralizable N for Various Soils. ... 83
5.13 Fractions of Inputed Reaction Rates for Various Temperature
Coefficients (&) 84
7.1 Frequency Analysis of Alternative Soil and Water Conservation
Practices Using the ARM Model 108
Vlll
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ACKNOWLEDGMENTS
This manual is a compendium of our experience in developing, testing, and
applying the ARM Model. The financial support for the portion of this work
related to sediment, pesticide, and nutrient simulation was provided by the
Environmental Research Laboratory in Athens, Georgia. Mr. Lee A. Mulkey was
the EPA project officer; his assistance and support of this work have been
instrumental to its successful completion.
At Hydrocomp, Mr. Anthony Donigian was project manager responsible for the
technical content and completion of this user manual. Mr. Harley Davis
prepared the nutrient-related portions of the manual and the samples of
model input and output. Mr. Douglas Beyerlein assisted in various aspects
of the project and reviewed the final draft report. The manuscript was
reviewed and edited by Ms. Donna Mitchell and Ms. Diana Allred. Graphics
and drafting expertise was provided by Mr. Guy Funabiki, and the typing was
prepared by Ms. Kathy Francies.
IX
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SECTION 1
INTRODUCTION
The purpose of this user manual is to provide detailed instructions and
guidelines for application and use of existing versions of the Agricultural
Runoff Management (ARM) Model. Data requirements and sources, model input
and output, parameter definition and evaluation, and calibration procedures
and guidelines are discussed. This manual describes the input sequence for
both the original version of the ARM Model (Donigian and Crawford 1976a) and
Version II (Donigian, et al. 1977). Also, the hydrologic and sediment
parameters and calibration procedures and guidelines are applicable to the
Nonpoint Source Pollutant loading (NFS) Model (Donigian and Crawford 1976b)
which includes similar hydrologic and sediment algorithms. This manual is
not intended to replace the discussions of modeling philosophy and
descriptions of model algorithms contained in the original reports. We
recommend that the model user be familiar with the algorithm descriptions in
the ARM Model reports since an understanding of the mechanisms and processes
of agricultural runoff and their representation in the ARM Model is critical
to successful application.
In general, the major steps involved in using the ARM Model are:
(1) data collection and analysis
(2) preparation of meteorologic data and model input sequence
(3) parameter evaluation
(4) model calibration and verification
(5) production of needed information and analysis of simulation results
The first three steps will often overlap as the input sequence of parameters
and meteorologic data are being prepared for calibration trials. Section 2
discusses the overall structure, composition, and operation of the ARM Model
while Section 3 defines general data requirements and sources. Section 4
describes the input sequence and format for model parameters and
meteorologic data, and the output information obtained from the model.
Examples of model output are included in Appendix B. Section 5 provides
descriptions of the model parameters and guidelines for evaluation, while
Section 6 discusses calibration of specific hydrology, sediment, pesticide,
and nutrient parameters. Verification of simulation results is also
discussed in Section 6. Section 7 explores the use and interpretation of
the ARM Model simulation results for applications in environmental analysis.
The appendices include sample input sequences (Appendix A), examples of
model output (Appendix B), and a description of parameter input under format
control (Appendix C) for computers that do not support the FORTRAN namelist
option.
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SECTION 2
THE AGRICULTURAL RUNOFF MANAGEMENT (ASM) MODEL
This section provides an overall description of the ARM Model and brief
discussions of the present versions of the major component programs. The
emphasis is on the functions and processes simulated by the component
programs. The reader is referred to the ARM Model reports (Donigian and
Crawford 1976a; Donigian, et al. 1977) for details of the simulation
algorithms.
2.1 MODEL STRUCTURE
The ARM Model simulates runoff (including snow accumulation and melt),
sediment, pesticides, and nutrient contributions to stream channels from
both surface and subsurface sources. No channel routing procedures are
included and uniform land use is assumed. Thus, the model is applicable to
watersheds with uniform cropping and management practices that are small
enough that channel processes and transformations can be assumed negligible.
Although the limiting area will vary with climatic and topographic
characteristics, watersheds greater than 2 to 5 sq km are approaching the
upper limit of applicability of the ARM Model.
Figure 2.1 demonstrates the general structure and operation of the ARM
Model. The major components of the model individually simulate the
hydrologic response (LANDS) of the watershed, sediment production (SEDT),
pesticide adsorption/desorption (ADSFB), pesticide degradation (DEGRAD), and
nutrient transformations (NUTRNT). The executive routine, MAIN, controls
the overall execution of the program; calling subroutines at proper
intervals, transferring information between routines, and performing the
necessary input and output functions. Table 2.1 describes the functions of
each of the ARM Model components and indicates its location in the source
code.
In order to simulate vertical movement and transformations of pesticides and
nutrients in the soil profile, specific soil zones (and depths) are
established so that the total soil mass in each zone can be specified.
Total soil mass is a necessary ingredient in the pesticide adsorption/
desorption reactions and nutrient transformations. Figure 2.2 depicts the
zones and depths assumed in the ARM Model. The depths of the surface and
upper soil zones are specified by the model input parameters, SZDPTH and
UZDPTH, with values of 2 to 6 mm and 5 to 20 cm, respectively. The upper
zone depth corresponds to the depth of mixing of soil-incorporated
chemicals. It also indicates the depth used to calculate the mass of soil
in the upper zone whether agricultural chemicals are soil-incorporated or
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INPUT
OUTPUT-
MAIN
EXECUTIVE
PROGRAM
NUTRNT
NUTRIENT TRANSFORMATION
AND REMOVAL
•*-CHECKR CHECK INPUT SEQUENCE
-*-NUTRIO READ NUTRIENT INPUT
— OUTMON, OUTYR OUTPUT SUMMARIES
LANDS
HYDROLOGY
AND SNOW
SEDT
SEDIMENT
PRODUCTION
PEST
res
YES
NUTR
NO
ADSRB
PESTICIDE ADSORPTION
AND REMOVAL
DEGRAD
PESTICIDE
DEGRADATION
Figure 2.1 ARM model structure and operation
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TABLE 2.1 ARM MODEL COMPONENTS
Major
Program
MAIN
Component
Subroutine
CHECKR
CHECKS
BLOCK DATA
NOTRIO
OUTMON
OOTYR
LANDS
SEDT
ERDBU3
ADSRB
DSPTN
DEGRAD
NUTRNT
TRANS
Function
Master program and executive control
routine
Checks input parameter errors
Checks input parameter errors
Data initialization for common
var iables
Reads and checks nutrient input data
Prints monthly output summaries
Prints yearly output summaries
Performs hydrologic simulation and
snowmelt calculations
Performs erosion simulation
Outputs to the printer erosion files
written to disk (for error checking)
Performs pesticide soil adsorption/
desorption simulation
Performs desorption calculations
Performs pesticide degradation
simulation
Performs nutrient simulation
Performs nutrient transformations
Beginning
Line Number
10.
1200.
1400.
1600.
6200.
9000.
2000.
2000.
4000.
4200.
5000.
5800.
6000.
7000.
7800.
-------
'
UZDPTH
PARAMETER
^fsZDPTH fARAMU
~
£fi
• • ••••••'• -
i . • • « • h
* "^
It
'•:
;- - . , . ,
•:
>: >:. >:
» * « *"
•
.*.* "."*" .
;;
SURFACE ZONE
•
UPPER ZONE
1.83 M
• .
-'''• '
LOWER ZONE
ui
GROUNDWATER ZONE
Figure 2.2 r-"odel soil layers for pesticide and nutrient storage
-------
surface-applied. The lower zone depth of 1.83 m has proved satisfactory in
testing to date.
The transport and vertical movement of pesticides and nutrients, as
conceived in the ARM Model, is indicated in Figure 2.3. Pollutant
contributions to the stream can occur from the surface zone, the upper zone,
and the groundwater zone. Surface runoff is the major transport mechanism
carrying dissolved chemicals, pesticide particles, sediment, and adsorbed
chemicals. The interflow component of runoff can transport dissolved
pesticides or nutrients occurring in the upper zone. Vertical chemical
movement between the soil zones is the result of infiltrating and
percolating water. From the surface, upper, and lower zones, uptake and
transformation of nutrients and degradation of pesticides is allowed. On
the watersheds tested, the groundwater zone has been considered a sink for
deep percolating chemicals since the groundwater flow contribution has been
negligible. However, on larger watersheds this contribution could be
significant.
2.2 MODEL OPERATION AND COMPONENTS
The model operates on a number of different time intervals. The major
interval of model operation is specified by the user and corresponds to the
time interval of available precipitation data; 5- or 15-min intervals are
allowed by the present version of the ARM Model. Hourly precipitation is
also accepted by the model, but the hourly values are divided into four
equal increments and the simulation is performed on 15-min intervals.
For days on which storms occur, the LANDS, SEDT, and ADSRB subprograms
perform calculations on the 5- or 15-min interval. For days on which storms
do not occur, the LANDS subprogram continues to operate on the 5- or 15-min
interval while the remaining programs operate on a daily basis. In the
present version of the model, the DEGRAD subprogram always operates on a
daily basis, and snowmelt calculations are performed hourly. The time
interval for nutrient transformations is determined by a user-specified
input parameter. The MAIN program monitors the passage of real time and
keys the operation of the separate subprograms at the proper time intervals.
The ARM Model simulates the major processes of importance in agricultural
runoff with the following components.
2.2.1 LANDS
The LANDS program simulates all flow components (surface runoff, interflow,
groundwater flow) and soil moisture storages by representing the processes
of interception, infiltration, overland flow, percolation,
evapotranspiration, and snow accumulation and melt. LANDS is basically an
accounting procedure for moisture above, at, and beneath the soil surface.
It is a modification of the Stanford Watershed Model (Crawford and Linsley
1966) and Hydrocomp Simulation Programming (Hydrocomp, Inc. 1976). Snow
calculations are based on an energy balance approach derived from work by
the Corps of Engineers (1965), Anderson and Crawford (1964), and Anderson
(1968). The LANDS algorithms are described in numerous publications
-------
/ P/N \
\ APPIICATIOM /
\
TOTAL UPTAKE
AND DEGRADATION ,
/
i
_ APPLICATION
P MODE
SOIL INCORPORATED
SUR
]
FACE APPLIED
UPTAKE AND -+ SURFACE P/N « ». SURFACE P/N
OEGRADAfiiJN ~~ STORAGE " *" INTERACTIONS
1 \
INFILTRATION
*
UPTAKE AND
DEGRADATION «
UPTAKE AND ^
DEGRADATION
UPPER ZONE P/N „-
STORAGE ' "*
T
P/N ON SEDIMENT
PESTICIDE PARTICLES
P/N IN OVERLAND FLOW
„ HPPFB jnur P/N P/N IN INTERFLOW
INTERACTIONS
PERCOLATION
*
LOWER ZONE P/N „ fc LOWER ZONE P/N
STORAGE ^~~*^ INTERACTIONS
LOSSES TO GROUNDWATER
*
GROUNDWATER <-_^G
P/N STORAGE
n CTDCAU ,- - . - -
KEY
( INPUT )
FUNCTION
STORAGE |
P-PESTICIDE
N- NUTRIENT
ROUNDWATERP/N_^
INTERACTIONS
1
Figure 2.3 Pesticide (P) and nutrient (N) movement in the ARM model
-------
(Donigian and Crawford 1976bf Crawford and Donigian 1973; Hydrocomp, Inc.
1976) and modifications are discussed in the ARM Model reports.
2.2.2 SEPT
The SEDT program simulates the erosion processes of soil particle detachment
by rainfall and transport by overland flow; overland flow values are
transferred from the LANDS program. Input parameters allow the user to
specify seasonal variations in land cover and the occurrence and impact of
tillage operations. The SEDT algorithms were initially derived from
sediment modeling research by Negev (1967) at Stanford University, and have
been substantially modified during the ARM Model development work based on
concepts presented by Meyer and Wischmeier (1969), Cnstad and Foster (1975),
and Fleming and Fahmy (1973). The SEDT algorithms and modifications are
described in the ARM Model reports and by Donigian and Crawford (1976c).
2.2.3 AD3RB
The ADSRB program in conjunction with the DSPTN subroutine simulates the
adsorption/desorption processes of pesticides in the soil profile. The
algorithms (Figure 2.4) are modifications of a standard Freundlich isotherm
plus an empirical constant, FP/M. This empirical term accounts for
pesticides that are permanently adsorbed to soil particles and will not
desorb under repeated washings. The user can choose to employ either
single-valued, reversible (Figure 2.4a) or non-single-valued, irreversible
(Figure 2.4b) adsorption/desorption equations. The operation of the
algorithms is described by Donigian and Crawford (1976a, 1976c). The model
(Version II) accepts initial pesticide concentrations in the soil and
multiple pesticide applications, but only one pesticide can be simulated
with each operation of the model.
2.2.4 DEGRAD,
The DEGRAD program calculates the combined degradation of applied pesticides
by volatilization, microbial degradation, and other attenuation mechanisms.
A step-wise first-order daily degradation algorithm is used in the current
ARM Model whereby different first-order degradation rates are specified by
the user for specific time periods following application. This approach was
chosen after evaluating both simpler and more sophisticated degradation
models (Donigian, et al. 1977).
2.2.5 NUTRNT
The NUTRNT program in conjunction with the TRANS subroutine simulates the
nitrogen and phosphorus components of runoff and transformations in the soil
profile. Figure 2.5 shows the nutrient forms and transformations simulated
in the current version of the nutrient model. The processes simulated
include immobilization, mineralization, nitrification/denitrification, plant
uptake, and adsorption/desorption. The model assumes first-order reaction
rates for all transformations (except plant uptake) and is derived from work
by Mehran and Tangi (1974) and Hagin and Amberger (1974). The nutrient
algorithms and assumptions are fully described in the original ARM Model
8
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Figure 2.4a Single-valued adsorption/desorption algorithm
T
II
M
J_
1-ADSORPTION
2-DESORPTION
3-NEW ADSORPTION
4-NEW OESORPTION
PESTICIDE SOLUTION CONC. (C) MG/ML
Figxire 2.4b Non single-valued adsorption/desorption algorithm
Figure 2.4 Pesticide adsorption/desorption algorithms
-------
N2
PLNT-N
KD KPL
NO2+NO3
NH4-A
K1
NH4-S
KAM
KIM
ORG-N
KKIM
A. Nitrogen transformations in ARM model
PINT
-P
PO4-A
KAS
—' •-.
KSA
KPL
PO4
-S
KIM
^•^MBMIMI
KM
ORG-P
B. Phosphorus transformations in ARM model
Figure 2.5 Nutrient transformations in the ARM Model
10
-------
report (Donigian and Crawford 1976a) while substantial modifications in the
ARM Model-Version II are discussed by Donigian, et al. (1977). Users of the
nutrient model should be familiar with the corresponding sections of both
reports.
2.3 COMPUTER REQUIREMENTS
The ARM Model is a large, relatively complex computer program comprised of
15 major subroutines and more than 5700 executable source statements written
in the FORTRAN IV language. The model was originally developed on an IBM
360/67 computer and much of the model testing has been performed on an IBM
370/168, both at Stanford University. On the IBM 370/168 using the FORTRAN
H compiler, the program requires approximately 360K bytes (92,000 words) of
storage for compilation of the largest subroutine. Program execution
requires up to 230K bytes (59,000 words) of storage depending on the model
options selected. Thus, a computer with a relatively large storage
capability is usually needed for use of the ARM Model. However, Version II
of the ARM Model has been adapted and run on a Hewlett-Packard 3000 Series
II computer which is substantially smaller than the IBM machines. Thus, the
model can be used on relatively small computers; the effort and model
changes needed to adapt the ARM Model to other computers will depend on the
specific computer installation. Since the HP 3000 does not support the
"namelist" option used for parameter input in the ARM Model, Appendix C
describes the necessary changes to the program to input parameters under
format control. The input format for this option is also described.
The ARM Model requires no special external storage devices (tape, disc,
etc.) other than the standard card reader input and line printer output.
However, the model includes an option to output simulated runoff and
sediment values to an external storage device as unformatted FORTRAN
records. The required input to access this output option is described in
Section 4.
Table 2.2 shows the expected range of program compilation and execution time
required for the ARM Model on the IBM 370/168 and the HP 3000. The smaller
machine requires considerably longer time of the central processing unit
(CPU). Also, execution time will vary with the specific quantities
simulated (hydrology, snow, sediment, pesticides, or nutrients) and will
increase with the options that produce more printed output. The values in
Table 2.2 should be used as a general guide since the time requirements will
vary with different computers.
11
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TABLE 2.2 EXPECTED COMPILATION AND EXECUTION RUN
TIMES FOR THE ARM MODEL a
Program compilation (min)
Program Execution (min/yr)
hydrology and sediment (without
snow)
hydrology and sediment (with snow)
hydrology, sediment,
pesticide (without snow)
hydrology, sediment,
pesticide (with snow)
hydrology, sediment,
nutrients (without snow)
hydrology, sediment,
nutrients (with snow)
IBM 370/168
0.6-0.8
1.5-2.0
1.8-2.3
2.0-3.0
3.0-5.0
6.0-7.0
7.0-8.0
HP3000
11.5-12.5
25.0-30.0
30.0-35.0
40.0-60.0
75.0-100.0
110.0-130.0
140.0-160.0
All values apply to simulation with 5-min precipitation data, and
hourly calculations for snow and nutrients.
12
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DATA
SECTION 3
AND SOURCES
Data requirements for use of the ARM Model include those related to model
execution, parameter evaluation, and calibration/verification. These
requirements and possible data sources are briefly discussed.
3.1 MODEL EXECUTION DATA
The basic data required for model execution is the input time series of
meteorologic data which is the driving mechanism of the ARM Model. The data
required for simulating hydrology, snowmelt, sediment, pesticides, and
nutrients is shown below.
TAHLE 3.1 METEOROLOGIC DATA
FOR THE ARM MODEL
Hydrology
Precipitation
Potential
Evapotrans-
piration
Snowmelt
Sediment
Precipitation
Potential
Evapotrans-
piration
Pesticides
Precipitation
Potential
Evapotrans-
piration
Nutrients
Precipitation
Potential
Evapotrans-
piration
Max-Min air
temperature
Precipitation
Potential
Evapotrans-
piration
Max-Min air
temperature
Wind Movement
Solar Radiation
Dewpoint
temperature
Normal operation for hydrology, sediment, and pesticide simulation requires
5-min, 15-min, or hourly precipitation and daily potential evapotrans-
piration. In addition, nutrient simulation requires daily maximum and
niminum air temperature, and snowmelt simulation further requires daily wind
movement, daily solar radiation, and daily dewpoint temperature in addition
to air temperature. Since the ARM Model is a continuous simulation model,
the period of record needed for each data series corresponds to the length
of time for which simulation is performed.
Although the model can be used to simulate short time periods or single
events, should be simulated to overcome the impact of initial hydrologic and
soil conditions (Section 6). The actual time period of simulation will
depend on the information needed and the type of analysis being performed.
There are no inherent limitation in the ARM Model on the length of the
simulation period. Frequency analysis of long-term output (5 to 10 yr) can
provide valuable information on the probability of nonpoint pollution from
13
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agricultural lands and management practices.
3.2 PARAMETER EVALUATION DATA
Data requirements of parameter evaluation pertain to ARM Model parameters
that are evaluated largely from physical watershed and pollutant
characteristics, land surface conditions, hydrologic characteristics,
climate, agricultural cropping, and management practices. Section 5 will
describe each parameter individually and indicate methods of evaluation,
references, and specific data sources. In general, the types of information
needed for parameter evaluation include:
•topographic maps
•soil maps and reports
•hydrologic/ineteorologic studies
•water quality studies
• surveys of cropping and fertilizer/pesticide applications
Any investigations related to the above topics for the watershed to be
simulated should be collected and analyzed as a source of information for
parameter evaluation.
3.3 CALIBRATION AND VERIFICATION DATA
Calibration is the process of adjusting certain model parameters to improve
agreement between recorded and simulated information. For the ARM Model,
observed runoff and water quality data (that is, sediment, pesticides, and
nutrients) are usually required for accurate evaluation of certain model
parameters. However, many pesticide and nutrient parameters can be obtained
frgjn the literature or from laboratory analyses.
If snow simulation is performed, recorded snow depth and water
equivalent information are needed to evaluate the accuracy of the
simulation. Ideally, the observed data should be continuous to allow an
accurate assessment of the continuous simulation produced by the ARM Model,
-------
3.3.1 Data for Hydrologic Calibration
Hydrologic calibration involves comparison of simulated and recorded runoff
volumes and individual storm hydrographs for a calibration period of 1 to 3
yr. The volume comparison can be made on a storm, daily, monthly, or yearly
basis depending on the watershed area, the length of the calibration period,
and the available data. Daily or monthly runoff volumes are needed to
determine if the model is correctly representing seasonal variations.
Since the ARM Model simulates runoff on 5- or 15-min intervals (hourly
precipitation is divided into equal 15-min increments), comparison of
simulated and recorded storm hydrographs can be made only when the simulated
and recorded data are on comparable time intervals. Thus, minor storms with
durations less than the simulation interval and major storms with data only
on 3-hr or 6-hr intervals will not provide sufficient hydrograph
definition for a valid comparison. In summary, data for hydrologic
calibration includes both continuous runoff volumes and selected storm
hydrographs throughout the calibration period.
3.3.2 Data for Sediment, Pesticides, and Nutrient Calibration
Water quality calibration for the nonpoint pollutants simulated by the ARM
Model is analogous to hydrologic calibration; simulated pollutant mass
removal on a storm, daily, monthly, or yearly basis, and individual storm
pollutant graphs for selected storms are compared with recorded data.
Ideally, water quality calibration is limited to sediment and soil
temperature parameters since the key pesticide and nutrient parameters are
measurable in laboratory experiments. However, in practice, differences
between laboratory and field conditions, insufficient funds for laboratory
experiments, or inadequate data from the literature requires some adjustment
or calibration of the pesticide adsorption coefficients, pesticides
degradation rates, and nutrient transformation rates.
Since nonpoint pollution data are scarce, calibration is often reduced to
comparison of grab sample measurements or selected storm pollutant graphs
with the simulated values. Thus, actual data requirements for water quality
calibration in the ARM Model are reduced to obtaining whatever water quality
runoff data are available for the watershed. The model also provides the
division between solution and adsorbed forms of the pollutants, but such
recorded data are rarely available for comparison.
The ARM Model simulates soil temperatures, pesticides, and nutrient forms in
the profile. Recorded data on soil temperature at various depths and at
daily or more frequent intervals are needed to evaluate soil temperature
regression coefficients. Similarly, pesticide and nutrient concentrations
in the soil for the specific forms being simulated are needed to adjust
pesticide degradation rates, nutrients transformation rates, and leaching
adjustment factors.
Since such detailed data are rarely available, analogous information from
watersheds with similar climatic, hydrologic, and soil conditions can be
used to estimate the expected range of values for the simulation watershed.
15
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This is a common procedure in hydrologic simultion; it will become more
prevalent in water quality modeling as additional relevant data is collected
on watersheds across the country.
3.4 DATA SOURCES
To satisfy the data requirements of the ARM Model, a thorough search of all
possible data sources is a necessary task in the initial phase of
application. Many agencies at all government levels are involved in the
collection and analysis of data relevant to nonpoint source pollution. This
includes meteorology, hydrologic, water quality, and land use-related
information needed for application of the model.
Several federal agencies are active in monitoring and collecting of
environmental data. With regard to meteorological data, the Environmental
Data Service (formerly the Climatological Service, Division of the Weather
Bureau) provides a comprehensive network of meteorologic stations and
regularly publishes the collected data. Table 3.2 lists publications of the
Environmental Data Service where selected meteorologic data can be found.
Most of these publications can be found in the libraries of colleges and
universities, or regional offices of the Environmental Data Service. The
EPA STORET and USGS NASQAN data systems should be consulted for water
quality data. The EPA STORET system includes data from many research and
experimental watershed studies, including the extensive data used in the ARM
Model development work. Regional EPA and USGS offices should be contacted
for information and procedures to access their data bases.
Table 3.3 presents a brief summary of selected federal agencies and data
categories related to nonpoint pollution that may be available. Agencies
listed in Table 3.3 should be contacted during the initial data collection
phase to uncover any data available for the specific watershed being
simulated or watersheds with similar characteristics. The Soil Conservation
Service, the Agricultural Research Service, and the EPA are the most likely
agencies with data pertinent to the ARM Model.
Unfortunately, the large jurisdiction of federal agencies precludes data
collection and monitoring on many small watersheds where the ARM Model would
be applicable. Also, the emphasis of the federal agencies has been directed
to major streams and river basins where water quality measurements include
the effects of nonpoint pollution, point pollutant discharges, in-stream
water use, and channel processes. Consequently, much of the available water
quality data may not be directly comparable with the ARM Model simulation
results; joint use of the ARM Model and a stream model may be needed.
Lacking specific data on the watershed to be simulated, research or
experimental watersheds with similar characteristics can provide estimates
of runoff, sediment, pesticide, or nutrient loads to evaluate the simulation
results. The extensive meteorologic data collected on these experimental
watersheds can be used directly if the climatic regimes are similar.
Many experimental watershed studies are conducted by federal agencies,
universities, and research organizations. In 1965, the American Geophysical
16
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TABLE 3.2 SELECTED METEOROLOGIC DATA PUBLISHED BY THE
ENVIRONMENTAL DATA SERVICE3
Data Type Publication
Precipitation: Daily Climatological Data
Hourly Precipitation Data
Hourly Hourly Precipitation Data
Local Climatological Data
(for selected cities)
Evaporation Climatological Data
Max-min Air Temperature Climatological Data
Local Climatological Data
(for selected cities)
Wind Climatological Data
Local Climatological Data
Solar Radiation Climatological Data-National
Summary
Dewpoint Temperature Local Climatological Data
(for selected cities)
Snowfall and Snow Depth Climatological Data
Soil Temperature Climatological Data
k formerly the Weather Bureau
The National Climatic Data Center, Asheville, North Carolina
can be contacted for assistance in locating published data and
can provide data on magnetic tapes or punched cards.
17
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TABLE 3.3 SELECTED FEDERAL AGENCIES AS POSSIBLE DATA SOURCES FOR THE ARM MODEL
""^^^ Data
^^^^^Category
Agency ^^\^
Environmental
Protection
Agency
U.S. Geologicalb
Survey
Forest Service
Bureau of
Land Management
Soil Conservation
Service
Bureau of
Reclamation
Agricultural
Research Service
Climatologic
*
*
*
*
Hydrologic
*
**
*
*
*
*
Water Quality
**
*
*
*
*
*
Land Use &
Agricultural
Practices
*
*
**
**
Soil & Geology
**
*
**
*
*
Topographic
**
*
*
*
00
*additional source
**major involvement
.Publications of the Environmental Data Service listed in Table 3.2 are a major source of climatological data
"Water Resources Data" is an annual publication of the USGS for each state. It provides data streamflow
values at all USGS sites in the state. Also, regional offices of the USGS can often provide bi-hourly
.storm hydrographs for selected events.
-------
Union conducted an inventory of representative and experimental watershed
studies conducted in the United States (American Geophysics Union 1965).
More recently, the U.S. Forest Service performed a survey and inventory of
forest and range land watersheds with appropriate data for modeling nonpoint
pollution sources (United States Department of Agriculture 1977). Leytham
and Johanson (1977) have compiled an extensive list of watersheds with
sediment discharge records (and supporting hydrologic, meteorologic, and
land use data) including watersheds operated by the Agricultural Research
Service. These publications and other watershed inventories should be
consulted to locate data for application of the model.
However, there is no real substitute for data collected on the watershed to
be simulated, and all efforts should be expended to uncover whatever data
are available. Local, regional, and state agencies and possibly private
firms located in the subject watershed can be important sources of pertinent
data. Local agencies will often exhibit great interest in water quality
because of direct and indirect impacts of pollution on their activities.
The types of agencies that should be contacted include:
•planning commissions
•soil conservation districts
•flood control districts
•water conservancy districts
•water resource and environmental agencies
•university departments of agriculture, soil science, or engineering
Planning commissions and soil conservation districts can be a source of land
use, soils, and topographic data. Flood control and water conservancy
districts will often establish meteorologic stations and monitor streamflow
and water quality. State water agencies and university departments are
usually active in projects and investigations of water resources and water
quality in the state. All agencies listed above should be consulted to
provide a sound base for application of.the ARM Model.
19
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SECTION 4
MODEL INPUT AND OUTPUT
4.1 MODEL INPUT SEQUENCE
The ARM Model accepts input of parameters and meteorologic data on a
sequential basis in either English or metric units. Table 4.1 demonstrates
the sequence of input data; sample input listings of parameters and
meteorologic data are included in Appendix A. Input of the ARM Model
parameters begins the sequence. Section 5 entitled "Model Parameters and
Parameter Evaluations" defines and describes the parameter input sequence.
TABLE 4.1 INPUT SEQUENCE OF PARAMETERS AND METEOROLOGIC DATA
ARM Model Parameters
Potential Evapotranspiration
Max-Min Air Temperature
Wind Movement } 1st Year
Solar Radiation
Dewpoint Temperature
Precipitation
Potential Evapotranspiration
Max-Min Air Temperature
Wind Movement
Solar Radiation > 2nd Year
Dewpoint Temperature
Precipitation
etc. .
• »
4.1.1 Meteorologic Data Input Format and Sequence.
The ARM Model parameters are followed by the meteorologic data. All
meteorologic data except precipitation are input on a daily basis as a block
of cards) with 12 values in each line. Thus, the resulting 31 x 12 matrix
corresponds to the 12 months of the year with a maximum of 31 days each.
Table 4.2 demonstrates the format for the daily meteorologic data and Table
4.3 describes units and attributes. The only change to the format in Table
4.2 is for daily max-min air temperature since two values are input for each
day. In this case, the six spaces allowed for each daily value are divided
in half. The first three spaces contain the maximum, and the second three
spaces contain the minimum air temperature for the day.
20
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TABLE 4.2 SAMPLE INPUT AND FORMAT FOR DAILY METEOROLOGIC DATA
lionth
Jan Feb Mar
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAF73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
i
7
18 74 60
18 90 170
IS 60 43
0 61 43
35 61 43
28 82 71
28 121 4
28 69 41
28 7 35
28 20 20
28 21 20
28 21 21
28 16 123
28 54 123
27 46 132
33 47 103
19 45 61
41 45 61
41 46 61
54 46 61
54 81 112
55 83 44
118 101 104
32 45 87
24 46 87
24 46 87
24 28 72
25 60 86
25
91
ll
50
31
3f
\
14 20 26
Apr May Jun Jul Aug Sep Oct Nov Dec
29 13 266 131 103 15 41 90 68 1
29 13 70 1C3 96 63 69 72 68 2
30 14 65 140 53 189 97 48 47 3
GO 4 70 156 162 12'- 104 48 52 4
112 202 171 145 34 115 117 114 47 5
15 99 8 185 122 24 130 54 42 6
15 100 72 87 55 161 124 12 31 7
15 34 70 145 105 92 90 0 57 8
15 135 37 62 130 145 117 78 36 9
15 210 108 185 36 218 159 72 10 10
16 202 63 175 139 185 76 60 57 11
15 219 142 133 162 145 34 48 36 12
113 145 132 185 4 99 110 48 57 13
113 176 90 154 72 211 117 5'- 36 14
113 192 156 246 208 125 76 24 36 15 n
113 222 121 140 115 158 83 24 104 16 uay
1 171 160 89 123 191 90 60 73 17
88 173 70 58 92 130 110 120 47 18
88 159 72 80 72 112 117 66 57 19
88 72 161 46 130 119 104 24 73 20
G8 103 84 IfiS 205 73 83 48 104 21
88 198 149 129 178 7? 83 36 109 22
88 154 183 135 143 132 83 66 99 23
13 232 62 141 122 152 77 36 83 24
13 153 262 71 112 112 71 30 10 25
19 114 109 65 136 92 65 48 42 26
332 90 126 27 52 33 59 24 68 27
58 152 59 43 170 66 53 78 36 28
58 3 137 148 37 79 48 54 16 29
58 153 213 155 249 165 69 204 47 30
1 198 1 103 38 1 14 1 63 31
i i i i i i i > i
32 38 44 50 F6 62 68 74 80
Column Number
Notes: 1. Columns 1-7 are ignored. They can be used to identify the data.
2. All data are input in integer form.
3. Identical format for evaporation, wind, solar radiation, and
dewpoint temperature.
4. For Max-Min air temperature data, the six spaces allowed for each
daily value (above) are divided in half; the first three spaces
contain the maximum temperature, and the second three spaces
contain the minimum temperature. See listing in Appendix A.
21
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TABLE 4.3 METEOKOLOGIC DATA INPUT SEQUENCE AND ATTRIBUTES*
to
NJ
Data
Potential-
Evapotranspiration
Max-Min
Air Temperature
Units
Interval English Metric
Comments
Daily
Daily
Daily
Daily
Daily
5 minutes
15 minutes
Hourly
in x 1000 mm
degrees F degrees C
miles/day km/day
langleys/ langleys/
day day
degrees P degrees C
in x 100 mm
Assumed equal to lake evaporation, and
lake evaporation = pan evaporation x pai
coefficient
1. Caution: Time of observation
determines whether the recorded values
refer to the day of observation or the
previous day.
2. Required only for nutrient and snow
simulation .
Required only for snow simulation
1. Total incident solar radiation.
2. Required only for snow simulation.
3. 1 langley = 1 calorie/cm
1. Required only for snow simulation.
2. Average daily value since variation)
during the day are assumed minor.
Wind
Solar Radiation
Dewpoint
Precipitation
* All meteorologic data are input in integer form. Format specifications are described in Table
-------
Table 4.4 indicates the format for precipitation data input on 5-min,
15-min, or hourly intervals. Except for precipitation, daily meteorologic
observations are needed. For hydrology, sediment, and pesticide simulation,
without snowmelt calculations, only precipitation and evaporation are
required in the present version of the ARM Model. For nutrient simulation,
max-min air temperature is an additional requirement, and for snow
simulation, the required data series include max-min air temperature, daily
wind movement, daily solar radiation, and daily dewpoint temperatures (in
addition to precipitation and evaporation). For further clarification of
these formats, see the sample input listings in Appendix A. The model
operates continuously from the beginning the the end of the simulation
period. To simplify input procedures and reduce computer storage
requirements, the meteorologic data are input on a calendar year basis.
Each block of meteorologic data indicated in Table 4.1 must contain all
daily values for the portion of the calendar year to be simulated. Thus, if
the simulation period is July to February, the model reads and stores all
the daily meteorologic data for the July to December period. The model then
reads the precipitation data on the 5-min, 15-min, or hourly intervals, and
performs the simulation day by day from July to December. When the month of
December is completed, the model reads the daily meteorologic data for
January and February, and then continues stepping through the simulation
period by reading the precipitation and performing the simulation day by day
for January and February. Thus the input data must be ordered on a calendar
year basis to conform with the desired simulation period.
4.2 MODEL OUTPUT
Since the ARM Model operates chronologically on the input meteorologic data,
output is provided sequentially as a function of the mode of operation,
simulation options, and the frequency of printing. The user specifies the
type of output desired through the use of simulation "control" parameters in
the parameter input sequence (Section 5). Appendix B includes samples of
all the types of model output discussed below.
The HYCAL and PRINT parameters determine the mode of model operation and the
resulting frequency and extent of printed output, respectively. The two
modes of operation allowed by the present version of the ARM Model are
referred to as calibration (HYCAL = CALB) and production (HYCAL = PROD).
The monthly and yearly summaries obtained from calibration and production
runs are identical. They provide the monthly and yearly totals for runoff
and loss of sediment, pesticides and nutrients, and storages of soil
moisture, pesticide, and nutrient forms in the soil layers on the last day
of the month or year (Table 4.5). In the examples in Appendix B, note that
the word BLOCK is used to indicate the areal-source zones (see Donigian and
Crawford 1976a) in order to prevent confusion with the vertical soil zones
(that is, surface, upper, lower, and groundwater).
4.2.1 Calibration Output
The basic difference between the calibration and production modes is the
type and form of information obtained for simulation periods between the
monthly summaries. A calibration run provides detailed information on
23
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TABLE 4.4 ARM MODEL PRECIPITATION INPUT DATA FORMAT
Column No. Description and Format
1 Blank
2-7 Year, Month, Day (e.g. January 1, 1940 is 400101)
8 Card Number: 5 and 15 minute data - each card
represents a 3-hr period.
Card #1 Midnight to 3:00 AM
#2 3:00 AM to 6:00 AM
#3 6:00 AM to 9:00 AM
#8 9:00 PM to Midnight
All eight cards are required if rain occurred any time
during the day. A card number of 9 signifies that
no rain occurred during the entire day, and no other
rainfall cards are required for that day.
Hourly data - Each card represents a 12-hour period;
thus, two (2) cards are required for each day when
precipitation occurs. Card #1 is for the 12 AM hours.
As with 15-min, a card #9 indicates no precipitation
occurred in that day.
9-80 Precipitation data (nm {00's of in.)).
15-min intervals;
6 column per each 15-min in the 3-hr period of each
card. Number must be right justified, i.e. number
must end in the 6th column for the 15-min period.
5-min intervals;
2 columns per each 5-min interval, i.e. the 15-min
period still occupies 6 columns, but it is broken
down into three 5-min intervals.
Hourly intervals;
6 columns per each hourly interval, i.e. the hourly
period occupies 6 columns, and only two cards
are needed for the entire day. Number must be
right-adjusted.
