vEPA
United" States
Environmental Protection
Agency
Environmental Research
Laboratory
Athens GA 30813
EPA/600/3-85/043
June 1985
Research and Development
Field Agricultural
Runoff Monitoring
(FARM) Manual
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EPA/600/3-85/043
June 1985
FIELD AGRICULTURAL RUNOFF MONITORING (FARM) MANUAL
by
C.N. Smith, D.S. Brown, J.D. Dean+, R.S. Parrish++, R.F. Carsel,
and A.S. Donigian, Jr.+
Technology Development and Applications Branch
Environmental Research Laboratory
Athens, Georgia 30613
+Anderson-Nichols, Inc.
2666 East Bayshore Road
Palo Alto, California 94303
++Computer Sciences Corporation
Environmental Research Laboratory
Athens, Georgia 30613
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30613
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DISCLAIMER
information in this document has been funded by the United States
Environmental Protection Agency, It has been subjected to the Agency's peer
and administrative review, and it has been approved as an EPA document.
Mention of trade names or commercial products does not constitute endorse-
ment or recommendation for use by the U.S. Environmental Protection Agency.
ii
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FOREWORD
As environmental controls become more costly to implement and the
penalties of judgement errors become more severe, environmental 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 and engineering tools to help
pollution control officials achieve water quality goals through watershed
management.
Because of the toxicity and persistence of many pesticides and their
extensive use in modern agriculture, the runoff losses of these chemicals
from agricultural fields and the resulting concentrations in surface water
bodies is an environmental concern. During the 1970s, several research
studies of pesticide runoff from fields were conducted by the U.S. EPA to
provide data for developing and testing nonpoint source runoff models.
Experience gained in this research prompted the development of this
field runoff protocol manual for use primarily by EPA's Office of Pesticide
Programs in assisting registrants in meeting data collection needs. Field
data collection is part of the pesticide registration process in which an
exposure assessment is performed to estimate potential exposure to humans
and other organisms resulting from the agricultural, si'lvicultural, and
other applications of pesticides. The manual is intended to assist in the
development of consistent data bases for exposure assessment modeling thus
producing a cost-effective procedure for both EPA and the registrant.
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, Georgia
111
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ABSTRACT
A field monitoring protocol was developed to provide comprehensive
guidelines for developing pesticide runoff data bases for use in conducting
environmental exposure assessments as part of the registration process
conducted by the office of Pesticide Programs (OPP). These data bases must
be carefully planned to insure that important measurements are made and
that both the appropriate quality and quantity of data are obtained for a
representative agronomic location and management scenario. Detailed guid-
ance is provided, therefore, on site selection, experimental design, data
requirements, sampling procedures, equipment, quality assurance planning,
data base management, data analysis, and exposure assessment modeling.
This report was developed in part (Sections 2, 5 and 6) by contract
68-03-3116 to Anderson Nichols Co., Inc., Palo Alto, CA, under the sponsor-
ship of the U.S. Environmental Protection Agency. The report covers a
period from September 1983 to April 1985, and work was completed as of
May 1985.
iv
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CONTENTS
Foreword ................ ........ iii
Abstract ....... .................. iv
Figures ix
Tables .................... .. xvi
Acknowledgment xviii
SECTION 1 INTRODUCTION 1
1 .1 Statement of Problem 1
1.2 Purpose of Manual 3
1 .3 Format of Manual 7
1.4 References for Section 1 7
SECTION 2 OVERVIEW OF DESIGN AND PERFORMANCE PROCEDURES. 9
2.1 Concept of Design 9
2.2 Formulation of Study Goals 10
2.2.1 Exposure assessment defined ...... 10
2.2.2 Motivation for use of models ..... 15
2.2.3 Currently available models ...... 15
2.2.4 Model data requirements ........ 15
2.3 Design Specifications 16
2.4 Cause and Effect Relationships in the
Pesticide Runoff Process .......... 17
2.4.1 Runoff and sediment erosion processes . 18
2.4.2 interactions and fate of pesticides
in soils ............... 19
2.4.3 Impact of runoff, sediment transport
and pesticide fate processes on
study design 22
2.5 References for Section 2.. 23
SECTION 3 SPECIFIC DESIGN CONSIDERATION 25
3.1 Site Selection 25
v
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CONTENTS (Con't.)
3.1.1 Delineation of unique areas ...... 25
3.1.2 Ranking candidate watersheds . . . « . 39
3.2 Site Description 40
3.2.1 Location and size ........... 40
3.2.2 Climate 41
3.2.3 Topography 41
3.2.4 Soils . , 41
3.2.5 Geology 41
3.2.6 History , 44
3.2.7 Photographs .............. 44
3.3 Measurement of Runoff 44
3.3.1 Equipment for runoff flow measurements. 45
3.3.2 Water stage measurement ... 46
3.3.3 Selection of automatic sampler .... 52
3.4 Meteorological Station ............ 55
3.4.1 Site location requirement 55
3.4.2 Precipitation ............. 56
3.4.3 Evapotranspiration .......... 57
3.4.4 Solar radiation ....; „ . 58
3.4.5 Mr temperature and relative humidity . 60
3.4.6 Wind „ . . 60
3.5 Soil Characterization 61
3.5.1 Series 61
3.5.2 Hydrologic group 61
3.5.3 Texture 62
3.5.4 Organic carbon content ........ 62
3.5.5 Bulk density ........ 64
3.5.6 pH . 64
3.5.7 Temperature ........ 65
3.5.8 Moisture content ...... 67
3.5.9 Infiltration rate 68
3.6 A Word of Caution Regarding On-line Field
Data Systems 68
3.7 Sampling Network Design ........... 70
vi
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CONTENTS (Con't.)
3.7.1 Estimating means and totals ...... 71
3.7.2 Subdivision of the study area 73
3.7.3 Systematic sampling , . . . 73
3.7.4 Determination of sample size 74
3.8 Agricultural Practices ....... 76
3.8.1 Crop fertilization 78
3.9 Pesticide Application ..... 78
'•
3.9.1 Liquid sprayer calibration 82
3.9.1.1 Tractor speed calibration . . 83
3.9.1.2 Nozzle delivery rate calibra-
tion . 84
3.9.1.3 Tank mix concentration
calibration 84
3.9.1.4 Cautions regarding tank mixes. 86
3.9.2 Granular applicator calibration .... 86
3.9.3 Timing of application ......... 86
3.9.4 Field safety 86
3.10 Pesticide Application Monitoring 87
3.10.1 Filter disc method 89
3.10.2 Field timing method 94
3.10.3 Soil sampling method 94
3.10.4 Plant-soil application distribution
measurements ...... 96
3.11 Soil Sampling after Runoff Events 97
3.12 Runoff Sampling 101
3.12.1 Particle size analysis and enrichment
ratio * 101
3.12.2 Sediment organic carbon 101
3.12.3 Sediment cation exchange capacity . . 103
3.12.4 Enrichment ratio 103
3.12.5 Equipment maintenance and monitoring . 104
3.13 Canopy Development 104
vii
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CONTENTS (Con't.)
3.13.1 Leaf area index 104
3.13.2 Foliar washoff 107
3'. 13.3 Canopy temperature .......... 108
3.13.4 Plant uptake of pesticides . 108
3.13.5 Crop yield , . 108
3.13.6 Pesticide remaining in the harvested
crop and post harvest crop residues. 109
3.14 Volatilization from soil and plants 109
3,15 Mass of Pesticide in Precipitation ...... 109
3.16 Transporting Samples 109
3.17 Field Operations Record Keeping . . 110
3.18 Analytical Methodology 110
3.19_ Runoff and Soil Core Data Computation .... 112
3.20 References for Section 3 121
SECTION 4 QUALITY ASSURANCE PLANNING .......... 127
4.1 References for Section 4.... ....... 131
SECTION 5 DATA BASE MANAGEMENT 132
5.1 Introduction 132
5.2 Integrating the Field Data Collection and
Data Management Designs .......... 133
5.3 Data Security and Tracking .......... 135
5.4 Storing Data on the Computer ......... 137
5.5 "Data Entry Verification 137
5.6 Data Management ............... 138
5.7 Questionable Values ..... 138
5.8 Identification of Systematic Errors ..... 138
5.8.1 Correction of systematic errors .... 139
5.9 Identification of Random Errors ....... 139
5.9.1 Correction of random errors ...... 140
5.10 Missing Values 140
5.10.1 Interpolation ..... 141
5.10.2 Interstation correlation 143
5.10.3 Time series modeling ......... 145
viii
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CONTENTS (Con't.)
5.10»4 Summary of techniques to evaluate
missing data 149
5.11 Archiving the Data Base «... 149
5.12 Data Base Documentation ........... 150
5.13 References for Section 5. 151
SECTION 6 DATA ANALYSIS AND MODELING 152
- 6.1 Data Analysis ........... 152
6.1.1 Model inputs 153
6.1.1.1 Conversion of breakpoint pre-
cipitation to interval data. 153
6.1.1.2 Disaggregation of precipita-
tion depths ........ 153
6.1.1.3 Snowfall 156
6.1.1.4 Soil moisture 157
6.1.1.5 Pesticide degradation rates . 159
6.1.1.6 Pesticide adsorption partition
coefficient ........ 160
6.1.2 Model outputs 163
6.1.2.1 Integration to produce volume
and mass ........... 163
6.1.2.2 Frequency analysis ...... 166
6.2 Model Calibration and Verification ...... 166
6.2.1 The Kolmogorov-Smirnov two sample test. 171
6.2.1.1 Tests for serial correlation . 173
6.2.1.2 Example application ..... 173
6.2.2 Linear regression analysis . 176
6.2.2.1 Significance tests for a and g. 179
6.2.2.2 Example regression analysis . 179
6.3 Model Application and Sensitivity Testing . . 186
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CONTENTS (Con't.)
6.4 Extrapolation of Site Specific Data to Other
Field Sites ....... 189
6.5 References for Section 6 189
SECTION 7 APPENDICES
A. Useful conversion factors for environmental data bases . 192
B. Random unit tables 193
C. Student's t-distribution 198
D. Soil names and hydrologic classifications ....... 199
E. Surface soil sampler design with transfer funnel
specifications ..................... 228
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FIGURES
Number • • Page
1.1 Schematic of field runoff situation ............. 2
1.2 Components of a pesticide field runoff study for
evaluating exposure/risk assessment using
mathematical models « .... 6
2.1 Schematic of the design process 11
2.2 Time series plot of toxicant concentration ......... 12
2.3 Frequency distribution of toxicant concentrations ..... 12
2.4 Cumulative frequency distribution of toxicant
concentrations ....................... 12
2.5 Time series of toxicant concentrations with moving
average window of duration tc .............. 13
2.6 Schematic of natural systems that produce environmental
time series of pesticide concentrations 14
3.1 Flow chart for selection of watershed for field studies
to support modeling exposure assessments ......... 26
3.2 Corn harvested for all purposes .............. 27
3.3 Sorghums harvested for all purposes except syrup ...... 27
3.4 ' Wheat harvested for grain 28
3.5 Cotton harvested 28
3.6 Soybeans harvested for beans ........ 29
3.7 Land in orchards 29
3.8 Vegetables harvested for sale 30
3.9 Average annual potential direct runoff 31
X3,
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FIGURES (Con't.)
Number Page
3.10 Relative potential contribution of cropland to
watershed sediment yields 32
3.11 Average annual values of the rainfall-erosivity
factor, R 34
3.12 Percent nitrogen in surface foot of soil .......... 36
3.13 Soils and topography, watershed P1, Watkinsville, GA . . . . 43
3.14 H-type flume with sloping floor approach .......... 45
3.15 Stilling well attached to flume .... ..... 52
3.16 Illustration of composite sampling failure to adequately
represent dynamic flow fluctuations within an event ... 53
3.17 Example of positioning sampler intake in H-type
flume with sloping floor approach ..... 54
3.18 Example weather station, raingages and samplers (A),
evaporation pan (B), strip recorder for measuring pan
3.19
3.20
3.21
3.22
3.23
3.24
3.25
Evaporation pan with anemometer (A) and total precipita-
Example record sheet for daily meteorological
Instrument shelter with recording hygrothenmograph
. 56
57
58
. 59
. 60
63
66
XI1
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FIGURES (Con't. )
Number Page
3.26
3.27
3.28
3.29
3.30
3.31
3.32
3.33
3.34
3.35
3.36
3.37
3.38
3.39
3.40
3.41
3.42
Thermistor harness consisting of label tag (A); jack
(B), which is plugged into monitoring instrument;
Illustration of a field design for characterizing
Illustration of pesticide applications monitoring
Examples of pesticide application methods include tractor-
mounted (A), pull-type (B), electrostatic (C), aerial
Usual starting dates of corn planting in United states ...
Usual starting dates of cotton planting in United States . .
Usual starting dates of soybean planting in United States. .
Atrazine persistence in top 2.5 cm of soil for a watershed
66
69
70
74
79
87
88
89
93
95
96
99
99
100
102
105
11 1
x
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FIGURES (Con't.)
Number gage
3.43 Example of raw field and pesticide analytical data
from a runoff event ................... 113
3.44 Example of a runoff event summary . . 114
3.45 Column heading key for runoff data computations 115
3.46 Example of annual pesticide runoff summary ......... 119
3.47 Example of soil core data computations 120
3.48 Example of pesticide residue summary, ug/kg ........ 121
5.1 Use of Cartesian Coordinate system to locate
measurements in a watershed 134
5.2 Example flow chart of a system for computer processing
and storage of digital precipitation data ........ 136
5.3 Interpolation of missing data using Lagrange
polynomials ............. 142
5.4 Computer subroutine for interpolation using Aitken's
iterated interpolation 144
5.5 Minimum and maximum ambient air temperature at
Panther Basin, September 1, 1978 to September 30,
1979. Flat sections of plots represent data gaps .... 146
5»6 Autocorrelation function of daily pan evaporation ..... 146
5.7 Autocorrelation function of standardized daily pan
evaporation 147
5.8 Enlargement of first 12 lags of autocorrelation function for
standardized pan evaporation and fitted AR(1 } model ... 148
6.1 Cumulative plot of breakpoint storm rainfall
from Table 6.1 154
xiv
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FIGURES (Cont.)
Number Page•
6.2 Adjustment factors for rain and snow based on wind speed . . 158
6.3 Graphical determination of a first-order pesticide
decay rate 161
6.4 Example of numerical integration of a hydrograph
using Trapezoidal and Simpson's rule ........... 165
6.5 Model vs. natural systems: inputs, outputs", and errors . . 169
6.6 Serial correlogram of residuals .............. 174
6.7 Serial correlogram of observed and simulated mean daily
flow for the Main Ploodway at Weslaco .......... 175
6.8 Regression analysis of observed and simulated flows
for the Arroyo Colorado 180
E1 Surface soil sampler design with transfer funnel
specifications ...................... 228
xv
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TABLES
Number Page
1.1 Field runoff study requirements . 3
2.1 Relative impact of selected management practices
on agricultural runoff processes/components
compared to conventional practices ............ 20
3.1 Indications of the general magnitude of the soil-
credibility factor, K 35
3.2 Values of the USLE topographic factor, LS, for specified
combinations of slope length and steepness ........ 37
3.3 Typical soil pedon description .......... 42
3.4 Rating tables for H flume 47
3.5 Rating tables for 4-foot HL flume 51
3.6 Surface soil (2,5 cm) temperature and moisture from time
of application to first runoff event ........... 65
3.7 Agronomic data for major agricultural crops in the United
States 90
3.8 Sampling techniques for use throughout the cropping season . 91
3.9 Herbicide application rates as monitored by various
techniques (kg/ha) .................... 92
3.10 Crop leaf canopy development «... 106
6.1 Hypothetical breakpoint precipitation record ........ 155
6.2 Interval rainfall data determined from the plot
in Figure 6.1 * •• 156
6.3 Pesticide residue measurements in the top centimeter
of an agricultural soil 160
6.4 Adsorption data for a pesticide in a soil-water system . . 164
xvi
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TABLES (Con't.)
Number Page
6,5 Independent observed and simulated flow time -series
for the Arroyo Colorado at Weslaco, Texas 167
6.6 Frequency analysis of flow data from the Arroyo Colorado . « 168
6.7 Critical values in the Kolmogorov-Smirnov tests ...... 177
6.8 Residual of regression of Arroyo Colorado simulated
and observed streamflow (S-(a + P) ) 182
6.9 Computation of the x^ statistic for the test of
normality of residuals .................. 183
6.10 Standard normal probabilities ... 184
6.11 Percentage points of x2 distributions ............ 187
6.12 Percentage points of t distributions ....... 188
A1 Useful conversion factors for environmental data bases ... 192
B1 Random unit tables ......... ..... 193
C Student's t-distribution 198
D Soil names and hydrologic classifications ......... 199
3W3.1
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ACKNOWL EDGMENTS
Hie authors express sincere appreciation to the technical reviewers
for their helpful suggestions. They included Dr. J. L. Baker, Iowa State
University; Dr. R. W. Hoist, U.S. Environmental Protection Agency; Dr. G.
W» Langdale, U. S. Department of Agriculture; M. N. Lorber, U.S. Environ-
mental Protection Agency; Dr. G. R. Oliver, Dow Chemical Company; and Dr.
D. D. Sumner, Ciba-Geigy Corporation.
The authors gratefully acknowledge Annie Smith's efforts in synthesi-
zing a typed original first draft from our many pages of pencilled notes,
and for persevering through long hours of corrections leading to the final
deadlines. A similar thanks is extended to Bob Ryans for his very able
editorial and photographic assistance.
Acknowledgement is extended to T. Prather, B. Bartell, S. Hodge, and
R. Jfoon of the Computer Sciences Corporation for drafting many of the
figures.
We thank Drs. E. D» Law and J. L. Chesness, University of Georgia, for
supplying photographs of the electrostatic sprayer and chemigation system.
xvi 11
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SECTION 1
INTRODUCTION
1.1 STATEMENT OP PROBLEM
As part of its mandate under the Federal Insecticide, Fungicide, and
Rodenticide Act (FIFRA), the u. S. Environmental Protection Agency is
required to register and approve pesticide compounds before they are
released for widespread use in the United States. FIFRA requires that
data on toxicity, health effects, etc. be submitted to the Agency in
support of the registration of a pesticide. As part of the registration
process, an exposure assessment is performed to estimate environmental
exposures of chemicals to humans and other organisms -resulting from the
applications of pesticides for agricultural, silvicultural, and other
uses. Because of the toxicity and persistence of many pesticides and
their extensive use in modern agriculture, the runoff losses of pesticides
from agricultural fields and the resulting concentrations in surface
water bodies is a major environmental concern.
Pesticide runoff problems are associated with nonpoint source pollu-
tion because agricultural chemicals are of widespread use in our modern
society and management of soil and water resources from agricultural
systems influences the use, fate, and transport of chemicals. Evaluation
of pesticide risk (probability of damage) to the environment by agricul-
tural runoff requires an understanding of pesticide properties and agro-
nomic practices, and a detailed description of the hydrologic cycle. The
system to be examined can consist of a single field, a watershed, or a
river basin. Bach type of system has definite physical boundaries and its
response to pesticide runoff is determined by the combination of physical
characteristics of soils, topography, geology, vegetation and drainage net-
works (Bailey and Swank, 1983),, Inputs to the system (Figure 1.1) include
both natural inputs (i.e., uncontrolled inputs such as weather conditions)
and man-induced inputs (i.e., chemical application and management practices).
In general, the field or watershed produces pesticide runoff loadings to
water bodies depending on the relative timing of applications and storm
events, soil and chemical characteristics, topographic and geologic charac-
teristics, and agronomic and engineering practices (Donigian et al., 1985).
After pesticide application, the chemical is subjected to numerous physical,
chemical, and biological processes that transform, and transport the compound
as the hydrologic cycle interacts with the soil-plant-pesticide system.
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Natural Inputs
Man's Inputs
Climatic Seasons
Precipitation — rain, snow
Evapotranspiration Spring —
Temperature
Solar Radiation
Wind
Relative Humidity
Reid System Summer -
Soils
Topography
Geology
Vegetation Fall —
Drainage network
Reid Response
Surface Runoff
Tillage
Starter Fertilizer Application
Pre—emergence Pesticide Application
Plant Crop
Post—emergence Pesticide Application
Cultivation
Secondary Fertilizer Application
Foliar Pesticide Application
Harvest Crop
Foliar
Washoff
Active Runoff Zone
(Soil Surface)
FIGURE 1.1. Schematic of field runoff situation
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1,2 PURPOSE OP MANUAL
The objective of .this work was to develop a standard methodology for
the collection of pesticide runoff data for use by the EPA's Office of
Pesticide Programs (OPP), as well as the pesticide industry. Runoff data
are required to conduct exposure assessments when new pesticides are sub^
mitted for registration, when new uses are proposed for existing pesticides,
and when existing pesticides are being re-evaluated because of concern for
human health or the environment.
A standard data collection procedure is of greater importance today
because of the wide-spread use of modeling to evaluate the environmental
exposure and risks of a specific pesticide. Environmental exposure
models require certain essential data and the observed data base must
address this need.
The suggested field and laboratory measurements discussed in this
manual are intended to provide guidance for conducting either detailed
research investigations or information for use in model calibration and
application. Extension of measured data through modeling is considered a
major element of exposure/risk assessments. The data collected from these
studies can, therefore, be used for conducting exposure/risk assessments.
Specific monitoring requirements (e.g., rainfall data, pan evaporation
data, etc.) will depend on the detailed study objectives, pesticide appli-
cation mode (e.g., foliar versus soil applied), and the model of choice.
Field studies involve tradeoff between costs and relative values of
information obtained. Table 1.1 provides some general guidance on the
kinds of information normally needed for the two most common objectives in
field runoff studies, i.e. , research and development and model calibration.
TABLE 1.1. FIELD RUNOFF STUDY REQUIREMENTS
Research Model Calibration/
Factors or Measurements and Development Exposure- Assessment
Site selection based on crop,
land and chemical use area X X
Area of Field X X
Meteorological
Precipitation depth
Interval X Xa
Evaporation X X*3
Solar radiation X Xb
Air temperature X Xb
Relative humidity X Xb
Wind speed/direction X Xb
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TABLE 1.1 (Cont'd). FIELD RUNOFF STUDY REQUIREMENTS
Research Model Calibration/
Factors or Measurements and Development Exposure Assessment
Total wind X Xb
Soil Characteristics
Series identification X X
Hydrologic group X
Texture X
Organic carbon X X
Bulk density X
pH X
Temperature X
Moisture X
Infiltration rate X
Erodibility X Xb
Sampling Network Design X X
Quality Assurance Plan X X
Pesticide Application Rate and
Distribution on Plant
Residue-Soil X X
Runoff (for each event}
Total volume X X
Sediment yield X X
Pesticide Runoff Sampling
(dissolved and sorbed)
Inter-event X
Whole-event Xc
Runoff Sediment characterization
Particle size analysis X
Organic carbon X
Cation exchange capacity x
Enrichment ratio X
Soil Sampling after Runoff Events
Individual sites X
Composite X
Mass of Pesticide in Precipitation X
Land/Crop Management (i.e., USLE
factors) X Xd
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TABLE 1.1 (Cont'd). FIELD RUNOFF STUDY REQUIREMENTS
Factors or Measurements
Research
and Development
Model Calibration/
Exposure Assessment
Canopy
Percent cover X
Leaf area index X
Pesticide foliar washoff X
Temperature X
Plant uptake X
Crop yield X
Residue remaining after harvest X
Pesticide Residue in Harvested
Material and Post-Harvest
Crop Residue X
Field Operations Record Keeping X
Pesticide Residue in Crop Cover at
Application Time (Conservation
Tillage) X
Data Storage X
Pesticide Degradation Rates
Soil X
Foliar (if foliar applied) X
Volatilization
Soil X
Foliar (if foliar applied) X
Pesticide Sorption Partition
Coefficient (K ) X
X
X
X
X
X
X
x
x
aSpecific interval (i.e., hourly, daily) is model dependent.
^Specific combinations are used in various models.
°Composited sample. Some models predict hydrograph dynamics? most
predict storm totals.
values can be estimated.
eValue can be either measured for field soils or estimated based on
soil organic carbon content.
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Field runoff research studies conducted by EPA and cooperators during
the 1970s provide excellent reference material (Smith et al., 1978; Johnson
and Baker, 1982 and 1984; Ellis et al., 1977). Other excellent sources of
reference include the USDA (1979), Southern Weed Science Society (1977),
and Wauchope (1985, in preparation). These reports can be obtained through
USDA and the National Technical Information Service (NTIS) in Springfield,
VA.
The research studies conducted jointly by EPA, USDA, and several
universities were designed to develop and test pesticide runoff loading
models. These studies provided data to develop the first EPA simulation
model designed to mathematically describe nonpoint source pollution
(Crawford and Donigian, 1973). This modeling effort was extended with the
development and testing of the Agricultural Runoff Model (Donigian and
Crawford, 1976; Donigian et al., 1977). Several runoff models have been
tested on these data bases and include the Hydrological Simulation Program—
FORTRAN (Johanson et al., 1984; Donigian et al., 1984} and CREAMS—Chemi-
cals Runoff and Erosion from Agricultural Management Systems developed by
USDA (1980). Recent field research and mathematical model development have
provided a mechanism to develop detailed exposure/risk assessments for
pesticides. The components required to effectively link observed field
runoff data and pesticide runoff models (for conducting exposure/risk
assessment) are listed in Figure 1 .2.
The information presented in this manual is designed to provide the
user with a detailed information framework for developing and designing
components of a pesticide runoff study based on current understanding. The
design of such studies will provide the Agency and the registrant with
sufficient information to conduct an exposure/risk assessment for the
registration process. Specific recommendations for sampling, instrumenta-
tion needs, sample processing, and data analysis are provided.
Select a site from cropping, land, and chemical use
characteristics.
Develop experimental and quality assurance design network.
Monitor relevant soils, crop and climatic characteristics and
pesticide volatilization transport/transformation properties
to obtain mass balance.
Analyze and format collected data into a systematic data set.
Calibrate model to observed pesticide runoff data.
Conduct exposure/risk assessment using appropriate methodology.
FIGURE 1 .2. Components of a pesticide field runoff study for evaluating
exposure/risk assessment using mathematical models
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1 .3 FORMAT OP MANUAL
This user manual is divided into six sections, inclusive of this
introduction. Section II contains an overview of the overall design and
performance procedures. Section ill describes specific design considera-
tions including: site selection, required site description information,
data requirements, sampling network design, sampling equipment and
instrumentation, and sampling procedures. Section IV provides essential
elements for quality assurance planning. Section V contains information
on the development of a data base management system including record
keeping, data entry, storing data on computer, data manipulation, archiv-
ing, and data base documentation. Section ¥1 provides information on
model calibration and testing. This is followed by several appendices.
Appendix A provides conversion factors for environmental data bases.
Appendix B consists of random unit tables. Appendix C provides percentage
points of the student's t-distribution. Appendix D is a list of soil
series names and hydrologic classifications. Appendix E contains the
design specifications for a surface soil sampler.
1 ,4 REFERENCES FOR SECTION 1
Bailey, G.W. and R.R. Swank, Jr. 1983. Modeling Agricultural Nonpoint
Source Pollution: A Research Perspective, In: Agricultural Manage-
ment and water Quality, F.W. Schaler and G.W, Bailey (Eds.). Iowa
State University Press, Ames, IA.
Crawford, N.H. and A.S. Donigian, Jr. 1973. Pesticide Transport and
Runoff Model for Agricultural Lands. U.S. Environmental Protection
Agency, Athens, GA. EPA-600/2-78-013.
Donigian, A.S., Jr., D.W. Meier, and P.P. Jowise. 1985. stream Transport
and Agricultural Runoff of Pesticides for Exposure Assessment (STREAM)
A. Methodology. U.S. Environmental Protection Agency, Athens, GA.
(In preparation).
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell, and J.L. Kittle, Jr.
1984, Application Guide for Hydrological Simulation Program—FORTRAN
(HSPF). U.S. Environmental Protection Agency, Athens, GA.
EPA-600/3-84-065.
Donigian, A.S., Jr. and N.H. Crawford. 1976. Modeling Pesticides and
Nutrients on Agricultural Lands. U.S. Environmental Protection
Agency, Athens, GA, EPA--600/2-76-043.
Donigian, A.S., Jr., D.G. Beyerlein, H.H. Davis, Jr., and N.H Crawford.
1977. Agricultural Runoff Management (ARM) Model—Version II,
Testing and Refinement. U.S. Environmental Protection Agency,
Athens, GA. EPA-600/3-77-098.
-------
Ellis, E.G., A.E. Erickson, A.R. Wolcott, M. Zabik, and R. Leavitt, 1977.
Pesticide Runoff Losses from Small Watersheds in Great Lakes Basin.
U.S. Environmental protection Agency, Athens, GA. 1PA-600/3-77-112.
Johanson, R.C., J.C. Imhoff, J.L. Kittle, Jr., and A.S. Donigian, Jr.
1984. Hydrological Simulation Program—FORTRAN (HSPF): Users Manual
for Release 8.0. U.S. Environmental Protection Agency, Athens, GA.
EPA-600/3-84-066.
Johnson, H.P. and J.L. Baker. 1982. Field-to-Stream Transport of Agri-
cultural Chemicals and Sediment in an Iowa Watershed: Part 1. Data
Base for Model Testing (1976-1978). U.S. Environmental Protection
Agency, Athens, GA. EPA-600/S3-82-032.
Johnson, H.P. and J.L. Baker. 1984. Field-to-Stream Transport of Agri-
cultural Chemicals and Sediment in an Iowa Watershed: Part II. Data
Base for Model Testing (1979-1980). U.S. Environmental Protection
Agency, Athens, GA. EPA-600/S3-84-055.
Southern Weed Science Society. 1977. Research Methods in Weed Science.
Second Edition, Auburn, Ala. B. Truelove (Ed.), p. 221.
Smith, C.N., R.A. Leonard, G.W. Langdale, and G.W. Bailey. 1978. Trans-
port of Agricultural Chemicals from Small Upland piedmont Watersheds.
U.S. Environmental protection Agency, Athens, GA. EPA-600/3-78-056.
U.S. Department of Agriculture. 1979. Field Manual for Research in
Agricultural Hydrology. U.S. Department of Agriculture, Washington,
DC. Agriculture Handbook No. 224. p. 547.
U.S. Department of Agriculture. 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management Systems.
Knisel, W.G. , Jr. (Ed.). U.S. Department of Agriculture, Washington,
DC. Conservation Research Report No. 26.
Wauchope, R.D. and D.G. DeCoursey. 1985. Measuring and Predicting Losses
of Herbicides in Runoff Water from Agricultural Areas. In; Research
Methods in Weed Science. Third,Edition. N.D. Camper (Ed.). Southern
Weed Science Society, (In preparation.)
-------
SECTION 2
OVERVIEW OF DESIGN AND PERFORMANCE PROCEDURES
This section provides an overview of the procedures involved in
designing a field program for pesticide runoff data collection. It is
comprised of four major subsections:
• design concept
• formulation of study goals
• overview of design specification by topical areas
• cause and effect relationships in the pesticide runoff process
The design concept should be an overriding theme throughout the design and
implementation of the field study. It encompasses two subordinate concepts:
the formulation of exact study goals and the specification of design alter-
natives, which should always be evaluated and implemented in light of the
goals. The last section is included to briefly highlight some of the cause
and effect relationships in the pesticide runoff process. An understanding
of these relationships is helpful in evaluating the impact of alternative
designs on the goals of the program.
2.1 CONCEPT OF DESIGN
Design is a concept with which almost everyone is familiar but of
which few have a full appreciation. The American Heritage Dictionary of
the English Language defines design as "the invention and disposition of
the forms, parts or details of something according to a plan." The key
words in this definition are "details" and "plan." in order to specify
the details, the plan must be carefully formulated; and in order to
develop a plan, the program goals must be clearly defined.
The design, then, consists of two distinct phases. The first is a
concise and well defined statement of the program goals or objectives.
The soundness of the design will depend heavily upon how well the objec-
tives are defined. The objectives should be as specific as possible.
When properly defined, they form a set of design criteria that can be used
to evaluate the appropriateness of the various alternatives used to achieve
them. If, at any time during the design process, the question "Why are we
doing this?" cannot be answered, then the project objectives have been
incompletely specified.
-------
Once the project goals or objectives are known, one can formulate a
list of pathways or alternatives to follow in order to achieve the goals.
The selected alternatives will be the "details" of the design. The alterna-
tives should always be compared and selected based upon their anticipated
impact on the design objectives. This implies that a cause and effect
relationship between an alternative and the resulting outcome is understood.
If such a relationship is unknown, it may be necessary to experiment to
determine it. Obviously, the designer benefits greatly by being familiar
with literature on the subject and ongoing research, if, at any time, the
question "Why are we doing this this way?" cannot be answered, then proper
cause and effect has not been established between design alternatives and
program goals. Figure 2.1 shows a general framework for the design process.
2.2 FORMULATION OP STUDY GOALS
For our purposes, the overall objective for conducting a field program,
as discussed in Section 1, has been specified. To reiterate, we are primar-
ily interested in collecting data through field studies that can be used to
perform exposure assessments with computer simulation models.
Questions that come to mind after establishing this objective are:
• What constitutes an exposure assessment?
* What models can be used?
• What data will be required?
2.2.1 Exposure Assessment Defined
An exposure assessment is a determination of the magnitude (concen-
tration) of a toxicant to which an organism will be exposed over a given
period of time (duration). The model produces a time series of toxicant
concentrations in a specific medium (e.g., water, air, soil) svich as appears
in Figure 2,2. The time series can be compared to a critical value of the
concentration y (this might be, for instance, the LC50 value, i.e., concen-
tration for 50% mortality). This type of analysis easily shows whether the
criterion is exceeded and gives a qualitative feel for the severity of the
exceedence state. If we determine how often it is at a particular level or
within a specified range, we can create a frequency distribution of the
values of "y" (Figure 2.33 . If, in addition, we choose any value of y in
Figure 2,2 and determine the area under the curve to the right of that
value we can plot Figure 2.4, which is a cumulative frequency distribution
of the toxicant concentration. In other words, it shows the chance that
any given value "y" that we select will be exceeded, if our example time
series is long enough, then the "chance" approaches the true "probability"
that "y" will be exceeded.
10
-------
Unanswerable
Establish
Design
Objectives
1
Establish
Design
Alternatives
Why
is this being
done?
Why is
his being done
this way?
Answerable
Unanswerable
Experiment
Answerable
Select
Alternatives
1
Execute
Design
FIGURE 2.1. Schematic of the design process.
11
-------
o
i
Q
§
o
Time (t)
FIGURE 2.2. Time series plot of toxicant concentration.
100
*
•o
*
*
o
X
o
>»
Concentration (y)
FIGURE 2.3. Frequency distribution of
toxicant concentrations.
FIGURE 2.4.
Concentration (y)
Cumulative frequency
distribution of toxi-
cant concentrations.
12
-------
Thus far, only the concentration to which the organism will be exposed
has been discussed and nothing has been said concerning the duration of the
event. If we take the same time series and impose a window of length t on
it at level yc (Figure 2.5), and move it incrementally forward in time, we
can make a statement concerning the toxicant concentration within the dura-
tion window. Normally, the average concentration within the window is used.
The resulting cumulative frequency distribution shows the chance that the
moving average of duration tc will exceed the critical value of "y", yc.
The moving average window should be the same length as that specified for yc.
For instance, if the 48-hour LC50 is the criterion, a 48-hour moving window
should be used to average the data in the simulated time series. The use of
the moving window for averaging the time series allows us to compare both
the concentration and duration against the standard.
The chance or probability that the moving average concentration exceeds
the survival standard of a given species is the essence of the exposure
assessment. This type of information provides an estimate of the risk
taken in using this chemical under the conditions of the model simulation.
In this manual we are discussing the design of programs to produce
pesticide runoff data. How, then, does this fit within the general frame-
work of an exposure assessment? Figure 2.6 demonstrates the relationship.
The pesticide is introduced to the watershed system at the top of the figure.
Precipitation events produce runoff and sediment transport events, which,
at the field scale, are intermittent. That is, runoff and transport only
occur during or immediately following rainfall (or snowmelt) events. The
pesticide either dissolves in water or attaches to sediments and moves off
the field into adjacent streams. In these streams, the dissolved pesticide
may be diluted by uncontaminated water and pesticide attached to sediments
may deposit to the stream bed. In general, because of these mixing processes,
C
S.
£2
(0
•»•»
0)
o
C
o
o
Time (t)
FIGURE 2.5. Time series of toxicant concentrations with moving
average window of duration tc.