Notes: 1. Appendix A contains a sample of input data.
2. At least one precipitation card is required for each day of
simulation.
3. Blanks are interpreted as zeros by the Model: consequently,
zeros do not need to be input.
4. Only integer values are allowed.
24
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TABLE 4.5
Hydrology
Snow
Sediment
Pesticide
INFORMATION PROVIDED IN MONTHLY AND YEARLY SUMMARIES
OF CALIBRATION AND PRODUCTION RUNS
Total runoff and components (overland flow,
interflow, impervious, and baseflow)
Groundwater recharge
Precipitation
Evapotranspiration (net and potential)
Crop cover
Soil moisture storages on the last simulation
interval of the month or year
Precipitation as snow
Rain occurring on snow cover
Combined snowmelt and rain
Melt components (radiation convection,
condensation, rain meet, ground melt)
Snowpack depth (water equivalent) and
density
Snow cover
Snow evaporation
Sediment loss
Sediment fines storage
Pesticide storage (crystalline, dissolved,
adsorbed) in each soil layer
Pesticide loss by overland flow, interflow,
and sediment
Pesticide degradation loss from each soil
layer
Nutrients
(all nutrient forms)
Nutrient storages in each soil zone
Nutrient loss by overland flow, interflow,
sediment, and percolation from each'soil
layer
Total nutrient loss to the stream
Nutrient loss by transformation from each
zone and by harvesting
25
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runoff, sediment concentration and mass removal, and pesticide or nutrient
concentrations and mass removal for each simulation interval (5- or 15-min).
The goal of calibration output is to provide the information needed to
compare simulated runoff, sediment loss, and pesticide or nutrient loss with
recorded values for storm events. Since information is provided in each
simulation interval the PRINT parameter must be specified for interval
output (PRINT = INTR) for all calibration runs. Due to output printing
limitations, pesticides and nutrients cannot be run simultaneously in the
calibration mode.
4.2.2 Production Output
The production mode of operation provides summaries of runoff, sediment,
pesticide, and nutrient loss, in addition to the amount of pesticide and
nutrients remaining in the various soil zones. Thus, the production mode
provides a complete picture of the mass balance of pesticides and nutrients
applied to the watershed. Pesticide and nutrient simulation can be
performed simultaneously in the production mode. The production output is
printed in tables similar to the monthly summaries. The frequency of
printing is controlled by the PRINT parameter which allows printing to be
done on each interval (PRINT = INTR), each hour (PRINT = HOUR), or at the
end of each day (PRINT = DAYS) or each month (PRINT = MNTH). Generally,
production runs will be employed for daily or monthly print intervals. Use
of the interval (INTR) or hourly (HOUR) printout in the production mode
should be restricted to short simulation periods due to the large amount of
printed output provided. For example, over 500 pages of output is provided
each day of simulation for a production run which prints output for each
5-min interval.
4.2.3 Disk Output
The ARM Model Version II includes the option to write total land surface
runoff (LSRO), overland flow (RROS), or erosion (EROS) simulated in each
time interval to an external storage device. This capability was developed
to interface the ARM Model with an in-stream sediment transport model to
simulate sediment movement in large watersheds (Leytham and Johanson 1977).
With use of the proper control parameters (Section 5) the user can instruct
the model to create data files of the above variables for subsequent
statistical analysis or interface with stream models. Two types of data
files can be created by Version II of the ARM Model: (1) uncompressed files
(LSRO and RROS data), and (2) compressed files (EROS data) . Both files have
the following characteristics.
(1) Fixed length records: Each record contains TBLKSZ data items. TBLKSZ
is the number of simulation intervals in a time block, and specifies
the number of intervals simulated before the resulting block of
information is written to disk. The choice of TBLKSZ affects the
execution of programs that access the created data files, and an
optimal value depends on the relative costs of core storage, CPU time,
disk storage, and I/O operations (Leytham and Johanson 1977). The ARM
Model Version II uses a time block size of 128 which was found to
26
-------
minimize the amount of disk storage required for data files on the HP
3000. To change this value, the dimensions of the arrays ISRO, EROS,
and EROS must be changed to the new 1BLKSZ value in line 2020.1 in the
LANDS program. Note that idiosyncrasies on IBM machines require
unformatted files to be treated as having variable length, blocked,
spanned (VBS) records.
(2) Binary files: The data are transferred to and from disk in binary form
without format control. This obviates the usual conversion of data
from character (ASCII or EBCDIC) form on the disk files to binary form
in core, or vice versa, thus expediting data transfer.
(3) Sequential access: All data are written and must be accessed
sequentially.
The first record on each file is a label which is written by the MAIN
program of the ARM Model (lines 353/354) before any data are transferred.
The format and contents of the label are shown in Table 4.6. Whenever a
file is read, the contents of the label should be printed by the reading
program so the user can check that the correct file has been accessed. The
records following the label contain the data themselves in units of inches
(mm) of water for LSRO and RROS files, and tons/acre (tonnes/hectare) for
EROS files for the area simulated.
The data are stored in either "uncompressed" or "compressed" format. With
the uncompressed format, data are stored in a purely sequential form.
Successive items in the record contain data from successive simulation time
intervals.
Compressed records were developed to save space when storing data for
processes which occur intermittently. They are useful, for example, in
storing information on simulated land surface erosion; a process which
occurs only when overland flow takes place. The idea is to eliminate the
large number of zeros which would otherwise appear in the file.
To achieve this, the program keeps track of the number of data intervals
which have elapsed since the start of the file. When filling the buffer
array in core, prior to writing to the file, nothing is stored until a
nonzero value is encountered. A negative number is then written. The
negative sign indicates that the number is a header or displacement
indicator, and the absolute value is the displacement (in data intervals)
since the start of the file. Data are then stored in succeeding elements of
the array in the conventional manner until another zero value is found.
This process is repeated until the array is full, at which time it is
transferred to disk as a single record, whereupon the buffer array starts to
fill again. A typical compressed file is shown in Figure 4.1.
The compressed format has been used to store erosion data simulated for Four
Mile Creek, Iowa. The files occupy only 5 percent of the disk space which
equivalent files in uncompressed format would require. In general, the
degree of compression achieved will depend on how intermittent the process
is.
27
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TABLE 4.6 FILE LABEL FORMAT
Element
Number Contents
1-20 Descriptive title for contents of the file. Title may
consist of up to 80 alpha-numeric characters.
21 Starting hour of the file (File starts with the first
interval of this hour.)
22 Starting date of the file
23 Starting month of the file
24 Starting year of the file
25 Ending hour of the file (File ends with the last interval
of this hour.)
26 Ending day of the file
27 Ending month of the file
28 Ending year of the file
29 File time interval in seconds
30 File type = I uncompressed diffuse load file (LSRD)
File type = 2 compressed diffuse load file (EROS)
File type = 3 uncompressed point load file
31 TBLKSZ - not used
28
-------
Label
Record
(TBLKSZ values)
-20.
4
4.5
V
-35.
9.2
8.7
etc.
Record
(TBLKSZ values)
9.0
7.0
-100.
2.
etc.
A
V
A
/ \ v
Header -> ^- Data Item
Figure 4.1 Format of compressed record
The compressed format has been used to store erosion data simulated for Four
Mile Creek, Iowa. The files occupy only 5 percent of the disk space which
equivalent files in uncompressed format would require. In general, the
degree of compression achieved will depend on how intermittent the process
is.
29
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SECTION 5
MODEL PARAMETERS AND PARAMETER EVALUATION
5.1 ARM MODEL PARAMETERS
The ARM Model includes parameters that must be evaluated whenever the model
is applied to a specific watershed. Since the model is designed to be
applicable to watersheds across the country/ the parameters provide the
mechanism to adjust the simulation for the specific topographic, hydrologic,
soil, and land management conditions of the watershed. The large majority
of the parameters are easily evaluated from known watershed characteristics.
Parameters that cannot be precisely determined in this manner must be
evaluated through calibration with recorded data. This section discusses
and defines the ARM Model parameters, the parameter input sequence, and
methods of parameter evaluation. Section 6 provides calibration procedures
and guidelines.
Table 5.1 includes a complete list and description of the ARM Model
parameters. They are listed by categories: control, hydrology, snow,
sediment, pesticide, and nutrients (reaction rates and storages) . The
control parameters allow the user to specify the mode of operation, the
units and type of input and output, and the specific simulation options used
in each model run. The remaining parameters describe watershed conditions,
pollutant characteristics, and/or agricultural practices and are used in the
simulation algorithms contained in the ARM Model.
In Table 5.1, parameters enclosed in brackets [ Jare included only in
Version II of the ARM Model, whereas parameters enclosed in parentheses ( )
are included in both versions, but application/definition of the parameter
has been modified in Version II. The modifications are subsequently
described in footnotes in Table 5.1. All remaining parameters are identical
in both model versions.
5.1.1 Control Parameters
The HYCAL and PRINT parameters are discussed in Section 4.2; they control
the mode of operation and frequency of printing output, respectively. The
BJPOT and OOTPUT parameters specify the units of the input information
(parameters and meteorologic data) and the desired units of output,
respectively, either English (ENGL) or metric (METR). Also, with OUTPOT=
BOTH, production mode output and summaries (monthly and yearly) in
calibration mode output are provided in both sets of units. This option
should be used sparingly due to the vast amount of resulting computer
30
-------
TABLE 5.1 ARM MODEL INPUT PARAMETER DESCRIPTION^*
TYPE NAME DESCRIPTION
Control HYCAL Specifies type of information desired
PROD-production run, prints full tables for each
interval as specified by PRINT
CALB-calibration run, prints removal values for
each interval as specified by PRINT
INPUT Input units, ENGL-english, METR-metric
OUTPUT Output units, ENGL-english, METR-metric, BOTH-both
PRINT Denotes the interval of printed output, INTR-each
interval, HOUR-each hour.- DAYS-each day, MNTH-each
month
SNOW NO-snowmelt not performed, YES,snowmelt calculations
performed
PEST NO-pesticides not performed, YES-pesticide calculations
performed
NUTR NO-nutrients not performed, YES-nutrients calculations
performed
ICHECK ON-checks most of the hydrology, snow (if used),
sediment, and pesticide (if used) input parameter
values and prints out error and warning statements
for input parameter values that are outside of
acceptable value limits, OFF-no check is made
[DISK] NO-no output written to disk YES-LSRO, RROS, and/or
EROS written to disk
[IDEBUG] OFF-no output to check values written to disk
ON-print echo of output written to disk
[CHAR]
[TITLE]
[DSNFLO]
[DSNERS]
[DSNROS]
INTRVL
HYMIN
AREA
BGNDAY
BGNMON
BGNYR
ENDDAY
ENDMON
ENDYR
RUNOFF-Lands Surface RunOff (LSRO) output
SEDIMENT-EROSion (EROS) from sediment output
OVERLAND-Runoff fRom Overland Surface (RROS) output
Title for data set on disk (80 char)
Data set number for LSRO file
Data set number for EROS file
Data set number for RROS file
Time interval of operation (5, 15, or 60 minutes)
Minimum flow for printed calibration output during a
time interval
Watershed area
Date simulation begins-day, month, year
Date simulation ends-day, month, year
(continued)
31
-------
TABLE 5.1
TYPE
Hydrology
(continued)
NAME
UZSN K
UZS 3
LZSN B
LZS 1
L I
SS t
NN b
A £
EPXM t
PETMUL I
(K3°) ]
INFIL 1\
INTER ]
IRC ]
K24L I
KK24 C
K24EL 1
9GW ]
GWS 1
KV I
ICS ]
OFS ]
IPS 1
Snow
DESCRIPTION
Nominal upper zone soil moisture storage
Initial upper zone soil moisture storage
Nominal lower zone soil moisture storage
Initial lower zone soil moisture storage
Length of overland flow to channel
Average overland flow slope
Manning's n for overland flow
Fraction of area that is impervious
Maximum interception storage
Potential evapotranspiration data correction factor
Index to actual evaporation on a monthly basis (12 values)
Mean infiltration rate
Interflow parameter, alters runoff timing
Interflow recession rate
Fraction of groundwater recharge percolating to deep
groundwater
Groundwater recession rate
Fraction of watershed area where groundwater is within
reach of vegetation
Initial groundwater storage
Initial groundwater slope
Parameter to allow variable recession rate for
groundwater discharge
Initial interception storage
Initial overland flow storage
Initial interflow storage
[SNOWPRINT]NO-hourly snow tables not printed during snow pack
periods
YES-hourly snow tables printed
RADCON Correction factor for radiation melt
CCFAC Correction factor for condensation and convection melt
SCF Snow correction factor for raingage catch deficiency
ELDIF Elevation difference from temperature station to mean
watershed elevation
IDNS Initial density of new snow
F Fraction of watershed with complete forest cover
DGM Daily groundmelt
WC Water content of snowpack by weight
MPACK Water equivalent of snowpack for complete watershed
coverage
EVAPSN Correction factor for snow evaporation
MELEV Mean elevation of watershed
TSNOW Temperature below which precipitation becomes snow
(continued)
32
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TABLE 5.1 (continued)
TYPE NAME
DESCRIPTION
Sediment
PACK Initial water equivalent of snowpack
DEPTH Initial depth of snowpack
PETMIN Minimum temperature at which PET occurs
PETMAX Temperature at which PET is reduced by 50 percent
VJMUL Wind data correction factor
RMUL Radiation data correction factor
KUGI Index to forest density and undergrowth
COVPMO Fraction of crop cover on a monthly basis (12 values)
(TIMTIL)°Time when soil is tilled (Julian day, i.e. day of the
year, e.g. January 1=1, December 31 = 365/366)
(12 dates)
(YRTIL) *-
(SRERTL)
JRER
KRER
JSER
KSER
SRERI
[SCMPAC]
Corresponding year (last two digits only) for
TIMTIL (12 values)
"Fine deposits produced by tillage corresponding to
TIMTIL and YRTIL (12 values)
Exponent of rainfall intensity in soil splash equation
Coefficient in soil splash equation
Exponent of overland flow in sediment washoff equation
Coefficient in sediment washoff equation
Initial fines deposit
Rate by which soil fines are decreased per day on
non-rain days
Pesticide PESTICIDE
Title word to begin the reading of pesticide input
parameters
APMODE Application mode, SURF-surface applied, SOIL-soil
incorporated
DESORP NO-single-valued adsorption/desorption used, YES-non-
, single-valued adsorption/desorption algorithm used
[PSSZ], Initial pesticide storage in surface zone
[PSUZ] , Initial pesticide storage in upper zone
[PSLZ], Initial pesticide storage in lower zone
[PSGZ] , Initial pesticide storage in groundwater zone
(TIMAP) -Time of pesticide application (Julian day) (12 values)
(YEARAP) Year of pesticide application (last two digits only)
(12 values)
(SSTR) Pesticide application for entire watershed (12 values)
CMAX Maximum solubility of pesticide in water
DD Permanent fixed adsorption capacity
K Coefficient in Freundlich adsorption equation
N Exponent in Freundlich adsorption equation
NP f Exponent in Freundlich desorption equation
[DDG]* Julian day when KDG(l) begins (max. of 12 values)
[YDG] Corresponding year in which KDG applies
[KDG] Pesticide decay rate (per day) (max. 12 values)
(continued)
33
-------
TABIE 5.1 (continued)
1YPE NAME
DESCRIPTION
Soil
Nutrient
[LZTEMP]
[AXZT]
[BSZT]
[AUZT]
[BUZT]
SZDPTH
UZDPTH
[BDSZ]9
[BDUZ]9
[BDLZ]9
[UZF]
[LZF]
TSTEP
Lower zone temperature on a monthly basis (12 values)
Slope of surface zone soil temperature regression
y-intercept of surface zone soil temperature regression
equation
Slope of upper zone soil temperature regression equation
y-intercept of upper zone soil temperature regression
equation
Surface layer soil depth
Upper zone depth or depth of soil incorporation
Bulk density of surface zone soil
Bulk density of upper zone soil
Bulk density of lower zone soil
Upper zone chemical percolation factor
Lower zone chemical percolation factor
Timestep of chemical and biological transformations,
must be an integer number of time steps in a day,
and an integer number of simulation intervals
(INTRVL) in a TSTEP, range of TSTEP is 5 or
15-min to 1440 minutes, but the solution
technique works best at 60 minutes or less.
NAPPL Number of fertilizer applications, values may range
from 0 to 5
TTMHAR Time of plant harvesting, Julian day of the year,
value may range from 0 to 366
[ULUPTK] Fraction of maximum crop uptake of nutrients for the
the upper layers (surface and upper zone) on a
monthly basis (12 values), should be 1.0 or less
[LZUPTK] Fraction of maximum crop uptake of nutrients for the
lower zone on a monthly basis (12 values), should
be 1.0 or less
Nitrogen Reaction Rates
(Kl)
(KD)
(KPL)
KAM
KIM
KKIM
KSA
i/D
(continued)
Nitrification (Oxidation) rate of solution ammonium to
combined nitrite and nitrate
Denitrification (Reduction) rate of nitrite and nitrate
to gaseous nitrogen
Uptake rate of nitrate by plants
Ammonification or mineralization rate
of ORG-N to ammonium in solution
Immobilization rate of solution ammonium
to ORG-N
Dnmobilization rate of nitrate (and nitrite) to ORG-N
Transfer rate of ammonium from solution to
adsorbed (adsorption)
34
-------
TABIE 5.1 (continued)
TYPE NAME DESCRIPTION
KAS Transfer rate of ammonium from adsorbed to solution
(desorption)
Phosphorus Reaction Rates
KM Mineralization rate of ORG-P to solution phosphate
KIM Immobilization rate of solution phosphate to ORG-P
KPL uptake rate of phosphate in solution by plants
KSA Exchange rate of phosphate from solution to
adsorbed form
KAS Transfer rate of phosphate from adsorbed to
solution form
THKM Temperature coefficients for corresponding reaction
rates, e.g. THKM is coefficient for the KM rate.
Nitrogen Storages
ORG-N Organic nitrogen in or attached to soil
NH4-S Ammonium in solution
NH4-A Ammonium adsorbed to soil
(N02-tN03)iNitrite and nitrate
N2 Gaseous nitrogen forms from denitrification
PLNT-fl Plant nitrogen
Phosphorus Storages
ORG-P Organic phosphorus in or attached to soil
P04-S Phosphate in solution
P04-A Phosphate adsorbed to soil
PLNT-P Plant phosphorus
Chloride Storage
CL Chloride
3 [ J designate parameters added to Version II of the ARM Model
(Donigian, et al. 1977) while ( ) indicate parameters whose application/
definition has been modified from Version I (Donigian and Crawford
, 1976a) to Version II. The remaining parameters are identical.
Version I includes a single average annual value for K3 while Version II
requires input of 12 monthly K3 values.
Version I accepts 5 values for TIMTIL, YRTIL, and SRERTL, while
, Version II accepts 12 values.
Version I allows only a single pesticide application as specified by
TIMAP, YEARAP, and SSTR; Version II allows up to 12 values (i.e.
pesticide applications) for these parameters in addition to the
(continued)
35
-------
TABLE 5.1 (continued)
capability to initialize the pesticide storage in each zone (i.e. PSSZ,
PSUZ, PSLZ, PSGZ parameters)
In Version I, the 5 values for SSTR pertain to the 5 areal
blocks and the total application is the sum of the 5 values;
whereas in Version II each SSTR value is the total pesticide
application to the entire watershed and 12 separate application
j values are allowed.
Version I requires a single pesticide degradation note, DEGCON,
while Version II allows up to 12 degradation rates applicable to
specific time periods, specified by DDG and YDG.
g Version I required the same soil bulk density value, BULKD for all soil
zones, whereas Version II allows different values for the surface
(BDSZ), upper (BDUZ), and lower (BDLZ) zones.
All nitrogen and phosphorus reactions have been changed from being based
on nutrient mass/hectare in each zone in Version I, to nutrient concentra-
tions in Version II (Donigian, et al. 1977 pp. 63-68) to eliminate
. reactions at low moisture levels.
1 Version I simulates N02 and N03 separately while Version II includes
combined NC>2 + N03, which is assumed to be mostly NC>3 except for short
periods when NC>2 is present. Thus, the K2 and KK2 transformation rates
between N02 and NOs have been eliminated in Version II, and Kl, KD, and
. KPL rates apply to the combined NC>2 + N(-*3 form.
3 In Version I, the Kl rate applies to transformations from adsorbed and
solution ammonium to NC>2, while in Version II the Kl rate applies to the
pathway frcm solution ammonium to the combined N02 + NO . The nitrifica-
tion path from absorbed ammonium has been eliminated.
k In Version I, KPL is multiplied by the crop canopy to obtain the
seasonal variation in plant uptake, whereas Version II includes the
ULUPTK and LZUPTK parameters to specify the monthly distribution of plant
uptake.
36
-------
printout. The calibration mode output for storm events is provided in a
mixed set of units (Appendix B). For example, solution concentrations are
always in mg/1, to simplify comparison of simulated and recorded values in
the calibration process.
Hydrology and sediment calculations are performed in each model run.
However, the user-specified SNOW, PEST, and NUTR control parameters specify
whether or not snowmelt, pesticide, or nutrient calculations, respectively,
will also be performed. As indicated above, pesticide and nutrient
calculations can be performed simultaneously in a production run but not in
a calibration run. An error message will be printed and execution will be
prevented if this rule is violated.
The ICHECK control parameter allows the user to direct the ARM Model to
check for errors and reasonableness of the parameter values; the CHECKR and
CHECKS subroutines perform this function. With ICHECKON, the model checks
the input sequence, indicates errors, and then stops if any errors are
found. After errors have been corrected the model can be run again with
ICHECK=ON in order to check corrections and to perform the simulations.
The DISK control parameter is used to activate the option to write land
surface runoff (LSRO), overland flow runoff (RROS), or erosion (EROS) values
to an external storage device, usually a magnetic disk or tape (Section
4.2.3). With DISK=YES, the IDEBOG, CHAR, TITLE, and data set number or
numbers (DSNFID, DSNERS, D6NROS) must be specified in the input sequence.
The IDEBUG parameter (ON or OFF) allows the user to have the model print in
the model output the values written to the external storage device. This
can be used to check the option or obtain a record of the data set. The
CHAR parameter is a keyword (RUNOFF, SEDIMENT, or OVERLAND) to indicate the
information written to the device, and is followed by the user-specified
TITLE (80 characters maximum) of the data set and the data set number. Thus
the CHAR, TITLE, and data set number must be ordered in sequence for each
file written to the external storage device. Any one or all of the LSRO,
RROS, and EROS files can be written to the external device in a single run.
For example, the proper sequence for writing LSRO and EROS files would be:
DISK=YES
IDEBUGON
RUNOFF
DNSFLO=<10>
SEDIMENT
<TITLE OF THE SEDIMENT FILE>
DSNERS=<11>
ENDDISK
The information contained in <> is user-supplied. This sequence would write
the LSRO file to data set number 10 and the EROS file to data set number 11.
The character string ENDDISK is used to indicate the end of information for
writing to the external device.
37
</pre><hr><pre>
-------
The remaining control parameters specify the simulation interval (INTRVL),
the minimum flow for hydrograph output (HYMIN), the area of the watershed
(AREA), and the beginning and ending dates of simulation.
5.2 PARAMETER INPUT SEQUENCE
As shown in Table 4.1, both parameters and meteorologic data are input on a
sequential basis. Model parameters are input in two different formats
depending on the simulation options chosen. The majority of the ARM Model
parameters (except the control and nutrient parameters) are input in the
FORTRAN namelist format. The input sequence and attributes for these
parameters are described in Table 5.2. The nutrient parameters (except for
the "nutrient control" parameters) are input under format control due to the
number of transformations, reaction rates, and storages which must be
defined. Table 5.3 describes the input sequence and attributes for the
nutrient parameters. Study of Tables 5.2 and 5.3 and comparison with the
sample parameter input listings in Appendix A should clarify the ordering of
the parameter input sequence for any desired simulation run.
As in Table 5.1, the brackets in Tables 5.2 and 5.3 indicate parameters
added to Version II of the ARM Model, parentheses indicate parameters whose
application/definition have been modified, and the modifications are
described in footnotes in the tables.
The first two lines of the input sequence provide space for specifying the
watershed name, pesticide or chemical name, and other information describing
the model run. Next, the control parameters described above and three
control namelists (CNTL, STRT, ENDD) are input.
Next in sequence are the five hydrologic parameter namelist statements
(LND1, LND2, LND3, LND4, and LND5). If snowmelt simulation is specified by
the SNOW control parameter (SNOW=YES), the next parameter is SNOWPRINT= (YES
or NO) followed by the four snow namelist statements (SN01, SN02, SN03, and
SN04). SNOWPRINT=^JO suppresses the printing of hourly snowmelt output in
the form of daily tables (Appendix B) .
The hydrology and snow namelists are followed by the sediment namelist
statements (CROP, MUD1, MUD2, MUD3, and SMDL). If neither pesticides nor
nutrients are being simulated, SMDL is the final namelist statement in the
input sequence before the meteorologic data. However, if pesticide
simulation is to be performed, the SMDL namelist is followed by the title
word PESTICIDE (starting in column 1), the pesticide parameters APMODE=
(SURF or SOIL), DESORP=(YES or NO), and the pesticide namelist statements
(PSTR, PST1, PST2, PST3, AMDL, DEGD, DEGY, DEGR). If nutrient simulation is
not also being performed, the soil namelist statement, DPTH, follows the
DEGR namelist. Otherwise the nutrient parameters follow DEGR. The DPTH
namelist is required for either pesticide or nutrient simulation. This
completes the parameter input sequence for hydrology, sediment, and
pesticides.
38
</pre><hr><pre>
-------
CNTL
STRT
ENDD
LND1
(LND2)
TABLE 5.2 ARM MODEL (VERSIONS I AND II) INPUT SEQUENCE
AND PARAMETER ATTRIBUTES
(Excluding Nutrient Input and Parameters)
Namelist Parameter Type
Name Name
English Units
Metric Units
Watershed name (up to 72 characters)
Chemical name and/or run information (up to 80 characters)
HYCAL character
INPUT character
OUTPUT character
PRINT character
SNOW character
PEST character
NUTR character
ICHECK character
[DISK] character
[IDEBUG] character
[CHAR] character
[TITLE] (up to 80 characters)
[DSNFLO] integer
[DSNERO] integer
[DSNROS] integer
[ENDDISK] character (string ENDDISK indicates end of informa-
tion for writing to disk)
minutes
cubic meters/sec
hectares
INTRVL
HYMIN
AREA
BQJDAY
BGNMCN
BGNYR
ENDDAY
ENDMON
ENDYR
UZSN
UZS
LZSN
LZS
L
SS
NN
A
EPXM
PETMUL
integer
real
real
integer
integer
integer
integer
integer
integer
real
real
real
real
real
real
real
real
real
real
minutes
cubic i
acres
inches
inches
inches
inches
feet
inches
millimeters
millimeters
millimeters
millimeters
meters
millimeters
(continued)
39
</pre><hr><pre>
-------
TABLE 5.2 (continued)
Namelist
Name
b
(LND3)
(IM)4)b
(LND5)b
Parameter
Name
(K3)c
INFIL
INTER
IRC
K24L
KK24
K24EL
SGW
GWS
KV
ICS
OFS
IFS
Type
real
real
real
real
real
real
real
real
real
real
real
real
real
English Ifriits
(12 monthly values)
inches/hour
inches
inches
inches
inches
[SNCWPRINT character]
SNO1
SN02
SN03
SN04
CROP
(MJDlf'e
[MUD2]
RADCCN
CCFAC
SCF
ELDIF
IDNS
F
DGM
we
MPACK
EVAPSN
MELEV
TSNOW
PACK
DEPTH
PETMIN
PETMAX
WMUL
muL
KUGI
COVPMO
(TIMTIL^
(YRTIL)d
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
real
integer
real
integer
integer
1000 feet
inches/day
inches
feet
degrees F
inches
inches
degrees F
degrees F
days (12 values)
year (12 values)
Metric Units
millimeters/hour
millimeters
millimeters
millimeters
millimeters
kilometers
millimeters/day
millimeters
meters
degrees C
millimeters
millimeters
degrees C
degrees C
days (12 values)
year (12 values)
(continued)
40
</pre><hr><pre>
-------
TABLE 5.2 (continued)
Namel 1st
Name
[MUD3]
SMDL
[PSTR]
[PSTl]
[PST2]
[PST3]
AMDL
[DEGD]
[DEGY]
[DEGR]
Parameter
Name
(SRERTL) d
JRER
KRER
JSER
KSER
SRERI
[SCMPAC]
PESTICIDE
APMCDE
DESORF
[PSSZ]
[PSUZ]
[PSLZ]
[PSGZ]
(TIMAP) f
(YEARAP)f
(SSTR) f
CMAX
DD
K
N
NP
[DDG]
[YDG]
(KDG)g
Type
real
real
real
real
real
real
real
character
character
character
real
real
real
real
integer
integer
real
real
real
real
real
real
integer
integer
real
***NUTRIENT PARAMETERS (Table
[LZTP]
[RETP]
[DPTH]
[LZTEMP]
[ASZT]
[BSZT]
[AUZT]
[BUZT]
(SZDPTH) e
(UZDPTH) e
real
real
real
real
real
real
real
English Units
tons/acre (12 values)
tons/acre
per day
pounds/acre
pounds/acre
pounds/acre
pounds/acre
day
year
pounds/acre
pounds/pound
Ibs. pesticide/
Ibs. soil
day
year
per day
Metric Units
tonnes/hectare
(12 values)
tonnes/hectare
per day
kilograms/hectare
kilograms/hectare
k i log r ams/hec tar e
kilograms/hectare
day
year
kilograms/hectare
kilogramsAg
kgs. pesticide/
kgs. soil
day
year
per day
5.3) ARE INPUT HERE WHEN NUTR=YES ***
degrees F
inches
inches
degrees C
millimeters
millimeters
(continued)
41
</pre><hr><pre>
-------
TABLE 5.2 (continued)
Namelist Parameter Type English Units Metric Units
Name Name
L real pounds/cubic ft grams/cubic cm
(BUDZ)" real pounds/cubic ft grams/cubic cm
(BUDZ) real pounds/cubic ft grams/cubic cm
[UZF] real
[LZF] real
, [ ] and ( ) have the same meaning as in Table 5.1.
In Version I, the hydrologic namelists and parameters are:
LND1 - UZSN, UZS, LZSN, LZS
LND2 - L, SS, NN, A, K3 EPXM
LND3 - INFIL, INTER, IRC, K24L, KK24, K24EL
LND4 - SGW, GWS, KV, ICS, OFSr IFS
,In Version I, K3 is a single annual value.
In Version I, TIMTIL, YRTIL, and SRERTL are contained in namelist MUD1 and
can contain up to five values each.
In Version I, namelist DIRT follows the namelist MUD1 and contains
^parameters SZDPTH, UZDPTH, and BULKD.
In Version I, TIMAP, YEARAP, and SSTR describe a single pesticide
application and are contained in namelist AMDL.
^In Version I, a single pesticide degradation rate parameter DEGCON is
, contained in the namelist DEG1 which follows namelist AMDL.
In Version I, a single soil bulk density parameter, BULKD, replaces
BDSZ, BDUZ, and BDLZ, and is contained in the namelist DIRT (note e).
42
</pre><hr><pre>
-------
5.2.1 Nutrient Parameter Input Sequence
When NUTR=YES, the block of nutrient parameters follows the DEGR namelist if
both nutrients and pesticides are simulated, or the SMDL namelist if only
nutrients are simulated. Reference to Table 5.3 and the sample parameter
input sequences in Appendix A is important to understanding the nutrient
input sequence.
The sequence begins with the title word NUTRIENTS (in column 1) and is
followed by the nutrient namelist statements (NUTRIN, PLANTU, PLANTL).
Except for the soil namelist statements (LZTP, RETP, DPTH, in Table 5.2),
the remaining input of nutrient parameters is done under format control.
Also, character strings are input and checked by the program to verify the
accuracy of the input sequence. The section begins with the character
string REACTION RATES and then the words NITROGEN or PHOSPHORUS to indicate
which rates are being input. First order reaction rates may be input for
both nitrogen and phosphorus chemical and biological transformations.
Separate rates are allowed for the four soil zones; SURFACE, UPPER, LOWER,
and GROUNDWATER.
Following the character string NITROGEN, the word SURFACE appears on the
next line; then eight reaction rates are listed in F8.0 format on the
following line. These reaction rates refer to the various nitrogen forms
described in Table 5.3. Following the surface rates, the word UPPER appears
in column 1, and the reaction.rates for the upper zone are input on the next
line. Lower zone and groundwater rates follow in a similar manner. The
words TEMPERATURE COEFFICIENTS appear after the groundwater rates and the
following line contains the eight constants used for correcting the
corresponding reaction rates for nonoptimal temperatures.
Phosphorus reaction rates and temperature coefficients are input in a
similar manner except that there are only five reaction rates appearing in
an F8.0 format (Appendix A). The word END terminates input of reaction
rates. Specifying nitrogen or phosphorus rates is optional, and if values
are not given, the program will default the rates to 0.0.
The next section of nutrient input specifies the initial nitrogen,
phosphorus, and chloride concentration present in the four soil layers. The
word INITIAL begins this section; title words are used in the manner
described above. The seven different nitrogen forms, four phosphorus forms
and chloride may be initialized as described in Table 5.3. Nutrient
concentration is input by soil layer. If initial values are not given for
the nitrogen, phosphorus, or chloride forms, the program defaults them to
0.0. The character string END terminates the initialization section.
The final section of the nutrient input sequence indicates the date and
amount of application of nutrients during the simulation period. Each
nutrient application begins with the word APPLICATION followed by the Julian
day of application (for example, 136 in Table A3). The words following
indicate which constituents are to be applied: NITROGEN, PHOSPHORUS, or
CHLORIDE. Below the constituent type, the application amounts are entered
for each form for the surface and upper zone only. The character string END
43
</pre><hr><pre>
-------
TABLE 5.3 AW MODEL (VERSION I AND II) NUTRIENT PARAMETER INPUT SEQUENCE AND ATTRIBUTES
Block,
NUTRIENT
Section & Name
Subsection
&NUTRIN
TSTEP
Type
Character
Character
Integer
[&PLANTU]
[&PLANTL]
NAPPL
TIMHAR
SEND
[ULPTK]
SEND
[LZ'JPTK]
SEND
Integer
Integer
Character
Character
Real
Character
Character
Real
REACTION RATES
NITROGEN
SURFACE
(continued)
(XI)b
(KD)b
(KPL)1
Column Units
Position English Metric
1-8
2-8
Any minutes minutes
Any
Any
Any
2-8
Any
Any
2-8
Any
Character
Character
Character
Character
Real
Real
Real
Any
1-14
1-8
1-7
1-8
9-16
17-24
day day
per day per day
per day per day
per day per day
Comments.
Name to indicate start of
nutrient input sequence.
Namelist name of nutrient
control information.
Length of timestep for
chemical and biological
transformations. There must
be an even number of time
steps in a day, and an even
number of simulation intervals in
a TSTEP. Range = 5 or 15 to 1440.
Number of nutrient applications
over a year of simulation.
Values may range from 0 to 5.
Time of plant harvesting,
Julian day of the year.
Value may range from
0 to 366.
Indicate end of namelist statement
Namelist name for upper
layers plant uptake informations.
12 values of fraction
of maximum monthly crop
uptake cf nutrients, should
be 1.0 or less.
Indicate end of namelist statement
Narnelist name for lower
zone plant uptake information.
12 values oi: fraction
of maximum monthly crop
uptake should be 1.0 or less.
Indicate end of namelist statement
Name to indicate start of
nutrient input sequence.
Indicates nitrogen reaction
rate will follow.
Surface layer reaction
rates follow.
Oxidation rate of solution
ammonium to nitrite and nitrate.
Reduction rate of nitrite
and nitrate to gaseous nitrogen.
Uptake of nitrate by plants.
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
01
(Continued)
Section & Name
Subsection
KAM
KIM
KKIM
KSA
KAS
UPPER ZCNE
(Kl)b
(KD)b
(KPL)b
KAM
KIM
KKIM
KSA
KAS
DOWER ZONE
(Kl)b
(KD)b
(KPL)b
KAM
KIM
KKIM
KSA
Type
Real
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Real
Real
Column
Position
25-32
33-40
41-48
49-56
57-64
1-10
1-8
9-16
17-24
25-32
33-40
41-48
49-56
57-64
1-10
1-8
9-16
17-24
25-32
33-40
41-48
49-56
Units
Enqlish Metric
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
Comments
Ammonification or mineralization
rate of organic-N to ammonium.
Immobilization rate of solution
ammonium to organic-N.
Immobilization rate of nitrate
and nitrite to organic-N.
Transfer rate of ammonium from
solution to adsorbed (adsorption).
Transfer rate of ammonium from
adsorbed to solution (desorption).
Upper zone reaction rates follow.
Oxidation rate of solution
ammonium to nitrite and
nitrate.
Reduction rate of nitrite and
nitrate to gaseous nitrogen.
Uptake of nitrate by plants.
Ammonification or mineralization
rate of organic-N to ammonium.
Imrobilization rate of solution
ammonium to organic-N.
Immobilization rate of nitrate
and nitrite to organic-N.
Transfer rate of ammonium from
solution to adsorbed (adsorption).
Transfer rate of ammonium from
adsorbed to solution {desorption),
Lower zone reaction rates folow.
Oxidation rate of solution
ammonium to nitrite and
nitrate.
Reduction rate of nitrite
and nitrate to gaseous nitrogen.
Uptake of nitrate by plants.
Ammonification or mineraliza-
tion rate of organic-N to
ammonium.
Immobilization rate of dissolved
ammonium to organic-N.
Immobilization rate of nitrate
and nitrite to organic-N,
Transfer rate of ammonium from
solution to adsorbed (adsorption)
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
(continued)
Section &
Subsection
Name
KAS
GRQUNDWATER
(Kl)b
(KD)b
(KPL)b
RAM
KtM
KKIM
KSA
KAS
TEMPERATURE
COEFFICIENTS
PHOSPHORUS
SURFACE
(THK1)C
(THKD)°
THKPL
THKAM
THKIM
THKKIM
THKSA
THKAS
Typg^
Real
Character
Real
Real
Real
Real
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Real
Real
Real
Character
Character
Column
Position
57-64
1-11
1-8
9-16
17-24
25-32
33-40
41-48
49-56
57-64
1-23
1-8
9-16
17-24
25-32
33-40
41-48
49-56
57-64
1-10
1-7
Units
Enqlish Metric
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
per day per day
Comments
Transfer rate of ammonium from
adsorbed to solution (desorption).
Groundwater reaction rates follow.
Oxidation rate of solution
ammonium to nitrite and
nitrate.
Reduction rate of nitrite
and nitrate to gaseous nitrogen.
Uptake of nitrate by plants.
Ammonification or mineraliza-
tion rate of organic-N to
ammonium.
Immobilization rate of solution
ammonium to organic-N.
Immobilization rate of nitrate
and nitrite to organic-N.
Transfer rate of ammonium from
solution to adsorbed (adsorption).
Transfer rate of ammonium from
adsorbed to solution (desorption).
Temperature coefficients for
reaction rates.
Temperature coefficients for
corresponding nitrogen
reactions, should be greater
than or equal to 1.0.
Indicates phosphorus
reaction rates will follow.
Surface layer reaction
rates.
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
Section &
Subsection
UPPER ZONE
DOWER ZONE
GRDUNDWATER
Name
KM
KIM
KPL
KSA
KAS
KM
KIM
KPL
KSA
KAS
KM
KIM
KPL
KSA
KAS
KM
Type
Real
teal
Real
Real
Real
Character
Real
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Character
Real
Column
Position
1-8
9-16
17-24
25-32
33-40
1-10
1-8
9-16
17-24
25-32
33-40
1-10
1-8
9-16
17-24
25-32
33-40
1-11
1-8
Units
English
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
Metric
per oV-iy
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
(continued)
Cortments
Mineralization rate of
Organic-P to solution phosphate
Immobilization rate of
solution phosphate to Organic-P.
Uptake of phosphate in solution.
by plants.
Transfer rate of phosphate
Crom solution to adsorbed.
Transfer rate of phosphate
from adsorbed to solution.
Upper zone reaction rates
follow.
Mineralization rate of
Organic-P to solution
phosphate.
Immobilization rate of
solution phosphate to Organic-P.
Uptake of phosphate in solution.
by plants.
Transfer rate of phosphate
from solution to adsorbed.
Transfer rate of phosphate
from adsorbed to solution.
Lower zone reaction rates
follow.
Mineralization rate of
Organic-P to solution phosphate.
Immobilization rate of
dissolved P04-P to Organic-P.
Uptake of phosphate
in solution by plants.
Transfer rate of phosphate
from solution to adsorbed.
Transfer rate of phosphate
from adsorbed to solution.
Lower zone reaction rates
follow.
Mineralization rate of
Organic-P to solution
phosphate.
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
OO
END
Section & Name
Subsection — — —
KIM
KPL
KSA
KAS
TEMPERATURE
COEFFICIENTS
THKM
THKIM
THKPL
THKSA
THKAS
Type
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Character
Column
Position
9-16
17-24
25-32
33-40
1-23
1-8
9-16
17-24
25-32
33-40
1-3
Units
English Metric
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
per day
INITIAL
NITROGEN
SURFACE
NBLK
Character
Character
Character
Integer
1-7
1-8
1-7
16
Comments
Immobilization rate of
solution phosphate to Organic-P.
Uptake of phosphate
in solution by plants
Transfer rate of phosphate
from solution to adsorbed.
Transfer rate of phosphate
from adsorbed to solution.
Temperature coefficients
for reaction rates.
Temperature coefficients
for phosphorus reactions,
should be greater than or
equal to 1.0.
'END1 terminates input of
rates. Nitrogen and phosphorus
rates are optional, program
defaults them to 0.0 if not
specified.
Initialization of soil
constituents follows.
Initial nitrogen forms follow.
Surface layer initialization
follows.
Number of blocks which will be
input. 0 or 1 indicate the
average concentration over the
surface layer in input on one
line, and NBLK=5 means five lines
of input follow, one line per
block. Only 0,1,5 allowed.
A blank in col. 16 is read as 0.
(continued)
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
Section S Name Type Column Units
Subsection Position English Metric
Conrnents
OKG-N
Real
NH4-S Real
NH4-A Real
(N02 + N03)d Real
N2 Real
PLNT-N Real
UPPER ZONE Character
NBLK Integer
QKG-N
NH4-S
NH4-A
Real
Real
Real
1-8 Ib/ac kg/ha Potentially mineraiizabie or
total organic nitrogen.
9-16 Ib/ac kg/ha Ammonium in solution
17-24 Ib/ac kg/ha Ammonium adsorbed to soil.
25-32 Ib/ac kg/ha Nitrite and nitrate
33-40 Ib/ac kg/ha Gaseous nitrogen from denitrification.
41-48 Ib/ac kg/ha Plant nitrogen
1~10 Upper zone initialization
follows.
16 Nmnber of blocks which will be
input. 0 or 1 indicate the
average concentration over the
surface layer in. input on one
line, and NBLK=5 means five lines
of input follow, one line per
block. Only 0,1,5 allowed.
A blank in col. 16 is read as 0.
1-8 Ib/ac kg/ha Potentially mineraiizabie or
total organic nitrogen.
9-16 Ib/ac kg/ha Ammonium in solution
17-24 Ib/ac kg/ha Ammonium adsorbed to soil.
(N02 + N03) Real »-
N2 Real
PLNT-N Real
LOWER ZONE Character
OPG-N Real
(continued)
NH4-S
Real
25-32 Ib/ac kg/ha Nitrite and nitrate
33-40 Ib/ac kg/ha Gaseous nitrogen from denitrification.
41-48 Ib/ac kg/ha Plant nitrogen
1-10 Lower zone initialization.
1-8 Ib/ac kg/ha Potentially mineraiizabie or
total organic nitrogen.
9-16 Ib/ac kq/ha Ammonium in solution
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
Section &
Subsection
GROUNDWATER
PHOSPHORUS
SURFACE
UPPER ZONE
Name
NH4-A
(N02 + N03)d
N2
PLNT-N
ORG-N
NH4-S
NH4-A
(N02 + N03)
K2
PLNT-N
NBUC
ORG-P
P04-S
P04-A
PLNT-P
NBLK
ORG-P
Type
Real
Real
Real
Real
Character
Real
Real
Real
Real
Real
Real
Character
Character
Integer
Real
Real
Real
Real
Character
Integer
Real
Column
Position
17-24
25-32
33-40
41-48
1-11
1-8
9-16
17-24
25-32
33-40
41-48
1-10
1-7
16
1-8
9-16
17-24
25-32
1-10
16
1-8
Units
English Metric
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
Comments^
Ammonium adsorbed to soil.
Nitrite and nitrate
Gaseous nitrogen from denitrification.
Plant nitrogen
Groundwater zone initialization.
Potentially mineralizable or
total organic nitrogen.
Ammonium in solution
Ammonium adsorbed to soil.
Nitrite and nitrate.
Gaseous nitrogen from denitrification.
Plant nitrogen
Initial phosphorus forms follow.
Surface layer.
Number of blocks which will
be input.
Organic phosphorus.
Phosphate in solution.
Phosphate adsorbed or combined.
Plant phosphorus.
Upper zone phosphorus
initialization.
Number of blocks which will
be input.
Organic phosphorus.
(continued)
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
APPLICATION
Section &
Subsection
LONER ZONE
GROUNDKATER
Name
P04-S
P04-A
PLNT-P
ORG-P
P04-S
P04-A
PLNT-P
ORG-P
P04-S
P04-A
Type
Real
Real
Real
Character
Real
Real
Real
Real
Character
Real
Real
Real
Character
Column
Position
9-16
17-24
25-32
1-10
1-8
9-16
17-24
25-32
1-11
1-8
9-16
17-24
l&ll.
Units
English Metric
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
Conments
Phosphate in solution.
Phosphate adsorbed or combined.
Plant phosphorus.
Lower zone initialization.
Organic phosphorus.
Phosphate in solution.
Phosphate adsorbed to soil.
Plant phosphorus.
Groundwater initialization.
Organic phosphorus.
Phosphate in solution.
Phosphate adsorbed or combined.
Name to indicate start .of
APDAY
NITROGEN
SURFACE
NBLK
ORG-N
(continued)
Integer
14-18
Character 1-8
Character 1-7
Integer 16
Real
1-8
Ib/ac kg/ha
nutrient application section,
expected number of applications
is greater than 0.
Application day of the year
(Jul ian Day).
Nitrogen applications follow.
Surface applications follow.
Number of blocks which will be
input, 0 or 1 indicate one
line follows containing the
average application over the
watershed. A 5 indicates
five lines follow, one line
for each block.
Potentially mineralizable or
total organic nitrogen
applied.
</pre><hr><pre>
-------
Table 5.3 {continued)
Block
ui
NJ
(continued)
.Section & .Name
Subsection
NH4-S
NH4-A
(N02 + N03)d
N2
PLNT-N
UPPER ZONE
NBLK
ORG-N
NH4-S
NH4-A
(N02+N03)d
N2
PLNT-N
PHOSPHORUS
"SURFACE
NBLK
ORG-P
PO4-S
PLNT-P
Type
Real
Real
Real
Real
Real
Character
Integer
Real
Real
Real
Real
Real
Real
Character
Character
Integer
Real
Real
Real
Column .Units
Position English Metric
9-16
17-24
25-32
33-40
41-48
1-10
16
1-8
9-16
17-24
25-32
33-40
41-48
1-10
1-7
16
1-8
9-16
17-24
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
Ib/ac
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
Garments.
Ammonium in solution.
Ammonium adsorbed to soil.
Nitrite and .Nitrate
Gaseous nitrogen from
denitrification
Plant nitrogen
Upper zone applications follow
Nurnber of blocks which will
be input.
Potentially mineralizable or
total organic nitrogen
applied.
Ammonium in solution.
Ammonium adsorbed to soil.
Nitrite and nitrate
Gaseous nitrogen from
denitr ification.
Plant nitrogen.
Note: nutrients can only
be applied to surface and
upper zone.
Phosphorus applications follow.
Surface layer application
Number of blocks which will be
input.
Organic phosphorus.
Phosphate in solution.
Phosphate adsorbed or
combined.
</pre><hr><pre>
-------
Table 5.3 (continued)
Block
Section &
Subsection
Name
Type
Column Units
Position English Metric
Garments
UPPER ZONE
NELK
ORG-P
PO4-S
P04-A
END
Character
Integer
Real
Real
Real
1-10
16
1-8
9-16
17-24
Ib/ac kg/ha
Ib/ac kg/ha
Ib/ac kg/ha
PIMT-P
CHLORIDE
SURFACE
NBLK
CL
UPPER ZONE
NBLK
CL
Real
Character
Character
Integer
Real
Character
Integer
Real
Character
25-32
1-8
1-7
16
1-8
1-10
16
1-3
1-3
lt)/ac
Ib/ac
Ib/ac
Ib/ac
kg/ha
kg/ha
kg/ha
kg/ha
Upper zone .application.
Number of blocks which will
be input.
Organic phosphorus.
Phosphate in solution.
Phosphate adsorbed or •
combined.
Plant phosphorus.
Chloride applications follow.
Surface layer application.
Number of blocks which will
be input.
Chloride applied.
Upper zone applications.
Number of blocks which will
be input.
Chloride applied.
"END" terminates input of
applications for that day.
NOTE: Nitrogen, phosphorus
and chloride do not need to
be specified in input sequence
if none are applied that day.
Program defaults all applica-
tions to 0.0.
[ ] and ( ) have the same meaning as in Table 5.1.
In Version I, the K2 (oxidation of N02 to NO,) and KK2 (reduction to NO, to NO,) reaction rates are input
the Kl rate. In Version II, these transformations have been eliminated^with a resulting modification to
meaning of the Kl, KD, and KPL rates (see Table 5.1).
In Version I, THK2 and TKKK2 follow THK1. See note b.
In Version I, separate values for N02 and N03 follow the value for NH4-A.
after
the
</pre><hr><pre>
-------
terminates the input of each separate nutrient application. For multiple
applications, the sequence is repeated with the character string APPLICATION
and the Julian day of application. Applications must be sequential with the
first one applied in the year appearing first in the input sequence. The
application section is followed by the soil namelist statements (LZTP, RETP,
DPTH) shown in Table 5.2. This completes the nutrient parameter input
sequence.
5.3 PARAMETER EVALUATION GUIDELINES
Guidelines for evaluating the ARM Model parameters relating to hydrology,
snowmelt, sediment, pesticide, and nutrient simulation are provided below.
The simulation control parameters are described by their definition in Table
5.1 and discussed in Section 5.1.1. Also, guidelines are provided below for
obtaining initial values of the calibration parameters. However, precise
evaluation of these parameters can only be obtained through calibration
procedures discussed in Section 6.
5.3.1 Hydrology Parameters
A A is the fraction representing the impervious area in the
watershed. Usually A will be negligible for agricultural
watersheds, except in cases of extensive rock outcrops
along channel reaches.
HYMIN HYMIN is a control parameter representing the minimum flow
above which storm output is printed, and should be chosen to
include the significant portion of the storm hydrograph and
pollutant graph. Investigation of recorded storm hydrographs
and pollutant graphs will indicate an appropriate value of
HYMIN. Also, a large value for HYMIN will prevent printing
of storm output during calibration runs.
, EPXM This interception storage parameter is a function of
cover density, and represents the maximum interception
attained during the year. The following values are expected:
grassland 0.10 in. 2.5mm
cropland (maximum canopy) 0.10-0.25 in. 2.5-6.5 mm
forest cover (light) 0.15 in. 3.5 mm
forest cover (heavy) 0.20 in. 5.0 mm
The effective interception on any day is calculated in the
model as a function of crop canopy. It is equal to EPXM
times the fraction of maximum canopy on that day:
interception (Day T) = EPXM * Canopy (Day T)
Maximum Canopy
UZSN The nominal storage in the upper zone is generally
related to LZSN and watershed topography. However,
54
</pre><hr><pre>
-------
LZSN
K3
K24L, K24EL
INFIL
agriculturally managed watersheds may deviate significantly
from the following guidelines:
low depression storage, steep slopes, limited
vegetation 0.06*LZSN
moderate depression storage slopes and vegetation 0.08*LZSN
high depression storage, soil fissures, flat
slopes, heavy vegetation
0.14*LZSN
The nominal lower zone soil moisture storage parameter is
related to the annual cycle of rainfall and
evapotranspiration. Approximate values range from 5.0 to
20.0 in. (125 to 500 mm) for most of the continental United
States depending on soil properties. Figure 5.1 presents an
approximate mapping of LZSN values for the United States.
This map was obtained by overlaying climatic, topographic,
physiographic, and soils information with LZSN values for
watersheds calibrated with various versions of the Stanford
Watershed Model hydrologic algorithms. Ihe watershed
locations are shown in Figure 5.2 and listed in Table 5.4
with various watershed characteristics and calibrated
parameter values. Since Figure 5.2 shows that many areas of
the country have few calibrated watersheds, Figure 5.1 and
Table 5.4 should be used with caution. Initial values of
LZSN can be obtained from this information, but the proper
value will need to be checked by calibration.
As an index to actual evapotranspiration, K3 affects
evapotranspiration from the lower soil moisture zone. The
area covered by forest or deep rooted vegetation as a
fraction of total watershed area is an estimate of K3.
Values generally range from 0.25 for open land and grassland
to 0.7-0.9 for heavy forest. Version II of the AKM Model
accepts 12 monthly values of K3 to better represent the
seasonal variations of actively transpiring vegetation on
agricultural cropland.
These parameters control the loss of water from near
surface or active groundwater storage to deep percolation
and transpiration, respectively. K24L is the fraction of the
groundwater recharge that percolates to deep groundwater
table. Thus a value of 1.0 for K24L would preclude any
groundwater contribution to streamflow and is used on
small watersheds without a base flow component from ground-
water. K24EL is the fraction of watershed area where shallow
water tables put groundwater within reach of vegetation.
This parameter is an index to the mean infiltration rate
on the watershed and is generally a function of soil
characteristics. INFIL can range from 0.01 to 1.0 in./hr
55
</pre><hr><pre>
-------
Ul
(Ti
LZSN
(INCHES)
+ -H
5.0
6.0
40-60 4.0-ELEVATIONS ABOVE 1000-2000FT.
6.0- LOWER ELEVATIONS
7.0
80-140 8.0-LOWER ELEVATIONS
14.0- HIGHER ELEVATIONS
Figure 5.1 Nominal lower zone soil moisture (LZSNT parameter map
</pre><hr><pre>
-------
Ul
-J
Figure' 5.2 Watershed locations for calibrated LANDS parameters
</pre><hr><pre>
-------
TABLE 5.4 WATERSHEDS WITH CALIBRATED LANDS PARAMETERS
en
oo
Watershed Information
No.
1
2
3
4
5
6
7
3
9
10
11
12
13
14
1-i
16
17
18
19
General Location
Seattle, Uashington
Spokane, WA
Aschoft, Oregon
Whites on, Oregon
Central Sierra
Snowlab, CA
between Chico and
Flenmiing, CA
Cloverdale, CA
Napa, CA
Lurlingame, CA
Santa Cruz, CA
San i'lateo Co, CA
Santa Ynez, CA
Santa (laria, CA
Goleta, CA
Santa Ynez, CA
Los Angeles, CA
Pasadena, CA
Upper Columbia
Snowlab, f!T
Denver, CO
30 mi . south of
Denver, CO
Name
Lower Green R
Middle Green R
Upper Green R
Lake Washington
Little Spokane R
bull Run
South YaRihill R
Upper Castle Creek
N Fork Feather R
Dry Creek
Dry Creek
Col ma Creek
Branci forte Creek
Denniston Creek
Sisquoc River
Santa Maria River
San Jose Creek
Santa Ynez River
Echo Park
Arroyo Seco
Sky land Creek
South Platte R
Cherry Creek
Area
(sq mi)
107
502
3.96
300
878
14.4
10.8
17.3
3.6
281
2.38
5.5
895
0.4
16
8.1
69
Type
plains, rural
rural , steep
forest
rural , rocky
forest
rural , steep
forest
rural , moderate
slope, chaparral
rural , moderate
slope, chaparral
urban , moderate
slopes
rural
rural , steep
chaparral
rural , steep
light chaparral
urban, flat
slopes
rural , steep
rural , steep
urban, steep
residential
urban, steep
rural , steep
rural , moderate
slope, grasses
rural , moderate
LANDS Parameters
ftodel
HSP
HSP
HSP
HSP
HSP
HSP
nus
NWS
HSP
SUM V
HSP
HSP
HSP
SUM IV
HSP
HSP
HSP
HSP
HSP
HSP
NWS
HSP
HSP
UZSN
3.0
1.15
0.9
0.5
0.56
0.75
1.20
0.70
0.8
0.8
0.8
0.25
1.0
0.95
0.7
0.3
0.5
0.74
0.04
0.20
1.83
0.1
0.8
LZSN
12.0
9.5
14.0
8.0
7.0
14.0
5.3
9.0
12.0
15.0
12.0
12.0
16.0
12.7
8.5
5.0
10.0
8.3
5.0
7.0
10.7
0.7
7.0
INFIL
0.06
0.10
0.05
0.05
0.20
0.08
0.24
0.08
0.12
0.03
0.025
0.07
0.04
1.35
0.18
0.02
0.03
0.035
0.03
0.05
0.071
0.03
0.005
•I;1TER
10.0
3.0
11.5
10.0
15
3.5
0.5
0.67
2.5
1.8
2.5
2.0
2.5
2.0
1.5
1.4
3.5
1.5
0
1.2
5.6
1.0
3.0
Comments
POKER=0.37
POWER=1.5
POWER=0.83
(continued)
</pre><hr><pre>
-------
TABLE 5.4 (continued)
Ul
vo
Watershed Information
No.
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
General Location
Sporry, OK
Austin, TX
bryon, TX
Lannesboro, HIJ
Rock Rapids, I A
Iowa City, IA
St. Janies, I'O
Steel ville, tK)
.lettleton, 130
Collins, HI
Chicago, IL
Morthbrook, IL
Chatnpaign/Urbana, IL
Selkirk, MI
Springfield, Oil
Green Lick
Reservoir, PA
Frederic, ID
E of Washington U.C.
in i1D
Rosman, NC
Swannanoa, fIC
Blairsville, GA
Fayetteville, GA
Alna, GA
Danville, VT
Passumpic, VT
.lame
Bird Creek
'Jailer Creek
Burton Creek
Root River
Rock River
Rapid Creek
Bourbeuse River
fjeramec River
Town Creek
Leaf River
North Branch,
Chicago River
U Fork N Branch
Chicago River
Boneyard Creek
S Branch Shepards
Creek
lad River
Green Lick Run
Monocacy River
W Branch of
Patuxent River
French Broad R
Seetree Creek
ilottcly River
Camp Creek
Hurricane Creek
Sleepers River
Passumpsic River
Area
(sq mi)
905
6.5
1.3
625
708
25.3
21.3
781
617
752
100
11.5
3.6
1.2
490
3.1
817
30.2
67.9
5.5
74.8
17.2
150
3.2
436
Tyoe
slope, grassland
urban, moderate
urban, flat
urban, flat,
rural
urban, flat
slope
rural , flat
rural , limestone
forest
rural
rural , forest
mountains
urban, hilly
forests
rural , forested
rural
rural
LANDS Parameters
•iodel
NWS
HSP
HSP'
NWS
MviS
HSP
HSP
NWS
NHS
MS
HSP
HSP
HSP
HSP
N'.IS
HSP
flWS
HSP
NWS
HSP
tws
tiws
NWS
NWS
,-IWS
UZSN
1.38
1.0
0.3
0.2
0.75
0.5
0.75
1.2
0.44
0.05
1.4
1.40
0.80
1.0
0.41
1.0
1.2
1.2
0.01
0.30
0.02
0.5
0.2
0.25
0.15
LZSN
10.0
8.0
5.0
5.0
4.0
7.0
5.0
12.7
7.35
7.5
7.5
7.5
7.5
5.0
4.1
8.0
1.75
7.0
5.30
3.0
3.4
5.0
2.0
4.55
5.0
INFIL
0.048
0.04
0.02
0.08
0.02
O.C35
0.02
0.043
C.066
0.33
0.18
0.18
0.05
0.04
0.125
0.007
0.058
0.02
0.8
0.10
0.45
0.16
0.13
0.40
0.33
II4TER
0.67
1.25
1.5
0.5
1.4
3.5
1.0
1.05
0.89
0.37
3.5
3.0
2.0
1.0
0.83
1.0
1.0
2.0
0.25
30
2.5
0.75
2.6
0.25
0.9
Comments
POWER=0.78
POUER=2.0
POUER=2.5
POWER=1.56
POWER=2.G
POWER=2.85
POWF.R=0.40
POWER=0.30
PCWEP.=0.3f.
POUER=2.0
POWER=2.0
POl.'ER=2.0
POWER=3.0
P0'0=3.0
(continued)
</pre><hr><pre>
-------
TABLE 5.4 (continued)
Watershed Information
Ho.
46
47
48
50
51
General Location
West Hartford, VT
Grafton, VT
Bath, IIH
Plymouth, ilH
Knightsville Cam, MA
others
52
53
54
55
56
57
Fairbanks, AK
Seattle, WA
Spokane, WA
Santa Cruz, CA
Ingham, Co. MI
Athens, GA
,Jarne
jjlhite River
Saxton River
Ammonoosuc River
Pemigewasset River
Sykes Brook
Chena River
Issaquah Creek
Hangman Creek
Neary's Lagoon
Deer Creek
Southern Piedmont
Area
(sq mi)
690
72.2
395
622
1.6
1980
55
54
1.0
lf>. 3
0.01
Type
rural
rural
rural
rural , steep
hoavy forest
agriculture
urban, steep
rural , flat
agriculture
small plot
watersheds
RANGES
LANDS Parameters
Model
NWS
SWM V
HWS
NWS
HSP
.'•IMS
HSP
HSP
HSP
HSP
PTR
UZSN
0.25
0.8
0.3
0.25
1.2
0.05
1.12
0.50
0.80
1.5
0.05
0.01-3.0
LZSH
5.0
8.0
5.0
5.0
8.0
5.0
14.0
7.0
11.0
r..o
18.0
1.75-18
INFIL
0.15
0.05
0.12
0.22
0.03
0.08
0.03
0.02
0.04
0.05
0.5
.005-
1.35
INTER
1.3
2.0
0.65
0.53
1.0
0.25
7.0
3.5
2.5
2.0
0.7
11.5-
25
Comments
POUER=0.95
P011ER=1.50
POWER=2.08
POUER=1.0
b.
HSP Hydrocomp Simulation Program
Sim IV Stanford Watershed ftodel IV
SHU V Stanford Watershed llodel V
HWS National Weather Service I'odel
PTR Pesticide Transport and Runoff Model
HSP and the SWM Models use a value of 2.0 in the infiltration function
while the NUS Model allows the user to specify this value with the POWER parameter.
values of POWER are indicated in the comments column.
The
</pre><hr><pre>
-------
depending on the cohesiveness and permeability of the soil.
Initial values for INFIL can be obtained by reference to the
hydrologic soil groups of the Soil Conservation Service
(1974) in the following manner:
INFIL
SCS Hydrologic Estimate Runoff
Soil Group (in./hr) (mm/hr) Potential
A 0.4-1.0 10.0-25.0 low
B 0.1-0.4 2.5-10.0 moderate
C 0.05-0.1 1.2S-2.5 moderate to high
D 0.01-0.05 .25-1.25 high
The SCS has specified the hydrologic soil group for various
soil classifications across the country (1974). As for
LZSN, the values of INFIL obtained above should be used
with caution and only as initial values to be checked by
calibration.
INTER This parameter refers to the interflow component of runoff
and generally alters runoff timing. It is closely related
to INFIL and LZSN and values generally range from 0.5 to
5.0. Figure 5.3 provides an approximate mapping of the
INTER parameter for the United States. This map was
obtained as described for the LZSN parameter. In addition,
INTER values in Table 5.4 provide an indication of
representative values. This information should be used only
to obtain initial values that need to be checked by
calibration.
L L is the length of overland flow obtained from topographic
maps and approximates the length of travel to a stream
channel. Its value can be approximated by dividing the
watershed area by twice the length of the drainage path or
channel. Values usually range from 100 ft (30 meters) to 300
ft (90 meters) since overland flow rapidly forms into
drainage ditches.
SS SS is the average overland flow slope obtained from
topographic maps. The average slope can be estimated by
superimposing a grid pattern on the watershed, estimating the
land slope at each point of the grid, and obtaining the
average of all values measured.
NN Manning's n for overland flow will vary considerably from
published channel values because of the extremely small
depths of overland flow. Approximate values are:
smooth, packed surface 0.05
normal roads and parking lots 0.10
disturbed land surfaces 0.15
61
</pre><hr><pre>
-------
CTv
NJ
4- + + H- \+ 4- 4- +
+ + +: +\ + + + 4
4- 4^ 4- 4- 4 4
+ + 4-4I4- <•.-!- + 4-4-
+ + •+ +J+ +\+ + + +
r 4- + + W + +s— ^.+~+~
.+ 4- + + '+ + + +,' + +
~' ff- + 4-
, + +J+ +' +—+T"fc-.±/ 44-4- I ,'
4- 4/ 4- 4 4- k- + ~l+ + + . !
^+^+l+ + + +,' + +1—fc,-±. + I
;,. 4- f 4- 4- 4- /4- 4 + H- +~4—-1—
+ 4<4- + t44-4-|4-
4- N- 4- 4- / + + + 1+
vl-4-V4-t + 4-4-|4- +
.4—4—4—;* —4-
30-50 3.0-LOWER ELEVATIONS
5.0-HIGHER ELEVATIONS
Figxire 5.3 Interflow (INTER) paraneter map.
</pre><hr><pre>
-------
turf 0.25
heavy turf and forest litter 0.35
FETMUL PE1MUL is a multiplier that adjusts the input potential
evapotranspiration data to expected conditions on the
watershed. Values near 1.0 are used if the input data has
been collected on or near the watershed to be simulated.
IRC, KK24 These parameters are the interflow and groundwater recession
rates. They can be estimated graphically by hydrograph
separation techniques (Linsley, et al. 1975), or found
by trial from simulation runs. Since these parameters are
defined below on a daily basis, they are generally close
to 0.0 for small watersheds that only experience runoff
during or immediately following storm events.
.„.-, _ Interflow discharge on any day
Interflow discharge 24 hours earlier
24 _ Groundwater discharge on any day
Groundwater discharge on 24 hours earlier
KV, GWS The parameter KV is used in conjunction with the groundwater
slope index, GWS, to allow a variable recession rate for
groundwater discharge. If KV = 1.0 the effective recession
rate for different levels of KK24 and the variable GWS
is:
GWS
KK24 0.0 0.5 1.0 2.0
0.99 0.99 0.985 0.98 0.97
0.98 0.98 0.97 0.96 0.94
0.97 0.97 0.955 0.94 0.91
0.96 0.96 0.94 0.92 0.88
GWS is higher during wet periods when groundwater is being
recharged and lower during dry periods. Thus KV affects
the seasonal distribution of groundwater flow; increasing
KV will increase baseflow during wet periods and decrease
it during dry periods with no significant effect on the
total baseflow volume.
For small watersheds without a groundwater flow component,
a value of 0.0 is generally used for both KV and the
initial value of GWS.
UZS, LZS, These parameters are the initial soil moisture conditions
SGW for the upper zone, lower zone, and groundwater zone,
respectively at the beginning of the simulation period.
SGW is the component of groundwater storage that contributes
63
</pre><hr><pre>
-------
to streamflow. It is usually set to 0.0 for initial
calibration runs. The factor (1.0-K24L) specifies the
fraction of the total groundwater component added to SGW,
while the outflow from active groundwater is determined
by the recession rate, KK24. UZS and LZS are generally
specified relateive to their nominal storages, UZSN and
LZSN. If simulation begins in a dry period, UZS and LZS
should be less than their nominal values; whereas values
greater than nominal should be employed if simulation
begins in a wet period of the year. UZS, LZS, and SGW
should be reset after a few calibration runs according to
the guidelines provided in Section 6.
5.3.2 Snow Parameters
RADCON, CCFAC
SCF
ELDIF
IDNS
DGM
These parameters adjust the theoretical melt equations
for solar radiation and condensation/convection melt to
actual field conditions. Values near 1.0 are to be expected
although past experience indicates a range of 0.5 to 2.0.
RADCON is sensitive to watershed slopes and exposure, while
CCFAC is a function of climatic conditions.
The snow correction factor is used to compensate for catch
deficiency in rain gages when precipitation occurs as snow.
Precipitation times the value of (SCF-1.0) is the added
catch. Values are generally greater than 1.0 and usually are
in the range of 1.0 to 1.5.
This parameter is the elevation difference from the
temperature station to the mean elevation in the watershed in
thousands of feet (or kilometers). It is used to correct the
observed air temperatures for the watershed using a lapse
rate of 3 F per 1,000 ft elevation change (5,5°C per 1,000 m)
This parameter is the density of new snow at 0 F.
The expected values are from 0.10 to 0.20 with 0.15 a
common value. The relationship for the variation in snow
density with temperature is described by Donigian and
Crawford (1976a).
This parameter is the fraction of the watershed that has
complete forest cover. Areal photographs are the best
basis for estimates.
DGM is the daily groundmelt. Values of 0.01 in/day (0.25
mm/day) are usual. Areas with deep frost penetration may
have little groundmelt with DGM values approaching 0.0.
This parameter is the maximum water content of the snowpack
by weight. Experimental values range from 0.01 to 0.05
with 0.03 a common value.
64
</pre><hr><pre>
-------
MPACK
EVAPSN
MELEV
TSNOW
PETMIN,
PETMAX
WMUL, KMUL
KUGI
MPACK is the estimated water equivalent of the snowpack for
complete areal coverage in a watershed. Values of 1.0 to 6.0
in. (25 to 150 ram) are generally employed. MPACK is a
function of topography and climatic conditions. Mountainous
watersheds will generally have MPACK values near the high end
of the range.
EVAPSN adjusts the amounts of snow evaporation given by an
analytic equation. Values near 0.1 are expected.
The mean elevation of the watershed in feet (meters).
Wet bulb air temperature below which snow is assumed to
occur. Values of 31° to 33° F (-0.6 to + 0.6° C)
are often used. Comparing the recorded form of
precipitation and the simulated form for a number
of years will indicate needed modifications to TSNOW.
These parameters allow a reduction in potential
evapotranspiration for air temperatures near or below 32 F
(0 C). PETMIN specifies the air temperature below which
potential evapotranspiration is zero. For air temperature
between PETMIN and PETMAX, potential evapotranspiration is
reduced by 50 percent while no reduction^is performed for
temperatures above PETMAX.
and 40" F (4.4
respectively.
o
Values of 35° F (1.7°C)
C) have been used for PETMIN and PETMAX,
These parameters are multipliers used to adjust input wind
movement and solar radiation, respectively, for expected
conditions on the watershed. Values of 1.0 are used if the
input meteorologic data are observed on or near the watershed
to be simulated.
KUGI is an integer index to forest density and undergrowth
for the reduction of wind in forested areas. Values range
from 0 to 10; for KUGI = 0, wind in the forested area is
35 percent of the input wind value, and for KUGI = 10 the
corresponding value is 5 percent. For medium undergrowth
and forest density, a KUGI value of 5 is generally used.
5.3.3 Sediment Parameters
JRER
JPER is the exponent in the soil splash equation of the
sediment algorithm; it approximates the relationship
between rainfall intensity and incident energy to the
land surface for the production of soil fines. Wischmeier
and Smith (1958) have proposed the following relationship
for the kinetic energy produced by natural rainfall;
Y = 916 + 331 log X
65
</pre><hr><pre>
-------
where Y = kinetic energy, ft/tons per acre/in.
X = rainfall intensity, in./hr
Using this relationship, various investigations have also
shown that soil splash is proportional to the square of the
rainfall intensity (Meyer and Wischmeier 1969, David and
Beer 1974). Thus, a value of about 2.0 for JRER is predicted
from these studies. In general, values in the range of
2.0 to 3.0 have demonstrated reasonable results on the
limited number of watersheds tested. The best value will
need to be checked through calibration.
KRER This parameter is the coefficient of the soil splash equation
and is related to the erodibility or detachability of the
specific soil type and land surface conditions. Presently,
limited experience indicates that KRER is directly related to
the K and P factors in the Universal Soil Loss Equation
(Wischmeier and Smith 1965) and can be initially estimated as
KRER = K*P. K values can be obtained with techniques
published in the literature or from soil scientists familiar
with local soil conditions. Table 5.5 provides a list of the
expected magnitudes of K values for various soil types, and
Figure 5.4 is a nomograph for general estimation of K from
soil properties. Other available information on K factors
for the specific watershed should be consulted. Table 5.6
provides values of P for various practices affecting land
surface conditions. The user should note that the practices
listed in Table 5.6 also affect other AEM Model parameters,
such as NN, UZSN, L, and SS. The impact of different
agricultural practices can only be evaluated with changes in
all relevant parameters.
The initial value of KRER will need to be checked through
calibration trials.
JSER JSER is the exponent in the sediment washoff or transport
equation and thus approximates the relationship between
overland flow intensity and sediment transport capacity.
Values in the range of 1.0 to 2.5 have been used on the
limited number of watersheds tested to date. The most
common values are between 1.6 and 2.0, but initial values
should be checked through calibration.
KSER KSER is the coefficient in the sediment washoff, or
transport, equation. It is an attempt to combine the effects
of (1) slope, (2) overland flow length, (3) sediment particle
size, and (4) surface roughness on sediment transport
capacity of overland flow into a single calibration
parameter. Consequently, at the present time calibration is
the major method of evaluating KSER. Terracing, tillage
practices, and other agricultural management techniques will
have a significant effect on KSER. Limited experience to
66
</pre><hr><pre>
-------
TABLE 5.5 INDICATIONS OF THE GENERAL MAGNITUDE OF THE
SOIL-ERODIBILITY FACTOR, K
Organic matter content
Texture class
Sand
Fine sand
Very fine sand
Loamy sand
Loamy fine sand
Loamy very fine sand
Sandy loam
Fine sandy loam
Loamy very fine sand
Loam
Silt loam
Silt
Sandy clay loam
Clay loam
Silty clay loam
Sandy clay ^
Silty clay
Clay
<0.5%
K
.05
.16
.42
.12
.24
.44
.27
.35
.44
.38
.48
.60
.27
.28
.37
.14
.25
2%
K
.03
.14
.36
.10
.20
.38
.24
.30
.38
.34
.42
.52
.25
.25
.32
.13
.23
0.13-0.29
4%
K
0.02
.10
.28
.08
.16
.30
.19
.24
.30
.29
.33
.42
.21
.21
.26
.12
.19
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, use of Figure 5.4 or
Soil Conservation Service K-value tables will provide much greater accuracy.