13
-------
AGRICULTURAL
FIELD SYSTEM
Ul O
O Ul
5 co
STREAM
SYSTEM
Z 111
_ Ul
O CO
<
INPUT
(PRECIPITATION, PESTICIDE)
I
A A
OUTPUT
(RUNOFF. SEDIMENT, PESTICIDE)
INPUT
(RUNOFF, SEDIMENT. PESTICIDE)
OUTPUT
(STREAMF.LOW,SUSPENDED/BED
SEDIMENT, PESTICIDE)
TIME SERIES FOR EXPOSURE ASSESMENTS
FIGURE 2.6. Schematic of natural systems that produce environmental
time series of pesticide concentrations.
14
-------
a larger stream system produces a more continuous time series of concentra-
tions, especially if the pesticide is not subject to rapid degradation. It
is these concentrations in the stream to which aquatic species or humans may
be exposed, and therefore are used in the exposure assessment. Thus, pesti-
cide runoff data developed from field scale programs must be linked with
.instream (or impoundment) conditions in order to perform a complete exposure
assessment.
2.2.2 Motivation for Use of Models
The issue of which models to use brings up the larger question of why
we are using models to perform this assessment. Perhaps the greatest
motivation is the prohibitive cost of performing field studies for extended
periods of time. Using models combined with limited field studies allows us
to "extend" the pesticide runoff record through simulation, thus obtaining
an equivalent period of data at substantially reduced costs. The purpose of
this extension is to obtain from the sample data a better estimate of the
true probability of exceeding the critical value. Another factor is the
long time required to perform field studies. Here again, once models are
calibrated and validated to the watershed of interest, the extension of the
record through simulation is more a matter of minutes or hours than of
years. These aspects of using models become attractive, for instance, to
the company attempting to put products on the market in a timely manner
or to the regulator charged with making timely decisions on chemical
usage that may affect human health.
The foregoing arguments are based on the premise that models are
available that can accurately represent the runoff losses from a given
watershed for a wide variety of pesticides, watershed, meteorologic and
management conditions. The previous decade has witnessed the birth and
"coming-of-age" of several simulation models for this purpose.
2.2.3 Currently Available Models
Two pesticide runoff models that are currently widely used in the
scientific/engineering community are HSPF (which includes ARM-Agricultural
Runoff Management Model), developed for the U.S. Environmental Protection
Agency (Johanson et al., 1984; Donigian et al., 1984) and CREAMS, which was
developed by the U.S. Department of Agriculture (USDA, 1980). While these
models are somewhat different in their approaches, they both require certain
basic information common to their usage. The data required of the field
study will be those data necessary to use these models, or models like
them, to extend the record of field study observations.
2.2.4 Model Data Requirements
The necessary data break down into three broad but distinct groups;
» input time series for driving the model
15
-------
• output time series for comparison to model simulations
4 data required to establish model parameters
It is normal that the input time series include precipitation and some
form of energy input. Precipitation inputs range in time increments of
daily down to 5 minutes. For energy inputs, either pan evaporation,
temperature and/or total incident solar radiation are normally required,
usually on a daily time step.
Output time series can include any number and type of observations
that can be made on the watershed. Examples are soil moisture, pesticide
soil concentrations, and runoff of water, sediment and pesticide.
The remaining category of data is that required for model parameter
evaluation. This is usually where most of the differences in data require-
ments between various models occur. The parameters break down into two
basic types, physically based and empirical. The physically based para-
meters (e.g., pesticide-soil adsorption coefficient, soil bulk density) are
normally measured either in the field or laboratory, or have been compiled
by previous researchers. Empirical constants have either been tabulated as
a result of previous observations or model simulations, and/or must be
evaluated as part of the model calibration exercise. Improvement in.simula-
tion of output time series can usually be accomplished by adjustment of
one or more of the empirical parameters required by the model after the
first estimate of the parameter(s) has been made.
2.3 DESIGN SPECIFICATIONS
The specifications of design alternatives are the.chief subject of
the remainder of this text. These specifications are divided into a
number of subject areas, namely:
• Site selection
• Data requirements
• Sampling network design
• Sampling equipment and watershed instrumentation
• Data base management
• Data analysis and modeling
Site selection is a critical issue because it defines the climatic regime
and soils/watershed characteristics. These affect virtually all aspects
of pesticide transport. Included in this choice are issues concerning
selection of soil and water conservation management practices and pesticide
use and application procedures. The impacts of climate, watershed and
16
-------
soils characteristics and management practices are discussed in Section 2.4.
Data required as products of the field study can vary depending upon the
computer model(s) selected to perform the exposure assessment. Thus, the
model or models to be used should be selected prior to initiation of the
s tudy.
Once the type of data to be collected is known, issues of the sampling
design can be addressed.
The sampling network design must specify the measurements of certain
variables or parameters at defined points in space and time. The frequency
of sampling will depend on the dynamics of the process being sampled. The
length of the sampling period will, in general, depend upon the persistence
of the chemical, although some time series measurements may be required
year round if a multiple year study is being done and continuous simulation
models are used. Degree of sampling in space depends upon the degree of
spatial heterogeneity that occurs in time series (e.g., precipitation) or
parameters (e.g., crop canopy).
Once the sampling network has been designed, procedures for taking
samples should be specified. These procedures will require that certain
equipment be available to collect, store, and transport samples. This
equipment may be portable or may require permanent or semi-permanent in-
stallation on the watershed.
Design of the data base and data base management system should also
be considered before the initiation of the data collection activity.
Included in this are issues such as:
• What data should be put into the computer?
• How should it be stored, checked, and verified?
• How should missing data be estimated, if necessary?
Decisions concerning these issues may affect the design of the sampling
program and the selection of equipment used to make measurements.
The type of data analysis to be performed should also be considered.
In some instances this can affect the sampling network design {for instance,
in the case of classical factorial or least squares experiments). For our
purposes, data analysis is considered to be the reduction of raw data for
model parameter estimation or for comparison of simulated model outputs to
observed data.
2.4 CAUSE AND EFFECT RELATIONSHIPS IN THE PESTICIDE RUNOFF PROCESS
To better select design alternatives for successfully conducting
a field program to measure pesticide runoff, a discussion of relation-
ships among the factors that control the process is in order.
17
-------
Pesticide lost, in runoff is the sum of that lost in the dissolved
phase (in water) and in the adsorbed phase (on sediment). A knowledge of
the runoff and erosion processes is essential to understanding the more
complex issue of pesticide runoff. These will be discussed first, followed
by a discussion of the impact of edaphic and conservation practices on soil
and water losses. Finally, the impact of management practices on chemical
losses will be addressed.
2.4*1 The Runoff and Sediment Erosion Processes
For these purposes, runoff will be defined as water that moves over
the land surface at some point from the time that it impinges upon the
land surface until it exits the field edge. There are two ways in which
runoff can occur (Freeze, 1981). The first, known as Hortonian flow, occurs
when the precipitation rate exceeds the hydraulic conductivity (capability
to transmit water) of the surface soil (Horton, 1933). Rainfall contacting
the soil surface begins to infiltrate downward in the soil profile as
moisture content increases close to the surface. At some point, ponding
occurs (i.e., the soil surface, becomes saturated). At this point the rain-
fall rate exceeds the infiltration rate and surface runoff (overland flow)
occurs. The second, known as Dunne-type flow is caused by the rising of a
shallow water table, which saturates the surface soil, thereby causing
overland flow to occur (Dunne, 1978). Hortonian flow is more common on
upland areas, whereas Dunne-type flow occurs on flatter slopes usually near
channels or in poorly drained areas.
As water moves over the surface of the land, it exerts a shearing force
on the soil surface. If the shearing force exceeds a critical value for a
particular soil particle (which is a function primarily of soil particle
size and soil structure) then that particle will be entrained by the flow
and move downslope. As long as the velocities in the flowing water remain
high, the particles will remain suspended. If velocities are decreased,
however, then larger, more dense particles will begin to settle out and be
redeposited to the soil surface. At this point they may roll along the soil
surface, or, as velocities drop further, they will stop moving altogether.
Naturally, the' smaller and less dense the particle, the easier it is
for runoff'water to entrain it. High intensity rainfalls tend to pulverize
soil aggregates into smaller particles which enhances the supply of fine
sediments available to be transported by runoff. On the other hand, de-
creasing soil moisture content tends to cause reaggregation or crusting of
the soil, near the surface. This tends to cause a decrease in the supply
of fine sediments. Quite naturally, tillage events that drastically alter
soil structure also increase fine sediment supply.
Runoff moving over" the soil surface will inevitably transport some
particles as long as the supply of particles lasts; however, the capability
to transport particles is limited. Thus, two cases can arise which limit
the quantity of particles transported: the availability of particles
(i.e., supply limitation) or the capability of the flow to transport parti-
cles (i.e., transport limitation).
18
-------
Based on the understanding of these processes, factors can be identi-
fied that are important in describing the processes of runoff and sediment
transport. These break down into four groups:
• climatic
• topographic
• soils
• management
The chief climatic factor is the intensity of rainfall. Intensities must
be great enough to exceed soil infiltration rates or to cause surface
saturation by rapidly rising water tables to produce runoff. High inten-
sity rainfalls, as noted previously, detach soil particles from aggregates,
thereby producing a larger supply of the fine sediments that are more easily
transported. Once runoff occurs, topographic features such as long, steep
slopes will tend to produce higher runoff velocities, increasing the capabi-
lity of flow to detach and transport sediment. In addition, some soils are
naturally more erodible than others. Generally, soils with a higher .fraction
of silt size particles and organic matter tend to erode more easily than
those having higher percentages of sand and clay size particles.
Management practices can affect characteristics of the watershed which
may have drastic impacts on runoff and sediment transport. Nonstructural
management practices are those that require no physical alteration of the
watershed itself. These include tillage practices, crop rotations and
improvement of soil fertility. Structural management practices include
terracing, grading and construction of grassed waterways, tile drains, etc.
A good review of the literature concerning the effects of management prac-
tices for both control of runoff and erosion can be found in Woolhiser
(1976) and Wischmeier (1976). As a quick reference, a qualitative summary
of the effects of certain management practices on runoff and sediment trans-
port is shown in Table 2.1 (Donigian et al., 1983). The designer should be
aware, at least qualitatively, of the impact of management options upon
quantities of runoff and sediment moving from the field areas.
2.4.2 Interactions and Pate of Pesticides in Soils
Superimposed upon the dynamics of the runoff and sediment transport
processes are the dynamics of pesticide behavior in soils. Although affected
by these transport processes, the quantity of pesticide that will appear in
runoff is primarily a function of the quantity of pesticide present in sur-
face soil and its partitioning between the dissolved and sorbed phases. The
partitioning behavior, usually expressed as the ratio of chemical concentra-
tion in he solid phase versus the liquid phase (Kp) has a direct influence
on chemical runoff behavior. Very weakly sorbed compounds move primarily in
the solution phase and are almost independent of sediment transport, strongly
sorbed materials are, however, transported mainly in the sediment phase and
the transport of sediments and chemical are closely related. Many processes
19
-------
TABLE 2.1. RELATIVE IMPACT OF SELECTED MANAGEMENT PRACTICES ON AGRICULTURAL
RUNOFF PROCESSES/COMPONENTS COMPARED TO CONVENTIONAL PRACTICES1
MANAGEMENT PRACTICE
Honstructural Measures
1. No Tillage
2. Conservation Tillage
3. Contour Farming
4. Grided Rows
5. Contour Strip
Cropping'
C. Spring Plowing3
7. Sod-Based Rotation4
1. Winter Crop Cover
9. Permanent Meadow
10. Mechanical Cultivation
11. Crop Rotation
STRUCTURAL MEASURES
1. Terrace*
2. Diversions
3. Craned Waterways5
4. Filter Strips6
S. Tile Drainage
6. Retention Basins
INPUT MANAGEMENT OPTIONS
1. Improve Soil Fertility
2. Eliminate Excessive
Applications
3. Optimise Timing of
Planting and
Chemical Applications
4. Control Release and
Transformation Rates
5. Biological Control
C. Incorporation of
Applied Chemicals
by Tillage
.Runoff-Related
Overland
Flow
—
—
-
-,+?
-
••
_
-
_
4-
-7
•
0
..
0
0
0
-
-
Subsurface
Flow
•f
+
0,+
+.-?
0,+
•f
+?
+
+?
•f
•f
0,+ .
-?
0
0
•f '
0
?
0
0
0
?.
?
Sedinant- Related
Detachment
by Rainfal!
-
-
0
0
-,o
-
»
+
0
0
0
—
0
0
_
0
0
0
•f
+
Dotachment/Scour
by Overland Plow
-
-
-
-
-
-
•
+
0
.
-
m
-,o
0
-,o
0
0
0
•(•
+
Transport by
Overland Flow
-
-
-
-?
-
-
—
+
0
0,-
+?
^
-,o
0
-,o
0
0
0
•f
•»•
Availabi llty/Production
of Sediment Fines
-
—
0
0
-,o
-.0
+,o
—
+
0
0
0
0
0
0
0
0
0
0
0
?
•f
Aggregation/
Compaction
-
-
0
0
0
0?
?
-•o
-1+
-
0
0
0
0
0
0
0
.
0
0
0
?
•
to
o
*Many practices have effects which are tine-dependent because they are applied in different seasons of the year. Comparisons
indicate long term deviations from base conditions.
^Processes are considered only within the strips of grass or close-growing crops growing between the cultivated crops.
The overall effects of shifting plowing from fall to spring are considered.
Processes are considered for the sod year only.
Processes are considered only within the grassed waterway.
Processes are considered only within the filter strip.
Sourcei Donigian et al., 1983
-------
affect the quantity of pesticide on the watershed and its partitioning.
The major processes can be organized into source, sink and intrasystem
transfer categories.
Sources include spills and application events. Chemical sinks are
photolysis (direct, sensitized); hydrolysis (acid, neutral, base catalyzed);
biodegradation; volatilization; plant metabolism; runoff, erosion and
leaching losses; and removal in harvested material. Intrasystem transfers
occur through foliar washoff, organism uptake (bioaccumulation), foliar
absorption, adsorption, acid/base equilibrium, and plant uptake.
Sources, for our purposes, are limited to application events although
spills may also be of concern. Sinks include photolysis, which may occur
at plant or soil surfaces, and hydrolysis, which may be catalyzed by acidic
or basic conditions, depending upon the compound. Organisms may play a
large part in the breakdown of the chemical, and volatilization acts to
remove the chemical from soil and plant surfaces. Chemical reactions can
convert the chemical between various forms and radically alter its environ-
mental properties. Runoff, leaching, and erosion losses are the major
processes by which substances are transported from the land or land surface
into water supplies.
Several processes also serve to cycle or move chemicals out of the soil
phase where they may be made temporarily unavailable for transport out of
the system. Plants can play a major role by adsorbing chemicals through the
foliage or roots and translocating it to other plant parts. This temporarily
bound residue may become available once again through leaching and decay of
dead plant material. Organisms can also temporarily immobilize portions of
the chemical. Speciation reactions can shift the chemical between the solid
or dissolved phases making the chemical less or more available to leach
through the soil profile.
In-depth discussions of the fate and transport of pesticides in soils
can be found in Pionke and Chesters (1973), Leonard et al. (1976), and
Donigian and Dean (1984). The field program designer should have prior
knowledge of approximate degradation rates and adsorption characteristics
of the pesticide as these will substantially affect the sampling program
design.
In addition to watershed management practices that modify the production
of runoff and erosion from agricultural watersheds, an additional set of
options for pesticide management also come into play. These options are;
• Application Mode
- Granular
incorporated
unincorporated
21
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— Liquid
incorporated
unincorporated
* Application Types
- Aerial
- Airblast
- Ground rig
- Chemigation
• Application Timing
— Season
- Time of day
• Formulation
- Emulsifiable concentrate
- Wettable powder
— Granules
- Solutions
- Flowable powders
— Micro encapsulated forms
• Application Rate
Application mode will obviously affect the amount of the chemical
applied directly to the soil and thus directly available for runoff. For
instance, spraying the soil surface directly, as with a preemergence herbi-
cidef would tend to make pesticide more directly available for runoff.
Soil incorporation, on the other hand, may place much of the compound below
the surface soil zone from which runoff is normally produced. Application
timing with regard to season and also to individual storm events is extremely
important, especially for short-lived chemicals. Pesticides degrade in the
environment. Therefore, the shorter the time between pesticide application
and a hydrologic event producing runoff, the higher th'e probability that
the pesticide will appear in runoff. Runoff potential is enhanced if pesti-
cides are applied during times of the year when the probability of runoff-
producing events is high. This is particularly true since antecedent
moisture contents also tend to be higher during periods of high rainfall
activity. Naturally, application rate and formulation have significant
impacts on pesticide availability. Caro (1976) has provided a discussion
of the impacts of various practices upon the runoff of pesticides in agri-
cultural applications.
2.4.3 Impact of Runoff, Sediment Transport and Pesticide Fate Processes on
Study Design
The principle concept concerning the collection of pesticide runoff
data is that the events tend to occur infrequently and in a, very short
22
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time frame when they do occur. In a pesticide runoff study in Iowa, for
instance (Baker et al., 1979), only two runoff events of any consequence
occurred between application and nearly complete degradation of the pesticide
over a 2-year period. On the other hand, in a pesticide runoff study per-
formed in Watkinsville, Georgia, 28 runoff events occurred during the 1972
growing season (Smith et al., 1978). Ten of these events yielded measurable
•concentrations of paraquat (a compound with slow degradation that was applied
above normal rates), however, while only seven yielded measurable concentra-
tions of trifluralin (a compound with higher degradation rates than paraquat).
The duration of some of the events from 1973 in the same study was approx-
imately 1.5 hours. Because of the exponential dependence of sediment trans-
port processes on flow, the bulk of the chemical may be transported in
still shorter time frames, when flows are at or near peak values.
For these reasons, pesticide runoff sampling tends to be more difficult
to perform than, for instance, leaching or instream sampling where the dyna-
mics of the events tend to be smoothed out. The designer of such studies
should be aware that in many cases the dynamics of a particular sequence of
events may require that the study be shortened or lengthened considerably
or that sampling schedules may change subject to the "whims of nature." In
general, the designer should attempt to build flexibility and the capability
to react quickly into the field program.
2.5 REFERENCES FOR SECTION 2
Baker, J.L. , H.P. Johnson, M.A. Borcherding, and W.R. Payne. 1979. Nutrient
and Pesticide Movement from Field to Stream: A Field Study. In:
Best Management Practices for Agriculture and Silviculture. R.C.
Loehr, D.A. Haith, M.F. Walter and C.S. Martin (Eds.). Ann Arbor
Science Publishers, Inc., Ann Arbor, MI., pp. 213-246.
Caro, J.H. 1976. Pesticides in Agricultural Runoff. Chapter 5. In:
Control of Water Pollution of Cropland Vol. 2 - An Overview. U.S.
Environmental Protection Agency, Athens, GA, and U.S. Department of
Agriculture, Washington, DC. EPA-600/2-75-026B or ARS-H-5-2, pp.
91-120.
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell, and J.L. Kittle, jr. 1984.
Application Guide for Hydrological Simulation Program—FORTRAN (HSPF).
U.S. Environmental Protection Agency, Athens, GA. EPA-600/3-84-065.
Donigian, A.S. Jr., J.L. Baker, D.A. Haith, and M.F. Walter. 1983. HSPF
Parameter Adjustments to Evaluate the Effects of Agricultural Best
Management Practices. U.S. Environmental Protection Agency, Athens,
GA. EPA-600/3-83-066.
Donigian, A.S., Jr. and J.D. Dean. 1984. Nonpoint source Pollution Models
for Chemicals. Chapter 4. In: Environmental Exposure from Chemicals.
G. Blau and B. Neely (Eds.). CRC Press. (In Press.)
23
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Dunne, T. 1978. Field Studies of Hillslope Flow Processes. In: Hillslope
Hydrology, M.J. Kirkby (Ed.). Wiley Interscience, NY. pp. 227-293.
Freeze, R.A. 1981. The Influence of Hillslope Hydrological Processes on
the Stochastic Properties of Rainfall and Runoff. In: Statistical
Analysis of Rainfall and Runoff. V.P. Singh (Ed.). Water Resources
Publications, Littleton, CO. pp. 155-172.
Horton, R.E. 1933. The Role of Infiltration in the Hydrologic Cycle.
Trans. Amer. Geophys. Union. 14;446-460.
Johanson, R.C., J.C. Imhoff, J.L. Kittle, Jr., and A.S. Donigian, Jr.
Hydrological Simulation Program—FORTRAN (HSPF): Users Manual for
Release 8.0. U.S. Environmental Protection Agency, Athens, GA.
EPA-600/3-84-066.
Leonard, R.A., G.W. Bailey and R.L. Swank, Jr. 1976. Transport, Detoxi-
fication, Fate and Effects of Pesticides in Soil and Water Environments.
In: Land Application of Waste Materials. Soil Conservation Society of
America, Ankeny, IA.
Pionke, H.B. and G. Chesters. 1973. Pesticide-Sediment-Water Interactions.
J. Environ. Quality. _2_(1): 29-45.
Smith, C.N., R.A. Leonard, G.W. Langdale, and G.W. Bailey. 1978. Transport
of Agricultural Chemicals from Small Upland Piedmont Watersheds. U.S.
Environmental Protection Agency, Athens, GA. EPA-600/3-78-056.
U.S. Department of Agriculture. 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management systems.
W.G. Knisel, Jr. (Ed.). U.S. Department of Agriculture, Washington,
DC. Conservation Research Report No. 26.
Wischmeier, W.H. 1976. Cropland Erosion and Sedimentation. Chapter 3.
In: Control of Water Pollution from Cropland. Vol. 2 - An Overview.
U.S. Environmental Protection Agency, Athens, GA, and U.S. Department
of Agriculture, Washington, DC. EPA-600/2-75-026B or ARS--H-5-2.
pp. 31-58.
Woolhiser, D.A. 1976. Hydrologic Aspects of Nonpoint Source Pollution.
Chapter 2. In: Control of Water Pollution From Cropland, Vol. 2 - An
Overview. U.S. Environmental Protection Agency, Athens, GA, and
U.S. Department of Agriculture, Washington, DC. EPA-600/2--75-026B
or ARS-H-5-2. pp. 7-30.
24
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SECTION 3
SPECIFIC DESIGN CONSIDERATION
The objective of this section is to provide specific design criteria
for evaluating pesticide runoff including site selection, required site
description information, data requirements, sampling network design,
sampling equipment and instrumentation, sampling procedures, transporting
samples, field record keeping, analytical methodology, and runoff and
soil data computation.
3.1. SITE SELECTION
The careful selection of the sites(s) on which pesticide runoff studies
are conducted will contribute greatly to the success of the field program
and will have a direct impact oh the value of the data obtained. if an
exposure assessment is the ultimate objective of the field study/modeling
program, the site to be chosen should be representative of the general area
in which the chemical is likely to be used. "Representativeness" requires
that the properties of the watershed that have major impact on the pesticide
runoff process not be significantly different from the area being represented.
Unfortunately, a significance level is difficult to determine, either in
the aggregate or for individual characteristics, and is left up to the
judgment of the designer.
A flow chart for the selection of watersheds is shown in Figure 3.1.
3.1.1 Delineation of Unique Areas
The pesticide(s) and crop(s) of interest are usually known. Major
crop growing areas can be easily identified. Areas of major corn,'grain
sorghum, wheat, cotton, soybeans, orchard and vegetable production are
shown in Figures 3.2 through 3.8.
Next, these areas should be subdivided according to the major factors
that affect pesticide runoff. These factors include:
• Climate
- Rainfall Depth
- Rainfall Intensity
- Evapotranspiration
25
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SELECT
PESTICIDE, CROP
IDENTIFY MAJOR
CROP-GROWING AREAS
1
SUBDIVIDE ACCORDING
TO FACTORS AFFECTING
PESTICIDE RUNOFF
DELINEATE
UNIQUE AREAS
CHOOSE
CANDIDATE
WATERSHEDS
i
RANK ACCORDING
TO LOGISTICAL
FACTORS
MAKE FINAL
WATERSHED(S)
SELECTION
FIGURE 3.1.
Plow chart for selection of watershed for field
studies to support modeling exposure assessments.
26
-------
FIGURE 3.2. Corn harvested for all purposes (U.S. Department of
Commerce, 1982)«
FIGURE 3.3. Sorghums harvested for all purposes except syrup (U.S.
Department of Commerce, 1982).
27
-------
FIGURE 3.4. Wheat harvested for grain (U.S. Department of Commerce, 1982)
FIGURE 3.5. Cotton harvested (U.S. Department of Commerce, 1982)
28
-------
FIGURE 3.6. Soybeans harvested for beans (U.S. Department of Commerce, 1982)
FIGURE 3.7. Land in orchards (U.S. Department of Commerce, 1982)
29
-------
FIGURE 3.8. Vegetables harvested for sale (U.S. Department
of Commerc e, 1982).
Topography
- Slope Length
- Slope Steepness
Soil
- Er edibility
-' Organic Matter
— infiltration characteristics
Management Practices
Climatically, both total rainfall depth and intensity affect total runoff
and peak runoff rates—both of which affect sediment transport* Crop
evapotranspiration affects the antecedent soil water condition prior to
runoff events. Antecedent soil water has a definite impact on runoff,
Langdale et al., 1983. Two topographical factors, slope length and steepness,
will affect runoff velocities and, hence, the capability of flow to detach
and transport sediment. The soil type is also of primary concern. Erodi-
bility determines the sediment yield per unit shear stress on the soil; the
30
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organic matter content affects credibility and the degree of pesticide
adsorption. Infiltration characteristics affect the timing and quantity of
runoff water. The impacts of management on runoff and sediment transport
have been overviewed in Section 2.4.
Fortunately, the effects of most of the above factors on runoff have
been integrated in a study done by the U.S. Dept. of Agriculture in coopera-
tion with the U.S. Environmental Protection Agency (USDA, 1975). The Depart-
ment of Agriculture has divided the 48 contiguous states into 156 land re-
source areas (LRAs). An LRA is defined as "a geographic area characterized
by a particular pattern of soil type, topography, climate, water resources,
land use and type of farming." Although differences exist among these factors
within a land resource area, a "representative" meteorblogic station, predom-
inant soil type and runoff curves numbers were assigned to each. Ten to
twenty-five years of meteorologic records were then used to simulate runoff
for each LRA using a simulation model. The results are shown in Figure 3.9.
The simulations that produced this map were for straight-row corn and the
actual numbers should not be used for other crop/management alternatives.
Mountain, forest, swamps, desert
or steep rainfall gradients
FIGURE 3.9. Average annual potential direct runoff (USDA, 1975).
31
-------
This map does, however, give a comparison of the relative integrated impacts
of the major watershed factors listed above on runoff, and as such, is use-
ful for indicating potential pesticide runoff problem areas.
Figure 3.10 is taken from the same study. In this figure areas of
potential contribution of cropland to watershed sediment yields have been
classified as low, moderate, high or very high. The rating is computed by
multiplying an erosion-potential index, which is based on soil erodibility
and topographic information, by the percentage of cropland in each LRA. Thus
it ranks the cropland contribution of sediment movement from the land surface
to instream sediment loads. For a given field area this map may be misleading
because a "high" ranking on the map may be as much due to cropland density
as to erodible soil conditions.
A more direct way of ranking the erosion from fields in different
physiographic areas is to make use of the Universal Soil Loss Equation,
USLE (Wischmeier and Smith, 1978). The equation is:
X = R K LS C P
(3.1)
FIGURE 3,10. Relative potential contribution of cropland to watershed
sediment yields (USD&, 1975).
32
-------
where X = average annual soil loss in tons/acre-year
R = rainfall erosivity factor
K = soil eredibility factor
LS = slope-length factor
C = cover management factor
P =• supporting practice factor
For the purposes of ranking sediment erosion potential based on climate,
soil and topographical differences, the C and P factors can be ignored
(i.e., set to unity). Thus the product RKLS alone can be used for ranking
if the crop and supporting management practices are the same in each
area.
The parameter R can be determined from the map in Figure 3.11. The
maximum recommended value is 350. For the shaded area on the map the value
should be adjusted to account for runoff from snowmelt (see USDA, 1974).
The soil erodibility factor 'K1 can be determined for ranking purposes
by looking up a value in Table 3.1 , providing the predominant soil type and
approximate organic matter content are known. In general, the soil organic
matter content can be estimated by knowing soil nitrogen. The nitrogen
content of unamended soils in the U.S. can be determined from the map in
Figure 3.12. The approximate soil organic matter content is related to
soil nitrogen by:
% OM = 20 (%N) (3.2)
The final factor LS can be determined by knowing the approximate average
length and steepness-of slopes in the area. Table 3.2 gives values of the
factor for various values of slope length and steepness. These parameters
can be determined from topographic maps, in most cases, Soil Conservation
Service personnel in the area may be a source of more .accurate values.
The organic matter content has more of an effect on pesticide trans-
port than simply modifying the erodibility of the soil. The adsorption
of most pesticides can be related to the organic matter in the soil. Given
the same pesticide in an area with higher soil organic matter, more of the
pesticide would theoretically be adsorbed to the soil. Therefore, relatively
more of the pesticide would be lost from the field area as a result of
sediment transport and relatively less would be lost in the solution phase.
By looking at runoff, sediment loss, and organic matter content of the
soils in a given area, a fairly good ranking of the areas can be made in
order to determine which area might yield the better pesticide runoff data
Base.
33
-------
50
35
FIGURE 3.11. Average annual values of the rainfall-erosivity factor, R.
-------
Table 3.1. INDICATIONS OF THE GENERAL MAGNITUDE OF
THE SOIL-ERODIBILITY FACTOR, K
Texture class
Sand
Fine sand
Very fine sand
Loamy sand
Loamy fine sand
Loamy very fine sand
Sandy loam
Fine sandy loam
Very fine sandy loam
Loam
Silt loam
Silt
Sandy clay loam
Clay loam
Silty clay loam
Sandy clay
Silty clay
Clay
<0.5%
K
0.05
.16
.42
.12
.24
.44
.27
.35
.47
.38
.48
.60
.27
.28
.37
.14
.25
Organic matter
2%
K
0.03
.14
.36
.10
.20
.38
.24
.30
.41
.34
.42
.52
.25
.25
.32
.13
.23
0.13-0.
content
4%
K
0.02
.10
.28
.08
.16
.30
.19
.24
.33
.29
.33
.42
.21
.21
.26
.12
.19
29
(Sources USDA, 1975)
35
-------
NITROGEN
Percent N
Fll Highly Diverse
l±tJ Insufficient Data
FIGURE 3.12. Percent nitrogen in surface foot of soil (Parker, 1946).
-------
TABLE 3.2. VALUES OF THE USLE TOPOGRAPHIC FACTOR, LS, FOR SPECIFIED
COMBINATIONS OF SLOPE LENGTH AND STEEPNESS (USDA, 1975)
% Slope
0.5
1
2
3
4
5
6
8
10
12
14
16
18
20
25
30
40
50
60
Slope length (fert)
25
0.07
0.09
0.13
0.19
0.23
0.27
0.34
0.50
0.69
0.90
1.2
1.4
1.7
2.0
3.0
4.0
6.3
8.9
12.0
50
0.08
0.10
0.16
0.23
0.30
0.38
0.4 S
0.70
0.97
1.3
1.6
2.0
2.4
2.9
4.2
5.6
9.0
13.0
16.0
75
0.09
0.12
0.19
0.26
0.36
0.46
0.58
0.86
1.2
1.6
2.0
2.5
3.0
3.5
5.1
6.9
11.0
15.0
20.0
100
0.10
0.13
0.20
0.29
0.40
0.54
0.67
0.99
1.4
1.8
2.3
2.8
3.4
4.1
5.9
8.0
13.0
18.0
23.0
150
0.11
0.15
0.23
0.33
0.47
0.66
0.82
1.2
1.7
2.2
2.8
3.5
4.2
5.0
7.2
9.7
16.0
22.0
28.0
200
0.12
0.16
0.25
0.35
0.53
0.76
0.95
1.4
1.9
2.6
3.3
4.0
4.9
5.8
8.3
11.0
18.0
25.0
•-
300
0.14
0.18
0.28
0.40
0.62
0.93
1.2
1.7
2.4
3.1
4.0
4.9
6.0
7.i
10.0
14.0
22.0
31.0
--
400
0.15
0.20
0.31
0.44
0.70
1.1
1.4
2.0
2.7
3.6
4.6
5.7
6.9
8.2
12.0
16.0
25.0
••
••
500
0.16
0.21
0.33
0.47
0.76
1.2
1.5
2.2
3.1
4.0
5.1
6.4
7.7
9.1
13.0
18.0
28.0
• •
••
600
0.17
0.22
0.34
0.49
0.82
1.3
1.7
2.4
3.4
4.4
5.6
7.0
8.4
10.0
14.0
20.0
31.0
• •
--
800
0.19
0.24
0.38
0.54
0.92
1.5
1.9
2.8
3.9
5.1
6.5
8.0
9.7
12.0
17.0
23.0
..
• •
--
1000
0.20
0.26
0.40
0.57
1.0
1.7
2.1
3.1
4.3
5.7
7.3
9.0
11.0
13.0
19.0
25.0
..
• •
-•
-------
As an example let us say that we are interested in the application of
a certain preemergent herbicide on corn. Let us assume that there are two
major application areas of interest—the north-central portion of Iowa and
the southern portion (coastal plain) of Georgia. According to Figure 3.9,
the average annual potential direct runoff is 1 .1 to 3 inches for north-
central Iowa and >7 inches for southern Georgia. Figure 3.10 indicates
that the relative potential contribution of cropland to watershed sediment
yields in north-central Iowa is high, but moderate to low in southern
Georgia.
Making use of the USLE, we can get a better picture of erosion poten-
tial. The value of R in north-central Iowa is approximately 150. The soil
type is predominantly a silty clay loam. Soil nitrogen is generally 0.20%
or greater. Therefore, the organic matter content of the soil is roughly
4%. Thus the soil erodibility 'K' should be approximately 0.26,. For
slopes of 2% and lengths of 1000 ft, LS would be about 0.40. Thus the RKLS
product for an area in north-central Iowa might be:
RKLS = 150(.26) (.40) = 15.6 tons/ac-year (3.3)
For the coastal plain of Georgia the R value is roughly 350. The soil
nitrogen is under 0.05, therefore a limiting value for soil organic matter
would be about 1.0%. Soils are generally loamy sands, therefore the 'K1
parmeter is roughly 0.11. For a slope of 2% with a 300-ft slope length,
LS = 0.28. Thus:
RKLS = 350 (0.11) (0.28) = 10.8 tons/ac-year (3.4)
Comparison of the two values indicates that, for the same crop with the same
management practices, the erosion potential is likely to be higher in the
north-central Iowa location than in the southern Georgia coastal plain area.
Caution should be used when using such numbers as the sensitivity to slope,
for instance, is quite high. If a 5% slope 300 feet in length is used for
the Georgia'coastal plain, the estimate becomes 36 tons/acre-year instead
of 10.8. Thus we see that, although more runoff might be generated at the
Georgia location, more sediment might be generated at the Iowa location.
This coupled with the fact that organic matter is lower in the Georgia soils
indicates that, for weakly adsorbed chemicals, more chemical runoff might be
generated at the Georgia site while for more strongly adsorbed chemicals,
the Iowa site might produce greater chemical runoff, if runoff model veri-
fication is the primary objective, the site producing greatest runoff would
yield the most useful data set. Sites that produce no runoff are not
useful fo'r this purpose.
It may be, however, that the study is required in a certain area. In
this case, steps 1 through 4 in the flow chart of Figure 3.1 would be
38
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omitted and the designer would go directly to the step in which candidate
watersheds are selected.
3.1.2 Ranking Candidate Watersheds
Once a specific region is decided upon, there may be several candidate
watersheds that can be identified. This done, they should be ranked
according to logistical factors that should have bearing on the final
selection. "Shese ranking factors include:
* Landowner/Manager Cooperation
• Capability for Growing Crop of Choice
» Capability to Implement Management Practice of Choice
• Homogeneity of the Watershed
* Accessibility
» Existence of Available Permanently installed Equipment
• Location with Respect to Environmentally Sensitive Areas
• Presence of Endangered Species
"fiie first factor that should be considered is the cooperation of the
landowner or manager to provide access to the site. In addition, he must
be willing to have the equipment installed and the watershed must be capable
of supporting the desired management practices and the crop of choice. The
watershed should be in an area in which it is possible to install equipment
easily. A power source is a consideration for some equipment. It should
also be possible to get to the watershed quickly in case of equipment
failures or for other reasons.