Source: Stewart, et al. 1975.
67
</pre><hr><pre>
-------
CO
Figure 5.4 Soil erodibility nomograph
Source: Wischmeier, Johnson, and Cross (1971), p. 190
</pre><hr><pre>
-------
TABEL- 5.6 VALUES OF SUPPORT-PRACTICE FACTOR, P
Land Slope (percent)
Practice
Contouring (P )
Contour strip cropping (P )
R-R-M-M"1- ^
R-W-M-M-
R-R-W-M
R-W
R-0
Countour listing or ridge planting
(P^
2 _
Contour terracing (Pfc) '
No support practice
R = rowcrop, W = fall-seeded grain, 0 = spring-seeded grain, M = meadow. The crops are grown in
rotation and so arranged on the field that rowcrop strips are always separated by a meadow or
winter-grain strip.
2
These P. values estunate the amount of soil eroded to the terrace channels and are used for
conservation planning. For prediction of off-field sediment, the P. values are multiplied by 0.2.
n = number of approximately-equal-length intervals into which the field slope is divided by
the terraces. Tillage operations must be parallel to the terraces.
Source: Stewart, et al. 1975.
1.1-2
0.60
0.30
0.30
0.45
0.52
0.60
0.30
0.6/v/n
1.0
2.1-7
0.50
0.25
0.25
0.38
0.44
0.50
0.25
0.5/v/n
1.0
7.1-12
Factor P
0.60
0.30
0.30
0.45
0.52
0.60
0.30
0.6/v/H
1.0
12.1-18
0.80
0.40
0,40
0.60
0.70
0.80
0.40
0.8//n
1.0
18.1-24
0.90
0.45
0.45
0.68
0.70
0.90
0.45
0.9/Vn"
1.0
</pre><hr><pre>
-------
SRERI,
SRERTL
CX)VPMO
SCMPAC
date has indicated a possible range of values of 0.01 to 5.0.
However, significant variations from this can be expected.
These parameters indicate the amount of detached soil fines
on the land surface at the beginning of the simulation
period (SRERI) and the amount produced by tillage operations
(SRERTL). Very little research or experience relates to
the estimation of these parameters. Thus, calibration is the
method of evaluation. For SRERI, one would expect that
spring and summer periods on agricultural watersheds would
require higher values than fall and winter periods due to
the growing season disturbances and activities on the
watershed. Values of SRERTL are related to the severity
or depth of the tillage operation, and must be input to
correspond with the dates of tillage operations (TIMTIL,
YRTIL). Values of these parameters on the limited number
of calibrated watersheds have ranged from 0.5 to 2.0
tons/acre (1.0 to 4.5 t/ha).
This parameter is the fraction land cover on the watershed
and is used to decrease the fraction of the land surface
that is susceptible to soil fines detachment by raindrop
impact. Twelve monthly values for the first day of each
month are input to the model, and the cover on any day is
determined by linear interpolation. Overhead photographs
at periodic intervals during the year are the most direct
means of estimating the fraction land cover.
COVPMO values can be estimated as one minus the C factor
in the Universal Soil Loss Equation, i.e. COVPMO = 1 - C,
when C is a monthly value. For cropland, the C factors
for the various stages of crop growth should be used in
estimating COVPMO.
Tables 5.7 and 5.8 pertain to the evaluation of C on
undisturbed lands and have been reproduced from the paper
by Wischmeier (1975). C factors for disturbed lands
(croplands, agriculture, and construction areas) have been
published in the USLE Report (Wischmeier and Smith 1965).
The COVPMO values estimated from C may need to be reduced
since the C factor includes considerations other than
crop canopy and raindrop interception.
SCMPAC is a soil compaction factor that reduces the amount
of detached soil particles available for transport. It is
a first-order decrease (per day) of the surface storage
of soil fines performed on a daily basis during nonstorm
periods. The SCMPAC parameter attempts to represent the
natural aggregation and mutual attraction of soil particles
and the compaction of the surface soil zone from which
erosion occurs. These processes are a complex function
of soil characteristics, meteorologic conditions, and tillage
70
</pre><hr><pre>
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TABLE 5.7 C VALUES FOR PERMANENT PASTURE, RANGELAND, AND IDLE LAND
Canopy
Type and Pet
height *> cover c
(1) (2)
None
Weeds or
short brush •
(0.5m).
25
50
[75
f 25
Brush or
bushes I 50
(2m).
L75
Trees, no
low brush
(4m).
25
50
75
Ground cover
Typed
(3)
f G
{w
.(G
\w
f G
(w
TG
\W
(G
|w
IG
\W
/G
1W
fG
(w
/G
(w
s.
f
(G
iw
Pet cover
0
(4)
0.45
.45
.36
.36
.26
.26
.17
.17
.40
.40
.34
.34
.28
.28
.42
.42
.39
.39
.36
.36
20
(5)
0.20
.24
.17
.20
.13
.16
.10
.12
.18
.22
.16
.19
.14
.17
.19
.23
.18
.21
.17
.20
40
(6)
0.10
.15
.09
.13
.07
.11
.06
.09
.09
.14
.08
.13
.08
.12
.10
.14
.09
.14
.09
.13
60
(7)
0.042
.091
.038
.083
.035
.076
.032
.068
.040
.087
.038
.082
.036
.078
.041
.089
.040
.087
.039
.084
80
(8)
0.012
.043
.013
.041
.012
.039
.011
.038
.013
.042
.012
.041
.012
.040
.013
.042
.013
.042
.013
.041
95-100 ~
(9)
0.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
a All values assume (1) random distribution of mulch or vegetation, and (2) mulch of substantial depth where
credited.
b Classified by average fall height of waterdrops from canopy to soil surface, in meters.
'" Percentage of total-area surface that would be hidden from view by canopy in a vertical projection.
G—Cover at surface is grass or decaying, compacted duff of substantial depth. W—Cover at surface is weeds
(plants with little lateral-root network near the surface) or undecayed residue.
TABLE 5.8 C FACTORS FOR WOODLAND
Stand
condition
Well stocked ....
Tree canopy
(pet of area) "
100-75
7fi in
40-20
Forest
litter
(pet of area)
100—90
Qfl— T\
70—40
Undergrowth0
C-Factor
0.001
003-0.011
002- .004
.01 04
003- .009
02 .09e
3 Area with tree canopy over less than 20 pet will be considered grassland or cropland for estimating soil loss (ta-
ble 2).
Forest litter is assumed to be of substantial depth over the percent of the area on which it is credited.
c Undergrowth is defined as shrubs, weeds, grasses, vines, etc. on the surface area not protected by forest litter.
Usually found under canopy openings.
d Managed—Grazing and fires are controlled. Unmanaged—Stands that are overgrazed or subjected to repeated
burning.
• For unmanaged woodland with litter cover of less than 75 pet, C-values should be derived by taking 0.7 of the
appropriate values in table 2. The factor of 0.7 adjusts for the much higher soil organic matter on permanent wood-
land.
Source: Wischmeier (1975), pp. 123-24.
71
</pre><hr><pre>
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practices for which a detailed simulation is not possible
at present. Values in the range of .001 to .1 are possible.
5.3.4 Soil Parameters
LZTEMP
ASZT, BSZT,
AVZT, BUZT
SZDPTH,
UZDPTH
This parameter is an array of the average monthly soil
temperatures of the lower and groundwater zones. Values
may be estimated from nearby soil temperatures given
by the Environmental Data Service or from groundwater
temperatures published in U.S. Geological Survey Water
Data publications.
These parameters are regression constants which relate air
temperature (AT) to surface soil temperature (STEMP) and
the upper zone soil temperature (UTEMP) to STEMP as follows:
STEMP = ASZT + BSZT*AT
UTEMP = AUZT + BUZT*STEMP
They must be determined by correlating air temperatures
and soil temperatures for the simulation period. The
ARM Model calculates hourly air temperatures with a
sinusoidal interpolation between the input max-min air
temperatures, assuming the minimum temperature occurs between
5 a.m. and 6 a.m. and the maximum occurs between 3 p.m.
and 4 p.m. Thus, the regression equations are used on an
hourly basis, but the constants can be developed from
max-min or daily air and soil temperature data.
These parameters refer to the depth of the active surface
zone (SZDPTH) and the depth from the land surface to the
bottom of the upper soil zone (UZDPTH). Although these
parameters specify soil depths, their major impact is on
the retention and concentration of adsorbed chemicals
(pesticides and nutrients) in each zone.
Very little experience exists for evaluation of these depths.
SZDPTH is expected to range from 0.06 in. to 0.25 in. (1.5
to 6.0 mm) with a value of 0.12 in. (3 mm) commonly used.
Adjustments to SZDPTH will affect the concentration of
adsorbed pollutants in surface runoff (Section 6.4).
UZDPTH is generally evaluated as the depth of incorporation
of soil-incorporated chemicals. It also indicates the depth
used to calculate the mass of soil through which interflow,
percolation, and associated chemicals are assumed to pass,
whether the chemicals are soil-incorporated or surface
applied. UZDPTH is expected to range from 2.0 to 6.0 in.
(5.0 to 15.0 cm) with a value of 3.0 in. (7.6 cm) commonly
used. UZDPTH must be greater than SZDPTH.
72
</pre><hr><pre>
-------
BDSZ, These parameters refer to the soil bulk density in each
BDUZ, BDLZ depth zone: surface (BDSZ), upper zone (BDUZ), and lower
zone (BDLZ). These values may be available from some soil
surveys, or from agricultural extension personnel when field
sampling is not available. Values generally range from 75 to
112 Ib/cu ft (1.2 to 1.8 g/cc) and 100 Ib/cu ft (1.6 g/cc) is
commonly used. Surface soils will normally have lower bulk
densities than deeper soils. Likewise, soils with much
organic content will have lower bulk densities than those
with little organic content.
UZF, LZF These parameters are the chemical leaching factors for the
upper zone (UZF) and lower zone (LZF). They adjust the
amount of chemical leached with infiltrating and percolating
water. Values between 1.0 and 5.0 have been used for UZF
with less chemical leaching with the higher values. Values
are related to soil porosity with the lower values used
for more porous soils (Donigian, et al. 1977). Calibration
of these parameters with the downward movement of tracers,
such as chloride, is recommended. Otherwise, these
parameters can be adjusted to represent reasonable leaching
of soluble chemicals. The LZF has not been studied
adequately to determine if a deviation from a value of 1.0 is
needed.
5.3.5 Pesticide Parameters
DD, K, N, NP These parameters define the adsorption/desorption functions
used in the ARM Model. DD represents the capacity of the
soil to permanently adsorb the applied pesticide so that
it will not desorb under repeated washings. Its units are
in pounds (kilogram) of pesticide per pound (kilogram) of
soil. K and N are the standard Freundlich constants defining
the single-valued adsorption/desorption isotherm. The
ARM Model reports contain complete descriptions of the
adsorption/desorption algorithms and parameters.
Ideally the values of these parameters should be determined
by laboratory experiments for each specific pesticide-soil
combination. Pesticide manufacturers will often have
parameter values for their own pesticides on various soils,
and the general literature (technical reports and journals)
can be consulted for values for the more common pesticides on
soils similar to those on the simulation watershed. However,
laboratory values may not accurately describe a pesticide
behavior under field conditions; they may require some
adjustment or calibration (Section 6.4).
DD is related to the cation or anion exchange capacity of
the soil depending on the chemical properties of the
pesticide. The effect of nonzero values of DD is to
specify the amount of pesticide that can be applied before
73
</pre><hr><pre>
-------
any can be detected in solution in the runoff water. This
permanently bound pesticide amount equals the product of
DD, the depth of the zone of application (either SZDPTH or
UZDPTH), the corresponding bulk density, and the watershed
area. For highly ionic pesticides, such as paraquat, the
assumption of permanent adsorption is reasonable, but most
pesticides will require extremely small values or zero for
DD (the DD value used for paraquat on Cecil soils was
0.0003).
K and N values are highly variable and dependent on the
specific pesticide-soil combination. The assumption of a
linear isotherm would use an N value of 1.0 with K being
the partitioning coefficient (the ratio of sediment to
solution concentrations).
The NP values used to date have been 2 to 3 times the
corresponding N value.
CMAX CMAX is the water solubility of the pesticide being
simulated. Literature values are generally used, no
temperature correction is performed, and the input value is
dimensionless (i.e. pesticide mass/water mass). Pesticide
simulation results appear to be relatively insensitive to
CMAX because the solution concentrations have been much less
than the input solubility value.
KDG KDG is the first-order pesticide degradation, or attenuation,
rate. Up to 12 values can be input to the ARM Model with
each value activated on the day and year specified by the
corresponding DDG and YDG parameters, respectively.
Thus KDG(l) is activated on day DDG(l) in year YDG(l) and
remains in effect until KDG(2) is activated on day DDG(2)
in year YDG(2) and so on. In this way, a single degradation
rate can be applied for the entire season, or different
rates can be applied to different time periods following
application. This latter approach is an attempt to use
different KDG values for degradation/attenuation processes
that predominate at different times following application,
as shown in Figure 5.5.
Degradation processes are the major mechanisms determining
the amount of pesticide available for transport from the
watershed throughout the growing season. Thus accurate
representation of these processes is critical to simulating
pesticide runoff to the aquatic environment. As with the
adsorption parameters, KDG values should be determined
for the specific pesticide, soil, and environmental
conditions of the watershed. Pesticide manufacturers and
the technical literature should be consulted if specific
degradation rate information is not available. Menzie (1972)
has reported the estimated half-life of many pesticides
74
</pre><hr><pre>
-------
o
c/i
o
H
DC
I-
2
LU
O
z:
o
o
LU
Q
O
CO
LU
Q.
Application losses
Volatility
Leaching, volatilization,
chemical breakdown, adsorption
Enzymatic (probably bacterial),
degradation (+ leaching and volatilization)
TIME
Figure 5.5 Theoretical degradation curve for soil applied pesticides
(Edwards 1964)
75
</pre><hr><pre>
-------
in soils, and Stewart, et al. (1975) have tabulated the
approximate persistence in soil (that is, time required for
90 percent or more degradation) for 60 agricultural
herbicides. These values reproduced in Table 5.9 can be
converted to daily degradation rates for input to the
ARM Model as follows:
KDG =
or
0.693/t5Q
where tgQ and t^n are the time period, in days, for 90
percent and 50 percent degradation, respectively, of the
applied pesticide. Literature values may need to be adjusted
or calibrated to field conditions if pesticide soil data are
available (Section 6.4).
5.3.6 Nutrient Parameters
The nutrient model has been applied to the P2 watershed in Watkinsville,
Georgia and the P6 watershed in East Lansing, Michigan. The nutrient-
reaction rates and temperature adjustment coefficients for these watersheds
are listed in Table 5.10 as general information for nutrient parameter
evaluation. Note that reaction rates are input for each soil zone and that
a value of 0.0 will eliminate a particular transformation (as shown in
Figure 2.5) and can be used to prevent reactions from being simulated in any
zone. Thus the groundwater reaction rates in Table 5.10 are 0.0 because
transformations in groundwater were not important to the simulation.
TSTEP This parameter designates the time step in minutes for the
chemical and biological nutrient transformations. Values
range from 5- or 15- to 1440-min (1 day). There
must be an even number of time steps in one day and an even
number of simulation intervals in a TSTEP. Most testing of
the model has been, with a 60-min time step. At time steps
much larger than 60-min, the solution technique may be less
accurate. A warning message will be printed if the time step
is too large for the solution technique.
NAPPL NAPPL is the number of nutrient applications. Application
information must be repeated NAPPL times following the
initial storage values in the input sequence. Nutrient
applications may be designated for fertilizer or for crop
residue remaining or incorporated after harvesting.
TIMHAR This parameter designates the time of crop harvesting at
which the plant nutrient storages in the model are reset to
zero. The amount of nutrients harvested is printed in the
monthly summary. Typically, other APM parameters referring
to crop canopy, uptake, and evapotranspiration should be
adjusted for the harvesting period.
76
</pre><hr><pre>
-------
TABLE 5.9 PERSISTENCE OF AGRICULTURAL CHEMICALS IN SOILS
Pesticide
DDT
Aldrin
Dieldrin
Isodrin/endrin
Heptachlor
Chlordane
Toxaphene
BHC
Parathion, ethyl
Parathion, methyl
Thimet3
Conmon Names
of
Herbicides
Alachlor
Ametryne3
Amitrole
Asulam
Atrazine
Barban
Benefin
Bensulide
Bifenox
Bromacil
Butylate
COM
CDEC
Chloramben
Chloroxuron
Chlorpropham
Cycloate a
2,4-D Acid
2,4-D Amine
2,4-D Ester
Dalapon
DCPA
Diallate
Dichlobenil
Dinitramine
Dinoseb
Diphenamid
Diquat
Diuron
EPTC
(Menzie 1972)
Approximate
Half-Life
in Soil
3-10 years
1-4 years
1-7 years
4-8 years
7-12 years
2-4 years
10 years
2 years
180 days
45 days
2 days
Pesticide
Chlorthion
DDVP
Dipterex3
Disyston
Demeton S
Methyl demeton S
Dursban3
Diazinon
Chlor f env inphos
Dimethoate
Approximate
Half-Life
in Soil
36 days
17 days
140 days
290 days
54 days
26 days
29-1930 days
6-184 days
14-161 days
122 days
(Stewart,-et al. 1975)
Approximate,
Persistence
in Soil, days
40-70
30-90
15-30
25-40
300-500
20
120-150
500-700
40-60
700
40-80
20-40
20-40
40-60
300-400
120-260
120-220
10-30
10-30
10-30
15-30
400
120
60-180
90-120
15-30
90-180
500
200-500
30
Common Names
of
Herbicides
Fenaca
Fenuron
Glyphosate
Isopropalin
Linuron
MCPA
Metribuzin
Molinate
Monuron
Naptalam
Paraquat
Pebulate3
Phenmedipham
Picloram
Profluralin
Prometofte3
Prometryne3
Pronamide3
Propachlor3
Propanil3
Propazine3
Propham
Pyrazon
Simazine
TCA
Terbacil
Terbutryne3
Triallate3
Trifluralin
Vernolate3
Approximate ,
Persistence
in Soil, days
350-700
30-270
150
150
120/
30-180
150-200
80
150-350
20-60
500
50-60
100
550
320-640
400
30-90
60-270
30-50
1-3
200-400
20-60
30-60
200-400
20-70
700
20-70
30-40
120-180
50 .
Trade name; no corresponding common name exists.
Persistence refers to time required for 90 percent degradation
77
</pre><hr><pre>
-------
TABLE 5.10 NUTRIENT HEACTION RATES AND TEMPERATURE COEFFICIENTS USED FOR THE
P2 AND P6 DmTERSHED
oo
P2 WATERSHED—Watkinsville, Georgia
Nitrogen Rates (day )
Surface
Upper Zone
Lower Zone
Groundwater
Temperature Coef.
Phosphorus Rates (day )
Surface
Upper Zone
Lower Zone
Groundwater
Temperature Coef.
P6 WATERSHED—E. Lansing, Michigan
Nitrogen Rates (day )
Surface
Upper Zone
Lower Zone
Groundwater
Temperature Coef.
Phosphorus Rates (day )
Surface
Upper Zone
Lower Zone
Groundwater
Temperature Coef.
Kl
1.000
.20000
.1000
.0000
1.050
KM
.0200
.0020
.0020
.0000
1.070
t
Kl
3. WO
1.2500
.7000
.0000
1.050
KM
.0150
.0015
.0015
.0000
1.070
KD
.0000
.0060
.0020
.0000
1.070
KIM
.0000
.0000
,0000
.0000
1.070
KD
.0000
.0500
.0000
.0000
1.070
KIM
.0000
.0000
.0000
.0000
1.070
KPL
.1000
.1300
.0250
.0000
1.070
KPL
.Moo
.7000
.8000
.0000
1.070
KPL
.2500
.4000
.0900
.0000
1.070
KPL
.0100
2.1000
1.7000
.0000
1.070
KM
.0000
.0020
.0020
.0000
1.070
KSA
17WOO
1.0000
1.0000
.0000
1.050
RAM
.0150
.0015
.0015
.0000
1.070
KSA
1.0000
.5000
.5000
.0000
1.050
KIM
.0000
.0000
.0000
.0000
1.070
KAS
.mso
.0015
.0050
.0000
1.050
KIM
.0000
.0000
.0000
.0000
1.070
KAS
.0100
.0060
.0050
.0000
1.050
KKIM
.0000
.0000
.0000
.0000
1.070
KKIM
.0000
.0000
.0000
.0000
1.070
KSA
1.0000
1.0000
1.0000
.0000
1.050
KAS
.2000
.2500
.2000
.0000
1.050
5.0000
.7500
1.0000
.0000
1.050
.7500
.3000
.4000
.0000
1.050
</pre><hr><pre>
-------
ULUPTK, These parameters refer to the combined surface and upper zone
LZUPTK layers (ULUPTK) and the lower zone. (LZUPTK) crop uptake
fractions. They are monthly fractions of 'the maximum monthly
uptake of nitrogen and phosphorus with values less than or
equal to 1.0. The month with the highest expected uptake
should be set equal to 1.0. This is usually the month with
the most crop growth. Some adjustment of these parameters
and the uptake rate (KPL) will be needed in order to represent
the expected crop uptake pattern. Figure 5.6 shows the
expected pattern of growth and uptake for corn.
KPL KPL is the maximum uptake rate and is input separately for
nitrogen and phosphorus. It is used with the above crop
uptake fractions to represent the crop uptake pattern from
each zone during the growing season. Little information is
presently available in the literature on first order reaction
rates of crop uptake, so calibration of this parameter may be
needed. Adjustment of KPL will often be the major effort in
nutrient parameter calibration.
Approximate nutrient contents of various crops are given in
Table 5.11, and uptake rates should be calibrated to provide
the expected pattern and level of total uptake. However,
these values will vary with location and environmental
conditions. Therefore, a local agricultural specialist should
be consulted. Figure 5.6 and similar information for other
crops can then be used to estimate the distribution of the
plant uptake from the different soil layers during the growing
season. Generally 4.0 percent of the total nutrient uptake_of
a mature normal crop with roots to a 5-ft depth (for example,
corn, sorghum, soybean, and peanuts) is thought to occur from
the top foot (30 cm) of soil.
Kl This parameter is the nitrification reaction rate. Oxygen
content is a major determinent of this parameter, so the
deeper the soil the lower the rate should be. Soils that are
saturated much of the time will also have lower rates.
Nitrification rates have been in the range of 0.2 and 3.0 per
day. The nitrification rate should be calibrated to ammonium
and nitrate soil storage data unless laboratory measurements
are available.
KD KD is the dentrification reaction rate. Denitrification rates
are larges under anaerobic conditions. However, Broadbent
and Clark (1965) estimated 10 to 15 percent of the annual
mineral nitrogen input to agricultural areas is lost by
denitrification under normal crop conditions. Since the
extent of denitrification is dependent upon fluctuating field
conditions, the rates should be estimated or calibrated. If
the field is under ordinary aerobic conditions, that is,
little flooding or stagnant water, the denitrification
79
</pre><hr><pre>
-------
Maturity
115 Days
50 75
DAYS AFTER EMERGENCE
Figure 5.6 Corn growth and nutrient uptake (Steward, et al. 1975)
80
</pre><hr><pre>
-------
TABLE 5.11 APPROXIMATE YIELDS ALO HtJTRIENT CONTENTS OF
SELECTED CEOPSa (Stewart et al 1975)
Crop
Alfalfa"
Apples
Barley
Beans
Bermudagrass
Bluegrass
Cabbage
Clover
Corn
Cotton
Cowpea Hay
Lettuce 5
Lespedeza
Oats
Onions
Oranges
Peache^
Peanuts
Potatoes
Rice
Rye
Sorghum
b
Soybean
Sugar beets
Sugar cane
Timothy
Tobacco
Tomatoes
grain
straw
nuts
tubers
vines
grain
straw
grain
straw
grain
stover
grain
straw
roots
tops
stalks
tops
fruit
Wheat
grain
straw
(dry)
red
white
grain
stover
silage
lint and seed
stalks
vines
grain
straw
Yield/acre
Lbs N/acre
4 tons
500 bu
40 bu
1 ton
30 bu
8 tons
2 tons
20 tons
2 tons
2 tons
150 bu
4.5 tons
25 tons
1 ton
1 ton
2 tons
20 tons
2 tons
90 bu
2 tons
7.5 tons
28 tons
600 bu
1.5 tons
400 cwt
1 ton
90 bu
2.5 tons
30 bu
1.5 tons
60 bu
3 tons
45 bu
1 ton
20 tons
12 tons
30 tons
13 tons
2.5 tons
1 . 5 tons
25 tons
1.5 tons
50 bu
1.5 tons
200
30
35
15
75
200
60
150
80
130
135
100
200
60
45
120
90
85
55
25
45
85
35
110
95
90
55
30
35
15
50
65
160
25
85
110
100
50
60
115
145
70
65
20
Lbs P/acre
18
4
6
2
10
30
8
16
10
10
24
16
30
12
6
10
12
8
10
8
8
12
8
6
12
8
12
4
4
4
10
8
16
4
14
10
20
10
10
10
20
10
14
2
Values can vary by a factor of two across the country (Stewart, et al.
1975)
bLegumes that do not require fertilizer nitrogen
clbs P = 0.436 Ibs P0
81
</pre><hr><pre>
-------
reaction rate could be considered as less than 0;001 or 0.0
per day. If there is too much nitrogen in the soil system
after other demands have removed nitrogen, a higher value
could be used with discretion. When estimating rates from
literature values, it must be remembered that the reaction
rate used in the model is based on the total nitrite and
nitrate content, and not merely the nitrite.
KM, RAM, These parameters refer to the mineralization and
KIM, KKIM immobilization rates of nitrogen (RAM, KIM, KKIM) or
phosphorus (KM, KIM). Typical laboratory information refers
to net mineralization rates. When using such net rates,
immobilization rates (KIM, KKIM) can be set to 0.0.
Extensive research has been done by the U.S. Soils Laboratory
on net nitrogen mineralization rates. Table 5.12 gives first
order rates for mineralizable N and the percentages of total
N which is mineralizable N. If total Organic N values are
used for initial storages, these rates should be multiplied
by the percent of mineralizable N to obtain the corresponding
rate. Otherwise, the Organic N values for initial storages
should refer to only the mineral izable organic N, and the
rates in Table 5.12 can be used directly (with conversion to
daily values).
According to Stanford and Smith (1972) the most reliable
estimate of the net mineralizable N rate was 0.054 + 0.009
week"3-. However, the fraction of mineralizable N of
total N varied widely, 5 to 40 percent, in this study.
Unless other values are available, the net mineralization
rate of phosphorus can be assumed to be the same as the
nitrogen rate. These rates should not have to be calibrated
unless the values in Table 5.12 are considered inapplicable
for the specific watershed conditions.
KAS, KSA These parameters correspond to the adsorption (KSA) and
desorption (KAS) rates for nitrogen or phosphorus. These
rates are useful in keeping the adsorbed ammonium and
phosphate forms in the soil system and not taken up by
plants, moved by water, or transformed. The cation exchange
capacity will influence the extent of ammonium adsorption,
while the amounts of complexing ions (Al, Fe, Ca) as well as
pH influence the extent of phosphorus adsorption. Typically,
most of the phosphorus is in the adsorbed phase. The extent
of adsorption is determined by the proportion of KSA to KAS;
the magnitude of the rates determine the actual rate of
adsorption and desorption. Little information is available
on these rates in the literature, but indications are that
complete adsorption of applied compounds occurs within days.
Calibration should be performed with observed data unless
adsorption isotherms are available.
82
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TABLE 5.12 PAST MANAGEMENT, SURFACE SOIL NITROGEN PROPERTIES,
AND NET MINERALIZATION RATE OF MINERALIZAELE N FOR
VARIOUS SOILSa (Stanford and Smity-1972)
Soil
LtGlgndtion. and
Location0
Amarillo fsl
IfXl
rtagerstown sil
(PA)
Grenada fsl
(Kiss.)
Corfu fsl
(VIA)
Minidoka sil
(ID)
Portneuf sil
(ID)
Shano sil
harden fsl
(hA)
Colby sil a
(CA)
Regent sicl
(ND)
Ritzville sil
Sprole sil
(MT)
Tenwik sil
(ND)
Walla Walla sil
<WA]
Weld sil
Cecil si
(GA)
Previous Management
Alfisol
Cotton and sorghum
Hay (some alfalfa) with little
fertilizer; 145 kg H/ha on
corn
Unlimea; Ber.nudograss; avg. N,
245, kg/ha
Limed; Bermuda, avg. M, 245
kg/ha
Old alfalfa field; limtu corn.
180 kg (Vha
Sagebrusn and bunchgrass.
Aridisol
Vihcatgrass and sagebrush
Potatoes, 125 kg N/ha/yr
Wheat-fallow
Wheat-fallow presently uncropped
and unirrigated
Elltisol
Wheat-fallow, no fertilizer
Smooth bromegrass, no fertilizer
wheat-fallow, no fertilizer
Wheat-fallow, no N or P
fertilization
Small grains, no N fertilizer
Wheat-fallow, 60 kg/U/ha (avg.)
on wheat
Wheat-fallow, no fertilizer
Ultisol
Corn, no N fertilizer; grass-
crimson
Corn, 180 kg N/ha/yr; grass-clover
4-yr rotation of corn (no N) and
Total N
t C
0.053
0.144
0.132
0.141
0.116
0.043
0.128
0.104
0.039
0.040
0.096
0.222
0.068
0.145
0.205
0.085
0.065
0.021
0.031
0.051
Hinoi
« of
Total
N
n.4
15.2
27.0
25.4
27.9
9.1
23.1
19.7
4.6
16.0
15.4
10.4
10.0
19.8
11.7
12.1
21.5
23.8
27.4
40.6
ralizabls N
Net Rat<
; of
Mineralization
Week "i
0.066 *_
0.063 t
0.042 +
0.056 +
0.062 t
0.056 *
0.071 t
0.082 t
U.095 +
0.058 *
0.069 +
0.047 +
0.083 +
0.056 +
0.042 +
0.047 +
0.045 +
0.035 t
0.076 +
0.052 +
.009
.U12
.007
.014
.011
.010
.011
.014
.031
.009
.011
.008
.003
.004
.003
.008
.005
.009
.010
.013
Goldsboro si
ISC)
Greenville fsl
(AL)
Leek Kill sil
(PA)
Norfolk fsl
(SC)
Holtville scl
Lakeland Is
(SCI
Quincy Is
(OR)
Aastad cl
(Hi)
Barnes 1
Kranzbunj sil
(SD)
Parshall fsl
(ND)
Palouse sil
(KA)
Pullman sicl
(TX)
Rsgo sil
3-yrs fescue-clover (80 kg
N/ha/yr)
4-yr rotation of corn (180 kg
N/ha) and 3-yrs fescue-clover
80 kg N/ha/yr)
Corn, 73 kg N/ha/yr
Uncropped, no N recently
General cropping, 225-335 kg
N/lia/yr
Barley-saeadow; 33 and 42 kg
N/ha
Corn; 7j kg N/ha/yr
Sugar beet-barley rotation;
manure and 1UO kg N/ha on each
crop
Sugar beet-barley rotation,
3bU and U5 kg tl/ha/yr, resp.
Alfalfa-sug^r boots-barley; no N
fertilizer
Corn, no fertilizer
Uncropped recently irrigated for
potato and alfalfa
ttollisol
Corn, no fertilizer
Bromograss, corn, soybeans, oats,
no fertilizer
Corn, oats, fallow, soybeans,
10 kg N/ha/yr (avg.) on corn,
oats
Alfalfa, corn, oats, W and 34 kg
N/ha on corn and oats
Spring whc.it, bromegrass no
fertilizer
Winter wheat, dry peas rotation,
84 kg N/ha/yr on wheat
Dry farmed; wheat, sorghum, and
cotton
Whejt-fallow, no fertilizer
0.049 36.1
0.039 11.0
0.046
0.049
O.US
0.030
0.127
21.0
17.1
25.7
13.3
24.0
0.066 19.0
0.086 17.7
0.031 10.0
0.039 29.2
0.211 10.9
0.234 13.6
0.1M 11.9
0.231 13.5
0.112 12.5
0.135 11.5
0.110 25.7
0.110 15.4
0.056 + .012
0.050 + .012
0.054 + .006
0.052 + .010
0.056 + .017
0.052 + .004
0.052 + .004
0.053 + .004
0.078 * .029
0.087 + .011
0.057 + .007
0.057 + .006
0.045 + .007
0.050 + .007
0.050 + .009
0.064 + .008
0.044 + .008
0.044 + .006
jaReftr to original paper
f ° fine, s = sand, Si =
cTotol N percent is from
for more detail.
• silt, c = clay, 1 3 loam
surface soils sampled to plow depth (15-20 on)
83
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Temperature Coefficients
The temperature coefficients correct the input reaction rates
for temperatures less than 35° C. Values should not differ
extensively from one location to another. Values found in
Table 5.10 which were used in prior testing should be used
unless other information to the contrary is found. Table 5.13
shows the effect of the temperature coefficient on the input
reaction rates.
Nutrient Storages
The nutrient storages should be obtained from analyses of
field samples whenever possible. Otherwise, values could be
obtained from soil surveys, estimates of prior fertilizer
application, or from agricultural extension personnel.
Estimates of surface zone sediment associated chemicals can be
made from analysis of the composition of eroded material. The
nutrient forms measured in the soil should be comparable with
those analyzed in the runoff. That is, the same laboratory
analysis techniques and measured nutrient forms should be used
for the soil core samples and the nutrient content of the
runoff.
TABLE 5.13 FRACTIONS OF INPUT REACTION RATES FOR VARIOUS
TEMPERATURE COEFFICIENTS (0)
Fraction of Input Reaction Rate
Soil
Temperature e=1.0 0=1.05 0=1.07 0=1.10
>35° 1.0 1.0 1.0 1.0
33" 1.0 0.90 0.87 0.83
30 1.0 0.86 0.71 0.62
25" 1.0 0.61 0.51 0.39
20° 1.0 0.48 0.36 0.24
15° 1.0 0.38 0.29 0.15
10° 1.0 0.30 0.18 0.09
5° 1.0 0.23 0.13 0.06
< 4° 0 0 0 0
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SECTION 6
CALIBRATION PROCEDURES AND GUIDELINES
Calibration has been repeatedly mentioned throughout this user manual; this
indicates the importance of the calibration process in application of the
ARM Model. At the risk of further repetition, the calibration process will
be defined and described in this section and recommended procedures and
guidelines will be presented. The goal is to provide a general calibration
methodology for potential users of the ARM Model. As one gains experience
in calibration, the methodology will become second nature and individual
methods and guidelines will evolve.
6.1 ARM MODEL CALIBRATION PROCESS
Calibration is an iterative procedure of parameter evaluation and refinement
by comparing simulated and observed values of interest. It is required for
parameters that cannot be deterministicly evaluated from topographic,
climatic, soil, or physical/chemical characteristics. Fortunately, the
large majority of ARM parameters do not fall in this category.
Ideally calibration of the ARM Model will be limited to the hydrologic and
sediment parameters to the extent possible. Although the key pesticide and
nutrient parameters are quantities measurable in laboratory experiments, we
have found that the literature often does not contain the necessary
information for the particular pesticides, nutrient forms, soils, crops, and
test watershed conditions. Also, laboratory experimental conditions can
produce values that may not be applicable to variable field conditions.
This is especially true for the nutrient parameters. All efforts should be
made to extract the necessary information from the literature. However,
when the literature is lacking parameter values for the specific test
conditions, extrapolation or adjustment of "similar" literature values is
essentially a calibration-type process. The literature values are adjusted
to improve the agreement between simulated and recorded values. Thus, some
calibration of certain pesticide and nutrient parameters, such as pesticide
degradation rates, adsorption constants, and nutrient transformation rates
may be necessary when pertinent information is lacking.
Calibration should be based on several years of simulation (3 to 5 yrs is
optimal) in order to evaluate parameters under a variety of climatic, soil,
and water quality conditions. However, due to lack of data on sediment,
pesticide, and nutrients, calibration for these constituents is usually
performed on whatever data are available.
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The areal variability of meteorologic data series, especially precipitation
and air temperature, may cause additional uncertainty in the simulation.
Years with heavy precipitation are often better simulated for hydrology
because of the relative uniformity of large events over a watershed. In
contrast, low annual runoff may be caused by a single or a series of small
events that did not have a uniform areal coverage. Parameters calibrated on
a dry period of record may not adequately represent the processes occurring
during wet periods. Also, the effects of initial conditions of soil
moisture and sediment pollutant storages can extend for several months
resulting in biased parameter values calibrated on short simulation periods.
Calibration should result in parameter values that produce the best overall
agreement between simulated and observed values throughout the calibration
per iod.
Calibration includes the comparison of annual, monthly, and storm event
values for runoff components (quantity and quality), and -soil storage values
of pesticide and nutrient content for simulation of soil profile processes.
Ideally all these comparisons should be performed for a proper calibration
and simulation of hydrologic, sediment, pesticide, and nutrient processes.
Hydrologic calibration must preceed sediment calibration which, in turn,
preceeds the pesticide and/or nutrient calibration. This is necessary
because runoff is the transport mechanism for sediment, and both runoff (and
vertical moisture movement) and sediment are the transport mechanisms for
pesticides and nutrients. Thus, the major steps in the overall calibration
process are:
(1) estimation of all ARM Model parameters, including calibration
parameters, from the guidelines provided
(2) hydrologic calibration of annual and monthly runoff volumes
(3) hydrologic calibration of storm events
(4) sediment calibration of annual and monthly sediment loss, and
storm events
(5) pesticide/nutrient calibration of soil processes (and soil
temperature simulation)
(6) pesticide/nutrient calibration of runoff components
Note that the calibration process is not entirely sequential; that is, some
iterative fine tuning of hydrologic and sediment parameters may be required
during the pesticide/nutrient calibration to better simulate runoff quality.
Pesticide and nutrient calibration are not interdependent; they can be
performed in any order following hydrology and sediment calibration. Also,
soil temperature simulation is required only for nutrient simulation.
Each of the major calibration categories (hydrology, sediment, pesticides,
and nutrients) are described below, along with suggestions and guidelines
for parameter adjustment. Although sufficient data may not be available to
perform all the comparisons in the calibration process, the user should
analyze and evaluate all the simulated information with respect to data from
similar watersheds, personal experience, and the guidelines provided.
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6.2 HYDROLOGIC CALIBRATION
Hydrologic simulation combines the physical characteristics of the watershed
geometry and the observed meteorologic data series to produce the simulated
hydrologic response. All watersheds have similar hydrologic components, but
they are generally present in different combinations; thus different
hydrologic responses occur on individual watersheds. The AFM Model
simulates runoff from four components: surface runoff from impervious areas
directly connected to the channel network, surface runoff from pervious
areas, interflow from pervious areas, and groundwater flow. Since the
historic streamflow is not divided into these four units, the relative
relationship among these components must be inferred from the examination of
many events over several years of continuous simulation. Periods of record
with a predominance of one component (for example, surface runoff during
storm periods, or groundwater flow after extended dry periods) can be
studied to evaluate the simulation of the individual runoff components.
6.2.1 Annual Water Balance
The first task in hydrologic calibration is to establish a water balance on
an annual basis. This balance specifies the ultimate destination of
incoming precipitation and is indicated as:
Precipitation - Actual Evapotranspiration - Deep Percolation
- ASoil Moisture Storage = Runoff
In addition to the input meteorologic data series, the parameters that
govern this balance are LZSN, INFIL, and K3 (evapotranspiration index
parameter). Thus, if precipitation is measured on the watershed, and if deep
percolation to groundwater is small, actual evapotranspiration must be
adjusted to cause a change in the long-term runoff component of the water
balance. LZSN and INFIL have a major impact on percolation and are
important in obtaining an annual water balance. In addition, on extremely
small watersheds (less than 100-200 hectares) that contribute runoff only
during and immediately following storm events, the UZSN parameter can also
affect annual runoff volumes because of its impact on individual storm
events (described below).
Recommendations for obtaining an annual water balance are as follows.
(1) Annual precipitation should be greater than or equal to the sum of
annual evaporation plus annual runoff if groundwater recharge through
deep percolation is not significant in the watershed. If this does not
occur, the input precipitation should be re-evaluated and adjusted to
insure that it is indicative of that occurring on the watershed.
Since precipitation is highly variable, especially in mountainous
and thunderstorm areas, a single gage may not accurately represent the
actual precipitation on the watershed. The water balance equation
(above) is often used to estimate the actual precipitation needed
to produce the observed runoff. The input precipitation values are then
adjusted accordingly.
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(2) Since the major portion of actual evapotranspiration occurs from the
lower soil moisture zone, increasing LZSN will increase actual
evapotranspiration and decrease annual runoff. Also, decreasing LZSN
will reduce actual evapotranspiration and increase annual runoff.
Thus, LZSN is the major parameter for deriving an annual water balance.
(3) The INFIL parameter can also assist in deriving an annual water balance
although its main effect is to adjust the seasonal, or monthly runoff
distribution described below. Since INFIL governs the division of
precipitation into various components, increasing INFIL will decrease
surface runoff and increase the transfer of water to lower zone and
groundwater. The resulting increase in water in the lower zone will
produce higher actual evapotranspiration. Decreasing INFIL will
reduce actual evapotranspiration and increase surface runoff. In
watersheds with no baseflow component (from groundwater), INFIL can be
used in conjuction with LZSN to establish the annual water balance.
6.2.2 Seasonal or Monthly Distribution of Runoff
When an annual water balance is obtained, the seasonal or monthly
distribution of runoff can be adjusted with use of the INFIL parameter.
INFIL, the infiltration parameter, accomplishes this seasonal distribution
by dividing the incoming moisture among surface runoff, interflow, upper
zone soil moisture storage, percolation to lower zone soil moisture, and
groundwater storage. Of the various hydrologic components, groundwater is
often the easiest to identify. In watersheds with a continuous baseflow, or
groundwater component, increasing INFIL will reduce immediate surface runoff
(including interflow) and increase the groundwater component. In this way,
runoff is delayed and occurs later in the season as an increased
groundwater, or base flow. Decreasing INFIL will produce the opposite
result. Although INFIL and LZSN control the volume of runoff from
groundwater, the KK24 parameter controls the rate of outflow from the
groundwater storage.
In watersheds with no groundwater component, the K24L parameter is used to
direct the groundwater contributions to deep inactive groundwater storage
that does not contribute to runoff (K24L = 1.0 in this case) . For these
watersheds, runoff cannot be transferred from one season or month to
another, and the INFIL parameter is used in conjunction with LZSN to obtain
the annual and individual monthly water balance.
K24L is normally set equal to 0.0 in watersheds with a signficiant baseflow
or groundwater component, and the KV parameter can then be used to adjust
the seasonal distribution of baseflow volumes.
6.2.3 Initial Soil Mpisture Conditions
Continuous simulation is a prerequisite for correct modeling of individual
events. The initial conditions that influence the magnitude and character
of events are the result of hydrologic processes occurring between events.
Thus, the choice of initial conditions for the first year of simulation is
an important consideration and can be misleading if not properly selected.
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The initial values for UZS, LZS, and SGW should be chosen according to the
guidelines in Section 5.3.1 and readjusted after the first calibration run.
UZS, LZSf and SGW for the starting day of simulation should be reset
approximately to the values for the corresponding day in subsequent years of
simulation. Thus, if simulation begins in October, the soil moisture
conditions in subsequent Octobers in the calibration period can usually be
used as likely initial conditions for the simulation. Meteorologic
conditions preceeding each October should also be examined to insure that
the assumption of similar soil moisture conditions is realistic.
6.2.4 Storm Event Simulation
When annual and monthly runoff volumes are adequately simulated, hydrographs
for selected storm events can be effectively altered with the UZSN and INTER
parameters to better agree with observed values. Also, minor adjustments to
the INFIL parameter can be used to improve simulated hydrographs; however,
adjustments to INFIL should be minimal to prevent disruption of the
established annual and monthly water balance. Characteristics of the
overland flow plane (i.e. NN, L, SS) also have a major affect on hydrograph
shape; the pertinent parameters should be checked to insure that their
values are reasonable.
Parameter adjustment should be concluded when changes do not produce an
overall improvement in the simulation. One event should not be matched at
the expense of other events in the calibration period. Recommended
guidelines for adjustment of hydrograph shape are:
(1) The interflow parameter, INTER, can be used effectively to alter
hydrograph shape after storm runoff volumes have been correctly
adjusted. INTER has a minimal effect on runoff volumes. As shown in
Figure 6.1 where the values of INTER were (a) 1.4, (b) 1.8, and (c)
1.0, increasing INTER will reduce peak flows and prolong recession of
the hydrograph. Decreasing INTER has the opposite effect. On large
watersheds where storm events extend over a number of days, the IRC
parameter can be used to adjust the recession of the interflow portion
of the hy3rograph to further improve the simulation.
Time
Figure 6.1 Example of response to the INTER parameter
89
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(2) The UZSN parameter also affects hydrograph shape. Decreasing UZSN will
generally increase flows especially during the initial portions, or
rising limb, of the hydrograph. Low UZSN values are indicative of
highly responsive watersheds where the surface runoff component is
dominant. Increasing UZSN will have the opposite effect, and high UZSN
values are common on watersheds with significant subsurface flow and
interflow components. Caution should be exercised when adjusting
hydrograph shape with the UZSN parameters to insure that the overall
water balance is not significantly affected.
(3) The INFIL parameter can be used for minor adjustments to storm runoff
volumes and distribution. As with UZSN, changes to INFIL can affect
the water balance; thus, modifications should be minor.
When the calibration of storm hydrographs is completed, the entire hydro-
logic calibration is finished, and sediment calibration can be initiated.
6. 3 SEDIMENT CALIBRATION
As indicated in the description of the calibration process, sediment
calibration follows the hydrologic calibration and must preceed the
adjustment of the pesticide or nutrient parameters.
Sediment parameter calibration is more uncertain than hydrologic calibration
due to less experience with sediment simulation in different regions of the
country. The process is analogous; the major sediment parameters are
modified to increase agreement between simulated and recorded monthly
sediment loss and storm event sediment removal. However, observed monthly
sediment loss is often not available, and the sediment calibration
parameters are not as distinctly separated between those that affect monthly
sediment and those that control storm sediment loss.
In general, sediment calibration involves the development of an approximate
equilibrium or balance between the generation of sediment particles on one
hand and the washoff or transport of sediment on the other hand. Thus, the
sediment storage on the land surface should not be continually increasing or
decreasing throughout the calibration period. Alternating dry and wet
periods of variable length and intensity, and man-made disturbance (for
example, tillage) will cause substantial variations in the detached sediment
storage. However, the overall trend should be relatively stable. This
equilibrium must be developed and exist in conjuction with the accurate
simulation of monthly and storm event sediment loss. The detached sediment
storage is printed in monthly and annual summaries and vfoenever modified by
tillage operations.
The following sections provide guidelines and recommendations to assist in
sediment calibration.
6.3.1 Sediment Balance
On pervious areas, KRER and SCMPAC are the major parameters that control the
availability of detached sediment on the land surface, while KSER and JSER
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control the sediment washoff. The daily compaction or removal of detached
sediments by SCMPAC will dominate sediment availibility for land surfaces
with high cover factors (COVPMO). On exposed land surfaces, sediment
generation by soil splash is important and is controlled largely by the KRER
parameter. To offset the sediment availability on pervious areas, the KSER
and JSER parameters control sediment washoff to prevent continually
increasing or decreasing sediment on the land surface. Thus, a balance must
be established between the KRER and SCMPAC parameters and the KSER and JSER
parameters to develop the equilibrium described above.
6.3.2 Primary Calibration Parameters
The exponents of soil splash (JRER) and sediment washoff (JSER) are
reasonably well defined. Thus, the parameters that receive major
consideration during sediment calibration are the coefficient of soil
splash, KRER, and the coefficient of sediment washoff, KSER. These
parameters should be considered first in establishing the sediment balance.
6.3.3 Sediment Fines Storage
In general, an increasing sediment storage throughout the calibration period
indicates that either soil fines generation is too high, or sediment washoff
is too low. Examination of individual events will confirm whether or not
sediment washoff is undersimulated. A continually decreasing sediment
storage can be analyzed in an analogous manner except the SCMPAC parameter
can be suspected of being too high. Also, tillage operations will usually
cause major changes in the detached sediment storage, so two or more years
of simulation may be needed to establish the overall behavior of the
sediment storage.
6.3.4 Transport Limiting vs. Sediment Limiting
The sediment washoff during each simulation interval is equal to the smaller
of two values; the transport capacity of overland flow or the sediment
available for transport from the land surface. To indicate which condition
is occurring, an asterisk {*) is printed in the calibration output whenever
sediment washoff is limited by the accumulated sediment in each areal block
(Appendix B). Thus, when no asterisks are printed, washoff is occurring at
the estimated transport capacity of overland flow in all blocks. Generally,
washoff will be at capacity (no asterisks) during the beginning intervals of
a significant storm event; this simulates the "first flush" effect observed
in many nonpoint pollution studies. As the surface sediment storage is
reduced, washoff may be limited by the sediment storage in the blocks
producing the most surface runoff during the middle or latter part of storm
events. However, for very small events, overland flow will be quite small
and washoff can occur at capacity throughout. Also, on agricultural and
construction areas, washoff will likely occur at capacity for an extended
period of time due to the large amount of sediment available for transport.
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6.3.5 Tillage Operations
The impact of tillage operations on sediment production is represented in
the model by resetting the detached sediment storage to the value of SRERTL
on the day of the operation as specified by TIMTIL and YRTIL. We expect
that storms occurring soon after tillage will transport sediment at or near
capacity (no asterisks printed), while storms occurring an extended time (2
to 3 months) after tillage will produce sediment limited by availability of
detached material (asterisks printed). The SRERTL and SCMPAC parameters
should be evaluated conjunctively so that conditions highly susceptible to
erosion exist soon after tillage, but not later in the growing season.
Also, the pattern of crop canopy development affects the erosion potential.
6.3.6 Soil Splash and Transport Exponents
Using the information provided by the asterisks (described above) minor
adjustments in JEER and JSER, can be used to alter the shape of the sediment
graph for storm events. When available sediment is limiting (asterisks
printed), increasing JRER will tend to increase peak values and decrease low
values in the sediment graph. Decreasing JRER will have the opposite effect
tending to decrease the variability of simulated values. When sediment is
not limiting (no asterisks printed), the JSER parameter will produce the
same effect. Increasing JSER will increase variability while decreasing it
will decrease variability. These parameters will also influence the overall
sediment balance, but if parameter adjustments are minor the impact should
not be significant.
6.3.7 Concentration vs. Mass Removal
Sediment calibration for selected storm events can be performed by comparing
simulated and recorded concentrations or mass removal. For sediment and
other nonpoint pollutants, including pesticides and nutrients, mass removal
in terms of mass per unit time (gm/min) is often more indicative of the
washoff mechanism than instantaneous observed concentrations. However,- the
available data will often govern the type of comparison performed.
6.4 PESTICIDE CALIBRATION
Ideally pesticide simulation should require little, if any, calibration
since all the pesticide parameters represent characteristics that can be
determined in laboratory experiments. However, inaccuracies in the
pesticide algorithms, discrepancies between laboratory and field conditions,
variability in measured laboratory values, or lack of pertinent laboratory
values will usually require some adjustment or calibration of initial
parameter values. Calibration should be done by comparing simulated values
with measured field data. If no field data are available, data from
watersheds under similar conditions and personal experience should be used
to evaluate the simulated values.
Presently very little experience exists as a basis for adjusting the
pesticide parameters. From applications of the ARM Model in Georgia and
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Michigan, the recommended procedures for pesticide calibration are to adjust
the parameters for the following processes in the order given:
(1) pesticide degradation
(2) pesticide leaching and vertical distribution
(3) pesticide adsorption/desorption characteristics
(4) pesticide runoff evaluation
Obviously, the above processes are interrelated and any calibration
procedure will involve iterative examinations of the simulation results as
the parameter values are further refined. The intent of pesticide
calibration is to: (1) obtain the correct time distribution of the
amount of pesticide in the soil following application by adjustment of the
degradation parameters (KDG, DDG, YDG); (2) obtain the correct vertical
distribution of pesticides in the various, soil layers by adjusting the
leaching factors (UZF, LZF); and (3) obtain the correct partitioning
between solution and sediment-associated pesticide by adjusting the
adorption/desorption parameters (DD, K, N, NP). With this procedure in
mind, the following steps and guidelines for pesticide calibration are
recommended.
6.4.1 Pesticide Degradation or Persistence
The degradation rates, KDG, and the corresponding time periods as specified
by DDG and YDG should be adjusted to represent the persistence curve of the
pesticide in the soil. This curve can be evaluated from the output of daily
production runs (HYCAL=PROD and PRINT=DAYS) which indicates the amount of
pesticide present in the soil at the end of each day.
Many pesticides will degrade to negligible levels in the soil within one to
two months following application. Also, decay rates will often be much
higher in the first days and weeks after application than later in the
season. Atrazine and diphenamid have been shown to exhibit degradation
rates that are substantially reduced after the first major rainfall event
after application. If this occurs, a single-first order degradation rate
will usually underestimate degradation immediately after application and
overestimate degradation later in the growing season. Thus the KDG, DDG,
and YDG parameters can be used to employ different rates to obtain a
stepwise approximation to the actual degradation curve.
Degradation often accounts for the loss of over 90 percent of the applied
pesticide. If no soil pesticide measurements are available, the degradation
rates can be adjusted to bring the simulated runoff concentration in line
with observed values. This assumes that the partitioning characteristics
are reasonably accurate.
6.4.2 Vertical Distribution and Leaching
After the correct pesticide persistence has been approximated, the vertical
distribution can be adjusted using the upper zone and lower zone chemical
leaching factors, UZF and LZF. Soluble chemicals applied to the surface
zone will be washed to the upper and lower zones with the first rainfall
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event after application. Considering the small depth of the surface zone,
this is not an unreasonable assumption, and can only be corrected with
additional research and model development (Donigian, et al. 1977).
Increasing UZF and LZF beyond their default values of 1.0 will decrease the
chemical leaching from their respective zones. On the other hand,
decreasing these factors to values less than 1.0 will increase chemical
leaching. Guidelines for evaluating UZF and LZF are included in Section
5.3.4.
6.4.3 Pesticide Adsorption/Desorption
(1) The DD parameter is used for pesticides that are irreversibly bound to
soil particles and will not detach under repeated washings. High
values of DD will retain all the applied pesticide in the surface zone,
and pesticide loss in runoff will occur only by attachment to the
eroded sediment. In these cases, the pesticide concentration on the
eroded sediment will remain reasonably constant during an event and
will decrease with time following application due to degradation. In
effect, the eroded pesticide concentration is approximately equal to
the soil pesticide concentration and its initial value is equal to the
pesticide application divided by the mass of soil in the surface zone.
For these irreversibly bound pesticides, concentrations on eroded
sediment can be uniformly adjusted over the entire growing season by
adjusting the parameters that affect the surface zone soil mass (BDSZ
or SZDPTH), and the decrease in concentration during the growing season
is affected by the degradation rates. Guidelines for evaluating DD are
provided in Section 5.3.5.
(2) For zero values of DD or pesticide application amounts that exceed the
permanently fixed capacity of the soil (as specified by DD), the
adsorption/desorption parameters (K, N, NP) determine the partitioning
between the solution and adsorbed phases. As shown in Figure 2.4,
pesticide amounts in excess of the permanent fixed capacity enter the
adsorption/desorption algorithms to evaluate the equilibrium solution
and adsorbed concentrations. These equilibrium calculations are
performed in each time interval and for each soil layer. The
calculated pesticide solution concentration determines the pesticide
mass lost by water movement, while the adsorbed concentration
calculates the pesticide mass that is lost by erosion from the surface
layer or the amount that remains adsorbed in the other soil layers.
Figure 6.2 shows the relationship between the K, N, and NP parameters
on a logarithmic graph. All three parameters are used when the
non-single-valued (NSV) algorithm is employed; only K and N are used
for the single-valued (SV) algorithm. Figure 6.2 shows that:
(a) The input K value is the adsorbed concentration (in ppm or g/gm)
at a solution concentration of 1.0 mg/1. Thus, increasing K will
increase the simulated adsorbed concentration, and vice versa, for
either the SV or NSV algorithms.
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(b) For the SV algorithm, the value 1/N determines the slope of the
line which rotates about point A. Thus, increasing N will
decrease the slope resulting in higher adsorbed concentrations
when c < 1.0 and lower adsorbed concentrations when c > 1.0.
Decreasing N will produce the opposite effect. Except for high
application amounts or immediately after application, pesticide
solution concentrations are generally less than 1.0 and thus
increasing N usually increases the adsorbed concentration.
(c) For NSV simulation, the NP parameter affects the slope of the
branching desorption curves. Thus, increasing NP will increase
adsorbed concentrations and vice versa. The affects of NP and N
are not analogous, since each desorption curve is defined by NP,
the maximum solution concentration attained before desorption, and
a new K value calculated by the model (Donigian and Crawford
1976a).
.2
.4 .6 .8 1.0 2 4
SOLUTION CONCENTRATION (C), mg/l
Figure 6.2 Relationships of pesticide adsorption/desorption paraiteters
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Additional research and testing is needed to determine whether the SV
or NSV algorithms or a dynamic approach best represents the field
behavior of pesticides. In general, the NSV algorithm simulates higher
adsorbed sediment concentrations and appears to better represent the
ratio of solution to adsorbed pesticide in runoff during the growing
season. However, the NSV algorithm requires more computer time and it
is not clear that different K and N values with the SV algorithm could
not produce equally representative results.
The user will note that changes in the adsorption/desorption parameters
will also cause changes in the vertical distribution, since a shift in
partitioning to higher adsorbed concentrations will decrease the
solution pesticide that can move vertically with infiltrating and
percolating water. Thus UZF and LZF may need to be readjusted as a
result of changes in the adsorption/desorption parameters.
6.4.4 Pesticide Runoff Calibration
Shifts in the partitioning of a pesticide will also cause changes in the
total pesticide loss because different transport components affect the
adsorbed and solution phases. For example, a shift to higher adsorbed
concentrations will generally lead to greater pesticide loss with the eroded
sediment and less pesticide loss by the runoff components of overland flow
and interflow. The reverse is also true: higher solution concentrations
will produce greater pesticide loss by overland flow and interflow. However,
the absolute changes will depend on the relative total amounts of sediment
loss and runoff.
For highly soluble pesticides (and nutrient forms), the loss of solution
pesticide has been found to be sensitive to changes in the hydrologic
interflow parameter, INTER. INTER controls the volume of the interflow
components of runoff and hence the division of surface water between
interflow and overland flow. Chemicals with minimal adsorption to soil
particles are simulated as being transported largely by interflow. Thus,
some adjustment of the INTER parameter may be needed to improve the
simulation of these chemicals. Increasing INTER will increase the interflow
component and the associated loss of soluble chemicals, and decreasing INTER
has the opposite effect.
6.4.5 Monthly and Storm Comparisons
To the extent possible, comparisons of pesticide loss in runoff should be
done for both storm graphs and cumulative monthly values. Annual values
generally have little meaning since most pesticide loss will occur within
two to three months following application. Also, storm comparisons of mass
removal (gm/min) may be more meaningful than pesticide concentrations since
the latter can be highly erratic with little impact on total pesticide loss.
Mass removal shows the direct relationship between pesticide loss and its
transporting component, either runoff or sediment. However, concentrations
are important for examining ecologic and toxic impacts on receiving waters.
The type of information used in comparing simulated and recorded values will
depend on the available data and the problems analyzed.
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Whatever comparisons are made, pesticide calibration should be performed on
periods when the transport components, runoff and sediment, are reasonably
well simulated. Some consistency should exist between the pesticide
simulation and the transport components. Thus, if sediment is the major
transport component and it is oversimulated, then the pesticides values
should be oversimulated also. This consistency will indicate that the
correct mechanisms are being simulated even if the simulated and recorded
values are not in complete agreement.
6. 5 NUTRIENT CALIBRATION
Nutrient calibration begins with analysis and comparison of the production
run soil storages (HYCAL=PROD, INTR=DAYS) with the observed soil nutrient
data. Soil nutrient data obtained from sampling throughout the watershed
for the period of calibration provides valuable information for the
calibration of the nutrient parameters of the ARM Model. If no soil
nutrient data are available, calibration consists of merely estimating
reasonable nutrient storages and comparing the recorded and simulated
nutrient runoff results. However, all the simulation results (storages and
runoff) should be evaluated for reasonableness based on personal experience
and data from similar watersheds.
With or without observed data, the order of calibration is the same and is
analogous to the pesticide calibration procedures. (Review of Section 6.4
may assist the understanding of this section.)
Nutrient calibration involves the establishment of reasonable soil nutrient
storages through adjustment of percolation parameters, plant uptake
parameters, and reaction rates, followed by evaluation of nutrient runoff
and refinement of pertinent parameters. The recommended order and steps in
the procedure are:
(1) adjustment of percolation factors
(2) calibration of plant uptake parameters
(3) calibration of remaining soil nutrient reaction rates
(4) evaluation of nutrient runoff and refinement of related parameters
The first three steps should be done by comparing simulated and recorded
soil storages. As with pesticide calibration, some iteration of the
steps is often required. Parameter values may need to be readjusted as
later steps affect prior adjustments, but the order designated should
help to minimize the number of iterations in the calibration procedure.
6.5.1 Nutrient Percolation
The percolation factors, UZF and LZF, should be calibrated on downward
movement of chloride. Chloride merely acts as a tracer. Increasing UZF
will decrease the leaching of chloride from the upper zone (see Sections
5.3.4 and 6.4 for discussions of these parameters). If necessary,
increasing the hydrology parameter UZSN will also decrease the leaching
since this will increase moisture retention in the upper zone. However,
changing UZSN can have a noticeable impact on the hydrologic simulation.
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Experience to date on small watersheds indicates that LZF may not have to be
adjusted from its default value of 1.0; larger watersheds with nutrient
contributions to groundwater may need larger values. These percolation
factors once calibrated should not have to be readjusted unless further
changes in the hydrology parameters are made.
6.5.2 Plant Uptake of Nutrients
The plant uptake factors, ULUPTK and LZUPTK, and both the nitrogen and
phosphorus reaction rates, KPL, should be adjusted following the percolation
factors. ULUPTK (for surface and upper zones) and LZUPTK (for lower zone)
can be used to distribute the estimated total uptake both over time and
between the zones. Mjustment of KPL, the maximum uptake rate, can be used
to obtain the desired amounts of nitrogen and phosphorus uptake. The
amounts and distribution can be estimated from the guidelines given in
Section 5.3.6. All the uptake parameters should be evaluated initially from
the guidelines provided. However, since plant uptake is dependent upon the
availability of solution nitrate and phosphate, these initial values will
usually need adjustment following calibration of the other reaction rates.
6.5.3 Soil Nutrient Reaction Rates
Once the plant requirements are satisfied, the other soil reaction rates can
be calibrated. These rates must also be adjusted separately for each soil
zone. The surface and upper zone rates and storages have a direct effect on
the nutrients transported by sediment, overland flow, and interflow. The
lower zone rates and storages affect nutrient percolation to groundwater.
The three major rates to be adjusted in these zones (and in groundwater when
groundwater reactions are simulated) are KD, Kl, and KSA/KAS. The
denitrification rate, KD, may have to be increased if too much nitrogen
remains in storage after the major removals by leaching and plant uptake
have been determined. The nitrification rate, Kl, can be adjusted to get
the proper balance between NC^-N and NF^-N. The proper balance depends on a
variety of factors including the timing and form of fertilizer application,
the growing crop, the season of the year, and soil characteristics.
Consultation with soil scientists and agricultural extension personnel may
be needed to assist the evaluation of this and other aspects of the soil
nutrient simulation.
The desorption, KAS, and the adsorption rate, KSA, will also affect
nitrification by its impact on the amount of solution NH4-W available for
nitrification (oxidation) by the Kl rate. In addition, the respective
nitrogen and phosphorus KAS and KSA reaction rates will influence the
leaching, uptake, and runoff of ammonium and soluble phosphate by
determining the amounts of each in solution form.
The user will note that all the soil nutrient reactions are inhibited when
zero moisture levels occur (that is, zero values for the soil moisture
storages) . This occurs frequently in the surface zone which contains
moisture only during or immediately following storm events. The upper zone
can also experience zero moisture when small UZSN values are used.
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6.5.4 Nutrient Runoff
Once the reaction rates have been calibrated with soil data, the focus can
be on the nutrient runoff results. Simulated monthly and daily nutrient
runoff amounts should be compared with observed data. From calibration run
output (HYCAL=CALB and PRINT=INTR), simulated nutrient mass removal and
concentrations should be compared with recorded data for individual storm
events. As with the pesticide simulation, some degree of consistency should
exist between nutrient runoff simulation and the runoff and sediment
simulation, since the nutrient simulation can only be as good as the
simulation of its transport components.
Some other adjustments may be necessary when comparing the runoff results.
The model's main pathway of soluble nutrient removal (mainly Cl, N03, and
P04) is by the interflow component of runoff. Therefore, adjustment of the
hydrology interflow parameter, INTER, has been very useful in calibration of
soluble nutrients in the runoff (Section 6.4).
Sediment associated nutrients are removed only from the surface layer in the
model. Consequently, the form and amount of adsorbed nutrient forms in the
surface zone controls the amount available for removal on eroded sediment.
Application of the fertilizer directly to the adsorbed phase in the surface
zone will cause more nutrients to be in the eroded sediments. In addition,
application of fertilizer in both the surface and upper zone in the adsorbed
phase will result in less fertilizer being leached from these zones after
application. The adsorbed nutrient forms will remain in the surface and
upper zones, and will thus be available for transport for a longer period of
time than if they were applied in the soluble form. In these cases, the
desorption rates for nitrogen, phosphorus, and the Kl rate controls the
conversion to the more mobile solution forms, which are readily transported
with the moving water.
In general, analysis of the nutrient runoff results will indicate needed
changes in the nutrient storages that are usually effected by refinements in
the reaction rates. Alternating analyses of nutrient storages, reaction
rates, and runoff results is usually iterated until a satisfactory
calibration is obtained (Section 6.6). 'The user should attempt to keep
parameter adjustment within the expected ranges discussed in the parameter
evaluation guidelines (Section 5.3.6) unless evidence exists to the
contrary.
6. 6 HOW MUCH CALIBRATION?
A common question that is asked by model users concerns the extent of
calibration or parameter adjustment necessary before one can say that the
model is "calibrated" to the test watershed. Obviously this depends to some
extent on how well the initial parameter values are estimated. But beyond
that, the question is really "How close should the simulated and recorded
values be before calibration can be terminated?" The answer to this
question depends on a number of factors including the extent and reliability
of the available data, the problems analyzed vs. the model capabilities, and
the allowable time and costs for calibration.
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6.6.1 Data Problems
The available data are often the most severe limitation on calibration
especially for water quality variables. A common mistake by model users is
to accept the observed data as being absolutely accurate. In fact, any
measurement obtained under field or natural conditions will usually contain
at least a 5 to 10 percent variation from the actual or true value.
Moreover, instantaneous or short time interval measurements commonly show
variations of 10 to 20 percent and greater for flow or concentration values.
Usually annual volumes and total loss measurements are the most accurate
except when a persistent bias exists in the measurement technique.
The assumption of uniform areal precipitation is a major source of error
with direct effects on the simulation since precipitation is the driving
force of the ARM Model. Precipitation is rarely uniform and is highly
nonuniform in thunderstorm prone regions of the country. This nonuniformity
makes simulation of thunderstorms difficult since the actual rainfall is
unknown if the recording gage does not adequately represent the rainfall
pattern.
The user should be aware of the measurement techniques and the resulting
confidence limits of the observed values for both the input meteorologic
data and the runoff or soil calibration data. Simulated values within the
confidence limits of the observed calibration data cannot be improved upon;
this signals a reasonable end to calibration. However, this is not an
absolute criterion since a good overall calibration can include simulated
individual storm events or instantaneous values with larger variations than
the accepted confidence limits. In such cases, analysis of the
discrepancies and personal judgement must be called upon to decide if
calibration is sufficient.
6.6.2 Problems Analyzed vs. Model Capabilities
Another source of frustration in model calibration is the attempt to
calibrate a model for conditions or processes that the model cannot
adequately represent. Prime examples in the ARM Model are the hydrologic
impact of tillage operations and simulation of watersheds where channel
processes are significant. The ARM Model cannot presently represent the
effects of specific tillage operations on runoff and soil moisture;
additional research is needed to determine how these effects can be
simulated. Storms occurring soon after a tillage operation may not be well
simulated for runoff, but this effect decreases with time since the tillage.
Calibration of parameters to better simulate these events will bias the rest
of the simulation and produce a biased set of hydrologic parameters.
Similarly, calibration of the ARM Model on watersheds where channel
processes are important will usually lead to biased hydrologic parameters.
The hydrograph delay that is reflected in the recorded data can lead to
calibration of unusually large interflow and overland flow length
parameters. Sediment parameters would also be biased. In effect, these
parameter adjustments are attempts to account for processes that the model
does not simulate.
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To avoid these problems, the user should have a basic understanding of the
processes that are occurring on the watershed, the processes simulated by
the ARM Model, and their method of representation in the model. Study of
the ARM Model algorithms provides an additional benefit since the user will
acquire a better understanding of the role of model parameters and the
impact of parameter adjustments. Calibration can be expedited with this
knowledge, and with the realization that certain processes affecting the
observed data are not represented in the model. Parameter adjustments to
circumvent such model limitations are both inappropriate and futile.
6.6.3 Guidelines
In many applications of the ARM Model, the time and costs budgeted to
calibration will determine the level of effort expended. Calibration is a
critical step in any model application and may require 30 to 50 percent of
the total project resources. Its importance cannot be understated. The
arguments provided above should not be used to justify reducing the time and
costs required for a reasonable calibration. However, our experience has
shown that many diligent users will often spend too much time on calibration
due to insufficient observed data, ignorance of the accuracy of the data,
and misconceptions of model capabilities and parameter sensitivities.
The agreement between simulated and recorded values required for an adequate
calibration is highly dependent on the specific watershed, data conditions,
and problems analyzed. Very little quantitative information exists to
provide guidelines for evaluating a calibration. However, from our
experience in applying the ARM Model and related models and within the
framework of the considerations discussed above, the following general
guidelines for characterizing a calibration are provided to assist potential
model users:
Difference Between Simulated and Recorded Values (percent)
Calibration Results
Very Good Good Fair
Hydrology <10 10-15 15-25
Sediment <15 15-25 25-35
Pesticides/Nutrients <20 20-30 30-40
The above percent variations largely apply to annual and monthly values for
runoff, sediment, and pesticide/nutrient loss. Individual events may show
considerably larger variation for many reasons with little impact on the
overall calibration. These values should be used only as approximate
guidelines. The user should attempt to obtain the best calibration possible
within the limitations of the available data, the model capabilities, and
the allowable budget.
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6.7 CONCLUSION
The use of a continuous simulation model provides insight into the
relationships among the various components of the hydrologic cycle and water
quality processes. A model cannot be applied without understanding these
relationships, yet the process of modeling itself is instructive in
developing this understanding. The calibration process described above
requires such an understanding of the physical process being simulated, the
method of representation, and the impact of critical AFM Model parameters.
It is not a simple procedure. However, study of the parameter definitions,
the algorithm formulation, and the above guidelines should allow the user to
become reasonably effective in calibrating and applying the ABM Model.
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SECTION 7
SIMULATION ANALYSIS AND APPLICATIONS
7.1 METHODS OF ANALYSIS
Since the ARM Model produces continuous runoff quantity and quality
information for any period of input meteorologic data, how this information
is analyzed is a critical consideration in any application of the model.
The possible methods of analysis include evaluation of (1) single or
so-called "design" storm events, (2) mean monthly, seasonal, or annual
values, and (3) frequency or probability distributions of runoff and
pollutant concentrations or loadings. Obviously each method of analysis
has different requirements of observed data, labor effort, technical
expertise, and computer cost. The analyst must consider these factors
in choosing a particular analysis procedure for the problem being analyzed.
However, each method of analysis does not produce the same information
and can lead to different decisions if choices are to be made for use,
management, or regulatory practices of agricultural lands.
The ARM Model can be used to produce the information necessary for each
of the above analysis methods or others. However, we strongly advise
against the use of the ARM Model in single or design storm event analysis
for the following reasons:
(1) The model should not be calibrated on single storms in separate model
runs because the initial moisture, sediment, and soil conditions are
usually unknown and will often bias the simulation and the calibrated
parameters. Model parameters must be calibrated with continuous runs
for extended periods of time.
(2) The choice of a single storm is usually an arbitrary decision. Often
the largest storm is chosen and no frequency can be assigned to specify
how often the storm will occur. Rainfall frequency cannot be assigned
to runoff, and neither rainfall nor runoff frequency can be assigned to
the runoff quality.
(3) Simulation results for a single storm event can be highly variable for
the reasons discussed in Section 6. Also, critical events for
pollutant loadings cannot be necessarily predicted. Alternative plans
should be evaluated under a variety of environmental and meteorologic
conditions.
Monthly, seasonal, or annual values and frequency distributions can be
obtained from information produced by the same ARM Model run. The model
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provides the monthly and annual values by summing the simulation results in
each time interval. These summary values of runoff, sediment loss, or
pesticide/nutrient loadings obtained from separate model runs for
alternative land use or management conditions can provide the basis for
deciding among the various alternatives. Simulation runs for at least 3 to
5 yr, and preferably up to 10 yr, should be performed to obtain the mean
monthly, seasonal, or annual values. The longer runs can also provide an
indication of the variability expected about the mean value. Runoff volumes
and pollutant loadings are the type of information that is usually reported
in this type of analysis because mean concentration values for long time
spans are not especially useful in characterizing the highly
intermittent problems of nonpoint pollution.
To fully exploit the information provided by continuous simulation,
frequency analysis of the simulated time series information is recommended
in order to characterize the frequency or probability of occurrence of
runoff and pollutant levels under a wide range of meteorologic and
environmental conditions. The use of derived frequency distributions
obtained from continuous simulation for evaluating water quality plans is
described by Donigian and Linsley (1976).