Watersheds that have permanently installed equipment such as runoff
weirs and those that have associated historical data should be given signi-
ficant priority in the ranking process especially if other selection criteria
are satisfied.
Test watersheds should not be located so that pesticide drift or runoff
would migrate into environmentally sensitive areas. The designer also should
avoid areas having populations of endangered species.
Homogeneity of the watershed soils and relief should be considered.
Obviously, the cost of sampling to characterize the system and collect
residue data will increase with increasing diversity in the watershed. In
addition, results of the study from more homogeneous areas will allow for
more facile interpretation of the data collected.
39
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3.2 SITE DESCRIPTION
The accurate acquisition and reporting of information describing the
watershed is an important step in the data collection process. It serves
a function beyond simply providing introductory materials for a report.
In many cases, watershed response can be explained by having knowledge of
conditions that exist or have existed in the area.
A generic site description should include information in the following
categories:
* Location and size
* Climate (meteorology)
» Topography
* Soils
• Geology
• History
3.2.1 Location and size
The approximate latitude and longitude of the watershed should be
determined. Also, other descriptive qualities should be given about the
location (e.g., the watershed is in the Atlantic Coastal Flatwoods Area).
Some agencies like the Dept. of Agriculture and the Dept. of Commerce have
divided the United States into provinces or areas that are considered to be
homogeneous with regard to their particular data acquisition activities.
If these qualifiers are also given, this knowledge may aid in the retrieval
of information by these agencies. Examples are the. Dept. of Agriculture
Soil Conservation Service Land Resource Areas (LRAs) or the Dept. of Commerce
Climatic Divisions.
The size of the watershed should be accurately reported. Improper
reporting of watershed area can lead to bias in water and material balances.
In general it is more accurate to determine areas through surveying directly
(if this is possible) as opposed to planimetering from topographic maps.
Maps showing the location and size of the watershed should be prepared.
Any prominent features in the watershed should be recorded on these maps.
Watershed area of 0.5 ha to 2.0 ha should be considered a minimally
acceptable size to avoid small scale effects that could negate the extent
to which the site would be considered 'representative1 of the region.
Larger sites are preferred. The upper limit of the site will be controlled
by the costs of sampling large areas. Model capabilities will also affect
the allowable size of the site.
40
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3.2.2 Climate
General climatic characteristics in the area should be reported.
Means and extremes of precipitation, snowfall, pan evaporation and tempera-
ture are frequently useful if they are known. This information can be
obtained from several sources. The best source is probably the Climatic
Atlas of the United States (U.S. Dept. of Commerce, 1968). Another source
that may contain additional useful information is the Water Atlas of the
United States (Geraghty et al., 1973).
3.2.3 Topography
Good topographical information is essential for watershed hydrologic
and transport modeling. Elevation above mean sea level of the watershed
outlet or stream gaging station should be reported. Usually, reliable
topographic maps can be obtained from the U.S. Geological Survey for larger
areas. If the watershed is small, surveying of the area may be necessary.
Survey information should be adequate to determine field slopes. This
information should be as accurate as possible, because of the dependency
of runoff and sediment transport, especially the latter, on slope.
Topographical and drainage alterations by man should be noted (e.g.,
land leveling, terracing, grassed waterways, tile drainage). In these
cases, dimensions of terraces, slopes and widths of runoff diversions,
locations and depths of tile drains, etc. should be noted. Topographic
maps showing general drainage patterns of water and showing source and
deposition areas of sediment can be .very useful.
3.2.4 Soils
Spatial mapping of the surface and subsurface soils should be per-
formed. In many cases, this mapping has been done by the Soil Conserva-
tion Service and is available in county soil surveys. If not, the mapping
should be done by a qualified soil scientist. Once these soils are identi-
fied, erosional info.rmation such as erodibility ( 'K' of the Universal Soil
Loss Equation) should be provided if available. Soil cores for each soil
series in the watershed should be taken and analyzed. Typical pedon infor-
mation should be recorded as shown in Table 3.3. Location of the soil
cores taken should also be recorded. More quantitative information is also
needed as discussed later in this chapter (Section 3.5). An example of a
map that shows information concerning soils and topography is shown in
Figure 3.13.
3.2.5 Geology
Geologic foundation materials should be described paying special
attention to their type and capability to transmit water. Types of and
depths of impermeable layers are of interest, especially if they are rela-
tively shallow. The impact of shallow ground water on direct surface
41
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TABLE 3.3. TYPICAL SOIL PEDON DESCRIPTION (Smith et al., 1978)
Soil: Cecil sandy loam*
Classification: Typic hapludult, clayey, kaolinitic, thermic
Location: Watkinsville, Georgia, Southern Piedmont
Conservation Research Center, 1 0 meters south-
east of Watershed P3
AP 0-20 cm Light brown (7.SYR 6/4 dry; 5YR 5/4 moist)
sandy loam; weak fine granular structure;
moderately friable, moist; gradual smooth
boundary; many fine roots.
B1 20-30 cm Light red (2.SYR 6/6 dry) to red (2.SYR 4/6
moist) sandy clay loam; weak medium sub-
angular blocky structure; moderately friable
moist; gradual wavy boundary, few coarse sand
grains, few medium roots.
B21t 30-64 cm Red (2.5 5/6 dry; 2.SYR 4/6 moist) clay;
moderate medium subangular blocky structure;
moderately friable to firm moist; gradual
wavy boundary; few coarse sand grains; few
medium roots.
B22t 64-102 cm Red (2.SYR 5/6 dry; 2.SYR 4/6 moist) clay;
moderate medium subangular blocky structure;
moderately friable moist; gradual wavy
boundary; few mica.flakes, few quartz gravel.
B3 102-132 cm Red (2.5YR 5/6 dry; 2.SYR 4/6 moist) clay
loam; moderate medium subangular blocky
structure; moderately friable moist; common
mica flakes; few schist and gneiss fragments.
C 132+ cm Weathered schist and gneiss material.
*Described by George C. Brock and C. L. Mclntyre, U.S. Soil
Conservation Service, Athens, GA.
42
-------
UJ
-------
runoff can be considerable. The possibility of regional ground water
inflows to the watershed system should be investigated, especially if there
is a chance that discharge of this flow occurs above the flow recording
device. To the extent possible, such behavior should be documented and
quantified.
3.2.6 History
The history of the watershed should be recorded to a practical extent
noting such occurrences as natural disasters (e.g., fires or extended
periods of flooding), the history of cultivation, installation of drainage
alterations (e.g., tile drains) or structures, and the recent history of
fertilizer and pesticide applications (if known).
3.2.7 Photographs
Photographs are useful for providing a hard copy of the overall field
site, installed instrumentation, various tillage operations, pesticide
application and monitoring techniques, and overall field crop canopy devel-
opment with time (see Section 3.13). Selected photographs are valuable to
include in the project report.
3.3 MEASUREMENT OF RUNOFF
Surface runoff occurs when the rainfall intensity and duration exceeds
the infiltration rate and depression storage of the soil. The amount of
runoff produced, however, is a function of site size, antecedent soil
moisture content, soil permeability, vegetation, evaporation rate, slope
and surface roughness (Wauchope et al., 1977), The pesticide concentration
in runoff is affected by these properties as well as- the properties of the
pesticide including water solubility, degradation rate in soil,, sorption to
soil and sediment and method of application (plant foliage, soil surface or
soil incorporated) (Wauchope et al., 1977). The flow from a field site is
intermittent and can vary from low to high flows with varying time intervals
(few minutes to several hours) depending on the rainfall intensity and
duration, and site characteristics.
Prior to installation of equipment to measure surface runoff from a
given field, several decisions must be made, such as: (1) selection of a
suitable site for runoff monitoring equipment, (2) estimation of expected
rainfall and flow rates, and (3) selection of monitoring equipment (i.e.,
flumes, water stage recorder, and sampler).
fls indicated in the site selection section, watersheds or field sites
with existing permanently installed equipment such as runoff flumes, samplers
and weather station with historical records should be given priority consi-
deration provided they meet the objective of the study. Several instrumented
sites are currently in use in various locations within the United States.
44
-------
For information regarding availability, it is suggested that contacts be
made with the USDA agricultural experiment station and state universities
(i.e., agronomy and agricultural engineering departments). If an existing
site cannot be located for use, then the selection of a new site should
begin. First, in the process of selecting a suitable site to study runoff
of pesticides, one must conduct site visits and determine whether runoff
does occur. Secondly, it must be determined that the field has a drainage
pattern that converges to a central draw or drainage channel and outside
boundaries are well established,, To ensure that runoff water from other
areas does not enter the site, a soil berm (embankment) or dike can be
established on the outside boundary of the field. In predominantly level
geographic areas where well defined drainage channels are not apparent, it
may be necessary to construct a system of barriers to direct runoff to a
central.collection point. Depending on the specific field situation, these
might be elaborate metal or concrete barriers or simple soil terraces.
3.3.1 Equipment for Runoff Flow Measurements
Devices for monitoring flow commonly consist of various type flumes
and weirs. For field-scale runoff studies involving intermittent storms,
H-type measuring flumes (H and HL type) are appropriate, see Figure 3.14.
These flumes have been used successfully in previous runoff studies.
Weirs, on the other hand, are inappropriate for monitoring intermittent
flows (i.e., ponding conditions are created as well as sediment deposition),
j / , V .*A
FIGURE 3.14. H-type flume with sloping floor approach.
45
-------
H-type flumes have the capability of measuring a wide range of flow
rates with a high degree of accuracy. They are more accurate at lower
flows as compared to other types (i.e., Parshall). When high sediment
concentrations are present, they are subject to deposition problems, which
can be minimized by using a sloping false floor (Wauchope et al., 1977,
1985). Flumes are designed to be self-cleaning and are essentially
maintenance free after installation (Grant, 1981). Calibration data for
various size standard flumes are tabulated in Tables 3.4 and 3*5.
Commercially available prefabricated flumes are usually made of Fiber-
glass® material or plastic materials. The use of these flumes in pesticide
runoff studies is questionable because of the high affinity of Fiberglass®
material or plastic for sorbing some pesticides. The impact of the flume
material on pesticide concentrations is difficult to estimate, but the
potential sorption problem should be recognized. In most cases, the problem
can be minimized or eliminated by either sampling upstream from the flow
measurement device or by constructing a 31 6 type stainless steel flume
designed according to specification by USDA (1979). It is important that
the flume be built to exact specifications. Previous pesticide runoff
studies (Smith et al, 1978; Johnson and Baker, 1982-1984; Ellis et al. ,
1977) report the use of stainless steel flumes. In addition to a stainless
steel flume, the approach box should also be made of stainless steel. The
approach box is located on the upstream side of the flume and is useful for
mixing runoff during sampling. The advantages of using the stainless steel
flume for pesticide runoff studies outweigh the convenience of other mater-
ials.
3.3.2 Water Stage Measurement
To determine the rate of flow and runoff volume, a continuous water
stage record for depth of flow (stage or head) with time during the event
is required. This is obtained using a water stage recorder in conjunc-
tion with a stilling well attached to the flume. A typical setup is shown
in Figure 3.15. A wide range of water level recorders are commercially
available that utilize electric, spring-wound or weight-driven clocks. An
important advantage of the mechanical types is that they are not subject to
failure due to power outages that may occur during storm events. Models
are available that can be set to operate over intervals of 6 to 192 hours.
Multiple units can be installed to ensure that stage height data are not
lost due to recorder failure. Johnson and Baker (1982) installed recorders
on opposite sides of the flume. The stage record from the water level
recorders (derived from breakpoint values or at even time intervals) are
converted into rates of flow, i.e. , discharge in ft^/sec, by using the
rating table for the type flume being used (see rating Tables 3.4 and 3.5).
The water stage recorders should be enclosed in an appropriate shelter.
The shelter can be hinged to allow easy access for maintenance of the recor-
der and changing of charts. For additional information see USDA (1979).
46
-------
TABLE 3.4. RATING TABLES FOR H FLUME (USDA, 1979)
[Discharge in cubic feet per •econd)
FLUME 0.5 FOOT DEEP
Head (ft)
0
0.1 .
0.2
0.3 ...
0,4
0.00
0
. .0101
.0431
.1057
.205
0.01
0
.0122
.0479
.1139
.217
0.02
0.0004
.0146
.0530
.1224
.230
0.03
0.0009
.0173
.0585
.1314
.244
0.04
0.0016
.0202
.0643
.1407
.267
0.05
0.0024
.0233
.0704
.1505
.271
0.06
0.0035
.0267
.0767
.1607
.285
0.07
0.0047
.0304
.0834
.1713
.300
0.08
0.0063
.0343
.0905
.1823
.315
0.09
0.0080
.0385
.0979
.1938
.331
FLUME 0.75 FOOT DEEP
0
0,1
0,2
0.3
0.4
0,5
0.6
0.7
Head (ft)
0
0.1
0.2
0.3
0.4
0.5 ..
0.6
0.7
0.8
0.9
0
0.1 .
0.2
0.3
0.4 „
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3 ,
1.4
0
.0126
. .0501
. .119
.224
. .370
.§66
.813
0.00
0
.0150
.0571
.132
.244
.398
.696
.851
1.16
1.63
0
.0200
.0711
.157
.283
.454
,672
.942
1.27
1.65
2.09
2.61
3.20
3,87
4,60
O
.0151
.0555
.128
.237
.388
.588
.841
0.01
O
.0179
.0630
.141
.257
.416
,621
.880
1.20
1.57
C)
0.0237
.0780
,168
.298
,473
.697
.972
1.30
1.69
2.14
2.67
3.27
3.94
4.68
0.0006
.0179
.0612
.137
.250
.406
.611
.869
0.02
0.0007
,0211
.0692
.151
.271
.434
.644
.909
1.23
1.61
0.0011
.0276
.0854
.179
.314
.493'
.722
1.002
1.34
1.73
2. IS
2.73
3.33
4.01
4.76
0.0013
.0210
.0672
.146
.263
.424
.635
.898
PLUME
0.03
0.0017
.0246
.0758
.161
.285
.453
.668
.939
1.27
1.66
FLUME
0.0023
.0319
.0931
.191
.330
.614
.747
1.033
1.38
1.78
2..7A
2.78
3.39
4.08
4.84
0.0022
.0242
.0735
.156
.277
.443
.659
.927
1.0 FOOT
0,04
0.0027
.0284
.0827
.172
.300
.472
.em
.969
1.30
1.70
1.5 FEET
0.0039
,0365
,1011
.203
.346
.535
.773
1.065
1.41
1.82
2.30
2.84
3.46
4.15
4.92
0.0032
.0278
.0802
.167
.291
,462
.683
.957
DEEP
0.05
0.0040
.0324
.0900
.183
.315
.492
.717
1.000
1.34
1.74
DEEP
0.0057
.0414
.1096
.215
.363
.557
.800
1.097
1.45
1.86
2.35
2.90
3.52
4.22
5.00
0.0046
.0317
.0872
.177
.306
.482
.708
0.06
0.0056
.0367
.0976
.194
.331
.512
.743
1.031
1.38
1.78
0.0078
.0467
.1183
.228
.380
.579
.827
1.130
1.49
1.91
2.40
2.96
3.59
4.30
5.08
0.0061
.0358
.0946
.188
,321
.502
.734
0,07
0.0075
.0413
.1065
.206
.347
.533
.769
1.063
1.41
1.83
0.0103
.0523
.1275
.241
.398
.601
.855
1.163
1.63
1.95
2.45
3.02
3.66
4.37
5.16
0.0080
.0403
.1023
.199
.337
.523
.760
0.08
0.0097
.0462
.1138
.218
.364.
.554
.796
1.096
1.45
1.87
0.0131
.0582
.1371
.255
.416
.624
.883
1.197
1,57
2.00
2.50
3.08
3.73
4.45
5.24
0.0101
.0451
.1104
.211
.353
.544
.786
0.09
0.0122
.0515
.1226
.231
.381
.576
.823
1.129
1.49
1.92
0.0164
.0645
.1470
.269
.435
,648
.912
1.231
1.61
2.05
2.56
3.14
330
4.52
5.33
See footnotes *t end of table.
47
-------
TABLE 3.4 (Cont'd). RATING TABLES FOR H FLUME (USDA, 1979)
[Discharge in cubic feet per second)
FLUME 2.0VEBT DEEP
Head (ft)
0
0.1
02
0.8
0.4
0,5
0.6
0.7
0.8
0.2
1.0
1.1
1.2
1.3
1.4
1.6 _,_
1.6
1.7
1.8
1.9
0.00
o
.0248
. .0860
. .183
.323
. .609
. .745
1.08
. 1.38
1.78
. 2.25
2.78
. 3,88
- 4.06
. 432
. 6.65
_ 6.68
. 7.58
_ 8.67
. 935
0.01
O
0.0283
.0930
.195
.339
,630
.771
1.07
1.42
1.83
2.30
2.84
3.46
4.13
4.90
5.74
6.67
7.68
8.78
9.97
0.02
0.0014
.0341
.1016
.207
.366
.662
.798
1.10
1,48
137
2,35
2.90
3.61
4.20
4.98
6.83
6.77
7.79
8.90
10.09
0.03
0.0031
.0392
.1103
520
.374
.674
326
1.13
1.49
1.92
2.40
2.96
3.68
4.28
6.06
5.92
6.87
7.90
9.01
10.21
0.04
0.0060
.0447
.1195
.234
.392
.697
354
1.16
1.63
1.96
2.46
3.02
3.65
4.35
5.14
6.01
6.97
8.00
9.13
10.34
FLUME 2.5 FEET
Head (ft)
0
0.1
0.2
03 .
0.4
0.5
0.6
0.7
0.8
0.9
1.0 ,
1.1 ,
13.
1.3 ,.,
1.4 . .
1.5
1.8
17
13
1,9
0,00
0
. .0298
. .0990
. ,209
. 363
.664
. 318
. 1.18
. 1.49
. 1.92
. 2.41
. 2.97
. 3.69
. 459
5.06
. 6.91
. 634
. 736
. 8.98
.10.2
0.01
CO
0.0350
.1081
522
.381
.687
346
1.16
1.63
1.96
2.46
3.03
3.66
4.37
6.16
6.00
6.94
7.97
9.10
103
0.02
0.0018
.0406
.1176
.238
.399
.611
376
1.19
1.57
2.01
2.51
3.09
3.73
4.44
553
6.09
7.04
8.08
952
10.4
0.03
0.0038
.0465
.1276
560
.418
.635
.904
1.23
1.61
2.06
2.67
3.16
3.80
4.62
631
6.18
7.14
8.19
934
10.6
0.04
0.0061
.0628
.1379
.266
.437
.659
.934
1.27
1.65
2.11
2.62
351
336
4.69
5.39
6.27
7.24
8.30
9.46
10.7
0.05
0.0073
.0606
.1290
.248
.410
.620
.882
1.20
1.57
2.01
2.61
3.08
3.71
4.43
6.23
6.11
7.07
8.11
9,24
10.47
DEEP
0.06
0.0089
.0595
.1486
.280
.467
.684
.965
1.30
1.70
2.16
2.68
3.27
3.93
4.67
5.48
637
7.34
8.41
9.67
103
0.06
0.0100
.0687
.1390
.262
.429
.644
.911
1.23
1.62
2.06
2.56
3.14
3.78
4.50
5.31
6.20
7.17
8.22
9.36
10.60
0.06
0.0121
.0666
.1597
.296
.478
.710
.996
134
1.74
2.21
2.74
3.33
4.00
4.75
6.86
6.46
7.45
8.63
9.70
11.0
0.07
0.0130
.0632
.1494
.276
.448
.668
.941
1.27
1.66
2.10
2.62
3.20
335
4.58
5.40
6.29
7.27
8,33
9.48
10.72
0.07
0.0158
.0741
.1713
312
.499
.736
1.027
1.38
1.78
256
2,79
3.40
4.07
4.82
5.66
6.66
7.65
8.64
9.82
11.1
0.08
0.0166
.0701
.1602
591
.468
.693
.971
1.30
1.70
2.15
2.67
3.26
3.92
4.66
5.48
6.38
7.37
8.44
9.60
1036
0.08
0.0200
.0820
.1834
.328
.520
.763
1.069
1.41
1.83
2.31
2,85
3.46
4.15
4.90
5.74
6.65
7.66
8.75
9.94
11.2
0.09
0.0205
.0774
.1714
.307
.488
,719
1,002
1.34
1.74
2,20
2.73
3.32
3.99
4.74
5.67
6.48
7.47
8.56
9.72
10.98
0.09
0.0247
.0903
.1960
.345
.542
.790
1.092
1.45
1.87
2.36
2,91
3.53
4.22
4.98
6.82
6.75
7.76
8.87
10.06
11.4
Sec footnotes »t end of table.
48
-------
TABLE 3.4 (Cont'd). RATING TABLES FOR H FLUME (USDA, 1979)
(Discharge in cubic feet per second)
FLUME 2.5 FEET DEEP—Con.
Head (ft)
2.0
2.1
2.2
23
2.4
0.00
11.5
. 12.9
. 14.4
16.0
. 17.6
0.01
11.6
13.0
14.5
16.1
17.8
0.02
11.8
13.2
14.7
16.3
18.0
0.03
11.9
13.3
14.3
16.4
18.2
0.04
12.0
13.5
15.0
16.6
18.3
FLUME 3.0 FEET
0
0.1
0.2
0.3
0.4
0.5
0.6 -
0.7
0.8
0.9
1.0
1.1
1.2
1.3 .
1.4
1.5 . .
1.6 - -
1.7 .
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5 .. .
2.6 ..
2.7
2.8
2.9
0
.0347
.113
.234
.402
. .620
. .890
. 1.22
1.60
2.05
. 2.57
3.15
. 3.80
4.53
5.33
. 6.20
7.16
8.20
9.33
10.5
11.9
13.3
14.8
16.4
. 18.1
19.9
.21.9
23.9
26.0
28.3
O
0.0407
.123
.249
.421
.644
.920
1.25
1.65
2.10
2.62
3.21
3.87
4.60
5.41
6.30
7.26
8.31
9.45
10.7
12.0
13.4
14.9
16.6
18.3
20.1
22.1
24.1
26.2
28.5
0.0021
.0471
.134
.264
.441
.669
.951
1.29
1.69
2.15
2.68
3.27
3.94
4.68
5.50
6.39
7.36
8.42
9.56
10.8
12.1
13.6
15.1
16.7
18.5
20.3
22.3
24.3
26.5
28.7
0.0045
.0538
.145
.280
.462
.695
.982
1.33
1.73
2.20
2.73
3.34
4.01
4.76
5.58
6.48
7.47
8.53
9.68
10.9
12.3
13.7
15.3
16.9
18.7
20.5
22.5
24.5
26.7
28.9
0.0073
.0610
.156
.296
.483
.721
1.014
1.36
1.78
2.25
2.79
3.40
4.08
4.84
5.67
6.58
7.57
8.64
9.80
11.0
12.4
13.9
15.4
17.1
18.8
20.7
22.7
24.7
26.9
29.2
0.05
12.2
13.6
15.1
16.8
18.5
DEEP
0.0105
.0686
.168
.312
.504
.748
1.047
1.40
1.82
2.30
2.85
3.46
4.15
4.92
5.76
6.67
7.67
8.75
9.92
11.2
12.6
14.0
15.6
17.2
19.0
20.9
22.9
24.9
27.1
29.4
0.06
12.3
13.8
15.3
17.0
18.7
0.0143
.0766
.180
.329
.526
.775
1.080
1.44
1.86
2.35
2.91
3.53
4.23
5.00
5.84
6.77
7.78
8.87
10.05
11.3
12.7
14.2
15.7
17.4
19.2
21.1
23.1
25.2
27.4
29.7
0.07
12.5
13.9
15.5
17.1
19.1
0.0186
.0851
.193
.347
.549
.803
1.113
1.48
1.91
2.41
2.97
3.60
4.30
5.08
5.93
6.87
7.88
8.98
10.17
11.4
12.8
14.3
15.9
17.6
19.4
21.3
23.3
25.4
27.6
29.9
0.08
12.6
14.1
15.6
17.3
19.1
0.0234
.0939
.207
.365
.572
.832
1.147
1.52
1.96
2.46
3.03
3.66
4.37
5.16
6.02
6.96
7.99
9.10
10.29
11.6
13.0
14.5
16.1
17.8
19.6
21.5
23.5
25.6
27.8
30.1
0.09
12.7
14.2
15.8
17.5
19.2
0.0288
.1032
220
.383
.596
.861
1.182
1.56
2.00
2.51
3.09
3.73
4.45
5.24
6.11
7.06
8.10
921
10.41
11.7
13.1
14.6
16.2
17.9
19.8
21.7
23.7
25.8
28.0
30.4
49
-------
TftBLE 3.4 (Cont'd). RATING TABLES FOR H FLUME (USDA, 1979)
(DiKhnrge in cubic feet per ueondl
FLUME 4.5 FEET DEEP
Head (ft)
0
0.1
0.2
0.8
0.4 ..
O.B
0.6
0.7 ,.
0.8
0.9
1,0
1.1 _ ,
12 ..
13
1.4 „
1.6 .
1.6
1.7
1.8
1.9
2.0 ..
2.1
23.
23
2.4
2.5
2.6
2.7
23
2.9
3.0
3.1
S2
3,3
3,4
3.G
3.6
3.7 ..
3.8
S.9
4.0
4,1 ,
42
43
4.4
0.00
. 0
.. .0496
.. .155
.. .311
.. .520
.. .785
.. 1.11
.. 1.49
., 1.94
._ 2.46
.. 3.04
., 3.69
.. 4.42
.. 6.22
.. 6.11
., 7.07
.. 8.12
,. 9.26
.. 10.6
.. 11.8
- 13.2
- 14.7
.. 16.3
., 18.0
— 19.7
— 21.6
-28.6
., 25.7
., 27.9
...30.2
.. 32.7
..352
— 37.9
__40.8
,.43.7
... 463
... 49.9
... 63.2
... 66.7
... 60.2
— 63.9
— 67.8
.. 71.7
763
.. 80.0
0.01
<*>
0.0678
.168
.330
.644
.815
1.14
163
199
2.51
3.10
3.76
4.60
5.81
6.20
7.17
8.23
9.37
10.6
11.9
18,3
14.8
16.4
18.1
19.9
213
23.8
25.9
28.1
30.4
32.9
36.6
S8J
412
44.0
47.1
603
63.6
57.0
60.6
643
682
72.1
782
80.6
0.02
0.0031
.0666
.182
.349
.669
.846
1.18
1.68
2.04
2.66
3.16
3.83
4.68
6.39
6.29
7.27
8.34
9.49
10.7
12.0
18.5
1B.O
16.6
18.3
20.1
22.0
24.0
26.1
2fc.4
3f-.7
33.2
36.8
38.6
41.3
44.3
47.4
60.6
53.9
57.4
51.0
64.7
68.6
72.6
76.6
80.9
0.03
0.0066
.0758
.196
.368
.594
.876
1.122
1.62
2.09
2.62
3.22
3.90
4.66
5.48
6.39
737
8.46
9.61
10.8
12.2
13.6
15.2
16.8
18.6
20.3
22.2
24.2
26.4
28.6
30.9
33.4
36.0
383
41.6
44.6
47.7
50.9
643
67.7
61.3
65.1
68.9
72.9
77.1
81.3
0.04
0.0106
.0866
.211
.388
.620
.907
1.26
1.66
2.14
2.68
3.29
3.97
4,73
6.67
6.48
7.48
8.56
9.73
11.0
12.3
13.7
16.8
16.9
18.7
20,6
22.4
24.4
26.6
283
31.2
33.7
363
39.0
41.9
44.9
48.0
51.2
64.6
58.1
61.7
66.4
69.3
73,3
77.6
813
0.05
0.0154
.0969
.226
.409
.646
.939
1.29
1.71
2.19
2.74
335
4.04
4.81
5.66
6.68
7,59
8.68
9.85
11.1
12.5
13.9
16.6
17.1
18.8
20.7
22.6
24.6
26.8
29.0
31.4
33.9
36.6
39.3
42.2
46.2
48.3
61.6
64.9
58.4
62.1
66.8
69.7
783
77.9
82.2
0.06
0.0208
.1067
.242
.430
.673
.972
1.33
1.76
2.24
2.79
3.42
4.12
439
6.74
6.68
7.69
8.79
9.98
11.2
12.6
14.1
15.6
17.3
19.0
20.9
223
24.9
27.0
29.3
31.7
34.2
36.8
39.6
42.6
45.6
48.6
61.9
653
58.8
62.4
66.2
70.1
74,2
78.3
82.6
0.07
0.0269
.1180
.289
.462
.700
1.006
1.38
1.80
2.29
235
3.49
4.19
4.98
5.83
6.77
7,80
8.90
10.10
11.4
12.8
14,2
15.8
17.4
19.2
21.0
23.0
26.1
27.2
29.5
31.9
34.4
37.1
39.9
423
46.8
49.0
52.2
55.6
69.2
62.8
66.6
70.5
74.6
783
83.1
0.08
0.0337
.1298
.276
.474
.728
1.039
1.41
1.84
235
2.91
3.55
4.27
5.06
5.92
6.87
7.90
9.02
10.22
11.6
12.9
14.4
16.9
17.6
19.4
21.2
23.2
263
27.4
29.7
32.2
34.7
37.4
40.2
43.1
46.1
49.3
62.6
66.0
59.5
63.2
67.0
70.9
76.0
795
83.6
0.09
0.0413
.1420
.298
.497
.756
1.073
1.45
1.89
2.40
2.98
3.62
4.34
5.14
6.02
6.97
8.01
9.14
10.35
11.6
13.0
14.5
16.1
17.8
19.6
21,4
23.4
26.6
27,7
30.0
32.4
36.0
37.7
40.5
43.4
46.4
49.6
52.9
56.3
59.9
63.6
67.4
713
76.4
79.6
84.0
1 Rating derived from tests made by the Soil Conservation Service at the Hydraulic Laboratory of the National
Bureau of Standards using l-on-8 sloping false floor.
* Trace.
50
-------
TABLE 3.5. RATING TABLES FOR 4-FOOT HL FLUME (USDA, 1975)
[Discharge in cubic feet per second]
Head (feet)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3 „
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6 _
2.7 .
2.8
2.9 ..
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4.0
0.00
0
.089
.278
.565
.940
1.42
. 2.01
2.71
3.53
4.48
5.56
6.76
8.06
9.55
11.2
13.0
14.9
17.0
19.2
21.7
24.3
27.0
30.0
33.1
36.5
39.9
43.6
47.5
51.6
55.9
60.3
65.1
70.0
75.0
80.5
85.9
91.9
98.0
104
111
117
0.01
O
0.103
.302
.600
.982
1.48
2.07
2.79
3.61
4.58
5.68
6.89
8.20
9.70
11.4
13.2
15.1
17.2
19.4
21.9
24.5
27.3
30.3
33.5
36.8
40.3
43.9
47.9
52.0
56.3
60.8
65.6
70.5
75.5
80.9
86.5
92.5
98.6
105
111
0.02
0.005
.119
.327
.635
1.03
1.53
2.14
2.87
3.70
4.68
5.80
7.02
8.35
9.90
11.6
13.3
15.3
17.4
19.7
22.1
24.8
27.6
30.6
33.8
37.1
40.6
44.3
48.2
52.4
56.7
61.3
66.1
71.0
76.0
81.5
87.1
93.1
99.2
106
112
0.03
0.012
.136
.352
.670
1.08
1.59
2.21
2.95
3.79
4.79
5.92
7.15
8.50
10.1
11.7
13.5
15.5
1.7.6
19.9
22.4
25.0
27.9
30.9
34.1
37.4
41.0
44.7
48.6
52.8
57.2
61.8
66.6
71.5
76.5
82.0
87.7
93.7
99.8
103
113
0.04
0.020
.152
.378
.705
1.12
1.64
2.28
3.03
3.88
4.90
6.04
7.28
8.65
10.2
11.9
13.7
15.7
17.8
20.2
22.7
25.3
282
312
34.5
37.8
41.4
45.1
49.0
53.3
57.6
62.3
67.1
72.0
77.0
82.6
88.3
94.3
100
107
113
0.05
0.029
.170
.405
.740
1.17
1.70
2.35
3.11
3.98
5.01
6.16
7.41
8.80
10.4
12.1
13.9
15.9
18.1
20.4
23.0
25.6
28.5
31.5
34.8
38.2
41.7
45.5
49.4
53.7
58.1
62.8
67.5
72.5
77.6
83.1
88.9
94.9
101
107
114
0.06
0.039
.190
.434
.780
1.22
1.76
2.42
3.19
4.08
5.12
6.28
7.54
8.95
10.5
12.3
14.1
16.2
18.3
20.6
23.2
25.8
28.8
31.9
36.1
38.5
42.1
45.8
49.8
54.1
58.6
63.2
68.0
73.0
78.2
83.6
89.5
95.5
102
108
115
0.07
0.060
.211
.465
.820
1.27
1.82
2.49
3.28
4.18
6.23
6.40
7.67
9.10
10.7
12.4
14.3
16.4
18.5
20.9
2SA
26.1
29.1
32.2
35.4
38.8
42.4
46.2
50.2
54.5
59.1
63.7
68.5
73.5
78.7
84.2
90.1
96.1
102
109
115
0.08
0.062
.232
.497
.860
1.32
1.88
2.56
3.36
4.28
5.34
6.52
7.80
9.25
10.8
12.6
14.5
16.6
18.7
21.2
23.7
26.4
29.4
32.5
35.8
39.1
42.8
46.6
50.7
54.9
59.5
64.1
69.0
74.0
79.3
84.8
90.7
96.7
103
109
116
0.09
0.075
.255
.530
.900
1.37
1.94
2.64
3.44
4.38
5.45
6.64
7.93
9.40
11.0
12.8
14.7
16.8
19.0
21.4
24.0
26.7
29.7
32.8
36.1
39.5
43.2
47.1
51.1
55.4
59.9
64.6
69.5
74.6
79.9
85.3
91.3
97.4
104
110
116
1 Rating derived from tests made at the National Bureau of Standards using flat floor.
•Trace.
51
-------
FIGURE 3.15. Stilling well attached to flume.
3.3.3 Selection of Automatic Sampler
To determine the amount of pesticide residue transported in runoff,
samples must be taken in proportion to flow volume or elapsed time during
the event. Pesticide concentration measurements and stage height measure-
ments must be "paired" in time sequence to allow calculation of pesticide
runoff quantities due to the dynamic nature of the runoff hydrograph. The
collection of composite samples representing whole events are not adequate
for runoff model development studies because they mask the dynamics of' the
event as illustrated in Figure 3.16. Composite samples will suffice if the
only goals of the study are calibration of models requiring only composite
information or evaluating total pesticide runoff quantities. The sampler
must be set to ensure an adequate number of samples are collected at low
flows (small runoff events) as well as obtaining a practical number of
samples during high flow storm events. More samples are required at frequent
intervals (e.g., 2 minutes) during the early stage of the hydrograph (dis-
charge plotted over time) with less frequent sampling during the falling
stage and the remainder of the event. If an evaluation of interflow and
groundwater flow components is a major objective of the study, it would be
advantageous to take more samples later in the storm hydrograph. Dynamic
sampling techniques are particularly important because of the non-linear
relationship between flow rate and sediment/pesticide transport.
52
-------
o
i-H
fc
Dynamic
Sampling
Composite
Sampling
Time during runoff event
FIGURE 3.16.
Illustration of composite sampling failure to
adequately represent dynamic flow fluctuations
within an event.
Several different runoff samplers have been used in past field runoff
studies including the traversing—slot device (Smith et al., 1978), the
pumping sampler (Johnson and Baker, 1982, 1984), and the coshocton wheel
device (Ellis et al., 1977). More recently, automatic samplers (e.g.,
ISCO®) are being used by various governmental agencies for runoff and waste
sampling studies (M. Koenig, U.S. EPA, personal communication, 1985).
These samplers are flexible in that sample times can be preset for timed
sampling during the event or proportional to flow by using an attached flow
meter. The sampler can be activated by AC or DC current when rainfall
starts by using an electronic conductance cell or by a mechanical linkage
to the stilling well float. Samplers can be set to trigger at a small
predetermined stage height to prevent initiation of sampling for rainfall
events which do not produce runoff.
If the sampler intake has a suction line strainer, it should be
removed to allow a representative sample of the sediment to be collected.
In addition, the strainers are usually made of plastics that can potentially
sorb pesticides.