Figures 7.1 and 7.2 are examples of frequency distributions obtained from
the analysis of ARM Model simulation runs for alternative soil and
water conservation practices.* This information was developed as part
of an ongoing research project by Cornell Lfriiversity and sponsored by
EPA to evaluate the effectiveness of soil and water conservation practices
for pollution control (Cornell University 1976). Figure 7.1 shows the
runoff, sediment concentration, and sediment flux (mass removal) curves,
while Figure 7.2 includes the curves for total pesticide flux and
concentrations in the runoff water and eroded sediment. Simulation runs of
3.4 years on the P2 watershed (1.3 ha) in Watkinsville, Georgia
provided the continuous time series information to develop these curves.
The various practices were represented by assuming changes in the relevant
hydrologic and sediment parameters.
The curves are presented in terms of the percent of time the particular
variable (for example, runoff in cms) is greater than the ordinate value.
Thus, Figure 7.1 shows that sediment concentrations under terracing and/or
contouring are greater than 8.0 gm/1 for 2 percent of the time (time during
which runoff is occurring), whereas no conservation practices would produce
sediment concentration greater than 11.0 gm/1 for 2 percent of the time.
Similarly, Figure 7.2 shows that the pesticide concentration in water for
1.0 percent of the time will be greater than 1.2 mg/1 for
base/non-conservation conditions and greater than 0.4 mg/1 for contouring
and terracing. In this way frequency curves can be analyzed to determine
how often specific runoff volumes, flow rates, pollutant concentrations, or
flux rates will occur. For ecologic impact, the frequency curves and
*Neither Version I nor Version II of the ARM Model includes the capability
to generate these curves. Slight modification of the code and a program to
perform the frequency analysis can be obtained from the Environmental
Research Laboratory, Athens, GA. Contact: Lee Mulkey, (404) 546-3581.
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w
s
1
30.0
25.0
20.0
15.0
10.0
5.0
20.0
16.0
12.0
8.0
4.0
560.0
480.0
400.0
320.0
240.0
160.0
80.0 J
i i i i i i i r \ \
o Base Conditions
v Contouring
D Contouring+ Terracing
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0
% OF TIME GREATER THAN NOTED VALUE
Figure 7.1 Runoff and sediment frequency analysis
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o Base Conditions
v Contouring
Contouring+Terracing
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.0
% OF TIME GREATER THAN NOTED VALUE
Figure 7.2 Pesticide frequency analysis
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toxicity data can be used to estimate how often acute or chronic pesticide
levels toxic to specific organisms will exist.
1b evaluate the net or overall impact of the alternative practices, the
area beneath the curve for each practice can be calculated and compared from
elementary decision theory this area represents the expected value of the
ordinate variable under all conditions; that is, the value of the variable
times its probability of occurrence, summed over all possible occurrences.
For example, the area beneath the base sediment curve in Figure 7.1 is the
expected sediment concentration without conservation practices. It is
measured in units of the y-axis, mg/1; each block (1 x-axis unit x 1 y-axis
unit) is 0.08 mg/1 (4 mg/1 x .02). The differences in area beneath each
curve, or the area between the curves, can be used to evaluate the impact of
a particular practice. Table 7.1 lists the area beneath each frequency
curve and the percent change for each practice from the
base/non-conservation conditions. Evaluation of the overall effect of
different practices is accomplished with this information for the runoff
components of interest.
In summary, frequency analysis of the output obtained from the ARM Model
simulation runs is recommended to effectively utilize continuous simulation.
Total and mean values for runoff and pollutant loadings can complement
the frequency analysis since both types of information are provided by the
ARM Model.
7.2 APPLICATIONS
The ARM Model is specifically designed as a tool to evaluate the quantity
and quality of agricultural runoff and the impacts of alternative management
practices. Although testing has been limited to small agricultural
watersheds, the model can be used in non-agricultural (and non-urban)
areas since the processes and mechanisms simulated are universal. Urban
areas cannot be simulated because the impervious land surface processes
are not adequately represented.
Possible applications for the ARM Model include:
(1) Quantifying the runoff, sediment, pesticide, and nutrient content of
agricultural runoff.
(2) Evaluating the runoff quality resulting from alternative levels of
pesticide and fertilizer applications.
(3) Providing runoff components (quantity and quality) from non-urban areas
as input to stream water quality models for comprehensive basin
modeling.
(4) Evaluating ecologic effects resulting from the runoff of toxic
substances.
(5) Evaluating the runoff quantity and quality resulting from alternative
agricultural land management practices.
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TABLE 7.1 FREQUENCY ANALYSIS OF ALTERNATIVE SOIL AND WATER
CONSERVATION PRACTICES USING THE ARM MODELa
Total Runoff, cms x 10
-2
Base
Conditions
1.183
Expected Value
Contouring
1.124
Contouring
and Terracing
0.717
Percent Change from
Base Conditions
Contouring
-5.0
Contouring
and Terracing
-39.4
Overland Flow, cms x 10
-2
Interflow, cms x 10
-2
1.130
0.110
1.076
0.108
0.629
0.119
-4.8
-1.8
-44.3
+8.2
o
00
Sediment Loss
Concentration, mg/1
Flux, kg/min
Pesticide Loss in Water6
Concentration, mg/1
Flux, gm/min
Pesticide Loss on Sediment
Concentration, ppm
Flux, gm/min
1.161
4.76
Total Pesticide Flux , gm/min 0.0215
0.0710
0.0206
0.3813
0.0011
0.875
3.02
0.0181
0.0428
0.0176
0.1680
0.0009
0.938
1.92
0.0115
0.0301
0.0109
0.1961
0.0006
-24.6
-36.6
-15.8
-39.7
-14.6
-55.9
-18.2
-19.2
-59.7
-46.5
-57.6
-47.1
-48.6
-45.4
values were obtained from simulation runs with the ARM Model for 3.4 years on the P2 watershed
. (1.3 hectares) in Watkinsville, Georgia.
Area beneath the corresponding frequency curve obtained from the simulated data. Not all of the
frequency curves are shown in Figures 7.1 and 7.2.
^Base conditions refer to cropping parallel to the land slope.
Contouring and terracing were represented by assuming changes in pertinent hydrologic and sediment
parameters.
Atrazine was the pesticide simulated.
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Other applications and variations of those mentioned above are possible
within the capabilities of the model and the ingenuity of the user.
Version II of the ARM Model is not a final product since further testing and
evaluation is continuing to uncover model deficiencies and improve
simulation of specific processes and agricultural practices. Further
research is needed to better represent erosion processes, the effects of
tillage operations, the transport of soluble substances, pesticide
adsorption and degradation mechanisms, and nutrient transformations.
However, in its present form the ARM Model can be an extremely useful tool
for analysis of agricultural nonpoint pollution when it is applied with an
awareness of its capabilities and limitations.
109
</pre><hr><pre>
-------
REFERENCES
American Geophysical Union. 1965. Inventory of Representative and
Experimental Watershed Studies Conducted in the United States. Prepared
for Symposium on Representative and Experimental Watersheds, Budapest,
Hungary. September 28-October 5, 1965. 153 pp.
Anderson, E.A. 1968. Development and Testing of Snow Pack Energy Balance
Equations. Water Resour. Res. 4(1):19-37.
Anderson, E.A., and N.H. Crawford. 1964. The Synthesis of Continuous
Snowmelt Runoff Hydrographs on a Digital Computer. Department of Civil
Engineering, Stanford University. Stanford, California. Technical
Report No. 36. 103 p.
Broadbent, F.E., and F. Clark. 1965. Denitrification. In: Soil Nitrogen,
W.V. Bartholomew and F.E. Clark (eds.), Madison, Wis., Am. Soc. Agron.
Agronomy Monograph No. 10. p. 344-359.
Cornell University. 1976. Effectiveness of Soil and Water Conservation
Practices for Pollution Control. College of Agriculture and Life
Sciences, Ithaca, New York. Ongoing research grant No. R804925-01-0 for
the U.S. Environmental Protection Agency, Athens, Georgia.
Crawford, N.H., and A.S. Donigian, Jr. 1973. Pesticide Transport and
Runoff Model for Agricultural lands. Office of Research and
Development, U.S. Environmental Protection Agency, Washington D.C. EPA
660/2-74-013. 211 p.
Crawford, N.H. and R.K. Linsley. 1966. Digital Simulation in Hydrology:
Stanford Watershed Model IV. Department of Civil Engineering, Stanford
University, Stanford, California. Technical Report No. 39. 210 pp.
David, W.P., and C.E. Beer. 1974. Simulation of Sheet Erosion, Part I.
Development of a Mathematical Erosion Model. Iowa Agriculture and Home
Economics Experiment Station. Ames, Iowa. Journal Paper No. J-7897. 20
pp.
Donigian, A.S., Jr., and R.K. Linsley. 1976. The Use of Continuous
Simulation in the Evaluation of Water Quality Management Plans. Prepared
for U.S. Department of the Interior, Office of Water Research and
Technology, Washington, D.C. Contract No. 14-31-0001-4215. 94 pp.
110
</pre><hr><pre>
-------
Donigian, A.S., Jr., and N.H. Crawford. 19"6a. Modeling Pesticides and
Nutrients on Agricultural lands. Environmental'Research Laboratory.
Environmental Protection Agency. Athens, GA. EPA 600/2-76-043. 263 pp.
Donigian, A.S., Jr., and N.H. Crawford. 1976b. Modeling Nonpoint Pollution
from the Land Surface. Office of Research and Development. Environmental
Protection Agency. Athens, GA. EPA 600/3-76-083. 292 pp.
Donigian, A.S., Jr., and N.H. Crawford. 1976c. Simulation of Agricultural
Runoff. In: Environmental Modeling and Simulation. Proceedings of an
EPA Conference in Cincinnati, Chio, April 19-22, 1976. EPA 600/9-76-016.
pp. 151-155.
Donigian, A.S., Jr., D.C. Beyerlein, H.H. Davis, Jr., and N.H. Crawford.
1977. Agricultural Runoff Management (ARM) Model - Version II: Testing
and Refinement. Office of Research and Development. Environmental
Protection Agency. Athens, GA. EPA-600/3-77-098. 310 pp.
Edwards, C.A. 1964. Insecticide Residues in Soils. Residue Reviews.
13:83-132.
Fleming, G., and M. Fahmy. 1973. Some Mathematical Concepts for Simulating
the Water and Sediment Systems of Natural Watershed Areas. Department of
Civil Engineering, Strathclyde University. Glasgow, Scotland. Report
HO-73-26.
Hagin J., and A. Amberger. 1974. Contribution of Fertilizers and Manures
to the N- and P- Load of Waters. A Computer Simulation. Report Submitted
to Deutsche Forschungs Gemeinschaft. 123 pp.
Hydrocomp, Inc. 1976. Hydrocomp Simulation Programming Operations Manual.
4th Edition. Palo Alto, CA. 115 pp.
Leytham, K.M. and R.C. Johanson. 1977. Development of the Watershed
Erosion and Sediment Transport Model. Draft Report for Research Grant No.
R803726-01-0. U.S. Environmental Agency, Athens, GA.
Linsley, R.K., M.A. Kohler, and J.L.H. Paulhus. 1975. Hydrology for
Engineers. 2nd Edition. McGraw-Hill. 482 pp.
Mehran, M., and K.K. Tanji. 1974. Computer Modeling of Nitrogen
Transformations in Soils. J. Environ. Qual. 3(4):291-395.
Menzie, C.M. 1972. Fate of Pesticides in the Environment. Annual Review
of Entomology. 17:199-122.
Meyer, L.D., and W.H. Wischmeier. 1969. Mathematical Simulation of the
Process of Soil Erosion by Water. Trans. Am. Soc. Agric. Eng.
12(6):754-758,762.
Ill
</pre><hr><pre>
-------
Negev, M.A. 1967. Sediment Model on a Digital Computer. Department of
Civil Engineering, Stanford University. Stanford, CA. Technical Report
No. 76. 109 pp.
Onstad, C.A., and G.R. Foster. 1975. Erosion Modeling on a Watershed.
Trans. Am. Soc. Agri. Eng. 18(2):288-292.
Stanford, G., and S.J. Smith. 1972. Nitrogen Mineralization Potential in
Soil. Soil Sci. Soc. Amer. Proc. 36:465-472.
Stewart, et al. 1975. Control of Water Pollution from Cropland. U.S.
EPA-ORD and USDA-ARS. EPA-600/2-75-026a, ARS-4-5-1. 188pp.
Soil Conservation Service. 1974. National Engineering Handbook, Section 4.
Hydrology: Part I. Watershed Planning. Soil Conservation Service, U.S.
Department of Agriculture. Washington, D.C. p 7.7-7.12.
U.S. Army Corps of Engineers. 1956. Snow Hydrology, Summary Report of the
Snow Investigations. North Pacific Division. Portland, OR. 437 p.
U.S. Department of Agriculture, Forest Service. 1977. Nonpoint Water
Quality Modeling in Wildland Management: A State-of-the-Art Assessment.
Volumes I and II. Interagency Agreement No. EPA-1AG-D5-0660.
EPA-600/3-77-036. U.S. Environmental Protection Agency, Athens, Georgia
156 pp.
Wischmeier, W.H. and D.D. Smith. 1958. Rainfall Energy and Its
Relationship to Soil Loss. Trans. Amer. Geophys. Union. 39(2):285-291.
Wischmeier, W.H., and D.D. Smith. 1965. Predicting Rainfall Erosion losses
from Cropland East of the Rocky Mountains. Department of Agriculture.
Agricultural Handbook No. 282. 47 pp.
Wischmeier, W.H., L.B. Johnson, and B.V. Cross. 1971. A Soil Erodibility
Nomograph for Farmland and Construction Sites. J. Soil Water Cons.
26(5):189-193.
Wischmeier, W.H. 1975. Estimating the Soil Loss Equation's Cover and
Management Factor for Undisturbed Areas, p. 118-124. In: Present and
Prospective Technology for Predicting Sediment Yields and Sources. U.S.
Department of Agriculture, Agricultural Research Service. ARS-S-40.
112
</pre><hr><pre>
-------
APPENDIX A
SAMPLE INPUT SEQUENCES FOR THE ARM MODEL
TABLES
Al Input Sequence for Hydrology (with snow) and Sediment Simulation with
Meteorologic Data
A2 Input Sequence for Hydrology (without snow) , Sediment, and Pesticide
Simulation with Meteorologic Data
A3 Parameter Input Sequence for Hydrology (with snow) , Sediment, and
Nutrient Simulation
A4 Parameter Input Sequence for Hydrology (without snow) and Sediment
Simulation with Runoff and Sediment Written to Disk
113
</pre><hr><pre>
-------
TABLE Al. INPUT SEQUENCE FOR HYDROLOGY (WITH SNOW) AND SEDIMENT SIMULATION WITH
METEOROLOGIC DATA
//HARL7508 JOB 'A19$X2,444, .25,40 ', 'SNOW SAMPLE'
/*JOBPARM HOLD=JOB
//JOBLIB DD DSNAME=WYL.X2.A19.HD7508.ARMLM.DP100677,
// UNIT=DISK,VOL=SER=PUB005,DISP=(OLD,KEEP>
//STEP1 EXEC PGM=ARM
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT05F001 DD *
MICHIGAN P6 SNOW SAMPLE
HYDROLOGY AND SEDIMENT
HYCAL=CALB
INPUT=ENGL
OUTPUT=ENGL
PRINT=INTR
SNOW=YES
PEST=NO
NUTR=NO
ICHECK=ON
DISK=NO
SCNTL INTRVL= 5
1.98
SEND
SEND
HYMIN= 0.010, AREA=
SSTRT BGNDAY= 1, BGNMON= 1, BGNYR= 1974
SENDD ENDDAY = 31, ENDMON= 1, ENDYR= 1974 SEND
SLND1 UZSN= 0.200, UZS= 0.500, LZSN= 9.00, LZS= 11.0 SEND
SLND2 L= 60.,SS= 0.060,NN= 0.2000,A= 0.0000,EPXM=0.1200,PETMUL=1.000 «END
SLND3 K3=0.20,0.20,0.20,0.20,0.30,0.30,0.50,0.45,0.40,0.30,0.20,0.20 SEND
SLND4 INFIL=0.03,INTER=0.80,IRC=0.00,K24L= 1.00,KK24= 0.00,K24EL=0.00 SEND
8LND5 SGW=0.00,GWS=0.00,KV=0.00,ICS=0.00,OFS=0.00,IFS=0.000 SEND
SNOWPRINT=YES
SSN01 RADCON=1.0,CCFAC=1.00,SCF=1.40,ELDIF=0.0,IDNS= 0.14,F= 0.0 SEND
SSN02 DGM=0.0,WC=0.03,MPACK=1.0,EVAPSN=0.40,MELEV= 892.,TSNOW=32.00 SEND
SSN03 PACK= 0.0,DEPTH= 0.0 SEND
SSN04 PETMIN= 35.0,PETMAX= 40.0,WMUL= 1.0,RMUL= 1.00,KUGI= 0.0 SEND
SCROP COVPMO = 0. 0,0. 0,0. 0,0. 0,0. 0,0. 05, 0.55, 0.9*0,0.. 9 0,0.80, 0.0, 0.0 SEND
SMUD1 TIMTIL= 140,136,0,0,0,0,0,0,0,0,0,0
SMUD2 YRTIL= 74,75,0,0,0,0,0,0,0,0,0,0 SEND-
SMUD3 SRERTL= 1
SSMDL JRER=2.2,
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
15
15
15
15
15
15
15
15
15
15
15
.00,0.
KRER=0
26
26
26
26
26
26
26
26
26
26
26
80,0.0
,0.0,0.0,0.1
1,0.0,0.0,0.0,0.0,0.0,0.0
.15,JSER=1.40,KSER=0.5,SRERI=1.000,SCMPAC=0.001
42
42
42
42
42
42
42
42
42
42
42
82
82
82
82
82
82
82
82
82
82
82
107
146
100
153
54
192
107
46
23
77
130
140
155
140
190
176
113
162
56
148
148
106
258
192
236
258
162
185
155
221
288
140
185
189
77
119
98
126
175
154
77
147
152
84
90
48
48
84
84
96
84
121
96
78
103
48
21
27
69
101
69
48
43
69
59
80
SEND
SEND
29
29
29
29
29
29
29
29
29
29
29
17
17
17
17
17
17
17
17
17
17
17
(oontinus)
</pre><hr><pre>
-------
TABLE Al (continued)
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
TEMP74
19
15
23
23
24
24
22
14
23
21
22
20
18
35
38
43
43
34
35
40
47
35
35
40
47
53
52
42
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
15
11
2
10
12
4
10
12
-7
9
11
12
3
5
17
23
20
21
20
27
30
34
33
31
25
29
29
34
30
28
19
20
20
15-
19
19
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
15
14
10
9
4
10
3
24-12
26
30-
26
45
43
33
24
33
33
36
40
43
42
43
30
21
28
35
46
52
6
4
7
19
28
6
2
14
18
15
31
20
31
31
15
7
4
13
25
40
50
48
70
70
52
59
56
55
49
46
42
42
32
41
42
39
40
41
41
39
38
42
43
22
33
39
37
37
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
30
35
44
39
33
38
44
32
32
38
27
31
20
17
31
32
34
27
30
18
27
21
19
4
3
30
25
27
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
115
69
61
161
69
38
123
169
222
153
138
260
230
153
161
107
8
15
169
92
197
148
141
141
21
28
56
134
204
155
141
92
155
204
190
141
212
282
146
46
64
65
61
45
49
50
37
41
57
58
67
69
68
51
56
53
61
53
63
71
71
56
54
58
72
80
78
32
36
44
44
39
25
33
32
21
26
44
47
50
49
33
29
33
41
23
30
49
54
31
27
31
36
50
61
63
66
65
59
58
50
52
53
47
62
65
62
50
73
71
58
70
66
70
73
85
84
74
70
62
64
62
63
38
39
39
34
36
29
26
38
36
35
38
43
35
44
48
46
54
47
49
44
49
63
53
44
46
43
39
46
73
73
75
83
83
82
81
80
84
84
65
69
72
87
76
72
57
71
77
85
84
79
70
70
75
79
79
79
46
50
47
58
60
64
65
66
70
66
46
46
51
52
57
48
47
50
58
58
65
56
41
46
47
53
50
52
185
221
251
325
199
170
221
244
244
207
74
30
118
140
111
310
280
288
244
199
85
87
90
91
74
81
89
92
92
89
79
82
91
95
95
81
83
87
87
87
80
80
71
81
81
87
87
89
56
65
73
67
53
56
56
65
68
69
52
47
59
72
62
54
56
70
66
52
45
58
58
56
60
62
66
53
147
231
147
161
119
70
161
182
175
182
189
168
168
140
161
224
77
105
126
36
96
72
115
90
109
90
96
115
72
109
90
115
139
163
139
48
90
54
27
37
11
43
48
85
64
27
32
37
101
75
64
59
69
91
80
69
11
238
76
79
78
77
76
82
81
81
80
81
82
83
82
79
81
82
76
81
85
88
89
86
86
80
80
89
89
71
52
59
64
52
56
55
52
62
60
58
67
60
63
54
54
60
61
53
57
62
63
62
65
57
48
58
65
51
69
66
64
66
68
71
71
79
81
80
84
84
80
62
70
70
77
76
82
82
64
61
57
62
65
79
78
73
49
47
42
40
41
43
46
55
57
60
65
65
59
42
50
37
50
45
45
52
46
35
30
39
50
40
49
61
48
43
52
66
71
71
65
58
58
69
71
70
54
62
54
58
58
53
39
40
51
66
65
66
63
62
65
72
69
37
25
27
37
51
57
36
31
44
35
43
54
33
50
37
32
45
26
25
23
20
36
51
33
46
32
37
42
72
69
64
49
40
42
52
58
58
52
70
46
40
33
35
41
49
55
52
50
41
43
59
58
38
29
31
32
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
29
61
43
49
37
32
36
29
32
30
41
43
37
30
23
20
19
35
37
39
35
31
29
33
37
20
13
22
25
32
39
38
29
32
36
36
35
27
35
36
36
35
34
36
35
35
28
32
29
29
31
41
37
32
28
34
34
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
27
23
21
13
16
11
27
26
16
17
21
32
33
31
30
31
27
16
13
21
22
19
26
31
21
21
22
28
(continue)
</pre><hr><pre>
-------
TABLE Al (continued)
TEMPTS
TEMP74
TEMP74
WIND?'*
WIND74
WIND?'*
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND?'*
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
WIND74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
33 27
50 28
50 27
90
50
30
120
50
50
190
90
120
50
110
110
80
200
250
200
180
70
130
160
210
100
150
150
80
40
230
220
100
130
230
149
82
101
196
147
105
189
151
190
74
155
253
146
150
130
210
100
130
60
210
160
30
60
100
240
40
60
160
80
110
160
130
50
150
50
140
320
90
110
110
150
80
71
170
187
265
188
101
316
392
294
143
179
262
128
270
33 26
38 32
40 32
170
100
110
70
250
80
80
160
130
150
70
180
130
70
100
40
220
140
130
60
130
140
220
190
150
190
60
120
170
70
150
125
90
155
19
376
299
387
33
126
375
171
415
473
406
72 58
69 58
140
235
135
190
190
120
140
180
130
80
90
180
225
180
305
85
110
115
105
80
145
195
250
140
70
40
100
110
150
95
191
484
215
169
152
578
122
361
544
515
150
292
379
175
73 60
75 57
75 57
105
120
120
60
80
155
85
95
115
20
145
115
185
150
175
90
50
70
80
95
30
55
130
150
130
55
30
55
40
50
100
628
413
326
492
227
364
658
71
142
565
154
307
513
212
81 52
82 62
50
40
30
60
60
90
140
80
70
100
140
50
90
70
50
120
95
60
47
37
85
54
101
61
59
51
49
40
41
102
572
453
296
610
477
402
326
343
774
260
380
646
505
412
89 59
78 56
78 54
71
87
112
88
78
31
39
41
55
56
73
28
37
65
105
42
31
90
78
64
35
42
52
21
37
16
61
52
80
103
83
250
274
632
376
612
654
212
563
543
307
678
674
502
548
75 51
78 57
79 60
64
53
38
51
82
18
29
14
43
72
83
47
49
53
39
41
52
57
51
38
41
51
38
63
27
54
80
29
30
36
104
288
310
449
587
592
525
455
318
435
341
220
472
481
553
72 47
54 38
43
48
66
37
10
32
28
51
33
39
61
51
106
75
105
31
82
73
65
60
60
90
20
55
115
115
65
40
140
110
•
379
41
443
458
401
416
349
411
384
255
333
230
209
302
67 49
72 57
73 61
140
102
18
59
90
73
121
38
51
50
48
58
72
135
103
51
50
88
32
71
40
119
50
46
107
76
66
50
69
97
64
181
209
391
278
292
164
234
222
271
330
275
46
189
52
37 24
30 20
116
22
40
80
102
65
54
36
20
56
103
101
123
92
215
99
121
106
58
127
279
83
89
139
122
88
138
74
59
60
237
214
22
41
39
32
222
226
199
71
22
35
48
71
38 30
38 29
36 26
232
243
174
47
21
50
24
150
196
128
37
27
16
54
201
108
144
149
160
57
22
45
202
28
152
137
113
127
105
105
30
40
180
80
182
97
134
49
74
139
164
85
39
33
43
(continue)
</pre><hr><pre>
-------
TABLE Al (continued)
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
RADI74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEUIPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEU1PT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
DEWPT74
99
150
101
64
148
36
16
30
81
200
250
150
75
75
101
218
196
10
11
0
0
-4
18
10
-2
14
17
16
-12
9
31
31
36
12
28
32
38
30
33
30
27
37
39
35
32
33
47
27
341
148
317
106
72
348
236
67
255
275
370
382
297
207
-24
12
5
3
3
-30
2
4
10
22
3
24
29
10
1
25
21
32
31
23
26
27
-1
0
-1
23
26
30
0
0
0
90
239
457
206
302
365
346
352
265
381
646
267
131
58
103
100
100
27
47
55
36
31
48
34
33
42
27
30
20
8
18
36
27
21
28
26
24
20
21
19
3
17
10
21
25
31
34
34
255
579
574
134
616
559
200
175
150
575
200
593
422
173
315
255
40
42
53
40
34
38
34
26
26
26
45
61
59
46
33
33
40
41
35
41
53
51
36
25
31
52
43
57
64
59
0
382
54
170
349
605
615
525
288
644
365
382
600
275
119
211
534
290
53
45
45
44
42
25
25
37
39
38
61
44
41
59
48
54
54
53
54
55
67
61
58
45
46
39
48
58
64
65
58
415
232
209
382
285
659
500
424
287
465
637
640
668
644
621
578
50
50
57
55
62
66
68
68
71
60
58
64
64
68
67
45
50
56
67
57
66
53
44
45
49
50
49
45
44
50
0
600
616
368
418
521
677
619
88
310
548
246
490
558
601
596
497
542
50
72
67
67
57
54
61
67
71
68
51
40
68
68
57
52
62
66
58
46
49
64
59
59
64
59
46
56
51
51
52
524
261
286
533
486
482
475
431
355
391
508
450
392
333
462
444
481
58
62
57
59
51
63
59
62
64
60
71
58
62
59
61
67
61
59
60
61
61
61
60
53
59
65
58
54
53
51
43
382
442
306
402
386
130
312
395
397
245
309
380
322
174
177
84
48
46
41
43
46
49
54
60
60
66
64
69
50
41
52
44
56
44
59
40
36
25
28
34
36
48
50
62
40
38
0
280
294
284
263
157
336
316
275
127
229
211
269
248
243
34
138
165
34
29
27
47
51
58
33
37
44
47
43
52
48
49
37
37
34
24
33
21
25
42
46
51
37
29
33
44
56
61
62
93
121
175
166
41
16
72
158
139
17
162
147
52
120
181
75
47
55
45
33
38
36
38
45
42
44
44
38
31
28
25
28
36
37
48
34
31
32
52
36
21
22
29
29
18
23
0
22
38
79
88
74
96
65
104
163
85
89
172
71
48
65
172
38
32
27
22
14
22
18
34
22
18
24
33
34
33
31
33
33
21
14
27
24
26
26
30
30
24
15
30
28
33
24
32
(continue)
</pre><hr><pre>
-------
TABLE Al (continued)
00 7401075
7401019
7401021
7401022
7401023
7401024
7401025 11 1
7401026
7401027
7401028
7401039
7401049
7401059
7401061
7401062
7401063 1
7401064 1
7401065 1 1
7401066
7401067 1
7401068
7401071
7401072 1
7401073
7401074
7401076
7401077
7401078
7401089
7401091 111111111111 1 1 1
7401092 11 1
7401093
7401094
7401095
7401096
7401097
7401098
7401101 1 11
7401102 1 1
7401103
7401104
7401105
7401106
7401107
7401108 1 1
7401111 111111111
7401112 11 1
7401113
(continue)
</pre><hr><pre>
-------
TABLE Al (continued)
7401114
7401115
7401116
7401117
7401118
7401121
7401122
7401123
7401124
7401125
7401126
7401127
7401128
7401139
7401149
7401159
7401169
7401179
7401189
7401199
7401201
7401202
7401203
7401204
7401205
7401206
7401207
7401208
7401211
7401212
7401213
7401214
7401215
7401216
7401217
7401218
7401221
7401222
7401223
7401224
7401225
7401226
7401227
7401228
7401239
7401249
7401259
7401261
2334211 1 1
133222312232646732211 111 1
111
1 111111
1 111111 1 11 11
11111111 1 111
1 11 111 111111 1 1
1 1 111
1 111
1 1
1 11 11
111 11 1 1
111 1 11 1
1 1
1
1 1
1 1
1
1
1 1
1 2
1
1 1
1 1
(continue)
</pre><hr><pre>
-------
TABLE Al (continued)
7401262
7401263
7401264
7401265
7401266
7401267 1223 11111 12222
7401268 653111 11
7401271
7401272 11 1
7401273
7401274
7401275
7401276
7401277
7401278
7401281
7401282
7401283
7401284
7401285 1111
7401286 111 11 1
7401287
7401288
7401299
7401309
7401319
</pre><hr><pre>
-------
TABLE A2. INPUT SEQUENCE FOR HYDROLOGY (WITHOUT SNOW) , SEDIMENT, AND PESTICIDE
SIMULATION WITH METEOROLOGIC DATA
//HARL7508 JOB 'A19$X2,444,.10,40','J7508 DAVIS '
/XJOBPARM HOLD=JOB
//JOBLIB DD DSNAME=WYL.X2.A19.HD7508.ARMLM.DP100677,
// UNIT=DISK,VOL=SER=PUB005,DISP=(OLD,KEEP)
//STEP1 EXEC PGM=ARM
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT05F001 DD *
P-2 = PESTICIDE RUN USING LITERATURE PARAQUAT VALUES X SZDPTH=0.125
PARAQUAT APPLIED: 1973, 1974, & 1975
HYCAL=CALB
INPUT=ENGL
OUTPUT=ENGL
PRINT=INTR
SNOW=NO
PEST=YES
NUTR=NO
ICHECK=OFF
DISK=NO
XCNTL INTRVL= 5, HYMIN= 0.0500, AREA= 3.2 SEND
XSTRT BGNDAY= 1, BGNMON=12, BGNYR= 1973 XEND
XENDD ENDDAY=14, ENDMON= 2, ENDYR= 1974 SEND
XLND1 UZSN= 0.500, UZS= 1.000, LZSN= 18.00, LZS= 24.00 SEND
XLND2 L=100.,SS= 0.025,NN= 0.2000,A= 0.0000,EPXM=0.1200,PETMUL=1.000 SEND
XLND3 K3=0.30,0.30,0.30,0.40,0.40,0.50,0.70,0.80,0.60,0.50,0.40,0.30 XEND
XLND4 INFIL=0.10,INTER=0.70,IRC=0.00,K24L= 1.00,KK24= 0.60,K24EL=0.00 &END
8LND5 SGW=0.00,GWS=0.00,KV=0.00,ICS=0.00,OFS=0.00,IFS=0.000 SEND
8CROP COVPMO = 0.6,0.6,0.6,0.6,0.0,0.15,0.60,0.85,0.75,0.60,0.60,0.60 8END
XMUD1 TIMTIL= 115,114,0,0,0,0,0,0,0,0,0,0 XEND
8MUD2 YRTIL= 74,75,0,0,0,0,0,0,0,0,0,0 XEND
XMUD3 SRERTL= 1.00,2.00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 XEND
XSMDL JRER=1.9,KRER=0.08,JSER=1.70,KSER=0.5,SRERI=2.000,SCMPAC=0.02 XEND
PESTICIDE
APMODE=SURF
DESORP=YES
XPSTR PSSZ=0.0, PSUZ=0.0, PSLZ=0.0, PSGZ= 0.0 XEND
XPST1 TIMAP= 131, 119, 141, 0, 0, 0, 0, 0, 0, 0, 0, 0 XEND
XPST2 YEARAP= 73,74,75,0,0,0,0,0,0,0,0,0 XEND
XPST3 SSTR= 2.10, 2.20, 1.70, 0.0, 8*0.0 XEND
8AMDL CMAX=1.0E-5,DD=0.0003,K=120.0,N=2.0000,NP=4.600 XEND
XDEGD DDG=131,119,141,0,0,0,0,0,0,0,0,0 XEND
XDEGY YDG= 73,74,75,0,0,0,0,0,0,0,0,0 XEND
XDEGR KDG= 0.002,0.002,0.002,0.0 XEND
XDPTH SZDPTH=.125,UZDPTH=6.125,BDSZ=99.9,BDUZ=99.9,BDLZ=99.9,UZF=3.,
LZF=1.5 XEND
EVAP73 18 74 60 29 13 266 131 103 19 41 90 68
EVAP73 18 90 170 29 13 70 163 96 63 69 72 68
EVAP73 18 60 43 30 14 65 140 53 189 97 48 47
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
EVAP73
7312019
7312029
7312039
7312041
7312042
7312043
7312044
7312045
7312046
7312047
7312048
7312059
7 T i on & 1
/ O 1 <L U O 1
7312062
7312063
7312064
7312065
7312066
7312067
7312068
0
35
28
28
28
28
28
28
28
28
28
27
33
19
41
41
54
54
55
118
32
24
24
24
25
25
91
17
5532
61
61
82
121
69
7
20
21
21
16
54
46
47
45
45
46
46
81
83
101
45
46
46
28
60
2 3
221
43
43
71
4
41
35
20
20
21
123
123
132
103
61
61
61
61
112
44
104
87
87
87
72
86
50
31
31
3
1 2
1 1
60
112
15
15
15
15
15
16
16
113
113
113
113
1
88
88
88
88
88
88
13
13
19
332
58
58
58
1
1 1
4
202
99
100
34
135
210
202
219
145
176
192
222
171
173
159
72
103
198
154
232
153
114
90
152
3
153
198
1
112
1 1
70
171
8
72
70
37
108
68
142
132
90
156
121
160
70
72
161
84
149
183
62
262
109
126
59
137
213
5 1
1
156
145
185
87
145
62
185
175
133
185
154
246
140
89
58
80
46
168
129
136
141
71
65
27
43
148
155
103
31311
162
34
122
65
105
130
36
139
162
4
72
208
115
123
92
72
130
205
178
143
122
112
136
52
170
37
249
38
985
124
115
24
161
92
145
218
185
145
99
211
125
158
191
139
112
119
73
79
132
152
112
92
33
66
79
165
239
104
117
138
124
90
117
159
76
34
110
117
76
83
90
110
117
104
83
83
83
77
71
65
59
53
48
69
14
722
48
114
54
12
0
78
72
60
48
48
54
24
24
60
120
66
24
48
36
66
36
30
48
24
78
54
204
11
1
2 4
52
47
42
31
57
36
10
57
36
57
36
36
104
73
47
57
73
104
109
99
83
10
42
68
36
16
47
68
410 5
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
to
00
7312079
7312089
7312099
7312109
7312119
7312129
7312139
7312149
7312151
7312152
7312153
7312154 3
7312155
7312156
7312157
7312158
7312161
7312162
7312163
7312164
7312165
7312166
7312167 1
7312168
7312179
7312189
7312199
7312201
7312202
7312203
7312204 2
7312205
7312206
7312207
7312208
7312219
7312229
7312239
7312249
7312251
7312252
7312253
7312254
7312255
7312256
7312257
7312258
7312261
232
1 1
1
1 1
1 1
233232323232
1 1
1113
1 1
1
5
1
4
1
2
1 1
1 1
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
to
7312262
7312263
7312264
7312265
7312266
7312267
7312268
7312279
7312289
7312299
7312301
7312302
7312303
7312304
7312305
7312306
"7^1 9 T fi 7
/ O J. £OU /
7312308
7312311
7312312
7312313
7312314
7312315
7312316
7312317
7312318
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
EVAP74
2
1
241
111
92
86
86
16
5
92
108
43
11
5
43
86
81
86
59
22
43
27
38
22
49
98
1 1
1 1 1
2 1
1232
76
69
88
69
158
132
94
82
69
94
132
50
50
94
69
69
19
50
126
69
107
69
1
1
1
1
2 2
126
141
126
111
148
7
0
155
111
126
118
148
133
170
170
141
104
89
141
81
89
111
1 1
1 1
3 1 1
1
322
98
84
252
175
217
175
252
189
196
133
140
140
161
147
175
7
14
140
98
140
49
105
112
1
1 1
1 1 1
4
1 1
2 1
1
221
140
158
176
201
95
22
169
119
123
164
144
171
199
190
205
119
187
93
271
145
57
68
2 1 1
254
1 3
1 1
112
122
77
34
38
237
120
157
192
0
140
203
45
325
202
156
72
260
195
207
92
110
211
271
1 1
371
1
1 1 2
206
120
217
174
109
28
84
120
102
210
217
150
151
77
85
94
197
92
510
133
158
163
1 1 1
1
246
342
80
181
103
70
113
175
185
0
49
136
213
76
61
116
209
195
71
144
224
206
301
132
1 1 2
111
1
822
177
155
57
172
131
7
. 20
32
34
63
84
125
100
92
26
98
66
46
117
101
11
95
4222
1233
1
62128 2
124
110
110
110
110
110
110
110
76
76
76
76
76
76
76
41
41
41
41
41
41
41
2 3
2 1
5 4
66
66
66
66
66
54
54
54
54
54
54
54
54
24
24
24
24
24
24
78
78
78
522
2 1 1
IOTA
o JLH
122
222
55
55
55
35
35
35
35
35
35
35
35
71
71
72
72
72
72
72
120
120
120
120
(continue)
</pre><hr><pre>
-------
TflBLE A2 (continued)
N)
Ul
EVAP74 54 101 104 210 210 276
EVAP74 22 69 37 175 103 211
EVAP74 5 82 118 147 188 171
EVAP74 22 113 30 147 239 122
EVAP74 32 158 59 203 4 365
EVAP74 43 82 59 168 171 530
EVAP74 65 133 196 195 134
EVAP74 38 15 196 147 181
EVAP74 43 185 156
7401011 11511
7401012
7401013
7401014
7401015
7401016
7401017
7401018
7401021
7401022
7401023
7401024
7401025
7401026
7401027
7401028
7401031
7401032
7401033
7401034
7401035
7401036
7401037
TAninTO KT 1 y \ "y *> 1 11 11 11
/HUluOojO .1 £ 3 £ c. Jl JLi IX 1 j
7401041
7401042
7401043
7401044
7401045
7401046
7401047 1 1114
7401048
7401059
7401069
7401071
7401072 1 111
7401073 11 1 1
7401074
7401075
82 215 95 76 78
13 126 110 76 78
236 147 11 76 78
44 146 37 76 78
20 130 31 76 66
16 123 30 76 66
136 156 104 76 66
140 207 137 76 66
300 76 76
1 1
5335
1223
10 1 1 1 1 1 1
1 1 1 124
120
120
120
120
10
10
10
10
10
1
6
1
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
7401076
7401077
7401078
7401089
7401099
7401109
7401111
7401112
7401113
7401114
7401115
7401116
7401117
7401118
7401129
7401139
7401149
7401159
7401169
7401179
7401189
7401199
7401201
7401202
7401203
7401204 1 11
7401205
7401206
7401207
7401208 21111111 111
7401211 113433453 1
7401212
7401213
7401214
7401215
7401216
7401217
7401218
7401229
7401239
7401241
7401242
7401243
7401244
7401245
7401246
7401247
7401248
1232
1 1
121332111111111112
1 111222111
122111
3211
1 1
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
to
7401259
7401269
7401279
7401281
7401282
7401283
7401284
7401285
7401286
7401287
7401288
7401291
7401292
7401293
7401294
7401295
7401296 1
7401297
7401298
7401309
7401319
7402019
7402029
7402039
7402049
7402059
7402061
7402062
7402063
7402064
7<ifi9fl & R
/ *T U £ U D 3
7402066 1
7402067 3 2
"? & n o n £ ft i
/ H UcUOO 1
7402071
7402072
7402073
7402074
7402075
7402076
7402077
7402078
7402081 1
7402082
7402083
7402084
7402085
7402086
11111 1
1 1 2
441 1112111
1 1121123343321 414 6532555532
32193222221111 1 1 1 1
121 1221
1
111
11111
(continue)
</pre><hr><pre>
-------
TABLE A2 (continued)
7402087
7402088
7402099
7402109
7402119
7402129
£ 7402139
co 7402141
7402142
7402143
7402144
7402145
7402146 1
7402147 1 1 1 1 11112222111
7402148 1 1 1 111
/*
</pre><hr><pre>
-------
TABLE A3. PARAMETER INPUT SEQUENCE FOR HYDROLOGY (WITH SNOW) , SEDIMENT, AND
'NUTRIENT SIMULATION
to
//HARL7508 JOB •A19$X2,444,.05,40','SNOW NUTR PROD'
/XJOBPARM HOLD=JOB
//JOBLIB DD DSNAME=WYL.X2.A19.HD7508.ARMLM.DP100677,
// UNIT=DISK,VOL=SER=PUB005,DISP=(OLD,KEEP)
//STEP1 EXEC PGM=ARM
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT05F001 DD *
MICHIGAN P6 SNOW SAMPLE
HYDROLOGY,SEDIMENT, AND NUTRIENTS
HYCAL=PROD
INPUT=ENGL
OUTPUT=ENGL
PRINT=DAYS
SNOW=YES
PEST=NO
NUTR=YES
ICHECK=ON
DISK=NO
SCNTL INTRVL= 5, HYMIN=
SSTRT BGNDAY=20, BGNMON= 1, BGNYR= 1974
SENDD ENDDAY = 21, ENDMON= 1, ENDYR= 1974
SLND1 UZSN= 0.200, UZS=
0.010, AREA=
SEND
1.98
SEND
SEND
0.500, LZSN= 9.00, LZS= 11.0 SEND
SLND2 L= 60.,SS= 0.060,NN= 0.2000,A= 0.0000,EPXM=0.1200,PETMUL=1.000 SEND
SLND3 K3=0.20,0.20,0.20,0.20,0.30,0.30,0.50,0.45,0.40,0.30,0.20,0.20 SEND
&LND4 INFIL=0.03,INTER=0.80,IRC=0.00,K24L= 1.00,KK24= 0 . 00 ,K24EL=0 . 00 SEND
SLND5 SGW=0.00,GWS=0.00,KV=0.00,ICS=0.00,OFS=0.00,IFS=0.000 SEND
SNOWPRINT=NO
8SN01 RADCON=1.0,CCFAC=1.00,SCF=1.40,ELDIF=0.0,IDNS= 0.14,F= 0.0 SEND
SSN02 DGM=0.0,WC=0.03,MPACK=1.0,EVAPSN=0.40,MELEV= 892.,TSNOW=32.00 SEND
SSN03 PACK= 0.0,DEPTH= 0.0 SEND
SSNO<i PETMIN= 35.0,PETMAX= ^0.0,WMUL= 1.0,RMUL= 1.00,KUGI= 0.0 SEND
SCROP COVPMO=0.0,0.0,0.0,0.0,0.0,0.05,0.55,0.90,0.90,0.80,0.0,0.0 SEND
SMUD1 TIMTIL= 140,136,0,0,0,0,0,0,0,0,0,0 SEND
SMUD2 YRTIL= 74,75,0,0,0,0,0,0,0,0,0,0 SEND
8MUD3 SRERTL= 1.00,0.80,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 SEND
SSMDL JRER=2.2,KRER=0.15,JSER=1.40,KSER=0.5,SRERI=1.000,SCMPAC=0.001 SEND
NUTRIENT
SNUTRIN TSTEP= 60, NAPPL= 2,TIMHAR= 275 SEND
SPLANTU ULUPTK=0.0,0.0,0.0,0.0,0.0,1.0,0.5,0.05,0.0,0.0,0.0,0.0 SEND
SPLANTL LZUPTK=0.0,0.0,0.0,0.0,0.0,0.30,0.75,0.055,0.0,0.0,0.0,0.0 SEND
REACTION RATES
NITROGEN
SURFACE
3.00 0.0
UPPER ZONE
1.25 0.05
0.25
0.40
0.015
0.0015
0.0
0.0
0.0
0.0
5.0
0.75
0.75
0.3
(continue)
</pre><hr><pre>
-------
TABLE A3 (continued)
LOWER ZONE
0.7
GROUNDWATER
0.0
TEMPERATURE
1.05 1
PHOSPHORUS
SURFACE
0.015 0.