The number of samples collected should be sufficient to represent
the runoff event as discussed previously. The limitation on the number
of samples is determined by the volume of sample required for analysis.
Sample volumes of 1000 mL or greater are often used. To obtain samples of
these volumes with an automatic sampler that has a capacity of 24-350 mL
glass bottles requires programming the sampler to allow a sample to be
collected in several bottles. This necessarily limits the number of sam-
ples collected per event. An additional sampler can be used to increase
53
-------
the number of samples that can be collected per event. The additional
sample could also serve as a back up unit. Amber glass sample bottles with
Teflon-lined caps® and Teflon® bottles are suitable runoff sampling con-
tainers. Plastic or Nalgene® are not desirable because of potential sorp-
tion problems (W.R. Payne, jr., U.S. Environmental Protection Agency,
personal communication, 1985).
Positioning the sampler intake in the flume to obtain samples is very
important and can be accomplished in a number of ways, all of which involves
compromises. One complication is that vertical sediment concentration
gradients in the flume are to be expected. One satisfactory experimental
set up that alters the sampling intake location with flow rate and which
does not obstruct the flow is shown in Figure 3.17. This method was used
by EPA Region IV (M. Koenig, U.S. EPA, personal communication, 1985). The
sampler intake is suspended with a cable or rope attached to a metal float
(a stilling well float can be adapted for this use) that allows the sample
intake to rise or fall with the incoming runoff. The intake should extend
one to two inches below the float. A metal weight is connected to keep the
sampler intake in an upright position in the water column. In addition,
the float and intake may need to be secured so that the device does not
drift to the flume outlet with the flowing runoff. Modifications in the
suggested design may be required to meet each user's need. In order to
transport sands, the sampler should have a pumping rate of >2.5 ft^/sec (75
cm/sec) transport velocity using a 1/4 in. ID or larger Teflon® sampling
tube (U.S. EPA, 1979).
Soil Embankment
• X Sampler -X
Intake
t
Weight
FIGURE 3.17. Example of positioning sampler intake in K-type
flume with sloping floor approach.
54
-------
3.4 METEOROLOGICAL STATION
Monitoring weather or meteorological parameters at the field site is
an essential data requirement because these parameters influence runoff and
persistence of pesticides. Runoff models require varying interval (e.g.,
hourly, daily etc.) records for the various meteorological parameters. The
"required data include precipitation (rain and snow), pan evaporation, solar
radiation, air temperature, relative humidity, and wind. Each of these
parameters will be discussed in a.general way to provide the user of this
manual with sufficient information and sources to establish a weather
monitoring station at the field site. An excellent source of information is
the USDA (1979). A typical weather monitoring station is shown in Figure
3.18.
3.4.1 Site Location Requirement
The weather station should be near the field in an open area isolated
from buildings, trees, vegetation and other obstructions. It also should
be located for easy access because daily observations are required. The
door of the instrument shelter should open to the north, and a mesh wire
fence is required to secure the station from animals. The use of concrete
or gravel should'be minimized because of possible temperature effects, and
a bird perch higher than the sampling instruments should be provided.
FIGURE 3.18.
Example weather station, raingages and samplers (A), evapora-
tion pan (B), strip recorder for measuring pan evaporation (C).
55
-------
An important factor to consider in establishing a weather monitoring
site is the use of recording instruments to provide continuous observations
with permanent records and reduce time of daily observation measurements.
One important, part of collecting these1 data is to assign one project member
(with an additional backup person) the responsibility of conducting routine
maintenance of all equipment as well as keeping field notes, and compiling
all data as they are being generated.
3.4.2 Precipitation
Precipitation includes both rainfall and snow accumulation depth mea-
surements. Hourly or break point data will cover requirements for most
runoff models. Precipitation gages in use include both recording and
non-recording types. There are three types of recording precipitation
gages: weighing, tipping bucket, and float types. The weighing type is
the only one that will measure both rainfall and snow accumulation. An
example rainfall chart from a recording gage is depicted in Figure 3.19.
The digital precipitation gage (a weighing type) has an advantage over
other types because the data are punched on paper tape (although the paper-
chart type allows on-site inspection of data). This provides rapid data
tabulation in a form suitable for computer analysis. For extensive details
on calibration, potential errors, maintenance, data tabulation for various
type precipitation gages see USDA (1979).
"" 1 7 • • II •• •' I /
•/::> --/- .•/.-- . ••H&irirt
FIGURE 3.19, Rainfall chart from weighing-type recording gages,
56
-------
The number of gages needed at a site depends on the size of the study
area. For small areas (<10 acres), two raingages located at oposite ends
or sides of the field are preferred because each will provide a backup to
the other if failure occurs (i.e., clock stopped, pen or ink problems,
etc.). In addition, it is helpful to locate an inexpensive, non-recording
gage (measurement of total precipitation) at the site for measurement
checks as shown in Figure 3.20. Discussion relative to the preparation of
observed precipitation records for use in modeling is presented in Section
6.1.1.1-6.1.1.3.
Along with precipitation measurements, instruments are available for
collecting samples of precipitation for determining the amount of pesticide
residue being applied to the field from other sources, such as atmospheric
fallout due to volatilization and drift of pesticides.
3.4.3 Evapotranspiration
Evaporation of water is measured from a standard National Weather
Service 4-ft. (1 .22M) diameter, Class A pan (Figure 3.20). Daily potential
evapotranspiration (ET) data are required for most runoff models. One
method for obtaining ET is to convert daily pan evaporation data by using a
pan factor (dimensionless number) as shown in Figure 3.21. Several other
methods are available that are used in various runoff models. Some methods
require different climatic measurements including solar radiation, wind
FIGURE 3.20.
Evaporation pan with anemometer (A) and total
precipitation check gage (B).
57
-------
FIGURE 3.21.
Pan evaporation correction factors (from U.S.
Weather Bureau).
speed and temperature. The measurements obtained should correspond to the
particular method for obtaining potential ET. In addition to pan evaporation
measurements, total wind (miles/day) measurements are required because wind
influences evaporation. Anemometers are available that measure total wind
for each day (counter records tenth mile (161m) increments). This instrument
requires the observer to visit the site daily to record the 24-hour wind
movement. Daily observations are also required when using a standard hook
gage to measure evaporation losses from the evaporation pan. An example
record sheet for daily observations is provided in Figure 3.22. A continuous
water level recorder system can be connected to the evaporation pan, as
shown in Figure 3.18 (Ellis and Thomas, 1968). Additional information
regarding these measurements are described in USDA (1979).
3.4.4 Solar Radiation
Radiation is the most important factor in the evapotranspiration
process. Daily radiation measurements are required for some runoff models
if snow accumulation is anticipated. Radiation is measured by use of
pyranometers coupled to a recorder system with an integrator providing 24-
hour values. For more information on solar radiation measurements, see USDA
(1979).
58
-------
Evaporation and Climatological Observations
19
Date
1
2
3
4
5
6
7
e
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Time of
Observation
Observer
Air Temperature, °F
Instrument Shelter
Maximum
Hiftjppim
Current
Evaporation
Anemometer
Dial (miles)
24 Hr. Move-
ment (miles)
H20 Tern
H&xlmum
perature
MiBimufxi
Hook Gage
(inches)
Tank Filled
(inches)
Evaporation
(inches)
FIGURE 3.22. Example record sheet for daily meteorological observations.
-------
3.4.5 Air Temperature and Relative Humidity
Daily maximum and minimum air temperatures are needed to determine
mean daily air temperature for computing potential evapotranspircition and
for modeling snowmelt. Instruments consist of maximum and minimum thermo-
meters mounted inside a standard U.S. Weather Service instrument shelter.
In addition, a recording hygrothermograph can be placed on the floor of the
shelter house to provide a measure of air temperature and relative humidity
as shown in Figure 3.23. Humidity of the air influences the rate of evapo-
ration losses and is required for computing potential evapotranspiration.
Daily observations on a specific time schedule are required.
3.4.6 Wind
A measure of wind (speed and direction) is a model requirement that
influences evapotranspiration and volatilization of pesticide residue in
surface soils as well as affecting snowmelt. wind velocity is measured with
anemometers that are positioned at standard heights. For general informa-
tion on speeds and velocities over a field site, the anemometer should be
at a height of 33 feet (10m). When windspeed data are being used in calcu-
lating potential evapotranspiration by the Van Bavel (1966) or Penman (1956)
methods, the anemometer should be at a height of 6.6 feet (2m). The weather
bureau method (Kohler et al., 1955) requires the anemometer to be positioned
at the evaporation pan height (2 ft or 61.0 cm) as shown in Figure 3.20.
Additional information is provided in USDA (1979).
FIGURE 3.23.
Instrument shelter with recording hygrothermograph (A)
and anemometer recorder (B).
60
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3.5 SOIL CHARACTERIZATION
The characterization of soil properties is an essential part of a
field runoff study because these properties affect sampling design and
runoff and erosion losses. It is preferred that soil core samples of each •
soil series be taken from the field site for analysis and characterization.
If on-site expertise (qualified soil scientist) or laboratory facilities
are not available, commercial organizations and cooperative extension
service offices (soil test laboratories) are well qualified to assist with
soil textural and chemical analyses. In addition, general soil characteri-
zation data are available from other sources:
• Soil survey data by county for each state. Available through
county or USDA-Soil Conservation Service (SCS) state offices.
• Soil survey investigation reports (SSIR) for most states are
available through SCS state offices. (The observed data are
limited since only a small segment of a particular state is
represented.)
• The USDA/SCS computerized and- interactive soils information
system data base is available for a fee through the U.S. Army
Corp of Engineers, Construction Engineering Research Laboratory,
Champaign, IL (Goran, 1983).
Specific soil characterization data that are essential include soil
series identification, hydrologic soil group, soil texture, organic carbon
content, bulk density, soil water content, soil pH, and temperature. These
data are needed with depth in the soil profile (i.e., to a depth of 150 cm
or lower extreme of the plant root zone).
3.5.1 Series
As discussed previously, soil survey reports that contain soil maps
are available through USDA/SCS. From these maps the soil series can be
identified for the selected site. Maps of this type, however, cover such
a broad area that resolution at smaller scale (field) is not adequate.
Therefore, the local USDA/SCS office should be contacted for assistance in
identifying the soil series as well as describing the soil profile, as
shown for a typical soil pedon description, Table 3.3. Once the soils are
identified, a soil map of the field should be constructed with boundaries
showing the distribution of soil types. This information will be useful in
selecting sites by weighting the different series within the field for
monitoring.
3.5.2 Hydrologic Group
Soils are classified into four hydrologic groups based on their infil-
tration rates (USDA, 1973):
61
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• Group A. (Low runoff potential). Soils having high infiltration
rates even when thoroughly wetted and consisting chiefly of deep,
well to excessively drained sands or gravels. These soils have a
high rate of water transmission.
• Group B. (Moderately low runoff potential). Soils having moderate
infiltration rates when thoroughly wetted and consisting chiefly
of moderately deep to deep, moderately well to well drained soils
with moderately fine to moderately coarse textures. These soils
have a moderate rate of water transmission.
• Group C. (Moderately high runoff potential). Soils having slow
infiltration rates when thoroughly wetted and consisting chiefly
of soils with a layer that impedes downward movement of water, or
soils with moderately fine to fine texture. These soils have a
slow rate of water transmission.
• Group D. (High runoff potential). Soils having very slow
infiltration rates when thoroughly wetted and consisting chiefly
of clay soils with a high swelling potential, soils with a permanent
high water table, soils with a clay pan or clay layer at or near
the surface, and shallow soils over nearly impervious material.
These soils have a very slow rate of water transmission.
Obviously, hydrologic soil group "A" would be less likely to produce
runoff than soil group "D". Infiltration capacity of the soil should be
given major consideration as it will affect the quantity of runoff produced
from the area. A list of soil series and their associated hydrologic
classification are found in Appendix D.
3.5.3 Texture
Soil texture deals with the distribution of various soil separates
(i.e., sand, silt and clay). All soils can be grouped into 12 textural
classes. The textural class is obtained by using the textural triangle as
shown in Figure 3.24 in conjunction with the percent sand, silt and clay
content. S.oil texture influences erosion and sediment losses during the
runoff proces s.
Methods to determine soil texture are presented by Day (1965) and Weber
(1977).
3.5.4 Organic Carbon Content
The soil organic carbon is recognized as the most important soil pro-
perty affecting sorption and transport of uncharged pesticides (Karickhoff,
1984). Carbon exists in soils in four forms as discussed by Weber (1977).
These include: (1) carbonate minerals; (2) highly condensed organic carbon
on charcoal, and coal; (3) little altered organic residues of plants,
animals, and microorganisms; and (4) altered and rather resistant organic
62
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Adopted from "Supplement to Soil Classification
System (7th Approximation)" SCS,
USDA, Second Printing, March, 1967.
/ vy V\/v
/y\/Vyyv\AAA/Y\A
/V\A /\/\/\7\ffii..\ A/XA/V
Percent sand
COMPARISON OFr PA RT 1 CL E - S I ZE SCALES
Sieve Openings in Inches
3 2 11/»lJ«_l2*!_
11111! II
U. S. Stmndsrd Sieve Numbers
iQ ?o 4Q go
200
I i I I I I It I I I i
USDA
UNIFIED
AASHO
GRAVEL
D*r»
Course
GRAVEL
Coarse
1 Fine
SAND
CoarsejMtdi^m
Fine | ff^J
CLAY
SAND
Coarce
GRAVEL OR STONE
Coarse
Medium I fine
MediuK |
Fin*
SAND
Coarst 1
Fine
SILT OR CLAY
SILT -CLAY
Silt J Ci«y
HUM i linn i i
100 50
FIGURE 3.24.
10
2 1 0.5X0.42 0,25 0.1 X 0.05 0.02 0.01 o'oOS 0.002 0,001
Grain Size in Millimetert 0.074
Guide for textural classification (U.S. Department
of the Interior, 1973).
63
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residues—humus. The sorption of most pesticides can be qualitatively
related to the organic carbon content of the soil. Methods have been
developed to estimate sorption of pesticides as characterized by partition
coefficients, Kp. Partition coefficients are normalized to the organic
carbon using the KQJ, defined by (Koc = Kp/fraction of organic carbon)
(Karickhoff, 1981).
Methods to determine organic carbon content for soil and sediment
are presented by Nelson and Sommers (1982), Plumb (1981), and Weber (1977).
3.5.5 Bulk Density
Bulk density is the mass of soil per unit volume expressed as grains per
cubic centimeter. This parameter is essential when converting soil sample
results that are analyzed on a dry soil weight basis to a volume or area
basis. Hie bulk density of freshly tilled soil is much less than that of
a soil that has undergone crusting and settling as the result of rainstorms
(Smith et al., 1978).
The range of bulk density of fine textured soils is 1.0 to 1.3, whereas
course-textured soils usually range between 1.3 and 1,8 g/cm""3 (Miller et
al., 1966).
Various methods of sample collection for bulk density determination
are discussed by Blake (1965) and Blake and Hartge (1985). The core method
has been extensively used. This method requires the collection of an
undisturbed soil core with known volume (metal cyclinder). Methods for
obtaining samples include impact procedures using driving devices and steady
load devices—hydraulic jacks and tractor—mounted hydraulic soil samplers.
The choice of equipment depends on the soil condition. Care must be
exercised to minimize compaction with steady load devices and shaking with
impact clriving devices. If water content measurements are desired for each
soil core, then the core should be wrapped in aluminum foil to retain the
sample near its field water content. For bulk density determinations, the
core is oven-dried at 105°C until a constant weight is obtained.
If bulk density samples cannot be collected, estimation techniques are
available (see Carsel et al., 1984).
3.5.6 pH
Hydrogen ion activity has an effect on the dissipation of pesticides,
i.e., some are less stable at low soil pH values and some at high pH,
depending on their functional groups (Nash, 1980). In addition, many
polar compounds undergo speciation reactions as a function of pH,, which
substantially affects their sorptive behavior. The method for determining
pH in soil is presented by McLean (1982) and Weber (1977).
64
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3.5.7 Temperature
Soil temperature affects the persistence of pesticides. The tempera-
ture of surface soils in loosely tilled condition (at the time when herbi-
cides are commonly applied) is much higher than air temperature. For
instance, Smith et al. (1978), showed that for a cecil sandy loam, surface
soil temperature measurements taken on planting day were 15°C higher than
prevailing air temperature (Table 3.6). Throughout the 7-day rainless
period, air temperature was generally about 25°C. After the first rainfall
event, surface soil temperature decreased markedly. Surface soil temperature
would be somewhat less under conservation tillage regimes.
A discussion of various ways to monitor temperature is presented by
Taylor and Jackson (1965). Thermistors with readings obtained by using a
wheatstone bridge provide a convenient way to obtain temperature profiles
with depth. They can be installed in a single hole using a 2.5 cm slide
hammer type insertion tool (Figure 3.25). This tool is of the type commonly
used for installing tensiometers., Several thermistors can be assembled in
a harness (as illustrated in Figure 3.26) to assure proper positioning
TABLE 3.6. SURFACE SOIL (2.5 cm) TEMPERATURE AND MOISTURE FROM TIME OF
APPLICATION TO FIRST RUNOFF EVENT (Smith et al., 1978)
Days after3
Planting
0
1
2
3
4
5
6
7
Soil
Temperature
45.2
30.4
33.1
49.0
38.2
29.2
29.2
18.7
Soil
Moisture13
7.0
5.4
2.9
1.8
1 .9
2.6
1 .4
3.6
aRunoff occurred on day 7 after planting.
^Determined gravimetrically.
65
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FIGURE 3.25. Tool for inserting temperature sensors.
7 is
§•;••
• *
* *
%. it |
!ff Awfe* Slip1
* If
.* V
: •--•:•••*. - '
1
• •*"»
.* 4. T|.-~ •$! '«!%. '"V'VJ' ,;H, *ig: .f riptf-^ijt
* it 'ft,*»f ' * *£&&•£!&" " •- •
L%,, '1*1%,,
i *''
..•"IT*!
* •» -* *
, 4 * **
, 's1*
"*** ,«-'' ii-" '^S'**A- i^'si'*"' ~'-
..*:»•*.-
":,~-i>i
|'i«'4'"-ri
1
y-: *%.*"•:," I
J'"^ '., -;- : •: ,. .<
•. / ;, if • • ,*•>,* •> •
FIGURE 3.26. Thermistor harness consisting of label tag (A); jack (B), which
is plugged into monitoring instrument; below ground sensor (c);
and surface sensor (D).
66
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while the insertion hole is backfilled with loose soil to provide good
thermal contact. Aluminum label tags of the type used on gas chromatograph
columns are convenient and durable devices for identifying sensor leads.
It is important for each monitoring site to have sensors located at consis-
tent depths. Soil temperature will vary with the diurnal cycle. Measure-
ments should be made at short time intervals until this cycle is clearly
apparent. Measurement schedules can then be optimized in accordance with
model data needs.
3.5.8 Moisture Content
The moisture content of field soils has a direct influence on the
dissolved-phase concentrations of a pesticide. Because of this, soil mois-
ture content also has a substantial impact on the attentuation and transport
processes. Soil moisture measurements are needed as a function of time and
depth.
For field measurements, several methods are available for monitoring
soil moisture that vary from collecting soil samples with depth (determined
by gravimetric analysis) to indirect measurements by use of electrical
resistance sensors (gypsum blocks, etc.), tensiometers, and radiological
methods.
When the objective is to collect soil moisture data on a minimum number
of samples and high precision is required, the gravimetric method is usually
preferred. If large numbers of samples are anticipated, consideration should
be given to possible field disturbances caused by the boreholes. The gravi-
metric method (Gardner, 1965) will require less time than other methods un-
less very time intensive measurements are required. If the objective is to
collect daily moisture data for a large number of sites (soil variability
known to be large) for a period of 2 to 3 years, then an indirect method
would probably be more appropriate, indirect methods require extensive cali-
bration and installation efforts, but are efficient once operational. For
multiple year studies, it is important to recognize that sensor installation
within the plow zone may have to be disturbed to accommodate normal tillage
operation. This fact should be acknowledged when considering trade off
between gravimetric and indirect methods. Metal detectors provide an effi-
cient means of locating permanent sensor leads that have been temporarily
buried to accommodate tillage. Smith and Carsel (1985) used discarded
automobile brake drums to protect and house buried sensor leads. The brake
drum provides a large target for the metal detector and yields high sensiti-
vity in relocating buried sensor leads.
Some runoff models require soil moisture release characteristic curve
(matric potential versus water content). These data range from saturated
hydraulic conductivity (0 bar) to -0.33 bar (field capacity) and -0.15 bar
(wilting point) potentials. For a discussion regarding these measurements,
see Peters (1965). Techniques for obtaining undisturbed soil core samples
for these measurements are discussed in Section 3.5.5. Both field capacity
and wilting point determinations can be made in the laboratory using pressure
chambers. Bulk density determinations discussed in Section 3.5.5 are
67
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required for these measurements to facilitate conversion of soil moisture
content from oven-dry basis to a volume basis as required in most models.
Additional information regarding the preparation of data for model use is
presented in Section 6.1.1.4.
3.5.9 Infiltration Rate
Mean soil infiltration rate is a model requirement because it affects
water runoff quantity and quality (see Section 2). The mean soil infiltra-
tion rate should be obtained at the site. A discussion of methods to
measure infiltration is presented by Bertrand (1965), Bower (1985), and
Peterson and Bubenzer (1985).
3.6 A WORD OF CAUTION REGARDING ON-LINE FIELD DATA SYSTEMS
On-line computerized data collection systems using a variety of elec-
tronic sensors offer an attractive alternative to manual sampling methods"
where intensive sampling schedules are planned. Soil moisture, soil tempera-
ture, air temperature, relative humidity, solar radiation, wind speed and
direction, and rainfall are examples of measurements that are be made by
this means. This apparently very labor efficient approach is, however,
subject to a number of very real limitations posed by the field environment.
Wind, rain, dust, high and low temperature extremes, and variable humidity
conditions all contribute to the difficulty of providing workable environ-
ments for field installed computers, stable line and backup power sources
must be provided. Buried sensor wiring harnesses can involve miles of
shielded wire and require major plot excavation during installation.
Various interfacing electronics hardware and software are required for
connection of sensors to the computer by means which provide automatic
sampling and data storage. These seemingly surmountable requirements
combine to yield complicated systems that are difficult to maintain in
fully operational condition. From past experience in the operation of a
automated field data acquisition system (Smith et al., 1975), the following
factors merit consideration prior to the establishment of such a system.
a) Lightning—Experience has shown that lightning is a major threat
to field electronic installations. Power poles located in open
fields, above ground sensor posts (i.e. , wind speed instruments)
and long lengths of buried sensor wires provide good targets for
lightning. Even somewhat distant strikes can cause stray currents
to be transported through sensor wiring harnesses to the central
computer. The result is almost always serious damage to the
sensors, interfaces or the computer. This is particularly
important for field runoff studies in that lightning strikes
almost always coincide with key rainfall events.
Lightning arresters or grounding systems can be installed to
minimize these problems. Figure 3.27 shows a field with an
established grounding system.
68
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FIGURE 3.27. Example of field grounding system.
b) Power outage—Power outages, like lightning activity, are often
keyed to storm events. A dependable backup power unit should be
considered along with failsafe data storage and automatic restart
computer facilities.
c) Vandalism, thieves, and pests—Unmanned, remotely located field
installations offer attractive targets for vandals, thieves,
stray livestock, rodents and birds. Good quality fences help to
control livestock and pests and offer some deterrent to thieves.
Nevertheless, some problems are to be expected from these sources
and should be considered in the design of field installation.
d) Equipment failure—Field systems made up of 'large numbers of
sensors, complex interfaces and supporting equipment are often
subjected to inordinate downtime. Component failures are more
frequent in the field due to lightning, power line surges, weather
extremes and unauthorized tampering. This situation is compounded
by the fact that failure of the most simple component can often
damage other parts of the system or take the whole system down.
The probability that all systems will be fully operational during
any given series of storm events is not high.
e) Maintenance—Because of problems noted in items a through d,
maintenance of a field based data acquisition system often
becomes a very labor intensive exercise. Daily service and
69
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thorough checks on proper system operation are usually required.
One should not be lured into the belief that automation will
eliminate the need for frequent visits to the field site.
f) The human factor—One of the most serious shortcomings of automated
data systems is that they tend to produce voluminous quantities
of data far in excess of the quantity that can be effectively
interpreted and used by the project staff. The excitement that
seems to be inherent in the process of designing and installing
an automated data system tends to promote a preoccupation with
"the system." The very presence of an efficient system can
generate a rash of new ideas for using even more intensive data
sets. It is not difficult to occupy the project staff with the
task of operating, maintaining and expanding the system. The
obvious pitfall is that little effort is left available for
critically reviewing and interpreting any of the data. It is
important that project objectives and real data requirements are
not lost to the excitement of building the data system,,
3.7 SAMPLING NETWORK DESIGN
Sampling network design involves selection of the sampling sites for
monitoring various parameters in space and time following application and
after individual runoff events. To facilitate sampling for pesticide resi-
dues, the field should be divided into sampling areas. Figure 3.28 illus-
trates a typical sampling grid overlaying a soil series map. By overlaying
Soil Series
I I Clarendon
Primary Monitoring Sites (20, ranked)
0 IS 3D matari 16 17
FIGURE 3.28. Illustration of a field design for characterizing
pesticide degradation and transport.
70
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a grid on the field unit, random sampling areas can be chosen at the grid
intersections. This double overlay method distinguishes sampling area
based on soil physical characteristics that may be important in influencing
runoff. It also provides for an overall estimate of the entire area. Care
should be taken to avoid degeneracy in sample labeling methods.
For grid sampling, grid line separations (resolution of grid) must be
selected (e.g., 15.3 M) to provide a sampling area where independence is
obtained. Independence is important because it will help to maximize the
information available from the sample when optimum spatial independence
is obtained. A sample size of 30 is regarded as reasonably large and
depends somewhat on sample variability and on the degree of precision that
is desired. A sample size of ten is regarded as the minimum number of
sampling sites, Bresler and Green (1982). Without some estimate of variabi-
lity, the required sample size cannot be projected with any real confidence.
In preliminary sampling to estimate variability, however, relatively small
sample sizes are reasonable. Obviously, more sites (n = 10 to 30) within a
given field will provide additional information on the variance for esti-
mating the true sample mean as well as addressing spatial variability
impacts on runoff losses.
The number of samples to be collected and processed for residue analysis
must be considered initially as a factor in the overall experimental design.
Various type samples may be collected throughout the crop growing season
that include: filter disc (monitoring application rates), soil core
(application rate and residue analysis with depth and time), plant tissues
(application distribution with foliar-applied insecticide), and runoff
collections (both water and sediment).
3.7.1 Estimating Means and Totals
Prior to a discussion of sample collection, it is useful to review
general statistical sampling principles for use in estimating means and
totals for a given field.
The estimation of a total amount of compound that has been applied to
a site can be considered in the context of estimating the mean value per
unit area in the site and then multiplying by a numerical constant that
yields the field total. Whenever sample data are obtained in the form of a
simple random sample, it is straightforward to obtain numerical estimates
of the population mean, the standard error of the estimate, and confidence
intervals. For example, the sample mean is an estimate of the population
mean; standard error of this estimate of the population mean is the sample
standard deviation divided by the square root of the sample size; confidence
intervals are based upon these two estimates.
Some standard formulas for calculation follow.
Population mean estimate = Sample mean
= SUM (observed value) / n
71
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where n = number of values in the sample
Sample variance
= SUM ((observed value - sample mean)2) / (n-1)
Standard error of the estimate of the mean
= SQRT ((sample variance) / n)
Confidence interval for the population mean:
(sample mean) _+ t(n-2,alpha/2) X (std error of mean)
alpha (i.e., level of risk) = 0.05
Estimate of the population total
= N X (sample mean)
where N = total number of "sample units" in the
population
Confidence interval for the population total:
N X (sample mean) +_ N X t(n-2,alpha/2) X (std error of mean)
To estimate a population total, one must know the number (N) of "sample
units" that comprise the population. For example, if filter discs are
being used to estimate a mean quantity per disc, then in order to calculate
how much is on the whole field, the area of the field must be considered with
regard to the area of the discs. That is, N = (area of the field site)/
(area of a filter disc). To calculate the total on .the field, the standard
error of this estimate, and confidence intervals, one need only multiply the
respective estimates for the mean by the constant N.
Ideally, the samples should be collected at random. "Ehis means that
all "candidate samples" have an equal chance of being selected at sampling
time. In the present case, precautions should be taken to ensure that
samples are taken far enough apart spatially so that the samples are not,
in effect, measuring the same location. Selection of sampling points can
be done from a grid overlay of the sampling area so that the intersection
points denote the set of possible sample locations. The grid si^e usually
should be chosen so that it is as fine as is practically possible, but not
so fine as to permit two adjacent intersection points to be considered
dependent. Hie set of all intersection points comprise the set of "candi-
date" sample points. In practice these can be numbered in any manner, and
then a random selection of these can be made prior to collection time by
using a table of random digits (see Appendix B).
72
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3.7.2 Subdivision of the Study Area
A large field site can often be subdivided into a few smaller areas on
the basis of easily observed criteria such as soil series, gradient, or
elevation. Such a division may or may not serve to enhance the efficiency
of statistical sampling plans. A division of this sort is often called a
"stratification" of the sampling area. Generally, it is desirable to stra-
tify an area if the stratification serves to produce subdivisions with
significantly reduced variability compared to the whole site, relative to
the variable that is to be measured.
An area that is properly and effectively subdivided can yield a more
precise estimate of the field total than could be obtained with simple
random sampling using the same number of samples. Such sampling techniques
fall under the general heading of "stratified sampling." Basically, to do
stratified sampling, one only needs to stratify the population and then
randomly sample each subpopulation. The estimate of the population mean
is, mathematically, just a weighted mean of the individual stratum means.
A question that arises almost immediately is in regard to the number
of samples that should be used within each stratum. This generally is
posed in the context of there being a fixed number of samples that are to
be collected and "allocated" to the strata. Two common techniques are
proportional allocation and optimal allocation. With proportional alloca-
tion, sample sizes are assigned in proportion to the sizes of the strata. .
With optimal allocation, the sample sizes are chosen so as to minimize the
standard error of the estimate of the population mean. If a population is
stratified, a general rule that should be followed is: Take a larger
sample in a stratum if (a) the stratum is larger or (b) the stratum is more
variable.
3.7.3 Systematic Sampling
Often there are situations that cannot be approached practically under
a random sampling plan. That is, the requirement to select samples at
random may place unacceptable hardships on the execution of the experiment.
Random sampling is desirable for two main reasons. First, estimates of
means and variances that are calculated are statistically rigorous. Second,
by randomizing, objectivity is more suitably guaranteed. Still, the prac-
tical aspects of a situation may prevent competely random sampling. There
is always a possibility, especially for small samples, that a random sample
will not be a proper representation of the site being monitored. For
larger samples, this is not a frequent occurrence. A systematic sample
frequently will ensure that a representative sample is obtained. This is
accomplished by selecting sample points in some uniform fashion over the
whole field. This causes each small area of the field.to be represented in
the whole sample. The main objective in any sampling scheme is to obtain a
sample that is representative of the underlying population and to do so in
a manner that is statistically acceptable for the ensuing estimation of
population parameters.
73
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An example may be given for a situation in which it is desired to
monitor the application rate of a spray-applied pesticide (see Figure 3.29).
In the example, 100 equally spaced locations along the path of the tractor
were chosen. Filter discs might be used to intercept a sample of the
compound as it comes from a spray rig with multiple nozzles, ideally, the
location of the filter discs would be randomized over the field site, but
it might not be possible to properly locate, pick up, and label the filter
discs while the spraying operation is proceeding. An alternative would be
to systematically place discs in front of the spray rig, collecting them in
sequence as the compound is sprayed onto them. Biis makes the practical
aspects more manageable and also ensures that the field will be adequately
covered, giving a representative sample.
For a systematic sample, a population mean is estimated by the sample
mean,
3.7.4 Determination of Sample Size
The question of sample size can be addressed rather easily provided
certain preliminary information is available, fhe term "sample size" refers
to the number of individual, independent measurements made on a particular
variable of interest. A given sample size may be appropriate for some cases
but not for others. It is important to realize that the size of a sample
required in a given instance depends upon:
CO
Application Uonitoring Sites (100)
Soil Series
Clarendon
l~f~l Tilton
Tractor travels southward on
solid lines and northward on
dashed lines.
15 30 metsr«
FIGURE 3.29. Illustration of pesticide applications monitoring
design using filter discs.
74
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» inherent variability of the material being sampled, relative to the
variable being measured;
« degree of accuracy required by the investigator;
• level of confidence that is needed in the estimate? and
* resources available to the study.
A sample size can be chosen large enough to ensure, with a certain
level of confidence, that the estimate will fall within a specified distance
of the true value. One cannot be absolutely certain that the estimate will
be within that distance without sampling the entire population, which is
usually impossible or impractical. Biere is always the possibility that
the sample will be particularly bad, causing the estimate to lie further
from the true mean than is desired. This will occur, on average, only
as frequently as is permitted by the level of confidence required, however.
For example, a decision to construct a 95% confidence interval admits a 5%
chance that the true mean will not be contained in the interval yet to be
determined. This risk (5%) is referred to as the error rate or alpha
level. The nature of the inherent variability of the material being sampled
cannot be changed, but by taking a large enough sample, the width of a
confidence interval can be narrowed to any desired size. The greater the
variability, the larger the required sample. Also, the higher the confi-
dence needed in the estimate or the better the accuracy required, the
higher the sample size. The degree of accuracy required normally will
depend upon external considerations, as well as the level of confidence
needed.
The general formula for sample size required is:
n _> (ts/d)2
Here, t is a percentile of the Student's t distribution, based on the
sample size and the level of confidence; s is an estimate of the popula-
tion standard deviation; and d is the largest acceptable difference be-
tween the estimate and the true mean value. The notation s relates to the
inherent variability and must be estimated prior to the main sampling
procedure; t is dependent on the investigator's specification of "alpha,"
i.e., his acceptable risk; and d is dependent on the investigator's accuracy
requirement.
Example: Projecting the sample size for a filter-disc sampling
experiment to determine application rate
Target application rate = 3.36 kg ha~1
Accuracy desired (i.e., acceptable deviation from true value)
= + or - 20% of the mean
alpha (i.e., level of risk) = 0.05
75
-------
Measure of variability (prior estimate):
coefficient of variation =30%
Disc area = 269 cm2/disc = 0.0269 M2/disc
Expected mean per disk:
3.36 kg ha-1 X 1 ha/10-4 m~2 X 0.0269 mz/d±sc = 9.04 x 1 0~6 kg/disc
Largest acceptable difference between true value and estimate:
d = accuracy * expected mean = 0.20 * 9.04 x 10~6 = 1.81 x 1 0~^
Prior estimate of the population standard deviation:
s • c.v. * expected mean = 0.30 * 9.04 x 10~6 = 2.71 x 10~6
A student's t-distribution table (see Appendix C) can be used to
determine a value, dependent upon n and alpha, that satisfies the inequality:
n _>_ (t x s / d)2
where t is the tabulated value (see Appendix C) corresponding to column
heading 0.975, and n-2 degrees of freedom.
Here, the value n = 12 is satisfactory because using t (10, .975) =
2.228, we obtain 11.13 as the value on the right hand side of the inequality.
This is the smallest value of n where inequality holds true. If accuracy
were required to within 10% of the mean rather than 20%, n would be 36,
In certain cases, s and d will be known in numerical terms only, rather
than in terms of the c.v. and the population mean, as above. Either way,
this expression can be used in a trial-and-error sense to find a value for n
that just works. It is in this manner that a sample size is determined. In
the above, all that is needed is a suitable estimate for s and one for d.
Values for s and d could be determined by a preliminary sample, as circum-
stances permit.