UPPER ZONE
0.0015 0.
LOWER ZONE
0.0015 0.
GROUNDWATER
0.0
TEMPERATURE
1.07
END
INITIAL
NITROGEN
SURFACE
69.4 0
UPPER ZONE
440.0 4
LOWER ZONE
1488.
GROUNDWATER
0.0 0
PHOSPHORUS
SURFACE
40.5 1
UPPER ZONE
220. 1
LOWER ZONE
800.
GROUNDWATER
0.0 0
CHLORIDE
SURFACE
0.00
UPPER ZONE
130.0
LOWER ZONE
00.0
0.0 0.090 0.0015 0.0 0.0 1.0 0.4
0.0 0.0 0.0 0.0 0.0 0.0 0.0
COEFFICIENTS
.07 1.07 1.07 1.07 1.07 1.05 1.05
0 0.01 1.00 0.01
0 2.10 0.5 0.006
0 1.70 0.5 0.005
0.0 0.0 0.0 0.0
COEFFICIENTS
1.07 1.07 1.05 1.05
.20 0.91 0.30 0.0 0.0
.29 10.00 19.9 0.0 0.0
20.0 50.0 152.0 0.0 0.0
.0 0.0 0.0 0.0 0.0
.3 2.6 0.0
.36 111.64 0.0
20.0 200.0 0.0
.0 0.0 0.0
(continue)
</pre><hr><pre>
-------
TABLE A3 (continued)
GROUNDWATER
0.0
END
APPLICATION 136
NITROGEN
SURFACE
0.0 0.3 1.0 1.3 0.0 0.0
UPPER ZONE
0.0 29.2 0.0 29.2 0.0 0.0
PHOSPHORUS
SURFACE
0.0 3.65 0.30 0.0
UPPER ZONE
0.0 112.0 0.0 0.0
CHLORIDE
SURFACE
5.8
UPPER ZONE
134.2
END
APPLICATION 176
NITROGEN
SURFACE
0.0 14.2 0.2 14.2 0.0 0.0
UPPER ZONE
0.0 14.1 0.0 14.3 0.0 0.0
PHOSPHORUS
SURFACE
0.0 0.0 0.0 0.0
UPPER ZONE
0.0 0.0 0.0 0.0
CHLORIDE
SURFACE
0.0
UPPER ZONE
0.0
END
UZTP LZTEMP=38.2,36.6,37.1,40.1,48.5,56.5,62.4,65.1,64.5,58.7,51.3,44.3 SEND
8RETP ASZT = 24.27,BSZT = 0..630,AUZT = 0.0,BUZT = 1.0 SEND
8DPTH SZDPTH=.125,UZDPTH=3.125,BDSZ=63.7,BDUZ=72.4,BDLZ=99.0,UZF=5.,LZF=1. SEND
</pre><hr><pre>
-------
TABLE A4. PARAMETER INPUT SEQUENCE FOR HYDROLOGY (WITHOUT SNOW)
AND SEDIMENT SIMULATION WITH RUNOFF AND SEDIMENT WRITTEN TO DISK
//HARL7508 JOB 'A19$X2,444,.25,40','DISK ON TEST'
/XJOBPARM HOLD=JOB
//JOBLIB DD DSNAME=WYL.X2.A19.HD7508.ARMLM.DP100677.
// UNIT=DISK,VOL=SER=PUB005,DISP=(OLD,KEEP)
//STEP1 EXEC PGM=ARM
//SYSPRINT DD SYSOUT=A
//FT06F001 DD SYSOUT=A
//FT10F001 DD DSN=WYL.X2.A19.ARM.TEST.LSRO,DISP=(NEW,KEEP),
// SPACE=CTRK,(10,3),RLSE),VOL=SER=PUB005,UNIT=DISK,
// DCB=(RECFM=VBS,LRECL=516,BLKSIZE=2068)
//FT11F001 DD DSN=WYL.X2.A19.ARM.TEST.EROS,DISP=(NEW,KEEP),
// SPACE=(TRK,(10,3),RLSE),VOL=SER=PUB005,UNIT=DISK,
// DCB=(RECFM=VBS,LRECL=516,BLKSIZE=2068)
//FT05F001 DD *
NO SNOW xxxxTEST**** DISK RUN
NO PESTICIDES OR NUTRIENTS
HYCAL=CALB
INPUT=ENGL
OUTPUT=ENGL
PRINT=INTR
SNOW=NO
PEST=NO
NUTR=NO
ICHECK=ON
DISK=YES
IDEBUG=ON
RUNOFF
TEST DISK OPTION
DSNFLO=10
SEDIMENT
TEST DISK OPTION
DSNERS=11
ENDDISK
XCNTL INTRVL= 5, HYMIN= 0.010, AREA= 1.98 SEND
&STRT BGNDAY=21, BGNMON= 8, BGNYR= 1975 SEND
8ENDD ENDDAY=21, ENDMON= 8, ENDYR= 1975
UND1 UZSN= 0.200, UZS= 0.301, LZSN= 9.00, LZS= 8.736
8LND2 L= 60.,SS= 0.060,NN= 0.2000,A= 0.0000,EPXM=0.1200,PETMUL=1.
8LND3 K3=0.20,0.20,0.20,0.20,0.30,0.30,0.50,0.45,0.40,0.30,0.20,0
&LND4 INFIL=0.03,INTER=0.80,IRC=0.00,K24L= 1.00,KK24= 0.00,K24EL=
SLND5 SGW=0.00,GWS=0.00,KV=0.00,ICS=0.00,OFS=0.00,IFS=0.000
8CROP COVPMO=0.0,0.0,0.0,0.0,0.0,0.05,0.40,0.75,0.85,0.80,0.0,0.0
8MUD1 TIMTIL= 140,136,0,0,0,0,0,0,0,0,0,0
SMUD2 YRTIL= 74,75,0,0,0,0,0,0,0,0,0,0
XMUD3 SRERTL= 1.00,1.00,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
&SMDL JRER=2.2,KRER=0.15,JSER=1.40,KSER=0.5,SRERI=0.893,SCMPAC=0.
SEND
JEND
000 SEND
.20 SEND
0.00 SEND
4END
&END
SEND
SEND
«END
01 SEND
132
</pre><hr><pre>
-------
APPENDIX B
SAMPLE OUTPUT FROM THE ARM MODEL
TABLES
Bl Output Heading - Hydrology (without snow), Sediment, and Pesticide
Simulation
B2 Output Heading - Hydrology (with snow), Sediment, and Nutrient
Simulation
B3 Monthly Summary - Hydrology (without snow), Sediment, and Pesticide
Simulation
B4 Monthly Summary - Hydrology (with snow), Sediment, and Nutrient
Simulation
B5 Daily Production Run Summary (HYCAL=PROD) - Hydrology (without snow) ,
Sediment, and Nutrient Simulation
B6 Daily Production Run Summary (HYCAL=PROD) - Hydrology (with snow),
Sediment, and Nutrient Simulation
B7 Storm Event Calibration Run Output (HYCAL=CALB) - Hydrology and
Sediment Simulation
B8 Storm Event Calibration Run Output (HYCAL=CALB) - Hydrology, Sediment,
and Pesticide Simulation
B9 Storm Event Calibration Run Output (HYCAL=CALB) - Hydrology, Sediment,
and Nutrient Simulation
BIO Daily Snovmelt Output (SNOWPRINT=YES) - Calibration Run, English Units
Bll Daily Snowmelt Output Definitions - Calibration Run, English Units
133
</pre><hr><pre>
-------
T&HT.R Bl. OUTPUT HEADING - HYDROLOGY (WITHOUT SNOW) , SEDIMENT, AND PESTICIDE SIMULATION
THIS IS A PRODUCTION RUN FOR PESTICIDES
WATERSHED: P-Z: PESTICIDE RUN USING LITERATURE PARAQUAT VALUES & szDPTH=o.i25
CHEMICAL: PARAQUAT APPLIED: 1973, 1974, & 1975
INPUT UNITS: ENGLISH
OUTPUT UNITS: ENGLISH
PRINT INTERVAL: EACH DAY
SNOWMELT NOT PERFORMED
ADSORPTION AND DESORPTION ALGORITHMS USED
PESTICIDE APPLICATION: SURFACE-APPLIED
LINE PRINTER OUTPUT ONLY
INTBVLs 3 HYMINB 0.0300 AREA* 3.2000
BGNDAYs 11 BGNMONs 5 BGNYRs 1973
ENDOAYs 26 ENDMONs 3 ENDYRs 1973
UZSNs 0.5000 UZS= 1.0000 LZSNs IS.0000 LZSr 24.0000
L= 100.0000 SS= 0.0230 NN= 0.2000 As 0.0 EPXMs 0.1200 PETMULs 1.
K3 = 0.30 0.30 0.30 0.40 0.40 0.50 0.70 0.SO 0.60 0.50 0.40 0.30
INFIL= 0.1000 INTER: 0.7000 IRC: 0.0 K24L= 1.0000 KK24= 0.6000 K24EL= 0.
SGW= 0.0 GWSs 0.0 KV= 0.0 ICS= 0.0 OFS= 0.0 IFSr 0.0
(continue)
</pre><hr><pre>
-------
TABLE Bl (continued)
u>
COVPMOr 0.60 0.60 0.60 0.60 0.0
TIMTIlr 115 lift 000
YRTIl = 74 75 0 0 0
SREPTL: 1.000 £.000 0.0
JBERs 1.9000
PSSZ =
0.0
KRER = 0.0600
PSUZ = 0.0
TIMAP= 131 119 141 0 0
YEARAP: 73 74 75 75 75
SSTRs 2.100 2.200 1.700
CMAXs 0.000010
DOr 0.000300
0.15 0.60 0.65 0.75 0.60 0.60 0.60
0.0
DDG = 131 119 141 000
YDG= 73 74 75 75 75 75
KDG= 0.002 0.002 0.002 0.0
0
0
0.0
0
75
0.0
00
0
75
0.0
000000
000000
0.0 0.0 0.0 0.0
JSEP = 1.7000 KSER=
PSIZ = 0.0 PI
000000
75 75 75 75 75 75
0.0 0.0 0.0 0.0
Kr 120.0000
00000
75 75 75 75 75
0,0 0.0 0.0 0.0 I
PSGZs
0.0
0.0 0.0 0.0
SRERI= 2.0000
0.0 0.0
2.0000
SCMPACs 0.0200
0.0 0,0
NP= 4.6000
0.0
0.0
0.0
HYCAlrPROD INPUT=ENGL OUTPUTrENGl. PRINT=DAYS SNOWrNO
APMODE=SU»C DESORP=YES
PESTrYES NUTRrNO
ICHECKrOFF
SOIL ZONES DEPTHS AND BULK DENSITIES
SZDPTHr 0.1250 UZDPTHs 6.1250
I CACHING FACTORS
UZF s 3.000 LZF = 1.500
BDSZ= 99.9000
BDUZs 99.9000
BDLZs 99.9000
</pre><hr><pre>
-------
TABLE B2. OUTPUT HEADING - HYDROLOGY (WITH SNOW) , SEDIMENT, AND NUTRIENT SIMULATION
THIS IS A PRODUCTION RUN FOR NUTRIENTS
WATERSHED: MICHIGAN P6 SNOW SAMPLE
CHEMICAL: HYDROLOGY,SEDIMENT, AND HYDROLOGY
INPUT UNITS: ENGLISH
OUTPUT UNITS: ENGLISH
PRINT INTERVAL: EACH DAY
SNOWMELT CALCULATIONS PERFORMED
U)
LINE PRINTER OUTPUT ONLY
INTRVl= S HYMINr 0.0100
BGNDAYr 20 BGNMONz 1
ENDDAYs 21 ENDMONs 1
AREA= 1.9600
BGNYRr 1974
ENDYR= 1974
UZSNs 0.2000 UZS = 0.5000 LZSN= 9.0000 LZS = 11.0000
L= 60.0000 SS= 0.0600 NN= 0.2000 A = 0.0
K3 = 0.20 0.20 0.20 0.20 0.30 0.30 0.50 0.45 0.40 0.30 0.20 0.20
INFIL: 0.0300 INTER: 0.6000 IRC= 0.0 K24L = 1.0000
SGW= 0.0 GWS= 0.0 KV= 0.0 ICS= 0.0
EPXMr 0.1200
KK24= 0.0
OFS= 0.0
PETMULs 1.
K24EL= 0.
IFS= 0.0
RADCON= 1.0000
DGM= 0.0
PACK= 0.0
PETMINs 35.0000
(csDntinue)
CCFACr 1.0000
UC= 0.0300
DEPTHS 0.0
PETMAXr 40.0000
SCFr 1.4000
MPACKs 1.0000
WMULr 1.0000
ELDIF= 0.0
EVAPSNs 0.4000
RMUL= 1.0000
IDNSr 0.1400
MELEVr 892.
KUGI= 0.0
F= 0.0
TSNOWs 32.
</pre><hr><pre>
-------
TABLE B2 (continued)
COVPMOs 0.0 0.0 0.0 0.0
TIMTIls 140 136 0 0
YRT1L = 74 75 0 0 0
SRERTLr 1.000 0.800 0.0
0.0
JRER= 2.2000
KRER = 0.1500
0.05 0.55 0.90 0.90 0.80 0.0
0.0
0 0
0 0
0.0
JSER =
0 0
0 0
0.0
1.4000
0
0
0.0
0
0
0.0
KSERr
0.0
0.5000
0.0 0.0 0.0
SRERI= 1.0000
SCMPAC= 0.0010
HYCAL=PROD INPUTrENGL OUTPUT=ENGL PRINTsDAYS SNOW=YES PESTsNO NUTRrYES ICHECK=ON
SNOWPRINT=NO
NUTRIENT
NUTRIENT SIMULATION INFORMATION
U>
TIME STEP FOR TRANSFORMATIONS =
NUMBER OF NUTRIENT APPLICATIONS =
DATE OF PLANT HARVESTING
FRACTION OF MAXIMUM MONTI
UPPER LAYERS = 0.0
LOWER ZONE = 0.0
NITROGEN REACTION RATES
SURFACE
UPPER ZONE
LOWEP ZONE
GROUNDWATER
TEMPERATURE COEF.
PHOSPHORUS REACTION RATES
SURFACE
UPPER ZONE
LOWER ZONE
GROUNDWATER
TEMPERATURE COEF.
'IONS =
:ATIONS
= 275
=
60 MIN
2
ILY UPTAKE
0.0 0
0.0 0
Kl
3.0000
1.2500
0.7000
0.0
1.050
KM
0.0150
0.0015
0.0015
0.0
1.070
.0
.0
0
0
0
0
1
0
0
0
0
1
0.0
0.0
KD
.0
.0500
.0
.0
.070
KIM
.0
.0
.0
.0
.070
0
0
0
0
0
0
1
0
z
1
0
1
.0 1
.0 0
KPL
.2500
.4000
.0900
.0
.070
KPL
.0100
.1000
.7000
.0
.070
.000
.300
0.500
0. 750
0
0
KAM
0.
0.
0.
0.
1.
1 .
0.
0.
0.
1.
0150
0015
0015
0
070
KSA
0000
5000
5000
0
050
0
0
0
0
1
0
0
0
0
1
.050 0.0 0.0
.055 0.0 0.0
KIM KKIM
.0 0.0
.0 0.0
.0 0.0
.0 0.0
.070 1.070
KAS
.0100
.0060
.0050
.0
.050
0
0
5
0
1
0
1
.0 0
.0 0
KSA
.0000
.7500
.0000
.0
.050
.0
.0
KAS
0.7500
0.3000
0. 4000
0.0
1.050
(continue)
</pre><hr><pre>
-------
TABLE B2 (continued)
NUTRIENTS - LB/AC
INITIAL STORAGES
SURFACE LAYER
CO
00
UPPER ZONE
OPQ-N NH3-S
NH3-A N03+N02
N2 PLNT-N ORG-P
PO4-S
PO4-A PLNT-P
CL
AVERAGE
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
69.
69.
69.
69.
69.
69.
0.200
0.200
0.200
0.200
0.200
0.200
0.910
0.910
0.910
0.910
0.910
0.910
0.300
0.300
0.300
0.300
0.300
0.300
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
41.
41.
41.
41.
41.
41.
1.300
1.300
1.300
1.300
1.300
1.300
2.600
2.600
2.600
2,600
2.600
2.600
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
AVERAGE
BLOCK
BLOCK
BLOCK
BLOCK
BLOCK
LOWER ZONE
STORAGE
GROUNDklATER
STORAGE
1
2
3
4
5
440.
440.
440.
440.
440.
440.
i486.
0.
4.
4.
4.
4.
4.
4.
20.
0.
290
290
290
290
290
290
000
0
10
10
10
10
10
10
50
0
.000
.000
.000
.000
.000
.000
.000
.0
19
19
19
19
19
19
152
0
.900
.900
.900
.900
.900
.900
.000
.0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
220.
220.
220.
220.
220.
220.
eoo.
0.
i
i
i
i
i
i
20
0
.360
.360
.360
.360
.360
.360
.000
.0
111
111
111
111
111
111
200
0
.640
.640
.640
.640
.640
.640
.000
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
130.
130.
130.
130.
130.
130.
0.
0.
000
000
000
000
000
000
0
0
TOTAL NITROGEN IN SYSTEM = 2255.000 LB/AC
TOTAL PHOSPHORUS IN SYSTEM = 1397.400 IB/AC
TOTAL CHLORIDE IN SYSTEM = 130.000 LB/AC
NUTRIENTS - LB/AC
APPLICATION FOR DAY 136
SURFACE LAYER
AVERAGE
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
(continue)
ORG-N
NH3-S NH3-A
N03+N02
N2 PLNT-N
ORG-P
PO4-S
P04-A PLNT-P
CL
0.
0.
0.
0.
0.
0.
0.300
0.300
0.300
0.300
0.300
0.300
1.000
1.000
1.000
1.000
1.000
1.000
1.300
1.300
1.300
1.300
1.300
1.300
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.
0.
0.
0.
0.
0.
3.650
3.650
3.650
3.650
3.650
3.650
0.300
0.300
0.300
0.300
0.300
0.300
0.0
0.0
0.0
0.0
0.0
0.0
5.600
5.800
5.800
5.600
5.800
5.800
</pre><hr><pre>
-------
TABLE B2 (continued)
UPPER ZONE
LO
VD
AVERAGE
APPLICATION
SURFACE
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
FOR DAY 176
LAYER
AVERAGE
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
UPPER ZONE
AVERAGE
BLOCK 1
BLOCK 2
BLOCK 2
BLOCK 4
BLOCK 5
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
29.
29.
29.
29.
29.
29.
14.
14.
14.
14.
14.
14.
14.
14.
14.
14.
14.
14.
200
200
200
200
200
200
200
200
200
200
200
200
100
100
100
100
100
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.200
.200
.200
.200
.200
.200
.0
.0
.0
.0
.0
.0
29
29
29
29
29
29
14
14
14
14
14
14
14
14
14
14
14
14
.200
.200
.200
.200
.200
.200
.200
.200
.200
.200
.200
.200
.300
.300
.300
.300
.300
.300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
LOWER ZONE MONTHLY SOIL TEMPERATURES r
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
38.2 36.6 37.1 40.1 46.5 56.S 62.4 65.1 64.5 56.7 51.3 44.3
SOIL TEMPERATURE REGRESSION EQUATION CONSTANTS
SURFACE ZONE: ASZT z 24.270 BSZTS 0.630
UPPER ZONE: AUZT = o.o BUZT= i.ooo
SOIL ZONES DEPTHS AND BULK DENSITIES
SZDPTHs 0.1250 UZDPTHs 3.1250
LEACHING FACTORS
UZF = 5.000 LZF = 1.000
BDSZr 63.7000
BDUZ= 72.4000
BDLZ= 99.0000
</pre><hr><pre>
-------
TABLE B3. MONTHLY SUMMARY - HYDROLOGY (WITHOUT SNOW) , SEDIMENT,
AND PESTICIDE SIMULATION
SUMMARY_FOR_MONTH OF
WATER. INCHES
MAY
BLOCK 1
BLOCK 2
1972
BLOCK 3
BLOCK 4
BLOCK 5
TOTAL
RUNOFF
OVERLAND FLOW
INTERFLOW
IMPERVIOUS
TOTAL
BASE FLOW
GRDWATEE? RECHARGE
PRECIPITATION
EVAPOTRANSPIRATION
POTENTIAL
NET
CROP COVER
STORAGES
UPPER ZONE
LOWER ZONE
GROUNDWATEP
INTERCEPTION
OVERLAND FLOW
INTERFLOW
2.901
0.063
2.963
2.696
0.141
2.636
2.537
0.199
2.736
2.391
0.251
2.642
2.253
0.297
2.550
2.556
0.190
0.0
2.746
0.0
0.509
5.50
5.50
5.50
5.50
5.50
5.50
2
2
1
23
0
0
0
0
.93
.72
.377
.139
.0
.016
.0
.0
2
2
1
23
0
0
0
0
.93
.72
.366
.139
.0
.016
.0
.0
2
2
1
23
0
0
0
0
.93
.72
.362
.139
.0
.016
.0
.0
2
2
1
23
0
0
0
0
.93
.72
.357
.139
.0
.016
.0
.0
2
2
1
23
0
0
0
0
.93
.72
.353
.139
.0
.016
. 0
.0
2
2
0
1
23
0
0
0
0
.93
.72
.13
.363
.139
.0
.016
.0
.0
WATER BALANCES 0.0
SEDIMENT, TONS/ACRE
ERODED SEDIMENT
FINES DEPOSIT
2.376
0.002
2.377
0.004
2.375
0.005
2.369
0.012
2.344
0.037
2.368
0.012
PESTICIDE, POUNDS
SURFACE LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
UPPER ZONE LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
INTERFLOW STORAGE
LOWER ZONE LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
GROUNDWATER LAYER
ADSORBED
CRYSTALLINE
DISSOLVED
PESTICIDE REMOVAL, LBS.
OVERLAND FLOW REMOVAL
SEDIMENT REMOVAL
INTERFLOW REMOVAL
1
1
0
0
0
0
0
0
0
0
0
0
0
.167
.167
.0
.0
.0
.0
.0
.0
.0
.130
.0
.130
.0
1
1
0
0
0
0
0
0
0
0
0
0
0
.167
.167
.0
.0
.0
.0
.0
.0
.0
.130
.0
.130
.0
1
1
0
0
0
0
0
0
0
0
0
0
0
.167
.167
.0
.0
.0
.0
.0
.0
.0
.130
.0
.130
.0
1
1
0
0
0
0
0
0
0
0
0
0
0
.167
.167
.0
.0
.0
.0
.0
.0
.0
.129
.0
.129
.0
1.
1.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
169
169
0
0
0
0
0
0
0
126
0
126
0
5
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.636
.636
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.647
.0
.647
.0
(continue)
140
</pre><hr><pre>
-------
TABLE B3 (continued)
PESTICIDE DEGRADATION LOSS, IBS.
TOTAL 0.237
FPOM SURFACE 0.237
FROM UPPER ZONE 0.0
FPOM LOWER ZONE 0.0
PESTICIDE BALANCE: 0.0
141
</pre><hr><pre>
-------
TABLE B4. MONTHLY SUMMARY - HYDROLOGY (WITH SNOW) , SEDIMENT, AND NUTRIENT SIMULATION
SUMMARY FOR MONTH OF JANUARY_1974
BLOCK 1 BLOCK 2 BLOCK 3 BLOCK 4 BLOCK 5 TOTAL
WATER, INCHES
RUNOFF
OVERLAND FLOW
INTERFLOW
IMPERVIOUS
TOTAL
BASE FLOW
GRDWATER RECHARGE
PRECIPITATION
SNOW
RAIN ON SNOW
MELT & RAIN
MELT
RADIATION
CONVECTION
J^ CONDENSATION
10 RAIN MELT
GROUND MELT
CUM NEG HEAT
SNOW PACK
SNOW DENSITY
X. SNOW COVER
0.696 0.403 0.212
0.066 0.216 0.270
0.704 0.616 0.461
1.70 1.70 1.70
0.060 0.024 0.263
0.279 0.252 0.221
0.0
0.360 0.276 0.504
0.0
0.365
1.70 1.70 1.70
1.13
0.57
1.10
-0.11
0.49
0.12
0.02
0.0
0.00
0.56
0.22
55.66
SNOW EVAP , 0.03
EVAPOTRANSPIRATION
POTENTIAL 0.03 0.03 0.03 0.03 0.03 0.03
NET .0.00 0.00 0.00 0.00 0.00 0.00
CROP COVER 0.0
STORAGES
UPPER ZONE 0.559 0.540 0.520 0.505 0.466 0.522
LOWER ZONE 11.183 11.163 11.183 11.163 11.163 11.183
GROUNDWATER 0.0 0.0 0.0 0.0 0.0 0.0
INTERCEPTION 0.0 0.0 0.0 0.0 0.0 0.0
OVERLAND FLOW 0.0 0.0 0.0 0.0 0.0 0.0
INTERFLOW 0.0 0.0 0.0 0.0 0.0 0.0
WATER BALANCES 0.0
SNOW BALANCE: 0.0
(continue)
</pre><hr><pre>
-------
TABIE B4 (continued)
SEDIMENT, TONS/ACRE
ERODED SEDIMENT
FINES DEPOSIT
NUTRIENTS - IB/AC
STORAGE
U)
SURFACE LAYER
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
UPPER ZONE
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
INTERFLOW
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
LOWER ZONE
GROUNDUATER
REMOVAL
ADVECTIVE
SEDIMENT
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK S
ORG-N
68.61
66.29
68.67
68.91
69.06
69.11
439.77
439.77
439.77
439.77
439.77
439 . 77
0.0
0.0
0.0
0.0
0.0
0.0
1468.00
0.0
0.33
0.85
0.46
0.22
0.06
0.02
0.178
0.622
NH4-S
0.013
0.013
0.013
0.013
0.013
0.013
1.611
2.087
1.664
1.466
1.417
1.398
0.000
0.001
0.0
0.0
0.0
0.0
20.562
0.709
0.0
0.0
0.0
0.0
0.0
0.0
0.097 0.047 0,
0.903 0.953 0.
NH4-A
0.665
0.661
0.664
0.666
0.667
0.668
9.431
9.610
9.477
9.398
9.346
9.322
0.0
0.0
0.0
0.0
0.0
0.0
50.000
0.0
0.004
0.010
0.006
0.003
0.001
0.000
N03+N02 N2
0.0
0.0
0.0
0.0
0.0
0.0
6.970
14.866
9.795
7.592
6.490
6.104
0.002
0.008
0.0
0.0
0.0
0.0
153.701
5.331
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.240
0.305
0.256
0.226
0.209
0.201
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
.016
.985
PLNT-N
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.005
0.996
ORG-P
40.16
39.65
40.08
40.21
40.30
40.33
219.89
219.69
219.69
219.69
219.69
219.69
0.0
0.0
0.0
0.0
0.0
0.0
600.00
0.0
0.19
0.50
0.27
0.13
0.04
0.01
0.069
0.932
P04-S
0.004
0.004
0.004
0.004
0.004
0.004
1.205
1.797
1.263
1.062
0.956
0.924
0.000
0.001
0.0
0.0
0.0
0.0
20.197
0.701
0.0
0.0
0.0
0.0
0.0
0.0
P04-A
2
2
2
2
2
2
111
111
111
111
111
111
0
0
0
0
0
0
200
0
0
0
0
0
0
0
.577
.558
.572
.581
.587
.589
.706
.626
.738
.686
.652
.636
.0
.0
.0
.0
.0
.0
.000
.0
.012
.032
.017
.006
.003
.001
PLNT-P
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
CL
0.0
0.0
0.0
0.0
0.0
0.0
51.600
66 .560
56 .708
43.707
37.160
34.644
0.009
0.044
0.0
0.0
0.0
0.0
42.044
0.875
0.0
0.0
0.0
0.0
0.0
0.0
(continue)
</pre><hr><pre>
-------
TABLE B4 (continued)
OVERLAND FLOW
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
INTERFLOW
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
TOTAL TO STREAM
PERCOLATION TO
GROUNDWATER
BIOLOGICAL - TOTAL
SURFACE
UPPER ZONE
LOWER ZONE
GROUNDWATER
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.33
0.0
0.0
0.0
0.0
0.0
0.0
0
0
0
0
0
0
1
0
0
1
1
1
1
0
0
0
0
0
0
.079
.223
.103
.046
.016
.004
.016
.506
.998
.161
.227
.167
.095
.709
.0
.0
.0
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.004
0.0
0.0
0.0
0.0
0.0
0.0
0
0
0
0
0
0
3
3
5
6
6
6
5
5
0
0
0
0
0
.000
.000
.000
.000
.000
.000
.756
.121
.602
.639
.799
.429
.756
.331
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.240
.0
.240
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.19
.0
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.025
.072
.033
.014
.005
. 001
.736
.369
.739
.655
.677
.630
.763
.701
.0
.0
.0
.0
.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.0
. 0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.012
.0
.0
.0
.0
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0. 0
35.272
16.942
35.328
40.610
41.609
39.671
35.272
0.875
0.0
0.0
0.0
0.0
0.0
HARVEST
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
MASS BALANCE
NITROGEN = -0.000
PHOSPHORUS = -0.001
CHLORIDE = -0.001
</pre><hr><pre>
-------
TABLE B5. DAILY PRODUCTION RUN SUMMARY (HYCAL=PROD) - HYDROLOGY
(WITHOUT SNOW) , SEDIMENT, AND PESTICIDE SIMULATION
24: 0 ON 26 MAY 1O73
BLOCK 1 BLOCK 2 BLOCK 3
WATER, INCHES
BLOCK 4
BLOCK 5
TOTAL
RUNOFF
OVERLAND FLOW
INTERFLOW
IMPERVIOUS
TOTAL
2. 889
0.053
2.947
2.690
0.13X
2.621
2.532
0.1S7
2.719
2.388
0.237
2.625
2.251
0.262
2.532
2.550
0.179
0.0
2.729
BASE FLOW
GRDWATER RECHARGE
0.0
0.403
PRECIPITATION
4.27
4.27
4.27
4.27
4. 27
4.27
EVAPOTRANSPIRATION
POTENTIAL
NET
CROP COVER
STORAGES
UPPER ZONE
LOWER ZONE
OROUNDWATER
INTERCEPTION
OVERLAND FLOW
INTERFLOW
WATER BALANCE: 0.0
SEDIMENT, TONS/ACRE
ERODED SEDIMENT
FINES DEPOSIT
SURFACE LAYER PESTICIDE
PESTICIDE, LBS
ADSORBED
CRYSTALLINE
DISSOLVED
PESTICIDE, PPM
ADSORBED
CRYSTALLINE
DISSOLVED
REMOVAL, LBS
SEDIMENT
OVERLAND FLOW
PERCOLATION
UPPER ZONE LAYER PESTICIDE
PESTICIDE, LBS
ADSORBED
CRYSTALLINE
DISSOLVED
INTERFLOW STORAGE
PESTICIDE, PPM
ADSORBED
CRYSTALLINE
blSSOLVED
0
0
1
23
0
0
0
0
2
0
1
1
0
0
40
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.15
.15
.377
.139
.0
.018
.0
.0
.377
.002
.169
.169
.0
.0
.299
.299
.0
.0
.130
.130
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0
0
1
23
0
0
0
0
2
0
1
1
0
0
40
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.15
.15
.368
.139
.0
.018
.0
.0
.376
.004
.169
.169
.0
.0
.301
.301
.0
.0
.130
.130
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0
0
1
23
0
0
0
0
2
0
1
1
0
0
40
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.15
.15
.362
.139
.0
.018
.0
.0
.374
.005
.169
.169
.0
.0
.304
.304
.0
.0
.130
.130
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0
0
1
23
0
0
0
0
2
0
1
1
0
0
40
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.15
.15
.357
.139
.0
.018
.0
.0
.368
.012
.170
.170
.0
.0
.316
.316
.0
.0
.129
.129
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0
0
1
23
0
0
0
0
2
0
1
1
0
0
40
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.15
.15
.353
.139
.0
.018
.0
.0
.343
.037
.171
.171
.0
.0
.362
.362
.0
.0
.128
.128
.0
. 0
.0
.0
.0
.0
.0
.0
.0
.0
.0
0.15
0.15
0.13
1.363
23.139
0.0
0.018
0. 0
0.0
2 .368
0.012
5.848
5.848
0.0
0.0
40.316
40.316
0.0
0.0
647
647
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(continue)
145
</pre><hr><pre>
-------
TABLE B5 (continued)
REMOVAL, IBS 0.0 0.0 0.0 0.0 0.0 0.0
INTERFLOW 0.0 0.0 0.0 0.0 0.0 0.0
PERCOLATION 0.0 0.0 0.0 0.0 0.0 0.0
LOWER ZONE LAYER PESTICIDE
PESTICIDE, LBS 0.0
ADSORBED 0.0
CRYSTALLINE 0.0
DISSOLVED 0.0
PESTICIDE. PPM
ADSORBED 0.0
CRYSTALLINE 0.0
DISSOLVED 0.0
REMOVAL. LBS 0.0
PERCOLATION 0.0
GROUNDWATER LAYER PESTICIDE
PESTICIDE, LBS 0-0
ADSORBED 0.0
CRYSTALLINE 0.0
DISSOLVED 0.0
PESTICIDE DEGRADATION LOSS, LBS.
TOTAL 0.012
FROM SURFACE 0.012
FROM UPPER ZONE 0.0
PROM LOWER ZONE 0.0
146
</pre><hr><pre>
-------
TABLE B6. DAILY PRODUCTION RUN SUMMARY (HYCAL=PROD) - HYDROLOGY (WITH SNOW) , SEDIMENT,
AND NUTRIENT SIMULATION
24!_0 ON 20 JANUARY 1974
BLOCK 1 BLOCK 2 BLOCK 3
BLOCK 4 BLOCK 5
TOTAL
MATER, INCHES
RUNOFF
OVERLAND FLOW
INTERFLOW
IMPERVIOUS
TOTAL
BASE FLOW
GRDWATER RECHARGE
PRECIPITATION
SNOW
RAIN ON SNOW
MELT ft RAIN
MELT
RADIATION
CONVECTION
CONDENSATION
RAIN MELT
GROUND MELT
CUM NEG HEAT
SNOW PACK
SNOW DENSITY
X SNOW COVER
0.487
0.036
0.523
1.61
0.332 0.196 0.060 0.024 0.224
0.105 0.166 0.221 0.226 0.151
0.0
0.438 0.364 0.300 0.252 0.375
0.0
0.190
1.61 1.61 1.61 1.61 1.61
1.13
0.48
0.72
-0.05
0.16
0.12
0.02
0.0
0.00
0.66
0.23
66.13
SNOW EVAP
0.00
EVAPOTRANSPIRATION
POTENTIAL
NET
CROP COVER
0.01
0.00
0.01
0.00
0.01
0.00
0.01
0.00
0.01
0.00
0.01
0.00
0.0
STORAGES
UPPER ZONE
LOWER ZONE
GROUNDWATER
INTERCEPTION
OVERLAND FLOW
INTERFLOW
0.579
11.092
0.0
0.0
0.014
0.001
0.570
11.092
0.0
0.0
0.012
0.003
0.559
11.092
0.0
0.0
0.006
0.004
0.541
11.092
0.0
0.0
0.004
0.006
0.516
11.092
0.0
0.0
0.0
0.005
0.553
11.092
0.0
0.0
0 .006
0.004
(continue)
</pre><hr><pre>
-------
TABLE B6 (continued)
WATER BALANCE:
SNOW BALANCES
0.0
0.0
SEDIMENT, TONS/ACRE
ERODED SEDIMENT
PINES DEPOSIT
0.133
0.667
0.064
0.917
0.044
0.956
0.016
0.964
0.005
0.996
0.056
0.944
NUTRIENTS - LB/AC
ORG-N
NH4-S
NH4-A
NO3+NO2
N2 PINT-N ORG-P
PO4-S
PO4-A PLNT-P
Cl
SURFACE LAYER
REMOVAL
it"
00
SEDIMENT
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
OVERLAND FLOW
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
PERCOLATION
BLOCK 1
BLOCK Z
BLOCK 3
BLOCK 4
BLOCK 5
BIOLOGICAL
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
69.03
66.66
66.90
69.06
69.22
69.27
0.27
0.64
0.40
0.21
0.06
0.02
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.014
0.014
0.014
0.014
0.014
0.014
0.0
0.0
0.0
0.0
0.0
0.0
0.049
0.111
0.073
0.042
0.015
0.004
0.347
0.264
0.323
0.354
0.361
0.392
0.0
0.0
0.0
0.0
0.0
0.0
0.601
0.797
0.600
0.602
0.603
0.604
0.003
0.006
0.005
0.003
0.001
0.000
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000
0.000
0.000
0.000
0.000
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.000
0.000
0.000
0.000
0.000
0.000
0.300
0.300
0.300
0.300
0.300
0.300
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0,0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0. 0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0,0
0.0
40.26
40.07
40.21
40.32
40.40
40.43
0.16
0.37
0.23
0.12
0.04
0.01
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.004
0.004
0.004
0.004
0.004
0.004
0.0
0.0
0.0
0.0
0.0
0.0
0.015
0.035
0.023
0.013
0.005
0.001
1.345
1.326
1.336
1.346
1 .356
1.360
0.0
0.0
0.0
0.0
0.0
0.0
2.586
2.572
2.581
2.566
2.593
2.595
0.010
0.024
0.015
0.006
0.003
0.001
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
(continue)
</pre><hr><pre>
-------
TABLE B6 (continued)
UPPER ZONE
STORAGE
BLOCK
BLOCK
BLOCK
BLOCK
BLOCK
INTERFLOW
BLOCK
BLOCK
BLOCK
BLOCK
BLOCK
1
2
3
4
3
1
2
3
4
3
439
439
439
439
439
439
0
0
0
0
0
0
.69
.69
.69
.69
.89
.69
.0
.0
.0
.0
.0
.0
2
2
2
2
1
1
0
0
0
0
0
0
.126
.914
.397
.022
.716
.363
.013
.004
.011
.013
.019
.017
9.663
9.932
9.900
9.676
9.660
9.647
0.0
0.0
0.0
0.0
0.0
0.0
12
17
14
11
9
8
0
0
0
0
0
0
.457
.919
.294
.712
.643
.717
.073
.026
.064
.069
.104
. 094
0
0
0
0
0
0
0
0
0
0
0
0
.146
.156
.150
.144
.140
.136
.0
.0
.0
.0
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
219
219
219
219
219
219
0
0
0
0
0
0
.93
.93
.93
.95
.93
.93
.0
.0
.0
.0
.0
.0
1.563
2.234
1.603
1.495
1.246
1.136
0.010
0.003
0.006
0.011
0.013
0.012
111
111
111
111
111
111
0
0
0
0
0
0
.706
.732
.713
.703
.694
.687
.0
.0
.0
.0
.0
.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
75.634
109.163
67.055
71.306
58.694
53.052
0.459
0.160
0.369
0.540
0.636
0.571
REMOVAL
VD
INTERFLOW
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
PERCOLATION
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
BIOLOGICAL
BLOCK 1
BLOCK 2
BLOCK 3
BLOCK 4
BLOCK 5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.773
0.265
0.609
0.853
1.053
1.094
0.796
0.333
0.625
0.642
1.019
1.159
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4.312
1.490
3.421
4.770
5.650
6.026
4.362
1.641
3.450
4.626
5,576
6.315
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.146
0.158
0.150
0.144
0.140
0.136
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0. 0
0.554
0.190
0.437
0.611
0.752
0.777
0.547
0.221
0.428
0.580
0.704
0.601
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0. 0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0 .0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
26.627
9.220
21.135
29. 445
36.096
37.240
27.038
11.455
21 .420
28.707
34.573
39.137
0.0
0.0
0.0
0.0
0.0
0.0
TOTAL TO STREAM
0.27
0.623
0.003
4.312
LOWER ZONE
STORAGE
1486.00 20.447 50.000 153.729
0.0
0.0
0.0
0.0
0.16
0.569
0. 010
600.00 20.201 200.000
0.0
0.0
26.627
26.798
(continue)
</pre><hr><pre>
-------
TABIE B6 (continued)
REMOVAL
PERCOLATION 0.0 0.349 0.0 2.633 0.0 0.0 0.0 0.346 0.0 0.0 0.261
BIOLOGICAL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
QROUNOUIATER
STORAGE 0.0 0.349 0.0 2.633 0.0 0.0 0.0 0.346 0.0 0.0 0.261
REMOVAL
BIOLOGICAL 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
DAILY SOIL TEMPERATURE IN DEGREE P
SURFACE ZONE MAXC4PM ) MIN(6AM)
49.3 43.2
UPPER ZONE MAXC4PM) MIN(6AM)
49.3 6.2
LOWER ZONE DAILY AVERAGE
37.3
</pre><hr><pre>
-------
TABLE B7. STOBM EVENT CALIBRATION FUN OUTPUT (HYCAL=CALB) - HYDFOLOCT AND SEDIMENT SIMJLATION
UATh TIME FLUMCFS-CMS) SfcJlMtmT (I.BS-KG-KG/M IN-GM/L )
SEPT-1BER
S6PTMBER
SEPT^ER
SEPTMBER
SEPTM3ER
ScPTMBER
SEPTMBER
SEPTMRER
SEPTM3ER
SEPTMBER
SEPTMBER
SEPTMBER
SEPTMRER
SEPTM6ER
SEPTMBER
SEPTMBER
SEPTM8ER
SEPTMBER
SEPTMBER
SEPTMBER
SEPT^BEP
SEPTMbER
SEPTMBER
SEPTMBER
SEPTM3ER
SEPTMBER
SEPTMBER
9
9
9
9
9
9
9
9
9
q
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
9
20:45
20:50
20:55
21:0
21: 5
21: 10
21:15
21:20
21:25
21:30
21:35
21 :40
21:45
21:50
21:55
22: 0
22: 5
22: 10
22:15
22:20
22:25
22:30
22:35
22:40
22:45
22:50
22:55
0.006
o. iba
0. 72i
1.724
1.450
0.678
0.545
0.427
0.514
2.649
4.624
2.954
1.693
0.837
0.510
0.334
0.214
0.138
0.092
0.067
0.044
0.030
0.020
0.013
0.009
0.006
0.004
0.000
0.005
O.C20
O.G49
o . <m
0.019
0.015
0.012
O.C15
0.075
0.131
O.C84
J.048
O.C24
0.014
O.C09
0.006
0 . C C4
O.C03
0.002
0.001
0.001
0.001
0.000
0.000
0.000
0.000
0.13
A0.53
ji.yo
/7.J/
44. td
10.21
o.i»b
4.01
6.2o
14(5.43
^76.02
U0.7o
37. 12
10.43
2.31
l.lo
O.fa2
0.23
u.ul
0.0
0.0
o.u
0.0
u.O
0.0
0.0
U.O
0.06
4. 78
14.51
35.13
20.19
4.64
2.97
2.09
2.84
66.48
125.31
59.36
16.85
4.73
1.05
0.53
0.37
0. 11
0.00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.01
0.96
2.90
7. 03
4.04
0.93
0. 59
0.42
0. 57
13.30
25.06
1 1.87
3. 37
0.95
0.21
0. 11
0.07
0.02
0.00
0.0
0.0
0.0
0. 0
0. 0
0.0
0.0
0.0
1. 11
3.00
2.37
2.40
1.64
0. 80
0.64
0.58
0.65
2.95
3. 19
2.3 I
1. 17
0.67
0.24
0. 19
0.21
0. 10
0.00
0.0
0.0
0.0
0.0
0.0
0. 0
0.0
0.0
#
#
**
*
*
#
*
*
*#*
***
***
**
*
*
*
*
Note: Asterisks (*) indicate that the detached fines storage is less than the overland flow
sediment transport capacity in an areal zone (or block), e.g. three asterisks (***)
indicate that this occurs in three such zones.
</pre><hr><pre>
-------
TABLE B8. STORM EVENT CALIBRATION RUN OUTPUT (HYCALf=CALB) - HYDROLOGY, SEDIMENT, AND
PESTICIDE SIMULATION
DATE
TIME
FLOW(CFS-CMS)
SEDIMENT (LBS-KG-KG/MIN-GM/L)
PESTICIDE (GM-GM/MIN-PPM)
WATER SEDIMENT
PESTICIDE APPLICATION OCCURS ON MAY
11 (TIMAP=131) WITH AN APPLICATION OF 3.100 LBS/AC
BEGINNING ON
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
MAY
24
24
24
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
MAY
0:55
10:20
10:25
3:55
4: 0
4: 5
4 MO
4>15
4:20
4:25
4:30
4:35
4:40
4:45
4:50
4:55
5: 0
5: 5
5MO
5M5
5:20
5:25
5:30
5:35
5:40
5:45
17:45
17:50
17:55
18: 0
18: 5
18MO
18: 15
18:20
18:25
18:30
18:45
18:50
11 (DDG=131) THE PESTICIDE DEGRADATION RATE (KDG) EQUALS 0.
0.056
0.080
0.064
0.171
0.448
0.793
0.813
1.059
2.209
5.706
7.617
5.009
3.743
2.726
2.080
3,054
5.638
3.521
1.020
0.501
0.295
0.205
0.152
0.112
0.079
0.060
1.333
8.956
5.224
0.969
0.442
0.250
0.171
0.128
0.095
0.068
3.974
7.231
0.002
0.002
0.002
0.005
0.013
0.022
0.023
0.030
0.063
0.161
0.216
0.142
0.106
0.077
0.059
0.086
0.160
0.100
0.029
0.014
0.008
0.006
0.004
0.003
0.002
0.002
0.038
0.253
0.148
0.027
0.013
0.007
0.005
0.004
0.003
0.002
0.112
0.205
0.72
1.89
0.53
8.51
32.94
68.63
63.63
105.94
314.89
1310.21
1779.38
947.73
612.35
367.56
249.83
499.56
1207.17
474.06
70.00
20.10
6.61
2.44
1.03
0.48
0.23
0.12
211.11
2327.65
849.71
68.47
17.69
5.23
1.75
0.68
0.29
0.13
729.48
1230.55
0.33
0.86
0.24
3.86
14.96
31.16
28.89
48.10
142 . 96
594.83
807.84
430.27
278.01
166.87
113.42
226.80
548.05
215.22
31.78
9.12
3.00
1.11
0.47
0.22
0.11
0.06
95.85
1056.75
385.77
31.09
8.03
2.38
0.79
0.31
0.13
0.06
331.19
558.67
0.07
0.17
0.05
0.77
2.99
6.23
5.78
9.62
28.59
118.97
161.57
86.05
55.60
33.37
22.68
45.36
109.61
43.04
6.36
1.82
0.60
0.22
0.09
0.04
0.02
0.01
19.17
211.35
77,15
6.22
1.61
0.48
0.16
0.06
0.03
0.01
66.24
111.73
0.68
1.26
0.44
2.66
3.93
4.63
4.18
5.34
7.62
12.27
12.48
10.11
8.74
7.21
6.42
8.74
11.44
7.20
3.67
2.14
1.20
0.64
0.36
0.23
0.16
0.11
8.46
13.89
8.69
3.78
2.14
1.12
0.55
0.28
0.16
0.10
r.si
9.09
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
.002
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.015
0.039
0.011
0.173
0.670
1.395
1.292
2.151
6.388
26.521
35.692
18.777
12.052
7.203
4.883
9.747
23.475
9.141
1.344
0.365
0.127
0.047
0.020
0.009
0.004
0.002
4.055
44.672
16.037
1.284
0.331
0.098
0.033
0.013
0.005
0.002
13.697
22.970
0.003
0.008
0.002
0.035
0.134
0.279
0.258
0.430
1.278
5.304
7.136
3.755
2.410
1.441
0.977
1.949
4.695
1.828
0.269
0.077
0.025
0.009
0.004
0.002
0.001
0.000
0.811
8.934
3.207
0.257
0.066
0.020
0.007
0.003
0.001
0.000
2.739
4.594
45.136
45.136
45.134
44.774
44.772
44.760
44.736
44.715
44.683
44.586
44.182
43.639
43.350
43.163
43.048
42.977
42.834
42.474
42.308
42.236
42.150
42.071
42.026
42.000
41 . 981
41.981
42.308
42.272
41.573
41.304
41.236
41.153
41.086
41.056
41.040
41.032
41.358
41.115
</pre><hr><pre>
-------
TABLE B9. STORM EVENT CALIBRATION RUN OUTPUT (HYCAL=€ALB) - HYDROLOGY, SEDIMENT,
AND NUTRIENT SIMULATION
DATE
JUNE
JUNE
JUNE
JUNE
M
Ul
w JUNE
JUNE
JUNE
JUNE
JUNE
JUNE
JUNE
TIME
5 2:40
5 2:45
5 2:50
5 2:55
5 3: 0
5 3: 5
5 3:10
5 3:15
5 3:20
5 3:25
5 3:30
FLOW
(CFS)
0.011
0.019
0.179
0.282
0.168
0.240
0.473
0.565
0.202
0.073
0.042
1
SEDIMENT
(LB)
(GM/L)
0.24
1.21
0.48
1.32
11.16
3.32
15.17
2.88
10.05
2.86
12.66
2.82
33.58
3.79
36.77
3.48
8.41
2.23
1.60
1.17
0.42
0.53
DISSOLVED IN MATER
N03+N02 NH4 P04 CL
(LB) (LB) (LB) (LB)
(M6/L) (MG/L) (M6/L) (MG/L)
0.010
49.9
0.017
47.6
0.022
6.5
0.025
4.8
0.041
11.6
0.052
11.7
0.061
6.9
0.066
6.3
0.072
19.0
0.076
55.6
0.079
100.0
0.001
6.5
0.002
6.2
0.003
0.9
0.003
0.6
0.005
1.5
0.017
3.7
0.013
1.5
0.013
1.2
0.011
2.9
0.010
7.5
0.010
13.2
0.001
3.8
0.001
3.6
0.002
0.5
0.002
0.4
0.003
0.9
0.007
1.6
0.006
0.7
0.006
0.6
0.006
1.6
0.006
4.3
0.006
7.6
0.071
357.1
0.123
340.8
0.157
46.8
0.181
34.3
0.293
83.2
0.376
83.7
0.435
49.1
0.477
45.1
0.514
136.2
0.543
398.6
0.568
717.6
1
NH4
(LB)
(PPM)
0.000
30.1
0.000
30.1
0.000
30.1
0.000
30.1
0.000
30.1
0.000
29.7
0.001
29.7
0.001
29.6
0.000
29.6
0.000
29.6
0.000
29.6
ADSORBED
ORG-N
(LB)
(PPM)
0.001
2399.4
0.001
2399.4
0.027
2399.4
0.036
2398.7
0.024
2398.0
0.030
2397.0
0.080
2396.6
0.088
2395.1
0.020
2393.3
0.004
2392-1
0.001
2391.1
TO SEDIMENT I
P04 ORG-P
( LB ) ( LB )
( PPM ) ( PPM )
0.000
89.9
0.000
89.9
0.001
89.9
0.001
89.9
0.001
89.8
0.001
89.8
0.003
89.8
0.003
89.7
0.001
89.7
0.000
89.6
0.000
89.6
0.000
1400.2
0.001
1400.2
0.016
1400.2
0.021
1399.8
0.014
1399.4
0.018
1398.8
0.047
1398.6
0.051
1397.7
0.012
1396.7
0.002
1395.9
0.001
1395.4
TOT-N TOT-P
( LB ) ( LB )
(MG/L) (MG/L)
0.012
59.3
0.021
57.0
0.052
15.5
0.065
12.4
0.071
20.1
0.100
22.3
0,155
17.6
0.169
16.0
0.103
27.2
0.090
65.9
0.091
114.5
0.001
5.6
0.002
5.6
0.018
5.5
0.025
4.7
0.018
5.1
0.026
5.8
0.056
6.4
0.061
5.8
0.018
4.9
0.008
6.0
0.007
8.4
</pre><hr><pre>
-------
U1
TABLE BIO. DAILY SNOKMELT OUTPUT (SNOWPRINT=YES)
CALIBRATION RUN, ENGLISH UNITS
SNChMELT OUTPUT FOR
OECtHBEK
HOUR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
PACK
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
DEPTH
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
3.0
2.9
2.9
2.9
2.V
2.8
2.8
2.8
2.8
2.0
2.8
2.3
2.8
SOEN
0.204
0.204
0. 204
0.205
0.205
0.205
0.204
0. 204
0.204
0.2C4
0.203
0.202
0.203
0.204
0. 2C5
0.20t
0.205
0.204
0.204
0.2C3
0.203
0.202
0.202
0.202
*IB£ 00
0.735
0.734
0.733
0.732
C.731
C.730
0.730
C.729
C.728
C.727
0.726
0.725
C.725
0.724
C.723
0.722
0.721
0.721
0.720
C.719
C.718
C.717
0.717
0.716
CLDF
1.000
1.000
1.000
1.000
l.OOU
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
l.OOU
1.000
1.000
NtGMELT
0. 01 j
0.017
0.021
0.02 J
0.024
0.025
0.0i4
0.022
0.016
o. oo v
O.UU1
u.uoi
0.0
0.0
0.0
o. o
O.u
0.0
0.0
O.U
0.002
O.OU5
o.oov
0.014
LiUM
0.018
J.018
0.018
0.018
0.018
0.018
0.018
0. 018
0.018
0.018
0.018
0.018
0.013
0.018
J.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
0.017
TX
23.77
22.61
21.74
21.16
20.58
20.00
20.38
21.52
24.18
27.60
31.40
34.63
37.10
38.24
38.81
39.00
38.05
36.72
34.82
32.35
29.50
27.03
24.75
23.23
RA
0.
0.
0.
0.
0.
0.
1.
2.
3.
4.
5.
5.
5.
5.
5.
5.
4.
3.
1.
0.
0.
0.
0.
0.
LW
-8.
-8.
-8.
-8.
-9.
-9.
-9.
-8.
-8.
-7.
-7.
-6.
-6.
-5.
-5.
-5.
-5.
-6.
-6.
-7.
-7.
-7.
-8.
-8.
PX
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
MELT
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.003
0.006
0.007
0.005
0.002
0.0
0.0
0.0
0.0
0.0'
0.0
0.0
CONV
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.002
0.005
0.006
O.C07
0.007
0.006
0.004
0.002
0.000
0.0
0.0
0.0
0.0
RAINH
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
o.o
0.0
CON0S
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
ICE
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
</pre><hr><pre>
-------
TABLE Bll. DAILY. SNOVMELT OUTPUT DEFINITIONS - CALIBRATION
RUN, ENGLISH UNITS
HOUR
PACK
DEPTH
SDEN
ALBEDO
CLDF
NEGMELT
LIQW
TX
RA
LW
PX
MELT
CONV
RAINM
CONDS
ICE
Hour of the day, nuirber 1 to 24
Water equivalent of the snowpack, inches
Snow depth, inches
Snow density in inches of water per inch of snow
Albedo, or snow reflectivity, percent
Fraction of sky that is cloudless
Heat loss from the snowpack, equivalent inches
of melt
Liquid water content of the snowpack, inches
Hourly air temperature, degrees Fahrenheit
Incident solar radiation, langleys
Net terrestrial radiation, langleys (negative
value indicates outgoing radiation from the pack)
Total snowmelt reaching the land surface, inches
Total melt, inches
Convection melt, inches
Rain melt, inches
Condensation melt, inches
Ice formation at the land surface, inches
155
</pre><hr><pre>
-------
APPENDIX C
FORMATTED INPUT SEQUENCE FOR THE ARM MODEL
The Formatted Input Sequence (FIS) option was developed and added to Version
II of the ARM Model for use on computers that do not support the namelist
input option. The Namelist Input Sequence (NIS) is the only input sequence
supported in Version I of the ARM Model.
FIS has been constructed to look as much as possible like NIS. FIS is
column-dependent, NIS is not, and so care must be taken when setting up FIS.
However, with the format displayed in Table Cl and the description below,
most problems can be easily avoided. We recommend using Table Cl as a form
for preparing the parameter input for the FIS option, and referring to
Tables 5.2 and 5.3 and Section 5 for the parameters required for the model
options used.
Table Cl includes shaded boxes, blank boxes, and keywords (not written in
boxes). The shaded boxes, which contain parameter names, are not read by
the program. They are for the user's convenience as they quickly identify
and position parameter values in the input sequence. The shaded boxes can be
left blank or used in any manner which helps the user identify the value for
any particular parameter. The names given in the shaded parameter boxes in
Table Cl are the same as, or abbreviate, the ARM Model input parameter
names.
The non-shaded or blank boxes that follow a shaded box are for the parameter
value assigned to the parameter name in the shaded boxes. Refer to Table 5.2
and 5.3 for the parameter type (REAL or INTEGER). For all parameters,
except those with more than one value, the value of the parameter is placed
in the blank box directly after the parameter name. Parameters containing
more than a single value (either monthly values or one to 12 sequential
values) have the values listed in order after the parameter name box.
Examples are K3 and COVPMO: 12 monthly values from January to December; and
TIMTIL, TIMAP, and DDG and their related special events (tillage, pesticide
application, and pesticide degradation rate). If the number of special
events is less than the maximum (12), then the unused blank boxes should
either be left blank or given the value of zero. Also, it is always a good
idea to set ICHECK equal to ON in the input sequence to check for input
errors.
The formatted portion of the nutrient parameter input sequence is identical
for both FIS and NIS. The keywords in Table Cl (that is, words not in
boxes) are required in the input sequence and specify the parameters that
156
</pre><hr><pre>
-------
follow the keyword (Section 5.2.1). The nutrient namelist statements have
been converted to FIS in the same manner as described above.
When using FIS the ARM Model Version II source code must be modified to
use the formatted READ statements. The letter C is removed from column 1
to activate a line of the formatted READ code. Similarly, a C is placed in
column 1 of the namelist code to deactivate it when using FIS. The
changes to the source code converting from namelist to formatted READ
statements are listed below:
Remove C in column 1 for line numbers:
284-312.2
377.64-377.65, 377.7, 6303.-6305.
Add C in column 1 for line numbers:
153.-169.8, 283.01-283.37, 377.62-377.63, 377.69, 6250.93-6250.95,
6302.93-6302.95
The reverse must be done when changing from the formatted READ to the
namelist option.
157
</pre><hr><pre>
-------
TABLE Cl FORMATTED INPUT SEQUENCE (FIS) FORMAT FOR THE ARM MODEL VERSION II
ARM Model Formatted Parameter Input Sequence
Watershed
Run InfnrmaUnn'
1234 5 ( 7 1 9 10 11 1213 14 15 IS 17 11 192021 22 2324 HM 27 21 2S 30 31 3233 34 35 M 37 31384041 4243 44 4J 4t 474149 M 51 52 53 54 55 5$ 57 515* Htl S2S3MUfSC7 Mil JO 71 72 73 J4 75 7t 77 71 7S 10
H
1
0
P
S
f
N
1
D
1
w
c
Y
M
U
R
N
E
U
C
1
D
a
h
C
f
T
1
0
S
T
H
S
E
t
e
A
H
P
N
W
T
it
E
K
B
e
m
L
T
1
T
r
:
:
C
•»
U
r
/'
:
s
T
w
K
6
s
c
m
-
:
h
a
data identifier
D
E
S
(
S
N
1
B
E
U
L
K
1
S
N
R
D
P
P
/
N
D
H
G
N
Z
;
3
N
G
0
A
6
A
E
t
F
D
T
N
D
S
HI
F
W
W
D
M
C
T
/
I
1
R
0
D
N
1
:
P
C
*•
K
M
e
0
S
Y
A
A
;
L
R
N
.
2
1
:
K
L
Y
Y
1
N
i
-
;
:
N
s
e
/
d
T
d
e
:
n
n
i
a
I
H
B
E
d
m
i
Y
G
N
e
e
/
n
i
Ml
NM
DM
S
1
G
C
W
S
N
W
C
C
t
0
c
N
0
0
U
=
T
S
F
:
D
P
/
r
a
•
N
H
Z
E
;
A
E
E
/
/
:
-
S
R
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c
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t
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G
N
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d
N
D
n
e
Y
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n
A
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1
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t
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;
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;
L
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W
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1
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0
n
A
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1
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5
2
C
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4
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1
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0
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2
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4
r
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K
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1
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1
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4
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:
0
L
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f
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f
f
f
f
1
01
00
</pre><hr><pre>
-------
TABLE Cl (continued)
ARM Me
Watershed
Run Inforn
xtel Formatted Parameter Input Sequence
•
nation'
1234 S ( 7 1 9 10 11 1213 14 IS It 1711 H 20 21 22 2324 25 » 27 21 M 3031 3233J435M37 3IM404I 42 43 44 45 « 47 41 49 50 51 S2 53 M 55 « 57 51 51 MSI (2 (3 M UK 17 M B 70 71 72 7374 7578 7771 7JH
P
A
D
N
R
N
S
u
L
C(
T
Yl
SI
)
E
P
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P
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Y
S
C
0
Y
K
U'
T
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L
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1
Ul
PI
OH
¥
M
T
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E
T
0
0
S
M
A
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A
G
6
6
R
T
U
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c
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t
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/
0
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it
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0
A
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1
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;
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A
2
;
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L
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^
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z
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N
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0
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2
Z
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N
N
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N
N
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N
s
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0
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:
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1
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<•
S
C
M
P
C
Ul
VD
(continued)
</pre><hr><pre>
-------
TABLE Cl (continued)
ARM Me
Watershed
Run Inforrr
del Formatted Parameter Input Sequence
latinn1
1 2 34 5 1 7 1 9 10 11 1213 14 15 U 17 11 H 20 21 22 2324 H2« 27 21 M 3031 32 3334 35 3i 37 JIM 40 41 42 4344 45 « 47 41 « MSI 5253 5455 M 57 MM MCI (2 S3 (4 UK (7 H H It 71 tt 73 M 75 S 77 Jinn
3
r
p
s
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L
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r
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/
N
s
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L
G
P
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E
H
U
P
O
R
E
N
N
1
U
P
0
R
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O
M
O
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p
W
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M
D
1
J
R
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.>
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U
p
s
F
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P
T
R
F
E
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U
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N
P
P
A
R
R
N
E
1
O
A
R
R
N
P
D
R
H
r*
D
R
A
G
C
D
H
IV
A
O
E
Z
Z
w
A
L
E
p
Z
Z
W
O
A
T
R
O
0
A
T
N
0
O
A
R
T
U
U
N
N
T
U
N
N
T
U
E
R
S
E
E
E
R
E
E
E
S
R
E
R
E
R
C
C
0
O
p
E
F
F
F
F
1
1
r*
C
1
1
E
E
N
N
T
T
S
S
•
(continued)
</pre><hr><pre>
-------
TABLE Cl (continued)
ARM Me
Watershed
Run Inforrr
•del Formatted Parameter Input Sequence
*
lation'
1234 S 1 7 1 ! » 11 1213 M 15 11 17 II W 20 21 22 2324 25 2S 27 21 21 3031 32 J3J4 J5M37 31 M40 41 42 4344 45«47 « OM 51 52 53 54 55 M 57 MM MSI (2 C3 MUM 17 it M ft 71 72 73 H 7571 11*
S
u
_
J
c
S
J
L
G
E
A
N
5
U
P
U
3
o
R
H
U
p
0
R
N
P
1
U
P
H
R
3
KV
0
i
-i
p
KV
o
D
P
T
R
P
0
F
E
-
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_>
f
E
E
U
L
R
F
E
§
A
R
R
N
R
A
R
R
N
1
O
A
R
P
C
D
1
^
D
C
G
C
H
:
2
7
w
r
:
Z
2
W
A
E
E
2
O
O
0
A
c
r
o
A
r
N
0
R
N
N
r
N
N
T
1
N
U
-
:
E
E
El
Ol
E
5
?
V
—
TIM
(continued)
</pre><hr><pre>
-------
TABLE Cl (continued)
ARM Model Formatted Parameter Input Sequence
Watershed
Run Information'
1 2 3 4 5 1 7 1 9 10 11 1213 M 15 I1 17 11 192021 222324252(2721293031 323334353*37 31394041 42 4344454*47 41 49 5« 51 52 53 54 555*57.51 59 Wtl t2*3H*5Bi7SI«»7fl 71 7273747578 77 717910
S
U
c
S
U
U
p
H
U
P
I
A
S
R
P
L
R
P
z
S
z
F
E
O
F
E
T
Z
D
A
R
R
A
R
E
T
P
c
/
c
M
;
T
E
Z
D
E
Z
P
H
o
E
O
;
;
N
N
E
E
B
U
S
Z
Z
0
T
P
2
T
H
M
A
B
U
D
Z
S
T
Z
z
i
B
D
B
U
U
Z
Z
;
T
.
•
B
D
L
Z
I
U
Z
F
L
Z
F
N)
</pre><hr><pre>
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/3-78-080
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
5. REPORT DATE
User's Manual for Agricultural Runoff Management
(ARM) Model
August 1978 issuing date
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
A. S. Donigian, Jr., and H. H. Davis, Jr.
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Hydroconnp Incorporated
Palo Alto, CA 94304
10. PROGRAM ELEMENT NO.
1BB770
11. CONTRACT/GRANT NO.
R803722-01
12. SPONSORING AGENCY NAME AND ADDRESS
Environnvgntal Research Laboratory—Athens, GA
Office of Research and Development
U.S. Environmental Protection Agency
Athens, GA 30605
13. TYPE OF REPORT AND PERIOD COVERED
Final, 7/77 to 11/78
14. SPONSORING AGENCY CODE
EPA/600/01
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This user manual provides detailed instructions and guidelines for using the
Agricultural Runoff Management (ARM) Model, Versions I and II. The manual includes
a brief general description of the ARM Model structure, operation, and components,
but the primary purpose of this document is to supply information, or sources of
information, to assist potential users in using, calibrating, and applying the ARM
Model.
Data requirements and sources, model input and output, and model parameters are
described and discussed. Extensive guidelines are provided for parameter evaluation
and model calibration for runoff, sediment, pesticide, and nutrient simulation.
Sample input sequences and examples of model output are included to clarify the
tables describing model input and output. The manual also discusses computer
requirements and methods of analysis of the continuous information provided by the
model.
This manual, when used with an understanding of the simulated processes and the
model algorithms, can provide a sound basis for using the ARM Model in the analysis
of agricultural nonpoint pollution problems and management practices.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
COSATI Field/Group
Simulation
Runoff
Water Quality
Planning
Land Use
Nonpoint Pollution
Model Studies
48G
68D
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (ThisReport)
UNCLASSIFIED
21. NO. OF PAGES
173
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
163
* U.S. GOVBfflWm PWNHHG omct 1978—7 57 -140 /1 381
</pre><hr><pre>
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