3.8 AGRICULTURAL PRACTICES
Agricultural practices affect the characteristics of the field site
that have an impact on runoff and sediment transport. Conventional agricul-
tural practices for a typical cropping year might include various activities,
including:
o Spring till-disk, chisel plow, or moldboard plow
o Apply fertilizer - broadcast
76
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• Disk incorporate fertilizer
• Plant and apply pre-emergence and post-emergence pesticide for weed
control
• Cultivate for weed control four to six weeks after planting and one
to two additional times prior to harvest (cultivation may not be
required if proper herbicides are applied at planting)
• Apply foliar application of insecticide for insect control (for
example, foliar applications to cotton begin when squares form and
continue at weekly intervals thereafter)
• Apply defoliant as required
• Harvest crop and determine yield
• Use rotary mower to chop stalks
• Fall plow
Agricultural practices are, however, constantly changing to increase the
production of food commodities for both domestic and world needs. The
development and implementation of conservation tillage is being promoted as
a means of reducing soil loss (sometimes runoff) and of providing savings
in energy and labor (Carsel et al., 1985). Conservation tillage systems
leave a protective crop residue on the soil that promotes greater infiltra-
tion. A recent model comparison study of no-till and disk harrowing using
three representative pesticides on agricultural fields located in Georgia,
Iowa, and New York indicated no-till reduced the potential for pesticide
runoff and erosion losses. Pesticide leaching potential was increased,
however, for two of the three sites (Carsel et al., 1985). Since conserva-
tion tillage practices are now in common use, studies to evaluate the
impact of conservation tillage on pesticide runoff, leaching and water
quality should be conducted. Conventional tillage is, however, still the
predominant practice used in most regions of the United States. Donigian
et al. (1985) reported the following percentages for conventional tillage
in various regions:
Southeast 52%
Delta States 82%
Cornbelt 62%
Northern Plains 67%
Southern Plains 82%
Generally, conventional tillage will produce greater runoff, erosion, and
pesticide losses in runoff. With respect to environmental assessments,
this would result in a worst-case (convervative) estimate (Donigian et al.,
77
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1985). Tillage practices used in field runoff studies should reflect the
most common practices for a given pesticide use area.
3.8.1 Crop Fertilization
Major fertilizer nutrients (N, P, K, lime) are typically applied by
several methods. These include:
* broadcast before primary tillage
• top or side dress after crop is established
• foliar application of micro nutrients (S, Mn, Mo, Zn, etc.)
The actual combination of practices selected is determined by the crop,
geographic location, weather conditions, and other local factors.
It is good practice to utilize services offered by commercial and state
soil testing laboratories in determining fertilizer application rates. Many
laboratories offer full recommendation services based on detailed knowledge
of local areas. These laboratories will require collection of representative
soil samples. Details on sample collection, containers, and labeling can
be obtained from the testing laboratory personnel, or local extension
service (agents), or fertilizer suppliers.
3.9 PESTICIDE APPLICATION
Pesticides are commercially available in liquid, wettable powder, and
granular formulations. The formulation and method of application have an
impact on runoff. Liquid and wettable powders are usually sprayed directly
on the soil or plant surfaces. Granular pesticides are applied only to the
soil, and in most cases, incorporated by tillage. Wettable powders that
are applied to the soil surface consistently show the highest long-term
losses of any general class of herbicides (Wauchope, 1977). Some surface-
applied pesticides are rapidly lost from the soil surface due to volatiliza-
tion or photochemical degradation processes. These compounds must be
incorporated into the soil at the time of application as recommended by the
manufacturers. Various methods of incorporation are typically used. These
include disk harrowing, rotary tilling and use of a variety of mechanical
incorporation implements designed for this purpose.
Typical application devices include conventional ground equipment,
aerial sprayers, and chemigation equipment, other less used methods are
wick applicators, controlled droplet applicators, and electrostatic equipment.
Typical ground equipment usually involves a trailer- or tractor-mounted
tank, pump and boom system as illustrated in Figure 3.30. Aerial application
systems have similar equipment built into specialized crop dusting planes
and helicopters. Chemigation systems involve blending chemicals with
irrigation waters, which facilitates chemical application as a part of the
78
-------
FIGURE 3.30. Examples of pesticide application methods include tractor-mounted (A) pull-type
-------
FIGURE 3.30 (Conf d). Rope wick type (E) and chemigation (F)
80
-------
irrigation process and eliminates additional field operations. Check valves
should be utilized to prevent backflow of chemicals into water sources
(wells, reservoirs, ponds, rivers, etc.) in the event of equipment failure.
Wiese (1977) gives an excellent review of various herbicide application
techniques, covering both small plot sprayers and conventional field equip-
ment. For the purpose of this manual, discussions will be limited to the
type of equipment used in actual field production situations. Very small
plots and associated equipment are not recommended (see Section 3.2.1 on
the minimum recommended field size). Weise (1977) indicates that all
sprayers have the following essential characteristics:
1. A tank (Piberglas® or stainless steel) for mixing and holding
liquid spray material that is dispensed through a strainer to
separate foreign material.
2. An energy source to propel or discharge the liquid spray from the
tank consisting of a mechanically driven pump (nylon, rubber roller,
gear, centrifugal, diaphragm or piston)»
3. A pressure regulator to adjust and control .spray pressure and
discharge volume from tank.
4. An agitator to provide constant and uniform mixing of the chemical
in the tank. Mixing is achieved by a return flow of liquid from
the pressure regulator through agitator and by-pass lines.
5. A spray boom and system of nozzles constructed of flexible hose
with angle iron support or a pipe with nozzle assemblies attached
at specified distances for uniform distribution (overlap) over
soil or plant surfaces. Nozzle type and height should be ad-
justed according to the type of application.
The amount of chemical actually applied to the target site depends on
the method of application. Aerial application usually results in greater
losses due to increased drift potential. Factors that influence drift
losses are: wind velocity, pump pressure, nozzle angle, time of application,
nozzle type, droplet size, formulation and nozzle height above the ground
(Shoemaker and Harris, 1979). Shoemaker and Harris also show that four to
five times more drift occurs from aerial application compared to high clear-
ance ground sprayers. In addition, drift losses are greater for insecticides
because the applications are usually at high pressure with small droplet
sizes (Shoemaker and Harris, 1979). In regard to chemigation, application
and runoff losses are virtually unknown. As new or novel techniques are
developed, their impact on soil deposition should be determined.
Whatever application procedure is used, it is important to determine
the actual amount of pesticide that reaches the field site. In the case of
a foliar-applied pesticide, the distribution of chemical between the
foliage and soil must be determined. This can be accomplished by monitoring
actual amounts received by the soil and foliage. Monitoring techniques are
discussed in Section 3.10. Careful calibration of application equipment
should be conducted as a check on the monitoring process.
81
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3.9.1 Liquid Sprayer Calibration
Calibrating the application equipment is an important step for pesti-
cide runoff studies because the actual amount applied to the field site
must be accurately known. Prior to the calibration exercise, nozzles must
be examined examined to assure that they are in good condition and are
adjusted to proper height and spacing for uniform coverage. Pump shaft
seals, hoses, nozzles and valves must be checked for leaks. Several steps
are involved in the calibration. These are included in the following
example for broadcast spraying of a liquid formulation.
Example—Overall sprayer calibration involves three separate calibra-
tion steps; one for tractor speed, one for sprayer delivery rate, and
one for tank mix concentration. Tractor speed is calibrated in terms
of time to traverse a unit area, and therefore includes boom width as a
factor. Sprayer delivery is measured in terms of volume delivered per
unit time and tank mix is in terms of chemical mass per unit volume.
Combining these factors results in the desired mass per unit area
calibration:
Tank mix Desired
Tractor speed Nozzle delivery rate concentration calibration
time volume mass mass
X X = (3.1)
unit area unit time unit volume unit area
Many pesticide labels express recommended application rates in
the English system of units. Conversion to the metric units has been
slow. For this reason, example sprayer calibration calculations will
be duplicated for each system of units.
In English units, Equation 3.1 becomes:
Tank mix
Tractor speed Nozzle delivery rate concentration Desired calibration
sec acre"1 X gal sec~1 X Ib gal"1 = Ib acre~1 (3.2)
Similarily, in metric units:
sec ha~1 X A sec~1 X kg A"1 = kg ha~1 (3.3)
The targeted application rate is usually specified for a given set of
field circumstances. For example, the herbicide metolachlor might be
recommended for a peanut crop at the rate of 3.00 Ib acre"1 (3.36 kg
ha~1). At least one of the factors in Equation 3.1 must therefore be
continuously adjustable so that an exact rate of 3.00 Ib acre"1 can be
obtained. Because tractor speed is limited by a finite selection of
82
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gear ratios and nozzle delivery rates by available nozzle sizes, the
tank mix concentration term is best suited for continuous adjustments.
Although it is true that tractor speed can be regulated by engine rpn
as well as gear ratio, rpm should be set at governor speed to assure
constant and repeatable performance in field operation. Likewise,
nozzle delivery rates could be varied as a function of pump pressure,
but optimum nozzle operation cannot always be obtained if pressure is
varied over wide limits. Pump pressure also is influenced by engine
rpm and it is best that both these factors are fixed at the outset.
Pressure should be set to give optimum nozzle performance, rpm should
be set to governor speed, nozzles should be selected for adequate
volume delivery to assure uniform coverage, and gear ratio should be
selected for a reasonable and convenient field working speed. After
these factors are fixed, the exact application rate is obtained by
adjusting the tank mix concentration.
3.9.1.1 Tractor speed calibration
Tractor speed is calibrated by measuring the time required to traverse
a measured distance in a field. This distance is multiplied by the width
of swath covered by the spray boom to obtain a time per unit area calibra-
tion. During this test the tractor should be operated (from a running
start) at full governor speed rpm in the gear selected for spraying and
should have the sprayer attached and pump running as if under actual opera-
tion in the field. It is convenient to select the test run distance such
that exactly 0.100 acre or other simple fractional area (0.1 ha) is covered
in the test.
For example: assune 4—row equipment and a 36—in. row spacing. In
this case the boom would spray a 12-ft swath. Under these conditions a
43,560 ft2/acre-1
linear distance of 363 ft ( X 10~1) would be required to
12 ft spray swath
cover 0.100 acre of area. A test course of this distance should be staked
off. Elapsed times required for the tractor to traverse the course should
be measured several times using a stop watch. Small variations are per-
missible due to slippage, etc., but enough trials should be performed to
support a good statistical average. Assume an average time of 51 sec
resulted from this test. The tractor speed calibration would then be 51
sec per 0.100 acre or 510 sec per acre.
The same example in metric units would involve 4 rows at 0.914 m
spacings or a total swath of 3.66 m. A linear distance of 273 m
104 m2 ha~1
( X 10~1) would be required to cover 0.100 ha. Since a
0.91 5 m
distance of 273 m would probably be inconvenient an equivalent test track
of 27.3 m based on a 0.010 ha area might be substituted. Assuming the
tractor required an average of 12.6 sec to traverse this distance, the
tractor speed calibration would be 12.6 sec per 0.010 ha or 1260 sec
per ha.
83
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3.9.1.2 Nozzle delivery rate calibration
After nozzle and pressure specifications have been selected, a test
should be performed to determine nozzle delivery rates. This test should
be performed with tractor engine rpm (pump rpm) and pressure regulator
settings as to be used under actual operating conditions and the same as
those used in the tractor speed trials (Section 3.9.1.1). Nozzles should
be calibrated in terms of volume per unit time delivery rates. All nozzles
should be included in this test and individual delivery rates should be
compared for uniformity. Times required to deliver unit volumes from the
individual nozzles should be measured and recorded in several trials. Once
a set of suitable average times have been determined and are found to be
uniform, an overall average for all nozzles should be calculated. This
value should then be multiplied by the number of nozzles on the boom to
arrive at a final calibration factor. These tests may be conducted with
only water in the spray tank unless there is reason to believe substantial
viscosity changes are expected when the formulation is present.
For example: assume the 4-row equipment noted earlier is outfitted
with a 12-ft boom using 7 nozzle assemblies spaced at 20.5 in. intervals.
One gallon containers should be held under each nozzle and times for complete
filling measured by stop watch. Selected pressures, rpm, etc., should be
maintained during the test. Assuming the mean nozzle delivery rate is
found to be 1 gallon per 111.5 sec per nozzle or 0.00897 gal sec"1 per
nozzle, the overall delivery rate for the whole boom would be 7 x 0.00897 =
0,0628 gal sec"1.
In metric units, the boom is 3.66 m wide and the 7 nozzles are spaced
at 0.523 m intervals. One liter containers would fill in an average time
of 29.5 sec and the nozzle delivery rate would be one liter per 29.5 sec per
nozzle or 0.0339 £ sec"1. All 7 nozzles would deliver at a combined rate of
7 x 0.0339 = 0.237 t sec"1.
3.9.1.3 Tank mix concentration calibration
The required tank mix concentration can be calculated from the tractor
speed calibration factor (Sections 3.9.1.1), nozzle delivery rate calibration
factor (Section 3.9.1.2), and the desired field application rate as follows.
Field application rate (Ib acre"1)
Tank mix concentration = • (3.4)
Tractor speed factor X Nozzle delivery
(sec acre"1) factor
(gal sec"1)
Assuming: Tractor speed factor =510 sec acre"1
Nozzle delivery factor = 0.0628 gal sec"1
84
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Desired application rate = 3.00 Ib acre"1
3.00 Ib acre~1
Tank mix concentration = •• • • (3.5)
(510 sec acre""1 ) (0.0628 gal sec"1)
= 0.0937 Ib gal-"1
A properly mixed tank solution should contain 0.0937 Ib of chemical
for each gallon of solution in the spray tank.
For the metric example:
Tractor speed factor = 1260 sec ha~1
Nozzle delivery factor = 0.237 S, sec"""'
Desired application rate = 3.36 kg ha""''
and
3.36 kg ha-1
Tank mix concentration = (3.6)
(1260 sec ha-1) (Q.237 S. sec-1 )
= 0.0113 kg £-">
A properly mixed tank solution would contain 0.0113 kg of chemical for
each liter of solution in the spray tank.
Care should be taken to avoid ambiguity in expressing application
rates. Rates may be expressed in terms of active ingredients or in terms
of formulations. Either basis may be used, but should be used consistently
throughout the calculation and tank mixing operations. Serious errors can
otherwise result.
The volume of tank mix required for a given acrea'ge can be calculated
from the tractor speed and nozzle delivery rate calibration factors as
follows.
Volume of tank mix
Tractor speed X Nozzle delivery = •••
(time/unit area) (volume/unit time) unit area
(3.7)
Continuing with the previous example:
510 sec acre"1 X 0.0628 gal sec""1 = 32.0 gal acre""1 (3.8)
85
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or, for the metric example
1260 sec ha-1 X 0.237 £ sec~1 = 299 A ha~1 (3,9)
Multiplications of these values by the number of acres (hectares)
to be sprayed will yield the total tank mix volume required. A slight
excess should be prepared. Spray tanks will not usually drain completely
on rough ground without adversely affecting the spray pattern. Excess
volume of solution remaining in the tank after spraying is completed should
be measured. This will provide an additional check on the application
rate actually delivered.
3.9.1.4 Cautions regarding tank mixes
Two or more chemicals can sometimes be mixed and applied together.
Caution should be exercised, however, to assure that the formulations are
compatible. Manufacturers normally supply this information for recommended
mixtures. If other mixtures are considered, the operator should conduct
tests to determine compatability.
3.9.2 Granular Applicator Calibration
The application of a granular pesticide requires that the applicator be
calibrated to deliver a given weight of pesticide per distance of row (if
banded) or to a known area (if broadcast). The calibration procedure is
similar to the liquid sprayer calibration except that the tank mix factor
does not apply. Some pesticides require incorporation into the soil and
should be thoroughly mixed using some type of mechanical incorporator.
3.9,3 Timing of Application
The application of pesticides must be keyed to the normal planting
operations and the crop growing season. The usual planting and harvesting
dates for some important field and seed crops for each state are presented
in Figures 3.31, 3.32, and 3.33. A more extensive compilation that includes
some agronomic data is given in Table 3.7.
The time of day the pesticide is applied affects the amount of chemical
delivered to the target area. The preferred application time is during the
early morning when relatively little wind movement is observed and the soil
temperature is near a minimum.
3.9.4 Field Safety
Experimental field work usually necessitates that several field per-
sonnel be present during, and immediately following, chemical applications.
86
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Before April 1
April 1 - April 19
20 " M*y 9
10 - May 30
After May 30
FIGURE 3.31. Usual starting dates of corn planting in United States
(Burkhead et al., 1972).
Potential exposures to chemical spray drifts and vapors are high during
this period. Field workers should utilize applicable forms of available
safety equipment whenever possible. Respirators, protective clothing, and
gloves are useful for this purpose. The usual cautions for safe operation
of heavy machinery should also be employed.
3.10 PESTICIDE ^PLICATION MONITORING
To follow pesticide persistence with time after application, it is
important to know as accurately as possible the amount of pesticide that
actually reached the target. The difference between the calibrated amount
of applied chemical and the amount intercepted by the soil surface (as
monitored by filter disc or other methods) should be small indicating field
losses -during application were insignificant. Inordinate discrepancies would
be indicative of high drift losses, inaccurate calibration or inadequate
sampling and measurement procedures. The time delay between chemical
87
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j Before April 1
| April 1 - April 10
H April 11 - April 20
laiji-l April 21 - April 30
Igj Alter April 30
FIGURE 3.32.
Usual starting dates of cotton planting in United
States (Burkhead et al., 1972).
application and monitoring should be kept to a minimum so that losses from
the field after application are not confused with application or monitoring
problems. Some compounds have half-lives of only a few hours.
Because pesticides can be applied directly to the soil surface, incor-
porated in the soil, applied to plant foliage, or distributed between the
plant and soil, different techniques are,required to monitor thesse various
application modes, see Table 3.8.
Several methods for monitoring herbicide application rates have been
investigated by Smith et al. (1978). These include (1) the use of filter
paper discs, (2) timing the sprayer during application and recording total
spray time on the field, and (3) use of a surface soil sampler, Table 3.9.
In addition, Smith et al. (1981), conducted a plot study with a foliar
applied insecticide that required monitoring the application distribution
between plants and soil to evaluate foliar washoff losses. Discussions on
the various monitoring techniques are covered in Sections 3,10.1, 3.10.2,
3.10.3, and 3.10.4.
88
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Before May 9
10 - May 19
| May 20 - May 31
i June 1 and Later
FIGURE 3.33.
Usual starting dates of soybean planting in United
States (Burkhead et al., 1972).
3.10.1 Filter disc method
This technique involves the use of filter discs (Whatman number 42 of
1 5 cm or greater diameter have been found to be acceptable) to intercept
the liquid spray at the soil surface. Pesticide penetration tests through
single filter discs have been checked and found to be negligible (Smith et
al., 1978). For high volume spray application, two layers of filter disc
should be used to eliminate potential bleed-through losses.
The optimum number of filter disc locations within the field will vary
depending on the study design, as discussed in Section 3.7. When using
ground application equipment, the area taken by the tractor tires must be
recognized and eliminated from the sampling area because filter discs would
otherwise be crushed by the tractor tires. Once filter disc locations are
determined, a metal rod or similar tool can be used as a jig to aid in
positioning the discs in the field prior to application (Figure 3.34).
89
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TABLE 3.7. AGRONOMIC DATA FOR MAJOR AGRICULTURAL CROPS IN THE UNITED STATES
(Carsel et al., 1984)
Crop
Representa-
tive States planting Window,
of Major Month, Day
Production5 (Julian Day)k
Crop Emer- Crop Range of
gcnce (Days Maturity Harvest Window, Average Active Plant
from (Days from Month, Day Yield/Acre Rooting
Planting Planting) (Julian Day)b 1977-1979° Depth (cm)
Corn
Soybeans
Cotton
Wheat
Potatoes
IA,
NE,
IA,
MS,
TX,
AZ,
KS,
ND,
MN,
IL,
OH
IL,
OH
MS,
AR
OK,
MT,
ID
IN,
IN,
CA,
CA,
WA,
Long Island
NY,
MS,
ID,
April 25 (115)
to June 15 (166)
May 1 (121 ) to
June 25 (176)
March 1 (60) to
May 25 (145)
[TX. to June 20
(171)]
Aug. 15 (227) to
Oct. 25 (298)
[WA to Nov. 20 (324),
CA to Feb. 15 (046)]
April 1 (091) to
May 1 (121)
5-15
5-15
5-15
5-15
5-15
110-130
110-130
110-130
200-225
150-170
Sept. 25
to Dec.
Sept. 15
to Dec.
Sept. 1
Jan. 1 5
[TX Aug.
to Dec.
June 1 5
Sept. 20
Sept. 1
(268)
10 (344)
(258)
10 (344)
(244) to
(015)
1 (213)
20 (354)]
(166) to
(263)
(244) to
110
35
670
40
335
bu
bu
Ibs
bu
cwt
60-120
30-60
30-90
15-30
15-45
Oct. 1 (274)
WA, CA, OR
Peanuts GA, TX, AL,
NC, VA
Tobacco NC, SC, TN,
KY, VA
Grain TX, KS, NE
Sorghum
April 5 (095) to
June 5 (156)
[TX Mar. 31 (090)
to July 20 (201)]
April 5 (095) to
June 20 (171)
TX Mar. 1 (060) to
July 1 (182)
KS, NE May 5 (125)
to July 1(182)
5-15
Planted in
Field as
Seedling
5-15
150-175 Aug. 10 (222) to 2550 Ibs 30-60
Dec. 15 (349)
120-150 July 1 (182) to 2000 Ibs 30-60
Oct. 1 (274)
120-150 TX July 1 (182) to 62 bu 15-30
Nov. 20 (324) KS,
NE Sept. 20 (263)
to Dec. 1 (335)
aBay, D. M. and Bellinghausen, R. P. Missouri Farm Facts.
May 1979.
bBurkhead, B. E., Max, R. C., Karnes, R. B., and Neid, E.
USDA, Agricultural Handbook No. 283. 1972.
cKirkbride, J. W. (Ed.). Crop Production Annual Summary.
CrPr 2-1. 1980.
Missouri Department of Agriculture.
Usual Planting and Harvesting Dates.
USDA, Crop Reporting Board publication
-------
TABLE 3.8. SAMPLING TECHNIQUES FOR USE THROUGHOUT THE CROPPING SEASON
Sampling purpose
Sampling technique
Background monitoring (prior to
tillage and pesticide application)
Split-tube core sampler or hand
auger
Monitoring applications on bare soil
- liquid broadcast on surface
- liquid broadcast incorporated
- granular broadcast and/or banded
in row
Filter disc* or surface soil sampler
Surface soil sampler
Surface soil sampler or aluminum
cans
Monitoring applications directed to
plant foliage
- Plant foliage
a) row crop
b) tree
- Soil
Whole plant
Leaf s ample s
Filter disc
Post application monitoring
Surface soil sampler
Post runoff event monitoring
Split-tube core sampler or hand auger
*Filter disc are preferred unless soil sampling using surface soil sampler
is planned for daily observations until first rainfall event.
91
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TABLE 3.9. HERBICIDE APPLICATION RATES AS MONITORED BY VARIOUS
TECHNIQUES (kg/ha), -1975 (Smith et al., 1978)
Water- Atra- Cyana- Diphena- Propa-
shed Sample type zine zine 2,4-D Paraquat mid zine
P1 Filter disc NA* NA NA 1 .66 NA 1 .66
Nozzle (timing) 1.29 1.78
Surface soil sampler
P2
P3
P4
Filter disc
Nozzle (timing)
Surface soil sampler
Filter disc
Nozzle (timing)
Surface soil sampler
Filter disc
Nozzle (timing)
Surface soil sampler
1.54
1 .65
1 .33
NA
1.45
1 .81
1 .55
1.61 1.68
1.44 1.12
1 .26
NA NA
1.35 1.55
1.55 0.86
1.52
1 .93 NA
1 .96
2.00
1.84 2.31
1.45 3.36
1.75 NA
0.86
3.70
NA
NA
NA
*NA = Not applied.
92
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FIGURE 3.34. Filter disc, jig, and "hold down" wire.
Because the preferred application time is in the early part of the
day, field work to position filter disc should be initiated at an early
hour. This process often requires considerable effort and should be care-
fully planned so that adequate manpower is available on the site, if there
are concerns for wind moving the filter discs prior to or during application,
simple "hold down" wires can be made using small diameter wire (Figure
3.34). During application, the sprayer passes over the discs, then each
disc is immediately removed (to reduce losses by volatilization) and sealed
in a glass jar. One-pint Mason jars with lids lined with aluminum foil are
convenient for this purpose. Jars should be labeled as to exact location
in the field. Samples may be kept in ice coolers during transport to
analytical laboratory. At the laboratory, samples should be kept frozen
until analysis. Jtoalytical results for each disc are usually reported as
mg/disc. The following is an example calculation using an 18.5 cm filter
disc.
1. Determine surface area (irr^) of the disc.
Example: 18.5 cm (0.607 ft) diameter disc
93
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Radius (r) = 9.25 cm = 0.304 ft
Disc area = (3,14) (9,25)2 = 269 cm2/disc
or = (3.14) (0.304)2 = Q.290 ft2/disc
2. Determine application rate (Ib/acre)
(mean) mg pesticide/disc 1 Ib 1 g 43,560 ft2
0.290 ft2/disc 454 g 1000 mg acre
Ib pesticide/acre (3.10)
or mg pesticide/disc X 0.331 = Ib pesticide acre"1
Similarily, the application rate in kg ha~1 would be
mg pesticide/disc 1 kg 1O4 cm2 1O4 m2
= kg pesticide ha~1 (3.11)
269 cm2/disc 103 mg m2 ha
or mg pesticide/disc X 372 = kg pesticide ha~l
3.10.2 Field Timing Method
This is a simple and easy method for monitoring application rates and
is useful in conjunction with other methods as a check. The total time the
sprayer is in operation on the field site is recorded (time required for
tractor to turn at end of rows is not included). Determination of the total
application by this method requires the collection of a nozzle sample,
Both the concentration of pesticide in the sample and the nozzle delivery
rate should be determined. Nozzle delivery rate can be determined as des-
cribed in Section 3.9.1.2.
3.10.3 Soil Sampling Methods
Various sampling techniques have been investigated for obtaining soil
samples immediately following pesticide application, i.e., split-tube core
sampler, and surface soil sampler. Smith et al. (1978) identified several
problems in verifying application rates using a split-tube samplers in
loose soils. These included: (1 ) core compression during sampling, (2)
lack of depth zone definition, (3) inter-depth zone contamination, and (4)
bulk density pertubations.
The surface soil sampler shown in Figure 3.35 produced reliable data
for sampling loose soil. These data agree well with measurement via filter
paper (Table 3.9) and have been used successfully in several field runoff
94
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FIGURE 3.35. Surface soil sampler and 1.9 cm split-tube sampler.
studies. Design of the sampler with transfer funnel and support stand is
shown in Appendix E. The sampler is also suitable for use in post applica-
tion monitoring. Figure 3.36 illustrates typical results of surface area
sampling over a 7-day interval between application day and the first
runoff event showing a sharp break in the disappearance rate for atrazine.
Precision of this quality could not have been obtained with other samplers.
When using the sampler, it is pressed into the soil to desired depth
(2.5 to 5.0 cm for monitoring surface-applied pesticides). Maximum depth
of sampling is 10 cm. Soil near the sampler is pulled away to allow the
insertion of the cutting blade through its slot. The soil in the sampler
is transferred to a 1-gallon, wide-mouth bottle using a transfer funnel
(see Appendix E), then weighed, and blended using a twin-shelled blender,
A subsample is taken for pesticide residue analysis and soil water determi-
nation. The amount of pesticide residue remaining in the soil can be
expressed on a unit area or, optionally, on a unit volume basis depending
on the particular application. Either way, the ambiguity of using an
unstable bulk density term in mass calculations is eliminated through use
of the surface sampler.
If a surface soil sampler is not available, shallow aluminum cans can
be inserted into the soil to obtain full-can core samples. Aluminum cans
95
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Q
m
IS1
1.4-1
1.3-
1.0-
0.8-
n
fce 0.6-
0.4-
&2 0.2-
"3J
0.0-
First runoff event
"—e
T"
4
T
8
16
20
84
28
32
TIME, days after planting
FIGURE 3.36.
Atrazine persistence in top 2.5 cm of soil for a
watershed in a Cecil soil near Watkinsville, GA.
approximately three and one-half inches in diameter and two inches deep are
convenient for this purpose, fhe sample volume is known from the dimensions
of the can. Wet weight is determined directly and total dry weight is
determined from separate soil moisture content analysis.
3.10.4 Plant-Soil Application Distribution Measurements
In the case of foliar applied pesticides, the application distribution
between the plants and soils is an important measurement. The amount
intercepted by plant foliage reduces the amount that reaches the soil upon
application and will have an impact on runoff losses. Smith and Carsel
(1984), have demonstrated this effect in simulation studies of several
insecticides applied to cotton.
96
-------
Application distributions can be determined using filter discs to
monitor the fraction received by the soil (see Section 3.10.1) and plant
tissue samples to monitor the fraction intercepted by the foliage. For row
crops, whole-plant tissue sampling is preferred. Individual plants are
wrapped in aluminum foil, labeled, sealed with tape, placed in ice coolers
for transport to the analytical laboratory, then frozen and stored until
analysis.
When the experimental site is some type of orchard, there are a few
additional facets of the design that merit consideration. Sampling that is
done to determine the amount of compound reaching the soil surface during
application should account for the various areas that might receive
systematically varying amounts of the compound. For example, ground area
directly beneath the canopy, area between trees, and areas where the spray
rig travels are likely to intercept different average amounts of the compound
and have differing variabilities. Relative areal measurements should be
obtained and a sample design such as proportional allocation could be used
so that a properly weighted estimate of the orchard total can be obtained.
Sampling to determine amounts of the compound that adhere to the trees
will usually involve sampling a number of leaves from a number of randomly
selected trees. The leaf sampling should account for possible variation
from top to bottom of the tree and from outside to inside. Generally, the
objective is to obtain an unbiased estimate of the average amount per leaf
or unit area of leaf. Sample sizes can be chosen to provide any needed
precision.
3.11 SOIL SAMPLING AFTER RUNOFF EVENTS
Soil samples are collected and analyzed for estimating pesticide
residues at various times and depths in the soil. These results are used
for determining degradation rates and leaching, runoff and erosion losses.
Most models require as input first-order degradation rate constants based
on average pesticide- residues intergrat'ed over the entire soil profile.
A single sampling technique is not available that is suitable for all
sampling needs. Table 3.8 provides a summary of preferred sampling techni-
ques for various purposes.
The period between application and the first runoff event is a critical
time for sampling. Degradation during this period can be extremely rapid.
Figure 3.36 shows a rapid decline in atrazine concentration during a 7-day
rainless period following application. The surface soil sampler or aluminum
cans are most suitable because they take volume samples. This eliminates the
need for estimating bulk density to facilitate mass balance computations.
Bulk density in freshly tilled soil is highly variable and difficult to
measure.
97
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The 1,9-cm, split-tube core sampler is a conventional method of obtaining
soil samples. As indicated earlier, this method is not suitable for monitoring
application rates in loose soil, but is reliable for background sampling
and post-rainfall events once compaction or soil settling has taken place.
To obtain sufficient quantity of soil for analysis, multiple core samples
are required. These should be randomly selected samples from a given area
and composited. Various sampling depths have been used in previous studies.
Typical soil sampling depth increments are 0-1 1-7.5, 7.5-15, and 15-30 cm,
for characterization of pesticide residue content with depth. For some
chemicals, it may be advantageous to concentrate sampling efforts in the
upper surface zone. In hot, dry conditions, some surface applied chemicals
degrade very rapidly in the upper crust at depths of less than 1 cm. It is
important for soil samples to be taken after each runoff event for all
depths and at each sampling segment within the field to evaluate residue
remaining and vertical movement and to obtain mass balance calculations.
Fields typically reach optimum moisture content for sampling within 1 or 2
days after rainfall events. Sampling can be extremely difficult, when soil
moisture content is inappropriate. Excess contamination usually results
when sampling extremely dry soils. Very high moisture contents usually
prevent field entry.
Split-tube sampling is accomplished by pushing the sampler into the
soil to the desired depth and gently pulling the sampler upward out of the
hole. A spatula can be used to trim away the exterior portion of the soil
core to minimize potential contamination from the dry surface soil. The
resulting core can be divided into the desired depth increments.
Other methods of soil sampling include the use of hand augers. A
variety of sizes is available, as shown in Figure 3.37. whatever size is
selected for use, it should be used consistently because the auger size can
have an effect on the results. These samplers are of sufficient, size that
a single soil core sample for a selected site provides an adequate volume
of sample for analysis. Care must be taken to eliminate potential conta-
mination. The central part of the auger core is least likely to be conta-
minated. The extreme upper and lower part of the core should be discarded
and the central part used for the sample. Sampling depth increments of
less than 6 inches (15 cm) is not practical.
It is helpful to construct a sample tray for carrying sampling cans
that organizes samples by depth as shown in Figure 3.38. The soil samples
can be stored in 3-inch diameter aluminum cans. Plastic containers should
not be used because of potential sorption problems. Sample labels should
include information on sample ID number, date, depth, and any appropriate
field notes. It is also prudent to establish sampling record sheets similar
to the one shown in Figure 3.39. The sampling cans can be sealed with
plastic tape to preserve moisture content and secure the lid. The samples
can be placed in ice coolers for transport to the laboratory-
98
-------
FIGURE 3.37. Various size hand augers.
FIGURE 3.38. Carrier tray with aluminum soil sample cans.
99
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Date
Sample
Obtained
Days
After
Planting
Sample
Number
Sampling
Depth
(CM)
Site
Number
Samples
Taken by
( Name )
and
Sampling
Remarks
Results of Chemical
Analyses
Pesticide A
(ppb)
Pesticide B
(ppb)
FIGURE 3.39. Example of soil sampling record keeping.
100
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3.12 RUNOFF SAMPLING
Runoff sampling equipment and techniques were discussed in Section
3.3.3. It is important that runoff samples are removed from the field soon
after runoff events so that sampling equipment is ready for new events.
Samples also should be analyzed for pesticide residues as soon as possible
to minimize errors due to degradation losses. Each sample should be labeled,
consistent with record keeping sheets similar to the example shown in Figure
3.40. Runoff samples are collected for each event after application until
the parent pesticide decreases in concentration to a level below the detec-
tion limit.
If information on particle size distribution is not desired, calcium
chloride can be pre-added to sample containers before they are placed in
the collection devices. This will promote flocculation of sediments and
aid in making phase separation.
Runoff samples can be transported to the laboratory in ice coolers and
stored in a refrigerator at 4°C until processed. If both chemical (pesticide
residue) and physical (particle size distribution) analyses are desired,
runoff samples should be subdivided. Fleming and Leonard (1973) designed
a sample splitter to subdivide large-volume samples containing sediment.
Separate pesticide analysis should be performed in the sediment and water
phases and sediment weights should be determined. The relative effort
exerted in analyzing either the sediment or solution phases should reflect
the main transport mode of the pesticide, strongly sorbed pesticides tend
to be transported in the sediment phase and weakly sorbed pesticides move
primarily in the solution phase.
3.12.1 Particle Size Analysis and Enrichment Ratio
Methods for conducting particle size distribution analysis were discussed
in Section 3.5.3. Automated methods also are available. Johnson and Baker
(1982) used a Sedigraph 5000® particle size analyzer to determine particle
size distribution in runoff samples from an Iowa watershed. Information on
particle size distribution in runoff is important because fines (silt, clay)
normally have enriched pesticide contents. This enrichment effect results
from the increased surface areas, cation exchange capacities, organic carbon
contents of the fine fractions. This is compounded by the fact that fines
also are enriched in runoff relative to the parent soil. Enrichment ratios
are discussed in Section 3.12.4.
3.12.2 Sediment Organic Carbon
Organic carbon content of runoff sediments is important because of the
influence organic carbon has in determining overall pesticide sorption
(Karickhoff, 1983). The carbon content of eroded sediments can vary with
time during runoff events. Carbon contents can be determined as outlined
in Section 3.5.4.
101
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Date (MMDDYY) [
Watershed
Flume Size
Stage Stage Sample Sample
Time Height, Number Time
HHMM M HHMM
Sediment
Concen-
tration,
Rain
Time
HHMM
Raxn
Level,
cm
FIGURE 3.40. Example of runoff sampling record keeping.
102
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3.12.3 Sediment Cation Exchange Capacity
The cation exchange capacity (CEC) is an expression of the number of
cation adsorption sites per unit weight of soil or sediment, expressed in
milliequivalents per 100 grains of oven-dry soil. CEC influences sediment
sorption capacity; particularly for cationic or polar compounds. Methods
for determining CEC in both arid land and acid soil is presented by
Rhoades (1982).
3.12.4 Enrichment Ratio
Enrichment ratios of several types are used in some of the current
erosion and pesticide runoff models. These ratios originate from the fact
that eroded sediments tend to have higher percentages of fines (silt and
clay) than the soil from which they came. Because the smaller size frac-
tions usually have higher specific surface areas, cation exchange capacities
and organic carbon contents, they also have increased sorption capacities
and tend to contain higher concentrations of pesticide. For highly sorbed
pesticides (which are transported primarily in the sediment phase), these
natural enrichment processes can have a dramatic influence on pesticide
transport. For studies involving compounds that partition strongly toward
the sediment phase (high Kp), the enrichment process should be evaluated.
Runoff losses of weakly sorbed compounds (Kp generally less than 5) are not
sensitive to enrichment ratios because the primary mode of transport is
through the solution phase. These criteria should be considered in deter-
mining whether enrichment ratio measurements should be conducted in a given
s tudy.
Examples of enrichment ratios used in some models include:
specific surface area of
Specific surface area eroded sediments
enrichment ratio =
specific surface area of
whole soil
This ratio is calculated by accounting for surface area contributions from
sand, silt, clay and organic carbon fractions and is used in the erosion
component of the CREAMS model (Foster et al., 1980).
An organic matter enrichment ratio (Tom) is used in the PRZM model
(Carsel et al., 1984) and is defined by
ln(Tom) =2 + 0.2 In (i|;e/Aw)
where \j;e refers to soil loss as calculated from the Modified Universal
Soil Loss Equation (MUSLE) used by Williams and Berndt (1977) and Aw is the
area of the watershed.
103
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3*12.5 Equipment Maintenance and Monitoring
The importance of having field equipment checked and serviced routinely
to assure proper operation can not be over-emphasized. It is advisable (if
not necessary) to have an operator on site during major events (runoff events
that occur soon after application) to see that automatic samplers are not
allowed to malfunction. This is especially important in new systems which
have not been fully debugged. New installations are often untrustworthy.
Samplers frequently become clogged with crop residues and other trash.
Usually, only minimal on-site effort is required to prevent major data
losses. On-site personnel visits are essential.
The on-site operator also should occasionally collect manual runoff
samples for comparison as an added check on the automatic equipment.
Johnson and Baker (1982) added sampling slots and Teflon transport tubes to
the inside flume wall for this purpose.
Rainfall events often occur at night or on weekends and the difficulty
(and cost) of maintaining around-the-clock surveillance is recognized.
Local weather forecasts should be utilized to assure that field personnel
are present during key periods, while minimizing the overall time commit-
ment,
It is important that one experienced person be assigned overall
responsibility of field operations including maintenance and operation of
all field equipment, sample collection, and data compilation.
3.13 CRNOPY DEVELOPMENT
Crop canopy development as related to the percent soil cover has
an impact on detachment of soil fines and erosion during rainfall events.
Estimates of canopy development can be obtained by taking photographs from
a step ladder looking down onto the crop canopy as discussed by Johnson and
Baker (1982). The resulting photograph can be placed over a grid to determine
the percentage of soil area covered by crop canopy (Figure 3.41). Examples
of canopy developments for corn and soybeans are provided in Table 3.10.
Measurements of the percent of soil area covered by crop canopy are needed
at regular intervals (weekly) throughout the crop growing season. One
additional post-harvest measurement of residue coverage is also advisable.
3.13.1 Leaf Area Index
Leaf area index measurements taken at various times throughout the crop
growing season are useful in estimating fraction of ground cover, potential
evapotranspiration, mass of pesticide on foliage at application,, washoff
and uptake per unit leaf area. Carsel et al. (1984) used leaf area index
measurements to estimate ground coverage as follows.
104
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FIGURE 3.41. Grid for crop residue determination.
COVMAX = (2.0 - ERFC (1.33 LAI £fjn - 2,))/2.1
where COVMAX = fraction of ground covered by the plant
LAI = leaf area index of crop, m, on day, &
ERFC = complimentary error function
Measurements of leaf area index should be taken at weekly intervals
during the crop growing season until harvest. A representative number of
plants (i.e., 20 to 30) should be utilized to obtain a representative field
average value. Both portable and laboratory area meters are available.
The portable area meters provide a means of monitoring of crop canopy
development without leaf destruction. If leaves are collected from the
field, measurements must be made before they shrink or curl.
105
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TABLE 3.10. CROP LEAP CANOPY DEVELOPMENT
(Johnson and Baker 1982)
Date
in
1978
5/23
5/31
6/7
6/14
6/21
6/29
7/7
7/13
7/24
8/1
8/14
9/19
9/21
'10/3
10/31
Pencentage of Soil Surface Area
Soybeans
0
3
6
12
19
35
49
63
86
94
85
97
--*
93
—
Covered by Crop Leaf Canopy
Corn :
1
4
14
23
37
67
88
91
95
98
—
—
85
:
95*
*After harvest.
106
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3.13.2 Foliar Washoff
Once a pesticide is applied and intercepted by plant foliage, it may
undergo several processes, including sorption, volatilization, degradation,
and washoff. After washoff occurs, the residue will either accumulate in
the soil or contribute to runoff.
A pesticide foliar washoff model was recently developed and evaluated
by Smith and Carsel (1984). This model provides an empirical simulation of
pesticide washoff from plant leaf surfaces as influenced by rainfall amount.
Daily rainfall amounts (cm day""') are required, along with a lumped first
order degradation rate constant (kf) for foliar transformation processes.
Foliar washoff can be monitored using collection vessels placed under
the canopy to intercept washoff and dislodgeable residue (Smith et al.,
1981; McDowell et al., 1984). Measurement sensitivity can be increased
substantially by using large area funnels to feed the collection vessels.
The number of collection vessels and their locations must be determined to
obtain the required random sampling and precision for estimating the amount
of washoff. Experimental design should follow rules similar to those
discussed in Section 3.7.4. Optimum sizes of collection vessel openings
are difficult to specify. Generally, larger vessels yield more represen-
tative samples. Each vessel location should be identified (i.e., wooden
stakes) to eliminate possible data discrepancies that might occur if vessels
were inadventently relocated after servicing (Smith et al. , 1981).
In an orchard, the collection vessels should be located within the tree
drip lines. Locations within tree lines should be chosen randomly from
among a number of locations that are predetermined on the basis of some
sort of grid pattern. The general idea is to ensure random sampling of the
under-canopy locations, in some cases, stem flow may be important and will
require monitoring. Typically, a tight fitting annular ring around the
trunk is used to collect flow, with a tube outlet for sample collection.
The trees actually used can be selected randomly from all that are available.
Usually it will be best to utilize as many trees as possible rather than
choosing a few trees and sampling more heavily under each. The objective
generally will be to obtain a precise estimate of the mean amount per
container per event.
Samples should be collected immediately following a rainfall/washoff
event to reduce potential attentuation losses (i.e., volatilization, etc.).
Once washoff occurs, the solution volume in collection vessels should be
measured and recorded. Samples should be packed in ice coolers for trans-
port to the analytical laboratory for analysis. To determine the amount of
washoff from the whole field, the area of the field must be considered with
regard to the area of the collecting vessels.
Random plant samples (whole plants preferred) or leaf samples (for
trees) are required to establish mass balance and degradation rates.
Foliage should be sampled before and after each pesticide application and
again after each rainfall event. Overall losses reflected in these
measurements will include degradation losses.
107
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Foliar degradation rates (losses) must be evaluated through an addi-
tional sampling time series performed entirely within a rainless period.
This series should include a minimum of three consecutive sampling dates and
should preferably traverse at least one half-life.
3.13.3 Canopy Temperature
Canopy temperature affects degradation rates and volatilization losses
of foliar applied pesticides. Measurements should be made at short time
intervals until a diurnal cycle is established. Measurement schedules can
then be optimized in accordance with model data needs. Several devices are
commercially available for measuring surface temperature, Omega Engineering
(1983).
3.13.4 Plant Uptake of Pesticides
The uptake of pesticides by plants is influenced by the mass and
volume of the crop root system, physical and chemical properties of the
soil matrix, plant metabolism and the pesticide. Uptake can be visualized
as occurring in two steps: (1) absorption by the root system and (2)
translocation into the above ground plant parts. Some pesticides do not
absorb well and/or translocate significantly, in these cases, the pesticide
effectively remains in the soil zone and is potentially available for runoff.
The influence of uptake processes on pesticide runoff is variable. The
effort expended in evaluating uptake should be in proportion to the expected
impact on runoff losses. Recycling of pesticide through washoff and release
from crop residues should be considered in this decision. Carsel et al.
(1984) report <1 .0 to 20% of applied pesticide can be removed by plant
uptake.
If uptake is deemed important, a sufficient number (see Section 3) of
random whole plant tissue samples should be collected to establish mass
balance. Sampling times should correspond to soil sampling dates. Addi-
tional measurements may be required if it is desired that the uptake process
itself be studied and evaluated.
3.13.5 Crop Yield
The average yield of the crop at harvest should be determined and
reported. Yield measurements are determined by obtaining the total weight
of the crop produced (e.g., kg, Mj. etc.) and dividing by the harvested
acreage. These determinations would typically be expressed as kg ha~1 and
M£ ha . The average yield of some selected crops were presented in Table
3.9. Commercial harvesting equipment is usually required for yield determi-
nations on areas of several acres or more. Conventional harvesting machinery
such as grain combines, cotton pickers, forage harvesters or other equipment
should be used as appropriate. Average grain moisture content should be
determined for converting total crop weight to a standard basis.
108
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3.13.6 Pesticide Remaining in "the Harvested Crop and Post Harvest Crop
Residues
Crop residues left on the soil after harvest provide a protective
layer that reduces erosion and soil runoff losses during the off season,
A measure of the percent of surface coverage by post-harvest residue is
required (see Figure 3.41). This can be determined photographically as
noted in Section 3.13. These crop residues may contain pesticide residue
which could serve as a source term at harvest time. To close the mass
balance at the end of the cropping season, residue samples should be
analyzed for pesticide content in both the harvested crop and post harvest
crop residue.
3.14 VOLATILIZATION PROM SOIL AND PLANTS
Volatilization may be an important mechanism for removal of some
pesticides applied to either soil or plant foliage. Pesticide loss due to
volatilization will effect both mass balance and degradation rates.
If volatilization is deemed important, measurements should be obtained
to establish mass balance. Measurements should be made frequently after
application. The number of measurements will largely be determined by both
pesticide properties and the occurrence of rainfall events after application.
Examples of measuring volatilization losses were provided by Harper et al.,
1976 and White et al., 1977 for soil; and Harper et al., 1983 and Willis
et al., 1983 for plants.
Other studies have been conducted comparing herbicide volatilization
losses under conservation and conventional tillage practices, Harper and
Bush (1985).
3.15 MASS OP PESTICIDE IN PRECIPITATION
If atmospheric fallout of pesticides due to volatilization and drift
is of concern, then the observer should consider obtaining samples of
precipitation. Instrumentation is available for collecting a composite
sample of precipitation for analysis. An example of a rainfall sampler was
shown in Figure 3.18. Fallout is usually not a major concern in runoff
studies.
3.16 TRANSPORTING SAMPLES
Soil samples taken from the field in ice coolers can be frozen and
stored for analytical processing. If the samples are to be shipped to a
analytical laboratory, it is important to freeze the samples to minimize
109
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degradation. For shipment, the samples should be packed in dry ice (if
possible) and sent by air express to minimize delivery time. .Arrangements
for sample pick up at the airport should be made with the designated labora-
tory.
A similar procedure is required for shipping runoff samples* if the
samplers can be received at the analytical laboratory within 1 -day travel
time, refrigeration for a short time prior to shipment is adequate; the
samples are then packed in ice for transport.
3.17 FIELD OPERATIONS RECORD KEEPING
Daily record keeping of field notes for various activities} is an
important task for the field operations manager. These records provide key
data when conducting runoff model application studies. An example of field
record keeping is shown in Figure 3.42.
3.18 ANALYTICAL METHODOLOGY
Specific pesticide analysis methods with sensitivities in the low parts
per billion range are usually required for runoff testing. Modifications
for extracting various media (filter discs, soil, runoff water and
sediments and plant tissues) also should be developed. "Production line"
analysis is necessary to provide a large sample throughput in a minimum
amount of time. Analysis for pesticide residue is commonly done by gas
chromatography or high pressure liquid chromatography. various; quality
control activities should be planned in the analysis schedule as discussed
in Section 4»
To minimize the effects of systematic bias attributable to laboratory
procedural techniques and to ensure objectivity in the associated measure-
ments, the samples generally should be analyzed in random order. Unless
randomization is employed, any trends that arise cannot confidently be
attributed to actual field phenomena or laboratory effects.
Detailed discussions of pesticide analysis methods are beyond the
scope of this manual because they will vary with the pesticide. Analytical
methods on some pesticides, however, have been developed by EPA's Environ-
mental monitoring and Support Laboratory, Cincinnati, OH. Other excellent
sources of references include U.S. EPA (1980), U.S. EPA (1983) and the
Pood and Drug Administration Pesticide Analytical Manuals, Vol. I (1985)
and II (1984).
Soil core samples are analyzed for pesticide residue on a dry weight
basis. Soil moisture content will vary from sample to sample and with
sampling date and depth. Separate soil moisture content determinations
are required for converting analytical data to a dry weight basis. A
110
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Date Description
1984
Oct. 9 Location of field site and agreement established with land
owner.
Oct. 20 Soil series identified by USDA-SCS personnel.
Oct. 25 Selected sampling sites identified.
Nov. 7 Installation of flume.
Nov. 15 Lime applied to site (2240 kg ha~1).
Dec. 20 Installation of weather station completed, including two
raingages, evaporation pan, instrument shelter (housing
maximum-minimum therometers), relative humidity, solar radiation,
and wind sensors.
Dec. 23 Fence constructed around weather station.
1985
Jan. 1 Installation of water stage recorders and automatic sampler.
Mar. 1 Soil samples taken from each designated sampling area for
background residue analysis at depths of 0-1, 1-7.5, 7.5-
15, 15-30 cm.
Apr. 15 Fertilizer broadcast applied at site (rate of N (38), P (33),
K (127), in kg ha~1) and disk incorporated.
Apr. 29 Field site disk harrowed in preparation for planting.
- Crop planted - seed information and planting rate (kg ha~^)
- Herbicide applied—target rate (3.36 kg ha~1 active ingre-
dient)
- Method of application and equipment—surface or
incorporated
- Application monitoring technique (i.e., filter
disc size, location and number)
May 15 Soil samples taken after runoff event from each sampling
segment at depths of 0-1, 1-7.5, 7.5-15, 15-30 cm.
Jun. 15 Soil samples taken (same as May 15).
Aug. 1 Full crop canopy established.
Sept. 1 Crop harvested.
Crop grain yield data (5400 kg ha~1); Stover (6800 kg ha~1).
Sept. 15 Post-harvest treatment (i.e., stalks mowed and/or winter cover
crop planted).
FIGURE 3.42. Typical record keeping of field operations.
111
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subsample for soil water determination should be taken at the time a
subsample is taken for pesticide residue analysis. This eliminates the
need to dry the sample to be used for pesticide analysis. The drying
process may cause pesticide loss or degradation.
Water content is expressed as a percent of the oven dry weight of soil:
Wet soil weight - oven dry
soil weight
Percent moisture = • ••• ••<• • x 100
by weight Oven dry soil weight
3.19 RUNOFF AND-SOIL CORE DATA COMPUTATION
Runoff samples are collected during small but finite time intervals
during individual runoff events, lach sample represents the average behavior
within a particular time segment. Several computational steps are required
in computing overall pesticide losses and evaluating dynamic aspects of the
event. The mass of data involved usually dictates the use of some form of
automated data processing equipment and software. A general discussion
concerning development of a computerized data base is presented in Section
5. Figures 3.43 through 3.48 give example data tabulations and calculations.
These include:
• Figure 3.43 - raw field and pesticide analytical data (see Figure
3.45 for column heading key)
• Figure 3.44 - runoff event summary (see Figure 3.45 for column
heading key)
* Figure 3.45 - column heading key for runoff data computations
• Figure 3.46 - annual pesticide runoff summary
* Figure 3.47 - soil core data computations
* Figure 3.48 - pesticide residue summary
112
-------
H
H
OJ
Input Data (Colum)
Runoff, 061373 PI
2.5 1973
Pesticide, 061373
PI
A
1802
1829
1844
1849
1909
1910
1920
1937
0
-1
C
P11A
P12A
P13A
P14A
PISA
P16A
P17A
P18A
P19A
_1
B
0.00
2.14
0.39
1.19
0.04
0.03
0.02
0.00
0.00
0.00
D
1810
1812
1814
1816
1818
1828
1832
1838
1937
C
P11A
P12A
P13A
P14A
P15A
P16A
P17A
P18A
P19A
H
100.0
70.0
30.0
35.0
35.0
30.0
25.0
30.0
30.0
0.0
D
1810
1812
1814
1816
1818
1828
1832
1838
1937
I
24.0
11.0
28.0
18.0
19.0
16.0
11 ;o
12.0
8.0
0.0
E F
G
47.19 1758 0.00
71.57 1805 0.62
58.99 1820 0.65
51.86 1825 0.75
44.34 0 0.00
47.51 0 0.00
59.26 0 0.00
38.68 0 0.00
21.65 0 0.00
0.00 0 0.00
J
0.489E+05
0.394E+OS
0.418E+05
0.415E+Q5
Q.384E+05
0.371E+05
0.308E+05
0.413E+05
0.426E+05
0.0
K
-99.0
-99.0
-99.0
-99.0
0.0
0.0
0.0
0.0
-99.0
0.0
L
0.150E+04
0.110E+04
900.0
900.0
200.0
700.0
600.0
600.0
400.0
0.0
M
600.0
600.0
400.0
600.0
0.340E+04
0.290E+04
0.140E+04
0.100E+04
100.0
0.0
FIGURE 3.43.
Example of raw field and pesticide analytical data from a runoff event
(Smith et al., 1978).
-------
NOPQRCSE TUV H
X
06/13/73 WATERSHED P-01 FLUME SIZE 2.5 FEET
USE ELAP STAG FLOH VOLUME SAMP SAMP §15. T.SED RAIN GA6E TRIFLURLN TRIFLURl
EOT TIME CM L7M LITERS NO TIME GM/L KG. TIME CM. SEO H20
If Z M
PESTICIDES (MG)
.N OJPHCNAMO DIPHENAMO PARAQUAT
SEO H20 SEO
66
PARAgUAT
1758 0 0.0
?02 0 0.0 0. 0. 4i
OS 3 7.2 230. 260. 7
§10 a 19.3 1557. 420*. P11A 8 *7.2 198.4 12
12 10 24*2 2482? 8219. P12A 10 71.6 287.4 I*
1814 12 29.0 3680. 1*3*8. P13A 12 59.0 361.6 16
1816 « 33.8 5143. 23130. P14A 14 51.9 455.4 18
1818 6 38.7 6896. 35126. P15A 16 44.3 531.9 20
a|| 8 43.5 8974. 50962. 22
Sit 26 lilt 20986! }&653*; P16A 26 47.5 6243.2 *
829 27 65.2 22860. 188457.
ill 99 §4.§ 15062. 244964. P17A 30 59.3 46*8,9
838 36 l3?2 4946* 301726. PISA §6 381? 2194.8
8*4 42 11.9 SB7. 315932.
1849 47 36.3 5989. 32976}.
909 6T 1*2 \ 0 * 369369 *
910 oO 5*9 6» 3693 rS*
920 78 0.6 3. 369422.
.9
.6
.6 19.84
.6 20.12
.6 10.85
.6 15.9*
.6 18.62
.7
** 187.30
ltt:B
1937 95 6.6 6. 369450. P19A 95 21.6 1466.2 43.99
cc
DO
EE
ff
66
HH
II
TOTAL. KG (NOTE 1) 369449.9 LITERS 16387.8 KG 498.71
MEAN. PP8 (NOTE 2) 44.4 GM/L
LN MEAN 3.8
TOTAL. MG (PRED. NOTE 3} 369450.0 LITERS 16387.8 KG
MEAN. PPB (PRED. NOTE 3) 44.4 GM/L
RAH DATA MAXIMUM (NOTE 2> 71.6 GM/L
RAH DATA MINIMUM (NOTE 21 21.6 6M/L
NOTE 1. ALL VALUES ARE IN MG UNLESS OTHERWISE NOTED.
NOTE 2. ALL VALUES ARE IN PPB UNLESS OTHERWISE NOTED.
NOTE 3. PREDICTED RESULTS BASED ON MISSING DATA
30.43
3.42
498.71
30.43
100.00
25.00
100.89
44.17
171.63
158.06
227.92
2102.54
862.94
680.91
541.79
4890.85
13.24
2.58
4890.85
13.24
28.00
8.00
297.56 2522.20 9700.36
316.10 2409.07 11322.06
325.43 2451.88 151 14.51
409.85 5268.64 18898. 55
106.38 40786.27 20425^01
4370.26 381085.93 231624.12
2789.35 109829.18 1*3186.50
1316.88 56742.68 90645.50
'586.48 6772.34 62460.63
10518.31 607868.07 603377.22
641.84 1645.33 36818.76
6.46 7.41 10.51
10518.31 607868.07 603376.98
641.84 1645.33 36818.76
1500.00 3400.00 48900.02
200.00 100.00 30800.02
0*00
0.00
0,00
0.00
0.00
0.00
0.00
o.oa
0.00
0.00
o.oe
FIGURE 3.44.
Example of a runoff event summary (Smith et al.,, 1978).
-------
Column Description
A. Time series of runoff (derived from breakpoint on water stace
recorder). . &
B Stage height (ft), corresponds to A's times.
C Sample numbers.
D Time sample was taken (obtained by mark on water stage recorder).
E Amount of sediment in sample, g/1.
F Time series of rainfall (breakpoint on rain chart).
G Accumulated rainfall (in.) per time (F).
H Concentration of trifluralin in sediment
I Concentration of trifluralin in water
J Concentration of paraquat in sediment
K Concentration of paraquat in water
L Concentration of diphenamid in sediment
M Concentration of diphenamid in water
N Time (EOT). Chronological time which signifies when rainfall began
and any change resulting in a break in the event such as an increase
in rainfall, sample being taken or an increase or decrease in the
runoff stage height.
0 Elapsed time of runoff or stage height change.
P Stage height in flume (cm).
Q Flow rate through the flume. This value is determined by taking
stage height in centimeters (column P) and converting it to feet. By
using the rating tables for the type flumes found in Agricultural
Handbook Number 224, one can determine the discharge in ftvsec.
FIGURE 3.45. Column heading key for runoff data computations
(Smith et al., 1978).
115
-------
Column
Description
"Example: stage height conversion factor £t3/sec x 60 sec/min x
28.32 liter/ft3 = flow (liter/min)
R Cumulative volume (liters). Volume is
integration using the trapezoidal rule.
determined by approximate
In the runoff event, each breakpoint on the water stage recorder
represents a value for stage height and a corresponding value for
time. These corresponding values enables one to compute flow from
the flow versus stage height table. This, in turn, provides a plot
of flow (liters/min) versus time (min). The flow versus time curve
is then.integrated by use of the trapezoidal rule to compute the
approximate value for the area under the curve. The area under the
curve is equal to the volume during the event.
The trapezoidal rule takes each corresponding flow value and time
value and computes the area by the following equation:
AREA
?s (flow A + flow B) (Time B - Time A)
flow
r i
mm
LB
T T
1 *
* D Time, min
This value is the volume that has passed through the flume for the
tine period (Tg - TA) . This value, in turn, is added to the volume
already accumulated. Each time increment is calculated and added to
the accumulated volume until the event has ended.
Example:
Time, min
FIGURE 3.45 (Cont'd)
Column heading key for runoff data computations
(Smith et al., 1978).
116
-------
Column Description
Volume I - %(f(1) + £(0)) CT(1) - T(Q))
Volume II - Volume I + Jj(f(2) + f(i)) CT(2) ' T(l)^
Volume III - Volume II + %(f(3) * £(2)) (T(3) - T(2))
S Elapsed time after runoff began and when sample was taken,
T Total sediment (kg) for the successive flow in column R (volume for
corresponding sample - volume for previous sample} x column E x 0.001
kg/g = T. Total sediment (kg) = 1 x g/1 x 1 kg/1000 g.
Example: (8219 - 4204 liters) x 71.6 g/1 x 0.001 kg/g - 287.4 kg.
In cases where runoff continued without sampling, the volume of
runoff is added to the last sample for computational purposes.
U Elapsed time after rain began.
V Accumulated rain gage values in on.
W Trifluralin in sediment, mg,
X Trifluralin in water, mg.
Y Diphenamid in sediment, mg.
Z Diphenamid in water, mg.
AA Paraquat in sediment, mg.
BB Paraquat in water, mg.
To calculate total mass of pesticide in the sediment, multiply total
sediment (column T) by the concentration of the input data for that
particular sample.
Total (kg) x concentration (g/kg) x 0.001 mg/1 =
Total pesticide (mg)
To calculate the total mass of pesticide in the water, multiply the
volume (column R) for that particular sample by the concentration
from input data for that sample.
Volume (liters) x concentration (g/1) x 0.001 mg/1 =
Total pesticide (mg)
FIGURE 3,45 (Cont'd), Column heading key for runoff data computations
(Smith et al., 1978).
117
-------
Column Description
CC Totals: Runoff volumes (liters), sediments (kg), pesticides (mg),
DD Mean values « (|)S) * WOO - g/1 of sediment.
Total pesticide (rag) in sedijnent 1nnn
_ Total sediment (leg) "
ppb pesticide in sediment
Total pesticide (ing) in water innft
_ "Total water (1) x 1UUU ~
ppb pesticide in water
BE In (mean values) .
FF Predicted values based on missing data. These values are calculated
from the samples taken before and after the sample following missing
data. These two values are averaged to determine an average
pesticide or nutrient concentration. This value is used for the
concentration of the missing sample. The concentration, is then
multiplied by the amount of water, in liters, for the missing sample
which will provide a value for total mass of pesticide in the water.
If the pesticide sample is a sediment sample, an average
concentration is determined and then multiplied times total sediment
(kg) for the total mass in the sediment phase.
G6 Mean values for predicted values.
ffl Maximm values from input data (all values in ppb unless noted) .
II Minimum values from input data (all values in ppb unless noted) .
FIGURE 3.45 (Cont'd). Column heading key for runoff data computations
(Smith et al., 1978).
118
-------
1 1
HUNOFFI EVENT 1 UAYS
EVENT DATE 1 *FT£H
NO. 1 PL ANT IMG
1
1
| |
1 105-23-7* 2*
I
ii 106-27-7*1 59
1 1 1
1 3 06-27-7*1 59
1 I I
1 * 107-27-7*1 89
S
08-16-7* 109
1 6 1 08-29-7*1 122
7
12-15-7*1 230
I d 112-19-7*1 23*
V
|
1 10
11
12
13
1*
|
12-29-7*1 2**
|
01-10-751 256
I
01-12-751 2S8
01-2*-751 270
02-0*-75 281
02-16-751 293
15 02-16-751 293
I I
RAIN
GAUGE 1
ICM> 1
1
!
6.88
5.33
3.30
7.65
*.**
2.5*
3.18
2.16
2.29
2.*6
3.12
1.27
0.0 •
2.62
1.52
1 16 1 02-18-75 1 295 *.*2
I j
17 l02-2*-7SI 301 2.*1
1 i 1
1 18 103-13-751 318 10.01
1 1 1
IV 103-16-751 J21
1
1.78
20 03-18-751 J23 2.82
21
03-2*-7sl 329
2.6*
1 1
TOTAL 1 72.8S
TOTAL
RAINFALL
(LIT£rtS»
968018.
750168.
46*390.
107518*.
6251*0.
357223.
'
**6529.
3036*0.
321501.
3*653*.
*39356.
178612.
(>.<•
367911.
21*33*.
62162*.
33936.:.
1*0 F51*.
250056.
396*61.
371568.
1
TOTAL TOTAL 1 RUNOFF
BUNOFF ISEDINENTI »
(LITERS* (KG) 1
1 1
I 1
1
|
26621. 1 18.3
I
2.75
89*98. 1 90.2 11.9
221**0.
366917.
6D908.
5U2*.
67*.
*•»!.
7078.
36758.
112355.
*5262.
71502.
b*29.
12967.
I7«U7»
69050.
769a7b.
12630.
95711.
41617.
I
3*5.8 1 *7.7
1
121.2 1 3*.l
*1.0
11.0
2.7 l.*l
0.7
2.6
5.2
72.1
11*. 0
*2.1
33.5
3.8
9.5
1*6.6
51.8
*3*.2
5.8
JO. 7
20.*
0.15
0.16
2.20
10.6
2S.6
25.3
INN. CONC.
PESTICIDE
IN SEP.
CHPBI
S68.2
197.3
22*. 0
0.0
0.0
l.*8 1
6.05 1
28.8 1
20.3
S>*.7
5.05
2*.l
11.2 !
1 1 1
TOTAL INN. CONC.I TOTAL 1 TOTAr
PESTICIDE (PESTICIDE PESTICIDE I AMOUNT
IN SEO. UN HATER I IN HATER 1 OF
(MS) | (PPB) I (MGt {PESTICIDE
1 1 1
,
OF
SEASON
TOTAL
LOSS
1
10.*
32*. 5
17.8 6.8
77.5
0.0
0.0
S.*
8639.S 1 86*9.9
I
609.6 i 627.*
1266.5
0.2 t 8*.* i S*.*
1
0.?
*aa I *e.i
1
i
81.0
5.88
11.9
0*79
0.*5
i
i
1
i
1
1 i
1
1 1
1 1 1
1 1 1
i
i i
t !
1 II II
Iu2*i>123.l 2239013.1 1592.21 I I 105.7 1 — i 10570.6 1 10676.31
• RAIN SAUliC STOPPED
FIGURE 3.46. Example of annual pesticide runoff summary (Smith et al., 1978).
-------
The total mass of compound in a segment at a specific depth zone is
calculated by the following:
Concentration „ Area of Segment „ Bulk Density
yg/kg x m2 x ~/~"3 x
Height of Zone
on
x 1 x 10
•5 _
g/cm3
Mass of compound in segment
per depth zone, g
After this calculation is performed, the mass of compound in each depth
zone are summed for total grams of compounds. Then, the weighted mean
concentration for each depth zone are computed.
Total Mass on Watershed/Zone, g
Total area of
Bulk Density, g/cm3 x Height of Zone, cm
Mean Concentration, yg/kg (ppb)
x 1 x 10
An example of the output data for each sampling interval are presented
below:
Sampling Date:
Days After Planting:
07/10/73
25
Depth zones,
on
0.0-1.0
1.0-2.5
2.5-5.0
5,0-7.5
7.5-15.0
15.0-22.5
22.5-30.0
Segment number
1
11.3
6.8
5.6
2.3
1.7
-
~
2
9.2
20.7
9*8
2.9
3.5
-
™
3
18.1
16.9
33.9
11.3
1.7
-
**
4
23.0
41.4
46.0
5.8
3.5
-
•
5
6.1
12.2
10.2
0.5
1.5
-
—
6
20.9
37.6
41.8
4.7
3.1
-
—
7
0.7
0.2
0.5
0.4
0.0
-
«.
8
0.2
0.1
0.1
0.0
0.0
.
*™
Totals, pg/kg,
g
89,,
136,
147,,
27,,
15,,
™
6
0
9
8
0
ppb
444.0
449.6
293.4
55.1
9.9
-
ff
FIGURE 3.47. Example of soil core data computations (Smith et al., 1978)
120
-------
SAMPLING DATE / DAYS AFTER PLANTING
DEPTH ZONES
TW
CENTIMETERS
0.0 - 1 .0
1 .0 - 2.5
2.5 - 5.0
5.0 - 7.5
7.5 - 15.0
15.0 - 22.5
22.5 - 30.0
04-19-74
-1 0
19.2
25.2
24.7
15.3
7.6
5.4
2.7
05-08-74
9
4246.7
1576.7
752.7
203.7
53.4
24.4
32.6
05-13-74
14
4640.7
1465.7
• 350.7
137.4
43.5
18.9
25.2
05-24-74
25
1805.4
1 141.9
522.4
159.1
30.6
15.5
15.3
07-29-74
91
98.3
140.4
83.4
44.8
8.6
1.9
2.6
FIGURE 3.48. Example of pesticide residue summary, /ag/kg
(Smith et al. , 1978).
3.20 REFERENCES FOR SECTION 3
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121
-------
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122
-------
Goran, W.D. 1983. an Interactive Soils Information System Users Manual.
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123
-------
Miller, C.E., L.M. Turk, and H.D. Poth. 1966. Fundamentals of Soil
Science. John Wiley and Sons, Inc., New York, NY. pp 51-52.
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Erosion from Agricultural Management Systems, W.G. Knisel (Ed.).
U.S. Department of Agriculture, Washington, DC. Conservation Research
Report No. 26, Chapter 17, pp 560-594.
Nelson, D.W. and L.E. Sommers. 1982. Total Carbon, Organic Carbon, and
Organic Matter. Methods of Soil Analysis, Part 2, Chemical and
Microbiological Properties. Second Edition. A.L. Page (Ed.)*
American Society of Agronomy, Madison, WI. No. 9 (Part 2), pp 539-577.
Omega Engineering. 1984. Temperature Measurement Handbook, Stamford, CT.
Parker, C.A., et al. 1946. Fertilizers and Lime in the United states.
U.S. Department of Agriculture, Washington, DC. Misc. Pub. No. 586.
Penman, H.L. 1956. Evaporation: An Introductory Survey. Netherlands
Journal of Agricultural Science. 4^1)s9-20,
peters, D.B. 1965. Water Availability Methods of Soil Analysis Part 1,
Physical and Mineralogical Properties, Including Statistics of
Measurement and Sampling. C.A. Black (Ed.). American Society of
Agronomy, Madison, WI. No. 9. pp 279-285.
Peterson, A.E. and G.L. Bubenzer. 1985. Intake Rate: Sprinkler Infiltra-
tion. Chap. 33. In: Methods of Soil Analysis Part 1, Physical and
Mineralogical Properties, Including statistics of Measurement and
Sampling, Second Edition. A. Klute (Ed.). American Society of
Agronomy, Madison, WI. (In preparation.)
Plumb, R.H., Jr. 1981. Procedure for Handling and •chemical Analysis of
Sediment and Water Samples. Prepared by Great Lakes Laboratory, State
University College at Buffalo, Buffalo, NY, for the U.S. Environmental
Protection Agency/Corps of Engineers Technical Committee on Criteria
for Dredged and Fill Material. Published by the U.S. Army Engineer
Waterways Experiment station, CE, Vicksburg, MS. Technical Report
EPA/CE-81-1,
Rhoades, J.D. 1982. Cation Exchange Capacity, Methods of Soil Analysis
Part 2 Chemical and Microbiological Properties. Second Edition. A.L.
Page (Ed.). American Society of Agronomy, Madison, WI. No. 9. pp
149-157.
Shoemaker, C.A. and M.O. Harris. 1979. The,Effectiveness of SWCPs in
Comparison with oother Methods for Reducing Pesticide Pollution. In:
Effectiveness of Soil and Water Conservation Practices for Pollution
Control. U.S. Environmental Protection Agencyr Athens, GA,,
EPA-600/3-79-1 06.
124
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Smith, C.N. and R.F. Carsel. 1984. Foliar Washoff of Pesticides (FWOP)
Model:- Development and Evaluation. J. Environ. Sci. Health. B19(3) ;
323-342.
Smith, C.N. and R.F. Carsel. 1985. A Stainless Steel Soil Solution Sampler
for Monitoring Pesticides in the Vadose Zone (In preparation).
Smith, C.N., R.A. Leonard, G.W. Langdale and G.W. Bailey. 1978. Transport
of Agricultural Chemicals from Small Upland Piedmont Watersheds.
U.S. Environmental Protection Agency, Athens, GA. EPA-600/3-78-056.
Smith, C.N., R.L. Estes, T.W. Culbertson, D.M. Cline, and G.W. Bailey. 1975.
Design and Use of Small Runoff Plots and a Continuous Environmental
Monitoring System to Aid in Modeling Pesticide Attenuation Processes.
U.S. Environmental Protection Agency, Athens, GA. (Unpublished report.)
Smith, C.N., W.R. Payne, Jr., L.A. Mulkey, J.E. Benner, R.S. Parrish, and
M.C. Smith. 1981. The Persistence and Disappearance by Washoff and
Dryfall of Methoxychlor from Soybean Foliage—A Preliminary Study.
J. Environ. Sci. Health. B16(6) -.777-794.
Taylor, S.A., R.D. Jackson. 1965. Temperature Methods of Soil Analysis,
Part 1, Physical and Mineralogical Properties, including Statistics of
Measurement and Sampling. C.A. Black (Ed.). American Society of
Agronomy, Madison, WI. No. 9. pp 331-344.
U.S. Department of Agriculture. 1972. SCS National Engineering Handbook.
Section 4, Hydrology. U.S. Department of Agriculture, Washington, DC.
U.S. Department of Agriculture. 1974. Soil Conservation Service, Portland,
OR. Agronomy Technical Note No. 32.
U.S. Department of Agriculture. 1975. Control of Water Pollution From
Cropland. Vol I. A Manual for Guideline Development. Report No.
ARS-H-5-1 .
U.S. Department of Agriculture. 1979. Field Manual for Research in
Agricultural Hydrology.. Coordinators D.L. Brakensiek, H.B. Osborn,
and W.J. Rawls. U.S. Department of Agriculture, Washington, DC.
Agriculture Handbook No. 224. p 547.
U.S. Department of Commerce. 1968. Climatic Atlas of the United states.
U.S. Department of Commerce, Washington, DC.
U.S. Environmental Protection Agency. 1979. NPDES Compliance Sampling
Inspection Manual. U.S. Environmental Protection Agency, Enforce-
ment Division, MCD-51.
U.S. Department of Commerce. 1982. 1978 Census of Agriculture, Vol. 5,
Pasrt 1 . AC78-SR-1 .
125
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U.S. Environmental Protection Agency. 1980. Manual of Analytical Methods
for the Analysis of Pesticides in Human and Environmental Samples.
R.R. Watts (Ed.). U.S. Environmental Protection Agency, Research
Triangle Park, NC. EPA-600/1-80-038.
U.S. Environmental Protection Agency. 1983. Methods for Nonconventional
Pesticide Chemicals Analysis of Industrial and Municipal Wastewater.
U.S. Environmental Protection Agency, Washington, DC. EPA-440/1-83/
079-C.
U.S. Food and Drug Administration. 1985. Methods Which Detect: Multiple
Residues. Pesticide Analytical Method. Washington, DC. Vol 1.
U.S. Pood and Drug Administration. 1984. Methods for Individual
Residue. Pesticide Analytical Method, Washington, DC. Vol. 2.
Van Bavel, C.H.M. 1966. Potential Evaporation: The Combination Concept
and its Experimental Verification. Water Resources Research, ^(3):455-
467.
Wauchope, R.D. 1978. The Pesticide Content of Surface Water Draining
from Agricultural Fields—A Review. J. Environ. Qual. j^:459-472.
Wauchope, R.D., K.E. Savage, and D.G. DeCoursey. 1977. Measurement of
Herbicides in Runoff From Agricultural Areas. In: Research Methods
in Weed Science, second Edition. B. Truelove (Ed.). Southern Weed
Science Society, Auburn, AL. pp 49-58.
Weber, J.B. 1977. Soil Properties, Herbicide Sorption, and Model Soil
Systems. In: Research Methods in Weed Science. Second Edition.
B. Truelove (Ed.). Southern Weed Science Society, Auburn, AL. pp
59-72.
White, A.W., Jr., L.A. Harper, R.A. Leonard, and J.W. Turnbull. 1977.
Trifluralin Volatilization Losses from a Treated Soybean Field. J.
Environ. Qual. 61105-110.
Wiese, A.F.' 1977. Herbicide Application. In: Research Methods in Weed
Science. Second Edition. B. Truelove (Ed.). Southern Weed Science
Society, Auburn, AL. pp 1-13.
Williams, J.R., H.D. Berndt. 1977. Sediment Yield Prediction Based on
Watershed Hydrology. Transactions ASAE. 20(6);1100-1104.
Willis, G.H., L.L. McDowell, L.A. Harper, L.M. Southwick, and s. Smith.
1983. Seasonal Disappearance and Volatilization of Toxaplhene and DDT
from a Cotton Field. J. Environ. Qual. 12:80-85.
Wischmeier, W.H. and D.D. Smith. 1978. Predicting Rainfall Erosion
Losses - A Guide to Conservation planning. U.S. Department of
Agriculture, Washington, DC. Handbook No. 537.
126
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SECTION 4
QUALITY ASSURANCE PLANNING
Quality Assurance (QA) is an important consideration for all projects
where measurements are made and in particularly in the case of environ-
mental exposure assessments. The development of a QA project plan is an
element that must be recognized prior to initiating any data collection
efforts. The objective of a QA project plan is to ensure that the data are
reliable and that they include measures of data quality (errors in the data
are recognized, described and/or quantified) for all operational steps.
Reference manuals relative to quality assurance that will be helpful includes
Sheraa (1981) and U.S. EPA (1979). A suggested prototype of QA planning
elements is summarized below (EPA 1980).
1. Title Page.
2. Table of Contents.
3. Project Description.
- State objectives of project and the intended use of acquired
data.
- State anticipated dates for starting and completing the
project.
- Site selection—discuss why the specific area and site was
chosen as a representative-area to evaluate runoff losses.
- Experimental design—indicate how sampling sites are located
within the field and include details of the statistical
design.
4. Project organization and responsibility. List key personnel
involved in the project and their major responsibilities.
5. QA objectives formeasurement data in terms of accuracy,precision,
completeness, representativeness, and comparability. Explain
project objectives for each measured parameter listed in the project
description in terms of precision, accuracy, completeness,
representativeness, and comparability.
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a. Accuracy» The degree of agreement of a measurement (or an
average of measurements of the same thing), x, with an
accepted reference or true value, T, usually expressed as
the difference between the two values, X-T, or the difference
as a percentage of the reference or true value:, 100 (X-
T)/T, and sometimes expressed as a ratio, x/T. Accuracy
is a measure of the bias in a system. In many research
projects accuracy is qualitative. In such cases there can
be no statistical or numerical measure for accuracy, but
the methods to be used for verification should be described
in considerable detail.
b. Precision. A measure of mutual agreement among individual
measurements of the same property, usually under prescribed
similar conditions. Precision is best expressed in terms
of the standard deviation. Various measures of precision
exist depending upon the "prescribed similar conditions."
c. Completene ss. A measure of the amount of valid data
obtained from a measurement system compared to the amount
that was expected to be obtained in the experimental design.
d« Representativeness. The degree to which data accurately
and precisely represent a characteristic of a population,
parameter variations at a sampling point, a process
condition, or an environmental condition.
e. Comparabili ty. The confidence with which one data set can
be compared to another.
Sampling Procedures. If applicable, the following items must toe
considered whether the samples are environmental samples or
synthetically prepared samples when comparable concentrations are a
primary objective of the project. (In other'cases, address as
appropriate.)
a. special conditions for the preparation of sampling equipment
and containers to avoid sample contamination
b. sample preservation techniques
c. reagent quality
d. sample preparation requirements
e. sample labelling procedures
f. sample transportation
g. collection of a representative sample
h. sample storage
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7« Sample custody—indicate who will be responsible for sample handling
at various stages (from collection to analysis).
8. Sample storage and recordkeeping—indicate how o-ften samples will
be taken, how many, sample number identification, sample containers,
storage procedures in field, during shipment and in analytical
laboratory.
- discuss how all field and laboratory record data are kept
9. Calibration procedures and frequency—report procedures for all
field monitoring equipment and laboratory analytical instrumentation
utilized in the project.
- frequency of calibration
- calibration standards used
10. Analytical procedures—reference all appropriate standards or EPA-
approved methods. Indicate proposed variances or other methods,
where anticipated; reference same where possible and emphasize
proper documentation of all procedures and techniques used in
laboratory notebooks, etc.
1 1. Recovery structure—indicate the percent of samples that will be
run to obtain recovery data for soil, plant, water and sediment.
12. Reagent QA—discuss the quality and source of standards, reagents,
solvents, and gases for use.
13. Internal quality control checks and frequency—blanks will be
prepared and analyzed? controls needed will be specified and
analyzed.
- split field sample analyses for all compounds being
studied
14. Stability studies—discuss the collection and reporting results of
representative samples during frozen storage with portions analyzed
periodically (from representative sample taken shortly after
application).
15. Data reduction, validation, and reporting,
- data reduction—provide appropriate documentation and flow
charting of computer programs used for statistical processing,
logging, storage and retrieval
- validation—transcribed data will be checked periodically
for accuracy by checking raw data (sheets or logs) against
transcribed data (tapes or printouts)
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- reporting—data must be compiled on magnetic tape with
appropriate documentation, compatible with most, computer
systems. Report data in standard units or as specified
otherwise according to modeling requirements
16. Quality assurance reports—QA data should be included in reports
submitted to EPA.
Functions that can be performed by both field and laboratory personnel
to assist in an overall quality control program are (Plumb 1981):
Field Personnel
a. Providing a representative sample for analysis,
b. Providing replicate samples to define variation at a single
point.
c. Providing a sufficient amount of sample to allow detection.
d. Spiking occasional samples to correct for sample decay
between collection and analysis.
e» Initiating analysis or appropriate storage procedures
immediately after collection.
f. Properly labeling and recording the dates and location of
sample collection.
laboratory Personnel
a. Using acceptable techniques for analysis.
b. Completing the analysis immediately (ideally) or within
prescribed storage limits that are parameter specific.
c. Performing replicate analysis on approximately 5 to 10
percent of samples processed.
d. Adding standard solution spikes to approximately 5 to 10
percent of samples processed and determining recovery,
e. Using an internal laboratory standard to check performance
of analytical method.
f . Analyzing externally prepared reference and performance
(unknown) samples on a routine basis.
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4.1 REFERENCES FOR SECTION 4
Plumb, R.H., Jr. 1981. Procedure for Handling and chemical Analysis of
Sediment and Water Samples. Prepared by Great Lakes Laboratory, State
University College at Buffalo, Buffalo, NY, for the U.S. Environmental
Protection Agency/Corps of Engineers Technical Committee on Criteria
for Dredged and Fill Material. Published by the U.S. Army Engineer
Waterways Experiment Station, CE, Vicksburg, MS. Technical Report
EPA/CE-81-1 ,
Sherma, J. 1981. Manual of Analytical Quality Control for Pesticides and
Related Compounds in Human and Environmental Samples. U.S. Environmental
Protection Agency, Research Triangle Park, NC. EPA-600/2-81059, 455 p.
U.S. Environmental Protection Agency. 1979. Handbook for Analytical Quality
Control in Water and Wastewater Laboratories. U.S. Environmental
Protection Agency, Cincinnati, OH. EPA-600/4-79-019.
U.S. Environmental Protection Agency. 1980. Interim Guidelines and
Specification for Preparing Quality Assurance Project Plans. U.S.
Environmental Protection Agency, Office of Monitoring Systems and
Quality Assurance, Washington, DC. QAMS-005/80.
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SECTION 5
DATA BASE MANAGEMENT
5 .1 INTRODUCTION
The purpose of this section is to describe the design, storage, manipu-
lation, archiving, and documentation of a data base created on computer
from data collected in a field program. The design of the data base should
be considered at an early stage of the design of the data collection program.
Some aspects of the field program can be affected by the data handling and
data base management procedures. Many potential problems can be prevented
by developing a proper design at an early stage in the planning,,
The first item to be considered is the type of data to be put onto the
computer. From this viewpoint, there are really two types of data:
» those that can be expressed purely numerically
* those that can be expressed only qualitatively
An obvious example of the former is a precipitation depth over a time
interval at a location on a watershed. Depth, time, as well as location
can all be expressed numerically. On the other hand a comment in a field
notebook made at the time of this observation such as "tipping bucket
mechanism was sticking and was lubricated" cannot be quantified!,
It is clear that the observations of the first type should be
computerized. The second type of information also can be entered as text
into a word processing system or into files on the computer. Although
this is time consuming and not essential, it does provide a back-up for
information in field or laboratory notebooks that otherwise might be lost
or destroyed.
In the final analysis, anything that can be written can be stored on
a computer. It is probably a good idea to store all data, whether quanti-
tative or qualitative in the data base. The remainder of this section,
however, will deal only with numerical data.
The numerical data breakdown into two typess
• time series, and ;
* spatial (3-D) data.
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An example of the former is, again, a precipitation time series at a
single location on the watershed. An example of the latter are pesticide
residue samples collected at several locations on the watershed at one
time. Although it is not necessary, it is likely that these data types
will be stored differently on the computer.
5.2 INTEGRATING THE FIELD DATA COLLECTION AND DATA MANAGEMENT DESIGNS
Each measurement taken in a field data collection program can be
characterized by four attributes;
* type
* time
• location
• va lue
Each measurement that will yield a numerical value should be assigned a
type identifier. These can be arbitrary but should be a string of four
or less integers or alphanumeric characters. For instance, precipitation
records might be identified by the type designation "P" or '15' or 'R653'.
If the data is to be entered into a comprehensive data base such as STORET,
it may be useful to assign parameter codes that are used in the large data
base. The only real requirement is that the identifier be unique.
When each measurement, is taken it is essential that the time be recorded,
that is, the year, month, day and in some cases the hour and minute of the
measurement. The precision of time reporting depends on the relative
dynamic nature of the quantity measured. For instance, minutes may be
necessary to report during rainfall events; whereas the year, month and day
may be sufficient for reporting percent crop canopy development.
The location of each measurement must also be reported. The location
should be identified by an x-y-z grid coordinate in the data base. It may
be easiest to mark a spatial location on a map and simply report the depth
of a soil sample taken in the field. In the data base, however, the spatial
location should be designated by an x-y-z coordinate. An appropriate datum
for all location coordinates is the watershed outlet, which can be either
specified as location (0, 0, 0) or (0, 0, MSL) in which MSL is the elevation
of the gage above mean sea level. The x and y axes of the grid can be
oriented conveniently; however, for consistency x due north and y due east
are recommended. The plane of the horizontal should be normal to the
direction of the force of gravity. A three dimensional grid of this type
is shown in Figure 5.1.
In this figure, two locations A and B are shown. Location B is
outside the watershed boundary and is a measurement made at the land
surface, its coordinates might be x = '50, y = -25 and z = 6 or 6 + MSL
133
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(Z)
FIGURE 5.1 . Use of a Cartesian Coordinate system to locate
measurements in a watershed.
of the outlet. A is a location within the watershed. The measurement
was taken beneath the land surface. Its coordinates might be x = 40, y =
10 and z = -2 or -2 + MSL of the gage. In this way any location in the
watershed can be uniquely specified. A variation on this method would be
to create a 3-dimensional grid, numbering each element within the grid.
Grid numbers (e.g., x25, y14, z3) could be used to locate positions.
The last attribute of this measurement is its value.
entry of a measurement in this data might bes
SWC8406301430252550.43
An example
1
type
da
military
time
te x, 5
local
vaflue
T, Z
tion
134
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The advantage of entering data in this manner is that the data can easily
be sorted by type, date or location in order to create files, hard copies,
or plots of the information. The designer of the data base also may want
to include columns for value units and for identifications of estimated
values or other notes.
It is not necessary, and perhaps not desirable, to put every piece
of data generated into one huge file. Large files are often difficult to
use, especially on smaller computers, it may make more sense to group
data according to type or function. For instance, one might put meteoro-
logical observations, watershed pesticide residue data, runoff and erosion
data, and pesticide runoff data into separate files. Of course, these
smaller files can always be generated from a master file containing all
the data or vice versa.
Where several investigators are involved on the project, separate
files for an individual's data may be desirable for proprietary purposes.
Record lengths should be kept to 80 characters. This will insure
ease of downloading to microcomputers and obtaining hard copies from most
printers.
5.3 DATA SECURITY AND TRACKING
A system should be established to keep track of the data from the
time it is collected until it is stored permanently in final form in the
data base. This will prevent the inadvertent loss of samples or informa-
tion and provide a method for assessing the status of data reduction.
The individual responsible for collecting samples should record the
date, time and location of the sampling. Any subsequent processing of
the samples in the field should also be dated and initialed, when the
samples are turned over to the laboratory for analysis, the collector
should sign the samples over to the receiving authority. Any processing
in the laboratory should also be dated and initialed.
The same type of security and tracking should be maintained during
the reduction, entry and analysis of data. Raw data sheets from the
collection or laboratory analysis phases should be signed over to the
data management facility authority. Any subsequent data analysis steps
should be dated and initialed upon completion. Archiving of important
milestone levels of data reduction also should be recorded. Archiving
will be covered later in this section.
A flow chart of the process of information reduction and storage
should be produced. An example of this is shown in Figure 5.2. There
may be several flow charts produced for different types of data. An
up-to-date record of the status of the data process can be kept on the
flow chart.
135
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"*•«?" V-JXE."'-
I Translator/
Mp«at Pro^rtaa \
2 and J-4-S \ _
Until All error* j
•re Rcooved /
Corrected *
- —— — -M Monthly
Hag. Tape i
PROGRAM 8.
Produce 1st Level
SuMMrlza clone
CoBblne Prev. Honth(«)
Data with Current Month
o|
Printout of Dally
Preclp. Monthly
Prod p. Freq. of
Avg. Dally Occurrences
For Month and Year
Printout of Dally
Preclp. Monthly
Prccip. Freq. of
Avg. Dally Occurrences
For Month and Year
PROGRAM 9.
Check of
6 Month Data
Tape
^
r
Printout of Selected
Gages, Months and Days
to Insure all Data
was Properly Recorded
PROGRAM 10.
Create Backup
to 6 Month
Data Tape
1
0 ^/ a"*"P \
-~—~ pi M«g. mpe 1
\ (6-Month3)/
FIGURE 5.2.
Example flow chart of a system for computer processing
and storage of digital precipitation data (Woody, 1975).
136
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5.4 STORING DATA ON THE COMPUTER
There are three means of storing data on the computer, that is, there
are three machine accessible media on which data can be stored:
* punched cards
<* magnetic tape
* discs
There are advantages and disadvantages to each of these media. Punched
cards have a long life and are unaffected by most environmental conditions
except moisture. They are cumbersome, however, take up a lot of storage
space, and need to be attended closely when being read by the machine.
Punched cards are much less common presently than tape or disc. Magnetic
tapes are also relatively long-lived but are more environmentally sensitive
to heat,, moisture, dust and magnetic fields. They require little room for
storage and only moderate supervision when information is taken from them.
Discs are sensitive to the same environmental conditions as magnetic tapes.
They usually require more storage room than tapes but require almost no
supervision when loaded onto or unloaded from.the computer.
Whatever medium is used, data must be recorded to some level of accuracy
(i.e., a certain number of significant digits). In the case of straight
raw data transcription, the digits reported from the field or laboratory
should be recorded. When changes are being made to raw data such as units
conversions or multiplications by other data, only the number of digits of
the least accurate data should be carried and recorded.
Most hydrologic data are only recorded to (at most) three or four
significant digits; therefore, round off and truncation should not be a
problem in manipulating these data on most machines.
5.5 DATA ENTRY VERIFICATION
Raw data that are entered on the the computer should be verified for
correct transcription. The most accurate way of doing this is to trans-
cribe small quantities of data at one time (20 to 30 values) and visually
check each piece of datum entered. It is a mistake to enter all the data
(say several thousand points) and try to verify them all at once.
Another useful way of checking or verifying entered data is to have
the computer pick out the maximum and minimum values. Many times such a
procedure will catch a misplaced decimal point or exponent. An equiva-
lent way of doing this is to plot the data. Data should be rechecked from
a hard copy once they are all entered. In this case, the output should be
in a format similar to the raw data sheets for ease of checking.
137
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5.6 DATA MANAGEMENT
Once the data are stored on the computer, the issues concerning the
validity of the values should be considered. Adjustments may have been
made to data between the field collection or laboratory analysis and data
entry in the case of obvious errors. In some cases, however, analysis of
the data values may be necessary to determine whether errors exist, and,
if so, how to correct them. Two cases are considered in this sections
* the identification and correction of questionable data values
» the estimation of missing values
Questionable or missing values that are estimated should be so indicated
in the data base.
5.7 QUESTIONABLE VALUES
Any measurement that is made has a certain amount of error associated
with it. Measurement errors may be divided into two categories—systematic
and random, A systematic error is one that occurs in the same direction
each time a measurement is made thereby creating consistently high or low
values. Examples are misalignment of a zero of a recording gage or an
investigator who consistently reads an indicator high or low. Systematic
errors are not necessarily of the same magnitude, even within the same
experiment. For instance, a slowly changing temperature may cause an
instrument zero to drift, introducing a trend or periodicity in the measure-
ments. A sticking mechanism may cause periodic jumps in a series of mea-
surements. These types of errors can be minimized by a number of means—
proper training of personnel, proper service of instruments and good quality
control.
Random errors, on the other hand, are caused by purely inexplicable
phenomena and occur in no consistent direction, with no consistent magni-
tude. Examples are simple mistakes (misplacing a decimal point, trans-
position of digits) and variation caused by uncontrollable variables.
5.8 IDENTIFICATION OF SYSTEMATIC ERRORS
By far, the best way of determining the presence of systematic errors
in a time series is to plot the data. Any trends, jumps and/or periodi-
cities will normally be apparent from such a plot. If an error is sus-
pected, the investigator will want to determine the cause, if possible,
Field notebooks or interviews with data collection personnel could be
helpful in this regard. In some cases, the trend or periodicity may be
perfectly normal. In others it may be desirable to correct the data base
by eliminating the error in the observed values.
138
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5.8.1 Correction of Systematic Errors
Of the three types of errors listed above--trends, periodicities and
jumps—the trend is probably the easiest to identify and correct. Essen-
tially a regression of the values versus time is performed. The values
gan then be corrected by subtracting the predicted value of the regression
from the observed value.
While linear trends are common (which would indicate a linear regres-
sion), this may not always be true. The appropriateness of the model to
the observed trend should be assessed. In addition, the assumptions of
the regression technique should be satisfied by the data set (see Section
6).
Correction of data for periodic effects follows the same principles
as for trends, but requires the fitting of more intricate models. In
this case a trigonometric function, such as the sine function may be
appropriate and, for asymmetric periodicities, the Fourier series may be
appropriate. The Fourier series representation of a parameter may be
written as:
_ m
u(t) = u + £ Aj cos (2 TT jt/w) + Bj sin (2 IT jt/w) , (5.1)
j=1
t = 1 ,... ,w
where u = the mean of the series
j = the harmonics of the function
A and B = Fourier coefficients
Thus, it is easy to see that the value of 'u1 at any time t can be approxi-
mated by the sum of a number of sine and cosine functions.
In general, by a jump we imply an abrupt change in the mean of the
series of measurements as opposed to a trend in which the mean changes
gradually. By evaluating the mean before and after the abrupt change occurs
the questioned data can be corrected. The difference in the two means is
simply added to or subtracted from the erroneous data, as required.
Computer programs used to perform regression or Fourier analysis are
included in most standard statistical packages on larger machines.
5.9 IDENTIFICATION OF RANDOM ERRORS
Random errors can also be identified by plotting data and looking for
extreme values or by running programs that identify maximum and minimum
139
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values in the data as was suggested in the section on data entry verifica-
tion. Other statistical methods are also available, but unfortunately they
are of limited practical use since statistics of the population (e.g., mean
and standard deviation) must be known.
5.9.1 Correction of Random Errors
If a single value in the data is suspiciously higher or lower than
related values, the best way to deal with it is to treat it as a missing
value and use techniques of the following section to estimate a value.
5.10 MISSING VALUES
There are many reasons for the occurrence of missing values in a data
set. Chief among them are equipment malfunctions, loss of sample, or impro-
per analysis of a sample. It may not be necessary, or desirable, in all
cases to estimate values for missing information. For the purposes of model-
ing it is usually necessary, however, to have complete unbroken records of
the required time series inputs such as precipitation or pan evaporation.
Therefore, we will focus on methods for filling in gaps in these types of
records.
The method of filling a time series record depends largely on its
structure. In this regard, there are two important types:
• intermittent
* continuous
An example of an intermittent series is precipitation, precipitation may
begin, last for a period of time, cease and begin again. This series is
characterized by "events" that occur more or less at random. A continuous
series is exemplified by a variable like temperature. A temperature reading
can be made at any time. There is never a "no occurrence" value as there
is in the case of rainfall. It is easy to see that there are some methods
such as interpolation that may work well for a variable such as temperature,
but would not work well for precipitation.
Several methods of estimating missing time series data are discussed
in the following sections. These include:
* interpolation
* interstation correlation
* time series modeling
140
-------
5.10.1 Interpolation
Interpolation can take on several functional forms. The most simple
is linear interpolation. This method would be most likely utilized where
there are a few missing data points in an otherwise intact record. Linear
interpolation, in general, should only be used when the function being
interpolated is smooth. The form of the linear interpolating function is:
Y2 ~ Y1
y* = y., + x*-x1 (5.2)
x2 - X1
where y* = interpolated value of the dependent variable.
x* = value of the independent variable at which y*
is desired.
y.j and y2 = end point values of the dependent variable.
x1 and x2 = corresponding values of the independent
variables.
Interpolation can also be accomplished with higher order polynomials. An
example in the hydrologic literature is found in Mills and Snyder (1971).
This is useful when there are more than a couple of missing data values
together and a linear relationship is not adequate. A versatile and easily
used method for finding an interpolating function of order 'n1 where there
are n + 1 data points is interpolation using Lagrange polynomials. It can
be shown that if f(x^) = y^, 0
-------
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o
w
Ul
oe
3
^
C
Ul
Q.
SE
Ul
27-
26-
26-
24-
23-
22-
21-
20-
19-
18-
17-
16-
16 -
/" \
/ \
/ \
/ \
' X
/ \
v
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x P (^S = E f (x , ) 1 , (x) \
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© OBSERVED DATA
X INTERPOLATED DATA
SH
i i i i i i r
1 2 3 4 56 7
DAY
FIGURE 5,3. Interpolation of missing data using Lagrartge polynomials.
P(4) = 21 £0(4) + 23 £.,(4) + 26 SL2(4) + 20 £3(4) + 19 £4(4)
Using Bjuation 5.4:
(4-2) (4-3) (4-6) (4-7)
............. ..................
(1-2) (1-3) (1-6) (1-7)
(4-1) (4-3) (4-6) (4-7)
• -
(2-1) (2-3) (2-6) (2-7)
0.200
= 0.900
142
-------
(4-1)(4-2) (4-6) (4-7)
£2(4) = • = 1.500
(3-1X3-2) (3-6) (3-7)
(4-1) (4-2) (4-3) (4-7)
£3(4) = : = 0.300
(6-1) (6-2) (6-3) (6-7)
(4-1)(4-2) (4-3) (4-6)
£4(4) = . =- 0.100
(7-15(7-2) (7-3) (7-6)
Therefore
p(4) = 21 (0.20) + 23 (-.90) + 26 (1.5)
+ 20 (0.3) +19 (.1)
= 26.60
Similarly p(5) - 24.00
The interpolating polynomial and the interpolated values y(4) and y(5)
are also shown. The number of points used on either side of the missing
data is arbitrary. The values of A-s(x) decrease, however, as one moves
away from the points being interpolated. Therefore the use of more than
three or four points on each side will not improve the interpolated values
appreciably.
Of course, there are other methods of interpolation. Fourier series
can be used as well as spline functions. The above is, however, adequate
for most applications where there are only a few missing points.
When performing interpolation a number of times, the above computa-
tional method becomes tedious. In this case, Mtkin's iterative interpo-
lation technique can be used. It is easily programmed and the program is
given in Figure 5.4. The user supplies the set of x and f(x), and the x at
which an interpolated value of f(x) is desired. The subroutine returns
the value of f(x) as P&LPH&. This method of evaluating f(x) gives the same
result as the above demonstrated method.
5.10.2 Interstation Correlation
Another useful technique for estimating missing data is by correlation
with other records of the same variables from other stations in the vicinity,
This technique is often used with rainfall, evaporation and streamflow
records in various guises.
143
-------
SUBROUTINE AIT INT{X,Y,ALPHA,PALPHA.N)
DIMENSION X(20),Y<20).PI 201,0(20)
C
C SUBROUTINE AITINT USES AITKEN'S ITERATED INTERPOLATION TO EVALUATE
C THE INTERPOLATING POLYNOMIAL, PCX), AT A POINT X=ALPHA, WHERE
C P(X(IH«=Y(I), 1*1,2....,N. THE CALLING PROGRAM MUST SUPPLY THE
C ARRAYS XII) AND Y(I), THE POINT ALPHA AND AN INTEGER N. THE
C SUBROUTINE RETURNS PIALPHAI AS PALPHA.
C
DO 1 K-l.N
1 PIK)«Y{K)
00 3 J-2.N
F-ALPHA-XU-1)
00 2 K(K)'QIKI
PALPHA>P(NI
RETURN
END
FIGURE 5.4. Computer subroutine for interpolation using Aitken's
Iterated Interpolation (Johnson and Riess, 1977).
The simplest method is, again, a bivariate linear regression. In
•this case, the value at the gage A is regressed on the values measured
for the same time period at gage B. The linear regression is then used
to estimate missing values at A given a recorded value at B. This type
of regression is subject to the same problems and assumptions as that
demonstrated in Section 6. In general, however, it is a useful and
widely used technique.
Better estimates can usually be made if multiple records are avail-
able on which to perform the regression. In this case, multivariate
regression is used. That is, the values at A are regressed on the values
at B and C, then estimates at A are made using recorded values a.t B and C.
Multivariate regression programs are available in most statistics and
applied mathematics packages for computers.
An extension of interstation correlation is the regression of the
record having missing values on the record of a different variable with
which there exists a cause and effect relationship. For instance, if pan
evaporation records are missing they may be estimated using relationships
with measured solar radiation and temperature, and so on. These relation-
ships will often yield better predictions than interstation correlation.
Of course, there is also the possibility that estimates can be made by
combining these two techniques. For instance, pan evaporation might be
estimated by making use of temperature, solar radiation and pan evapora-
tion from another station. A number of possibilities arise in different
situations. The last group of techniques mentioned use still another
source of information for estimating missing values; the time series
itself.
144
-------
5.10«3 Time Series Modeling
Time series models can range from the very simple to the very complex,
As with any of these techniques, volumes could be written. Only simple
models are considered here, along with references for more complex techni-
ques.
Time series of natural phenomena usually have very complicated struc-
tures. They are made up of various combinations of trends, periodicities
and random components. Figure 5.5 shows an example. This daily temperature
series shows a long term period of one year overlain by many shorter term
periods induced by the passage of warm and cold fronts. There is obviously
randomness superimposed on these periodicities.
The essence of time series modeling is to decompose the problem into
its components by modeling each component (analysis phase) and then to
generate a new statistically equivalent series by regenerating the series
from the components (synthesis phase). Missing values can then be selected
from the same period in the synthetic series.
Dean and Mulkey (1982) modeled a daily pan evaporation time series
which had a structure very similar to the daily temperature series in
Figure 5.5. The existence of the annual period is demonstrated by the
presence of the peak in the autocorrelation function of the daily pan
evaporation series (Figure 5.6). The annual periodic component was modeled.
This was done by fitting a Fourier series to the .mean daily values grouped
by the month. The standard deviation of the daily values grouped by month
was also determined. The original series was then standardized by subtract-
ing the monthly means from each value and dividing the differences by the
monthly standard deviations, as follows.
xi - xm
sm
m = 1 ,...,12 (5.5)
where rj_ = new series of standard normal residuals
X^ = observed daily value
Xm = mean daily value for month m
Sm = standard deviation of daily values for month m
The fact that the annual period is removed by this procedure is demonstrated
by the autocorrelation function of the residuals which shows no period of
365 days (Figure 5.7). Some residual autocorrelation still remains due to
the shorter period passage of fronts. The autocorrelation function of
Figure 5.8, which is a "close up" of the first 12 lags of Figure 5.7, shows
this. It also shows that this residual correlation can be effectively
removed by using a one lag autoregressive model (Markov model). This
145
-------
30 -
20 -
S 10
Ul
E
O
Ul 0
Ul
cc
Q. ***"
Ul
-40 -
,-50
"T 1 1 1 1 I 1 1 1 1 1 1 1 1
30 60 00 120 1BO 180 210 240 27O 30O 330 380 300 42O
DAYS SINCE SEPTEMBER 1. 1078
FIGURE 5.5. Minimum and maximum ambient air temperature at Panther Basin,
September 1, 1978 to September 30, 1979. Flat sections of
plots represent data gaps, Chen et al., 1982).
CORRELATION
EFFICIENT
0.4
0.3
0.2
O.I
0.0
D
>oo0 Oo0o°o
^•V^ \J ^^
-
°o o
o,. ^
-0.3
'O
I
o^
±
J_
50
IOO ISO
200
LAG
J
250 300 350 400
FIGURE 5.6. Autocorrelation function of daily pan evaporation.
146
-------
u/»
duJ o. i
cro
Qp- U.U
°UJ.o.I
^°-02
cc u
Ld
(/) -0.3
-O.4
—
o o OOQ o^ o Oo o o o o
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u ww O 95% Confidence Bond
—
—
I t 1 1 1 I i i
50 100 150 200 250 300 350 400
LAG
FIGURE 5.7. Autocorrelation function of standardized daily pan
evaporation.
model simply states that the value of a pan evaporation residual depends
only on the values of its immediate predecessor in time plus some error,
or:
r± = jZr^., + e^ (5.6)
where 0 = value determined from auto regression
e£ = error of the regression
The errors of the regression (e^) were shown to be normally distributed
with a zero mean and unit variance, N(0,1) (see Section 6).
Thus a time series of pan evaporation statistically equivalent to
the original series can be generated in three steps:
1) Select a normal random number with zero mean and unit
variance (N(0,1)
2) Select a starting residual value and generate the next
residual by using Equation 5.6s
r± + 1 = 0ri 4- e±
147
-------
0.6r
Z 0.5
O
u.
u3 a4
o
o
Z 0.3
GJ 0-2
or
o:
o
o o.i
o
UJ
t o.o
o
Q_
o-o.i
o
-0.2
O—O Sample Correlation Coefficient
•- -• Population Correlogram
(AR (I) Model)
95% Confidence Limits
JL
±
3456789
NUMBER OF LAGS
10 II 12
FIGURE 5.8. Enlargement of first 12 lags of autocorrelation
function for standardized pan evaporation and
fitted AR{1 ) model.
148
-------
3) Generate a value of pan evaporation by destandardizing
the residual using Equation 5.5:
~ ri-M sm + Xm
Of course, this is only one example of a relatively simple time series
model. These types of models usually become more complex for shorter
time intervals (e.g., hourly as opposed to monthly). Complications also
arise for intermittent processes such as precipitation. For generation
of short interval rainfall depths, "event-based" models may be used
(e.g., Egbuniwe, 1975; Grace and Eagleson, 1966; Dean and Mulkey, 1982).
For daily or longer intervals, Markov models are frequently used (e.g.,
Haan et al., 1976? Chin, 1977).
5.10.4 Summary of Techniques to Evaluate Missing Data
Three techniques, interpolation, regression, and time series modeling,
have been advanced as possible methods for estimating-missing values in
time series data sets. For continuous variables (e.g., pan evaporation,
temperature) where only a few data are sporadically missing, some form of
interpolation will provide reasonable estimates. These methods are easy
to use and require little data analysis, when longer periods of missing
data exist, especially if the time series is intermittent, some form of
interstation or cause^effect correlation, or time series modeling is pre-
ferred. , The correlative methods usually require a little less work than
statistical time series models and are more generally used. The method of
estimation can also depend on the ultimate use of the data set. If the
data set is to be used for model calibration or verification then regressive
methods will give estimates closer to the actual values missing. Statistical
time series models will not necessarily do this. They are particularly
well-suited, however, for extrapolating records to be used as inputs to
deterministic models for the pupose of performing frequency analyses of
model outputs.
5.11 ARCHIVING THE DATA BASE
Archiving refers to the permanent storage and safekeeping of the data
base. In general, milestone steps in the processing of the data should be
archived. At least three stages stand out:
o original data (e.g., breakpoint rainfall)
o data that has been further reduced or converted (e.g., interval
rainfall derived from breakpoint)
o data in which missing values have been estimated (i.e., model-ready
rainfall input files)
149
-------
Archiving at these major stages can save many hours of work if an error
is found at a later date.
Archived data should be carefully stored. All canisters, boxes or
other storage containers should be clearly labeled. Usually, taipes in a
tape library are numbered. The numbering system should be store;d both in
the vicinity of the data storage facility and at an alternative location.
Tapes or discs should be stored in a clean environment. Temperatures
should correspond to those comfortable for humans. No smoking, eating or
drinking should be permitted in the storage area. Tapes and discs should
be stored away from any sources of electromagnetic radiation, cchey should
remain in their protective canisters when not in use. There should be at
least one back—up copy of the complete data base stored at another location.
Two copies are preferable.
Tapes should be rewound every year to relieve tension on certain parts
of the tape surface. Information on tapes should be rearchived every 3-5
years to avoid possible deterioration of any kind.
5.12 DATA BASE DOCUMENTATION
The data base will be much more useful and usable if it is properly
documented. The following items should be discussed in the documentation:
1) What is in the data base. This should contain a detailed discussion
of the type of data stored, how it is grouped and, in the case of
multiple discs or tape reels, which data reside on which discs or
tapes.
2) How the data is stored and formatted. The documentation should contain
the format statements with which the data were written to storage.
Also included should be the key to data type, location and units of
the values, in addition to the specifications for the tapes (e.g.,
EBCDIC, 1600 bpi, 9 track, etc.)
3} Estimated values. Any procedures that have been used to estimate
questionable or missing values should be clearly documented. Also any
procedures that have been used to reduce or convert data should be
documented. If computer programs were used for these purposes, they
should be identified in the documentation along with instructions for
their usage. Plow charts showing the use of various programs and
procedures should be included.
4} Supporting programs. Any programs that have been written to retrieve,
output or plot data should be included for the convenience of users.
Example outputs should be included.
5} Storage. A log should be kept indicating the last time tapes (or
discs) were inspected, rewound, or rearchived.
150
-------
Usage' An up-to-date list of the organizations or individuals to
whom the data base has been sent should be maintained at all times.
Two examples of documentation of computerized hydrologic data bases can
be found in Woody (1975) and Hibbert and Cunningham (1966).
5.13 REFERENCES FOR SECTION 5
Chen, C.W., J.D. Dean, S.A. Gherini and R.A. Goldstein.. 1982, Acid Rain
Model: Hydrologic Module, Journal of Environmental Engineering
Division, ASCE. 1 08(EE3);455-472.
Chin, E.H. 1977. Modeling Precipitation Occurrence Process with Markov
Chain. Water Resources Research. 1 3(6);949-956.
Dean, J.D. and L.A. Mulkey. 1982. Stochastic-Deterministic Modeling:
The Effects of Supplemental Irrigation of Watershed Hydrology and
Transport. In: Statistical Analysis of Rainfall and Runoff. V.J.
Singh (Ed.). Water Resources Publications, Littleton, CO. pp.
273-290.
Egbuniwe, N. 1975. Generation of Hourly Rainfall, water Resources
Bulletin. 11(4);706-713.
Grace, R.A. and P.S, Eagelson. 1966. The Synthesis of short Time
Increment Rainfall Sequences. Massachusetts Institute of Technology,
Cambridge, MA. Report No. 91 .
Haan, C.T., D.M. Allen and J.D. Street. 1976. A Markov Chain Model of
Daily Rainfall, Water Resources Research. 12(3);443-449.
Hibbert, A.R. and G.B. Cunningham. 1966. Streamflow Data Processing
Opportunities and Application. In: Forest Hydrology, Proceedings
of a National Science Foundation Advanced Science Seminar, The
Pennsylania State University. Pergammon Press, New York, NY.
Johnson, L.W. and R.D. Riess. 1977. Numerical Analysis. Addison-Wesley
Publishing Co., Reading, MA.
Mills, W.C. and W.M. Snyder. 1971. Algorithm for Adjusting Stream Stage
Records. Journal of the Irrigation and Drainage Division, ASCE.
97(IR1 );51-58.
Woody, T.K. 1975. A System for Computer Reduction of Digital Precipitation
Data. Presented at the National Symposium on Precipitation Analyses
for" Hydrologic Modeling. June, Davis, CA.
151
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SECTION 6
DATA ANALYSIS AND MODELING
Information in this section provides a link between the finished data
base and the actual use of the data base for calibrating and verifying
models and time series simulations for performing exposure assessments. As
such, it is divided into two sections—data analysis and model calibration
and verification.
Data analysis, in this case, refers to preparation of data to be used
in the model. Recall that checking or verifying such data and estimating
missing values were covered in Section 5.
The section on model calibration and verification does not address the
nuances of these problems for specific models, but rather discusses useful
statistical methods by which simulated and observed data can be compared.
The subject of model verification or "model performance testing" is
rather broad. In essence,-any technique that serves to effect a relevant
comparison between observed and model-predicted values can be useful and
informative in this context. There are some simple statistical techniques
that can be employed to objectively test how well two sets of values agree.
The choice of methods used in a given situation ought to depend upon how
well the methods in question test important aspects of comparison or high-
light differences that are of real significance. The few statistical tech-
niques discussed here should not be construed as exhaustive, but they are
likely to be generally quite useful in this type of comparison.
6.1 DATA ANALYSIS
Because the idiosyncrasies of the data produced by specific field
studies and models are manifold, discussion of data base analysis for the
purposes of using individual models is impossible. This section will
discuss problems generally encountered, however, and will deal with some
specific analysis techniques in detail. Many of these problems can be
avoided by consideration of models to be used or analyses to be performed
in the design of the data collection plan.
As mentioned earlier in this manual there are three types of data that
the field study provides:
152
-------
• data to be used directly as model input (e.g., precipitation)
for the purpose of running models
• data to be used indirectly as a means of estimating model para-
meters that are subsequently used as model inputs
• data to be used directly or indirectly for the purpose of cali-
brating and/or verifying models (e.g., runoff data)
The most common problem involving the preparation of data for use in models
is conversion of units. A table of useful conversions can be found in
Appendix A.
6.1.1 Model Inputs
Data required as inputs to models can be further subdivided into two
groups:
» time series
» discrete spatial data
Time series usually include precipitation, pan evaporation, temperature,
and/or incident solar radiation. Models normally require that these data
be input on equal time intervals—:for example, hourly, daily or monthly. A
common problem is that these data may not have been measured at equal
intervals. This is most likely to occur with precipitation when "break-
point" values instead of "interval" values may have been-reported, in this
case, the data will have to be converted,
6.1.1.1 Conversion of breakpoint precipitation to interval,data
A typical breakpoint precipitation record is shown in Table 6.1. The
simplest way of converting breakpoint to interval data is by interpolation.
This is demonstrated graphically in Figure 6.1 for the storm of 5 August,
1973, from Table 6.1. The cumulative rainfall is plotted versus time and
the cumulative interval totals can be read from the graph. By differentiating
the cumulative interval totals, the interval incremental amounts can be
determined. Resulting 10-minute-interval data a're shown in Table 6.2. Of
course, for large amounts of data this process is more easily accomplished
on the computer. ,
6.1.1.2 Disaggregation of precipitation depths
Another frequently encountered problem is that data are recorded on a
longer timestep than is usable by the model. In this case, the longer
timestep data must be disaggregated to form interval data on a shorter
timestep. This process can be quite complicated if done rigorously because
of the stochastic nature of the rainfall process. A technique for generating
153
-------
z
o.
IU
Q
Z
2
P
o
Ul
DC
0.
7_
-
.6-
.6-
.4-
.8-
.2-
.1-
ARROWS INDICATE
10 MINUTE INTERVAL
VALUES
1:30
FIGURE 6.1
2:00
3:00
CLOCK TIME
Cumulative plot of breakpoint storm rainfall
from table 6.1 (Precipitation depth in inches).
154
-------
TABLE 6.1. HYPOTHETICAL BREAKPOINT PRECIPITATION RECORD
Date
2 Aug 73
3 Aug 73
4 Aug 73
5 Aug 73
Time
8:20 a
8:45 a
8:50 a
9:10 a
11 :40 a
No Precipi-
tation
No Precipi-
tation
1:30 p
1:45 p
1 :55 p
2:05 p
2:30 p
2:50 p
2:55 p
Time Interval
(minutes }
25
5
20
150
15
10
10
25
20
5
Accumulated
Depth
(in)
0.0
0.2
0.24
0.38
0.42
0.0
0.25
0.30
0.45
0.60
0.62
0.64
Interval
Depth
(in)
0.2
0.04
0.14
0.04
0.25
0.05
0.15
0.15
0.02
0.02
155
-------
TABLE 6.2. INTERVAL RAINFALL DATA DETERMINED FROM THE PLOT
IN FIGURE 6.1
Time
1 :30
1:40
1 :50
2:00
2:10
2:20
2:30
2:40
2:50
3:00
Time Interval
(minutes)
10
10
10
10
10
10
10
10
10
10
Ac cumulated
Depth
(in)
.0
0.165
0.275
0.37
0.48
0.54
0.60
0.61
0.62
0.64
Interval
Depth
(in)
0.165
0.11
0.095
0.11
0.06
0.04
0.01
0.01
0.02
disaggregated time series is given by Valencia and Schaake (1973). other
stochastic disaggregation techniques have been discussed by Egbuniwe (1975),
Grace and Eagleson (1966), Austin and claborn (1971)' and Dean and Mulkey
(1982). simpler techniques based on typical observed storm patterns (which
ignores random effects) are discussed by Hjelmfelt (1980).
While the stochastic techniques are difficult to use and require
substantial data analysis, the simpler techniques may be substantially in
error for a given location. The best advice is to plan the acquisition of
precipitation data on a timestep equal to the shortest timestep on which
the model would be run. If a longer timestep is desired, the data can
always be summed to give the proper interval.
6.1.1.3 Snowfall
In areas where snowfall is a substantial portion of precipitation
during the winter, accurate snowmelt measurement are important in deter-
mining the annual water balance. It also may affect the antecedent
moisture conditions in the soil at the time of spring pesticide applica-
tion and runoff events.
^
156
-------
Snowfall is normally measured by rain gages in the same manner as
precipitation. The measurement of snowfall in precipitation gages, however,
can underestimate total snowfall due to the effects of wind on the gage
catch. One way of correcting the gage catch is to make snow course measure-
ments on the watershed. If this has not been done, the snow catch of the
gage can be adjusted if wind speed is known. The adjustment factor is
shown in Figure 6.2 as a function of wind speed. These data sets were
taken from Snow Hydrology (U.S. Army Corps of Engineers, 1956). The appro-
priate adjustment factor is chosen using the average wind speed occurring
on the day of the snowfall. This factor is then multiplied by the water
equivalent snow depth.to estimate the actual snowfall in water equivalent.
6.1.1.4 Soil moisture
Soil moisture, as discussed in Section 3, can be determined by a
variety of methods. Among these are gravimetric, soil tension, electrical
resistance, and radiological methods. If gravimetric methods are used, the
data will most likely be reported on a weight basis as a percentage. The
formula is:
ww - wd
• w
(100)
(6.1)
where Pw = percentage by weight of water in the soil
Ww = wet weight of the soil
Wd = dry weight of the soil
Usually, models require this measurement to be input on a volume basis
instead of a weight basis. In this case, the weight basis percentage can
be converted to the volume basis percentage by:
Pv = Pw PS/PW
(6.2)
where
Pv
pw
PS
PW
volume basis percentage
weight basis percentage
bulk density of the soil
density of water
157
-------
A 0.9540.203V
3 -
-A, =0.962 + 0
r2- 0.964
Af =1.348i-0.10Sv
r 2 =0.99?
A f = 0.950.984 *- 0.133v
r2= 0.998
A f =1.355 +-0.030v
r 2= 0.968
O SNOW ADJUSTMENT
X RAIN ADJUSTMENT
A SNOW ADJUSTMENT
3 6 10
I
•* 1 2.6m/**c.
I 1
20 30
WIND SPEED (m/»«c)
FIGORE 6.2. Adjustment factors for rain and snow based on wind speed
(U.S. Army Corps of Engineers, 1956).
158
-------
If the densities of water and bulk soil are expressed in g/crn^, the value
of pw is conveniently equal to 1.
Soil tension, electrical resistance, and radiological methods have to
be calibrated and the result can be expressed in weight or volume units as
needed. Hillel (1980) contains a good discussion of soil water contents
from the measurements produced by these various methods. In addition, the
operator's manuals for these various instruments should contain adequate
instructions.
6.1.1.5 Pesticide degradation rates
Most pesticide runoff models require that a first-order" degradation
rate of the chemical be entered. The field study should yield residue
measurements at various times and depths in the soil. These measurements
can be used to establish first-order degradation rates. It is important
that, during the time period of the measurements utilized to establish
degradation rates, no mass movement of the pesticide occurred by volatili-
zation, leaching, runoff, or erosion from the soil zone. If mass movement
does occur, the sum of pesticide residues in the entire profile may be used
to estimate the degradation rate of the chemical because runoff losses are
usually a small fraction of the total application.
Normally, volatilization can be lumped into the degradation process
because runoff models do not usually account for this process separately.
The same technique discussed below can be used for foliar degradation of a
chemical, subject to one or twa additional assumptions.
A typical set of pesticide residue measurements for the top centimeter
of an agricultural soil are shown in Table 6,3.
The first-order degradation model is given by:
C(t) = C0e~kt (6.3)
where C(t) = concentration at any time t
Co = initial concentration
k = first-order pesticide degradation coefficient
t = elapsed time
To determine k, the above model would be rearranged to solve for k. Thus?
Ln (C/C0) = -kt (6.4)
159
-------
This indicates that a plot of In (C/C0) versus time should yield a straight
line with a slope of —k. Figure 6.3 shows this plot, "file computation of
In (C/C0) is shown in Table 6.3. The k can be determined by a best-fit
analysis of a staight line to the data points.
TABLE 6.3. PESTICIDE RESIDUE MEASUREMENTS IN THE TOP CENTIMETER
OF AN flCRICULTURAL SOIL
Date
5 April
7 April
1 0 April
15 April
30 April
Intervening
Elapsed
Time
(days)
(Appl. Date)
2
3
5
15
Cumulative
Elapsed
Time
(days)
2
5
10
25
Residue
Level
(PPb>
5.0
4.3
4.0
2.9
1.4
C/Co
1 .00
0.86
0.80
0.58
0.28
In (C/C0)
: o.o
0.1508
- .2231
0.5447
-1 .273
In some cases, a simple first-order model may be inadequate for des-
cribing the degradation process. The modeler, should select one of three
options:
1. Assume a"first-order model even though the fit of the data
is not good
2, Investigate breaking the time after application into intervals
during which single first-order decay models give adequate fits
3. Change to different models or model algorithms
6.1.1.6 Pesticide adsorption partition coefficient
The adsorption partition coefficient describes the distribution of
pesticide between water and sediment phases. It is partly a function of
soil properties and partly a function of chemical properties. For com-
pletely reversible, linear, instantaneous adsorption (a characteristic
assumption of most runoff models) the coefficient is defined as;
160
-------
©
o
o
^
o
-1.3
k = -0.05/day
ELAPSED TIME (DAYS)
FIGURE 6.3. Graphical determination of a first-order pesticide
decay rate.
161
-------
cs
Kp = — (6.5)
where iC = adsorption partition coefficient (L /M)
Cg = concentration of pesticide adsorbed to soil
materials at equilibrium (M/M)
Cw = concentration of pesticide in the dissolved
phase at equilibrium (M/L^)
Ibis ratio can be determined by measurement of the adsorbed and dissolved
phase pesticide concentrations. It can also be estimated from:
Cs (OC) Koc
(6.6)
Cw 100
where (OC) = percent of organic carbon in the soil
= organic carbon partition coefficient
The concentrations Cs and Cw» or the organic carbon content of the surface
and subsurface soils should be determined by the field study in order to
determine Kp.
Another formulation of the partition coefficient iss
(6.7)
where Cg = that portion of the pesticide that is permanently fixed to
soil materials once applied
nis formulation is used to account for pesticides that do not completely
desorb once adsorption has occurred.
Other formulations take into account the non-linearity of the
adsorption/desorption algorithm. One such formulation is the Freundlich
equation:
162
-------
= C_/CW1/N (6.8)
where K£ = Freundlich coefficient
N = Freundlich exponent
For N = 1.0, the Freundlich isotherm reduces to the simple linear isotherm.
At low concentrations the assumption of linearity is often justified.
To evaluate the adsorption parameters required by these algorithms, the
data can be plotted or regression analyses can be performed. In either
case, "best fit" straight lines are estimated for the data.
Consider the adsorption data of Table 6.4. Regression analysis yields
the information shown at the bottom of the table. The model cs = K
-------
TABLE 6.4
ADSORPTION DATA FOR A PESTICIDE IN A SOIL-WATER SYSTEM
(ppb)
i
'
Adsorption
Desorption
0.
1.
5.
10.
50.
100.
500.
100.
50.
10.
5.
1.
0.
0
0
0
0
0
0
0
0
0
0
0
0
0
Model
Ce «= KflC«
log Cw
0.
0.
1.
1.
2.
2.
2.
1.
1.
0.
0.
0000
6990
0000
6990
0000
6990
0000
6990
0000
6990
0000
CS
(ppb)
0
40
185
428
1856
3850
18000
3900
1900
500
250
50
35
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
Intercept
1
2
2
3
3
4
3
3
2
2
1
1
Slope
36. 231
log Cs
.6021 .
.2672
.6314
.2686
.5855
.2553
.5911
.2788
.6990
.3979
.6990
.5441
R2
(all data)
cs . c£
(all data)
log Cs - log
(all data, except zeros)
80.56
1.6649
35.99
0.9605
0.9996
0.9971
1 Slope - Kd =
_ £(CsCw)
' 164
-------
At n-1
Q = /J q(t) dt [q(o) + q(n) + 2 I q(j)] (6.10)
2 j=1
Simpson's rule, which is, in general, more accurate is given by:
At n-1 n-2
Q = /Jj q(t)dt = — [q(0) + q(n) + 4^ q(j) + 2^ q(j)] (6.11)
3 j=1 j=2
j odd j even
To use either of these formulae, the data must be divided into equally spaced
intervals. This may entail deriving an equal interval hydrograph from a
breakpoint hydrograph. Once this is accomplished, these integration formulae
(or quadratures) are easily used. An example is shown in Figure 6.4. The
volume derived (Q) is the area under the q(t) curve. When a constituent in
the water is involved, the same integration procedure can be used to determine
INTEGRATION BY TRAPEZOIDAL RULE
O. = 38.85 LITERS
BY SIMPSON'S RULE
.6.45
Q = 38.40 LITERS
.8
TIME (MINUTES)
FIGURE 6.4. Example of numerical integration of a hydrograph using
Trapezoidal and Simpson's Rule.
165.
-------
the total amount of material in the runoff water. An additional step is
required, however. The concentration of the constituent in the water is*
first multiplied by the flow rate to give a mass flux (i.e., M/L x L/T =
M/T) . The mass flux curve is then integrated to obtain the total mass.
This procedure is denoted by:
M = J| q(t> Cw(t) dt (6.12)
!
Likewise, for a constituent adsorbed to sediment,
M = JQ q(t) Cs(t} S(t} dt (6.13)
where S(t) = sediment concentration in the runoff water at time 't*
6.1 .2.2 Frequency analysis
Many tiroes it is useful to compare the frequency with which events of
certain magnitudes occur in both the observed and simulated moclel outputs.
This is accomplished by grouping the data to intervals and counting the
number of occurrences in each interval in the observed and simulated data
sets.
The construction of frequency histograms is a relatively straight-
forward procedure. Consider the flow data in Table 6.5. Twenty-one inter-
vals of 25 ft^/sec each were established (Table 6.6) and the number of flows
in each of the intervals was counted. These counts appear in columns 3 and
4. The collective of these interval counts is called a frequency histogram.
When the count in each interval is divided by the total number of occurrences
in the histogram {columns 7 and 8) a relative frequency histogr«un results
(i.e., the area under the histogram is unity). When these relative counts
are summed over each interval, a cumulative relative frequency histogram
results. This cumulative histogram can be used in statistical testing of
model simulation results as discussed in the next section.
6.2 MODEL CALIBRATION AND VERIFICATION
The calibration process is performed to adjust model parameters so
that simulated and observed system outputs agree within some measure of
acceptance. Normally, this is accomplished in several stages. The logical
order is to calibrate terrestrial hydrologic processes followed by stream
hydraulics in order to properly simulate watershed hydrologic response.
Simulated watershed outputs and changes in storage are compared whenever
possible to observed values. Next/ sediment washoff from the land surface
followed by instream sediment process calibration is performed. Normally,
after the system is calibrated, the simulation results are verified by
166
-------
TABLE 6.5. INDEPENDENT OBSERVED AND SIMULATED FLOW TIME SERIES
FOR THE ARROYO COLORADO AT WESLACO, TEXAS
O S O S O
124.95
133.79
192.50
130.98
140.75
114.95
119.38
172.73
162.89
117.13
138.81
138.17
160.38
359.02
104.71
88.97
92.15
116.61
105.28
91 .82
80.14
72.80
219.67
78.48
90.69
126.
114.
113.
102.
92.5
95.7
141.
155.
106.
108.
140.
111.
168.
307.
119.
149.
171.
174.
157.
139.
122.
112.
325.
132.
133.
86.33
82.96
80.51
78.72
107.33
81 .31
87.85
267.97
104.67
99.2
96.28
93.52
136.07
223.16
123.13
120.19
119.05
117.02
171.82
124.32
126.95
138.16
146.92
2465.2
222,22
113.
149.
98.5
121 .
111.
114.
107.
121 .
92.90
89.0
92.5
101 .
94.3
188.
82.3
117.
105.
152.
138.
136.
150.
148.
144.
1410.
131.
151.45
237.98
144.17
134.161 .
135.26
125.33
119.13
113.86
108.86
104.64
177.07
112.92
107.71
439.09
132.14
123.83
207.73
132.62
142.56
128.76
162.97
145.48
138.00
304.
266.
137.
156.
161.
168.
139.
136.
127.
114.
129.
11 1 .
135.
123.
143,
168.
142.
129.
149.
106.
94.6
87.6
106.
simulating the outputs for a different period of time and comparing them to
observed outputs.
Simulations rarely match observations exactly. There are several
possibilities for explaining these discrepancies (Young and Alward, 1983):
* There may be errors in the mathematical description of the real-
world process (i.e., the model algorithms)
• There may be errors in the observed measurements that have been
made
• There may be errors in the input parameters or time series required
to use the model
167
-------
TABLE 6.6. FREQUENCY ANALYSIS OF FLOW DATA FROM THE ARROYO COLORADO
Interval Range
(1) (2)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
2 -25
25+-50
50+-75
75+-100
100+-125
125-f- 150
150+-175
175+-200
200+-225
225+-250
250+-275
275+-300
300+-325
325+-350
350+-375
375+-400
400+-425
425+-450
450+-475
475+-500
500+
# Occurrences
(3) (4)
S
0
0
1
15
22
18
6
2
4
1
1
0
0
0
1
0
0
1
0
0
1
o
0
0
0
10
23
24
10
1
0
1
1
0
3
0
0
0
0
0
0
0
1
Re 1 . Frequency
(5) (6)
S
0
0
0.0137
0.2055
0.3014
0.2466
0.0822
0.0274
0.0548
0.0137
0.0137
0
0
0
0.0137
0
0
0.0137
0
0
0.0137
O
0
0
0
0.1370
0.3151
0.3288
0.1370
0.0137
0
0
0.0137
0
0.0411
0
0
0
0
0
0
0
1 .0137
Cum . Frequency
(7) (8)
S
0
0
0.0137
0.2192
0.5206
0.7672
0.8494
0.8768
0.9316
0.9453
0.9590
0.9590
0.9590
0.9590
0.9727
0.9727
0.9727
0.9864
0.9864
0.9864
1 .0000
O
0
0
0
.1370
0.4521
0.7809
0.9179
0.9316
0.9316
0.9316
09453
0.9453
0.9864
0.9864
0.9864
0.9864
0.9864
0.9864
0.9864
0.9864
1 .0000
TOTAL
73
73
1.0000 1.0000
Figure 6.5 schematically shows where errors, or differences, can occur
whenever model results are compared to field measurements from a natural
system, such as a watershed. Similar to the categories listed above,
Donigian (1982) has discussed the various types of errors that can occur in
a model application, in terms of input errors, parameter errors, and output
errors. Whenever a measurement or observation is made, a potential source
of error is introduced. Although these errors may be difficult to detect,
users of the data should be informed of the potential uncertainty involved
and should consider these uncertainties during the model application.
In spite of these errors or differences, as discussed in section 5 and
noted above in the discussions that follow, the observed phenomena are
assumed to be perfectly measured and the sources of error rest with the
model and its various inputs. We will concern ourselves mainly with the
analysis of simulated and observed time series to determine how well these
two agree (i.e., goodness-of-fit) discounting any potential errors in the
observed measurements. Goodness-of-fit can be used for two purposes: to
ascertain when our calibration effort is good enough, and to ascertain if,
168
-------
SYSTEM INPUTS
MODEL
(System Representation)
Parameter
Estimation
Calibration
I Parameter
i Adjustment
1
NATURAL
SYSTEM
Calibration/Verification
Observed
Values
MODEL VALIDATION
System
Outputs
FIGURE 6.5. Model vs. natural systems:
errors (Donigian, 1982).
inputs, outputs and
given a good calibration, we can expect the model to perfom well during
another time period (verification).
Goodness-of-fit techniques can be applied to a number of statistics
derived from the observed and simulated time series. Aitken (1973) demon-
strated several techniques of model analysis including mean, standard
deviation, coefficient of determination (r2), coefficient of efficiency,
serial correlation coefficients, sign tests, and residual mass curve coeffi-
cients. Young and Alward (1983) used the coefficient of variation and the
Kolmogorov-Smirnov test in determining goodness-of-fit of ARM and HP'S model
calibrations. Chen et al. (1984) used student's t and F tests to test mean
and variance, and the sign test, Kolmogorov-Smirnov (K-S test), Pearson
product-moment correlation coefficient, and the McCuen-Snyder index (McCuen
and Snyder, 1975) to indicate goodness-of-fit.
The fact is that there are a number of tests that can be used to test
differences in various aspects of two time series. No single test will be
best for all circumstances; multiple test statistics should be generated
and analyzed. Goodness-of-fit tests should also be tailored to those :
aspects of the t4 e series important to the problem at hand. For instance,
if one is concerned with flooding, the tests should concentrate on peak
169
-------
flows; whereas if one is interested in dissolved oxygen, correct simulation
of low flows would be most important.
Thus we are guided to the question, "Which variables and corresponding
statistical tests should be used to evaluate the calibration of models for
simulation of pesticide runoff?". Obviously volume of water is important
purely from a dilution standpoint. Because pesticides are transported in
runoff events from the watershed, peak storm flows are important. Because
pesticides are partitioned between sediment and water, the simulation of
sediment movement also is important. The simulation of peak flow, then,
becomes increasingly important for strongly adsorbed pesticides because
sediment transport, either from the land surface or instream, can be des-
cribed as power functions of flow with exponents >1.0.
Proper simulation of velocity (or flow) is important for strongly ad-
sorbed chemicals when the velocities are above the scour and deposition
shear velocities of particles to which they are attached. For weakly
adsorbed pesticides, sediment and, hence, peak flows should probably be de-
emphasized with proper simulation of runoff volumes given more attention,
The key concept here is that the emphasis for analysis may shift depending
upon pesticide properties, if we are interested in calibrating the hydrology
and sediment transport model to enable us to simulate pesticides with wide
ranges of adsorption properties, it is important to simulate both flow rate
(as well as velocity) and volumes properly, over the full range of watershed
response.
In addition, models can be calibrated in the frequency or real time
domains* That is, we can use tests to tell us whether the frequency
responses of the simulated and observed data are not different or if the
point-to-point (real time) simulated and observed results are not statis-
tically different.
The K-S test is sensitive to a wide range of alternatives, such as
differences in central tendency as well as differences in dispersion. It
keys on the maximum difference between the cumulative frequency distri-
butions of two data sets. Thus it considers mass of the distributions
(i.e., volume) as well as the closeness of frequency response of the two
distributions.
If point-to-point simulation of observed values is important (e.g.,
timing of peaks and valleys) then a "real-time" approach, rather that one
based purely on frequencies/ must be taken. Regression analysis is a
suitable tool for this for two reasons:
• it measures the point-to-point correlation between observed and
simulated data
• tests are available for inference concerning the slope and
intercept of the regression line
It can also be used to provide information about the relative masses of
the two samples because the means of both enter into the calculations of
the least squares method.
170
-------
These two methods have been chosen for discussion as ways to test
observed and simulated flow and sediment concentrations for model calibra-
tion purposes. There are some pitfalls, however, in using these methods
on time series data that should be avoided. These caveats are discussed
in the following sections, in which is discussed the analysis of data and
application of the tests.
While the value of statistics in calibration and verification is
enormous, rigorous comparisons of time series data for these purposes have
been largely neglected. Typically, the judgment of the modeler has been
the key criteria in judging goodness-of—fit. What is advocated here is
intelligent use of the tools available to us for making .the judgement of
"how-good-is-good." Certainly, the simulated and observed data should in
every case be plotted and inspected visually. We can usually tell an
"excellent" fit from an "atrocious" one. In the "atrocious" case, statis-
tical measures may be of little value. When observed and simulated values
are closer, however, statistical measures can be valuable in determining
whether changes in model parameters are actually "improving" the fit.
Valuable time and effort can be wasted by trying to "perfect" the calibra-
tion.
6.2.1 The Kolmogorov-Smirnov Two-Sample Test
The Kolmogorov-Smirnov two-sample test (K-S test) requires that cumula-
tive frequency distributions be developed from the data. In our applications
these data are almost always a time series and are nearly always serially
correlated to some extent. This fact requires some special preprocessing
of the data before the K-S test can be applied. This will be discussed
later.
First, let us show how the K-S two-sample test is used. The following
discussion is taken from Siegel (1954).
"The Kolmogorov-Smirnov two sample test is a test of whether two
independent samples have been drawn from the same population (or from
populations with the same distribution). The two-tailed test is sensi-
tive to any kind of difference in the distributions from which the two
samples were drawn - differences in location (central tendency), in
dispersion, in skewness, etc. The one-tailed test is used to decide
whether or not the values of the population from which one of the
samples was drawn are stochastically larger than the values of the
population from which the other sample was drawn. This two-sample test
is concerned with the agreement between two cumulative distributions.
If the two samples have in fact been drawn from the same population
distribution, then the cumulative distributions of both samples may be
expected to be fairly close to each other, inasmuch as they both should
show only random deviations from the population distribution. If the
171
-------
two-sample cumulative distributions are "too far apart" at any point,
this suggests that the samples come from different populations. Thus a
large enough deviation between the two sample cumulative distributions
is evidence for rejecting H0 (i.e., the hypothesis of same distribu-
tions) . Applying the Kolmogorov-Smirnov two-sample test, we make a
cumulative frequency distribution for each sample of observations, using
the same intervals for both distributions. For each interval, then,
we subtract one step function from the other. The test focuses on the
largest of these observed deviations.
Let sn.. (X) = the observed cumulative step function of one of the
samples, that is, n^ (X) = K/n^, where K = the number of scores equal
to or less than X. And let sn^ (X) = K/n2« Now the Kolmogorov-Smirnov
two-sample test focuses on:
D = maximum [Sn1 (X) - Sn2 (X)] (6.14)
for a one-tailed test, and on:
D = maximum Sn1 (X) - Sn2 (X)| (6.15)
for a two-tailed test. The sampling distribution of D is known and the
probabilities associated with the occurrence of values as large as an
observed D under the null hypothesis (that the two samples have come
from the same distribution) have been tabled.
Notice that, for a one-tailed test, we find the maximum value of D
in the predicted direction [by equation 6.14] and that, for a two-tailed
test, we find the maximum absolute value of D [by equation 6.15], i.e.,
we find the maximum deviation irrespective of direction. This is because
in the one-tailed test, H-j (the alternative hypothesis) is that the popu-
lation values from which one of the samples was-drawn are stochastically
larger than the population values from which the other sample was drawn?
whereas in the two-tailed test, H-| is simply that the two .samples are
from different populations."
The analysis of data to produce a cumulative frequency distribution was
discussed earlier. When using the K-S test, it is desirable to use as many
grouping intervals as are feasible. If .individual values are available, it
is recommended that they be used in forming the empirical cumulative distri-
bution functions for subsequent comparison by the K-S technique.
One of the assumptions of the K-S test is that observations within a
sample are independent. The problem with applying the K-S test to time series
data is that, more often than not, this assumption is not met. This is due
to the fact that most natural time series (e.g., flow, sediment concentrations,
etc.) are serially correlated. This is especially true for larger watersheds
where streamflow, sediment transport, etc. are continuous. Less serial corre-
lation will be evident in data from smaller areas. In order to use a K-S
test, any serial correlation in the data must be removed from the sample.
172
-------
6.2.1.1 Tests for serial correlation—
The way to detect serial correlation in a data set is to compute the
Pearson product-moment correlation coefficient of each data point with its
preceding value, which is called the lag 1 serial-(or auto-) correlation
coefficent and is denoted r(1). If the data sets are lagged again so that
each data point is correlated with the second preceding point then the lag
2 coefficient, r(2), results. This process can be continued and serial
correlation coefficients r(k) can be computed (see Yevjevich, 1972 for a
complete discussion). A plot of r(k) versus k is called a serial correlogram.
An example is shown in Figure 6.6.
Notice that generally the r(k) decreases as k increases until they
hover close to or cycle around zero. Confidence limits (dashed lines) can
be computed around zero. Once the correlation coefficients consistently
fall inside these bands, we can say that they are statistically not different
from zero for this sample size and confidence level (for computation limits
see Anderson, 1942 or Jenkins and Watts, 1969). In the example correlogram
of Figure 6.7, then, we can say that approximately every 10th point (stream-
flow value for every 10th day, in this case), is uncorrelated (i.e., indepen-
dent) . To apply the K-S test, then, these two time series could be sampled
by taking every 10th point and using this new series to form the cumulative
distribution histograms.
While this method will virtually guarantee independence, others might
also be tried. Another method would be to aggregate data and perform the
test on monthly as opposed to daily data, for instance, while more aggregated
series generally have less serial correlation, this method does not guarantee
i ndependence.
6.2.1.2 Example application
Data from the HSPF calibration of the Arroyo Colorado watershed in
Texas (Dean et al., 1984) were used to construct the following example of
the application of the K-S test to serially correlated daily streamflow
data.
From the serial correlogram in Figure 6.7, it was noted that every
10th point (or day) in both the simulated and observed time series is
independent. Therefore the original two time series of 730 values were
sampled by selecting every 10th value in each. The resulting independent
subsets were shown in Table 6.5.
Table 6.6 shows the frequency and cumulative frequency distributions
of the observed and simulated independent series. The final column, |D| is
the absolute difference between the observed and simulated cumulative
frequency distributions. The maximum of these occurred in interval 4,
therefore Dj^y, = 0.0822. The value of D at a 0.05 (5%) probability level
is calculated from:
173
-------
z
Ul
u.
u.
Ul
o
0
z
o
Ul
c
£E
O
o
tu
CO
.6-
.6 -
»4 —
.3-
.2-
.1-
LAQ
FIGURE 6.6. Serial correlogram of residuals.
174
-------
Ul
8 10
NUMBER OF LAGS
FIGURE 6.7. Serial correlogram of observed and simulated mean daily flow
for the Main Floodway at Weslaco.
-------
D0.05 = 1 «36 ^ (n1+n2)/(ni n2) , (6.16)
Table 6.7 shows how D is calculated for different significance levels,
where n-j and n2 are the respective sample sizes (in this case n^ = n2 = 73).
Thus the value of D0>05 is calculated to be 0.225. Thus, Dmax < DQ.OS and
therefore there is no evidence to suggest that the distributions were drawn
from different populations.
6.2.2 Linear Regression analysis
Simple linear regression analysis involves the point-to-point compari-
son of an independent and a dependent variable. This is accomplished by
fitting a line through the x, y pairs of data. The line is fit by minimizing
the sum of squares of the deviations in the y-direction of each point from
the best-fit line. The line can be described by two parameters: a slope
and a y-intercept. The model, of course, is:
yj_ = a + 0 X£ + % , i - 1 , .. . , n (6.17)
where y^ — dependent variable
a = y-intercept
8 = slope of the linear relationship
x^ - independent variable
e = error :
The method for determining the coefficients in Equation 6.17 can be found
in a number of texts ( Bhattacharyya and Johnson, 1977? Haan, 1977; Fisher,
1981).
The major interest in applying regression analysis for the comparison
of simulated and observed time series is to test the slope and intercept
(ce and (5) of the regression equation. The aim in model development is
to obtain an a not statistically different from zero, and a 3 not statis-
tically different from 1 . Confidence in our inference about a and 3 is
enhanced, however, by knowing that the linear model is a good one and that
the good fit is not just fortuitous. This can be done simply by visual
inspection of the line plotted on a scattergram of the points. A more
objective method is by computing the coefficient of determination from the
data. This coefficient is computed by:
r2 = 1 - SSE (6.18)
SST
176
-------
.TABLE 6.7. CRITICAL VALUES IN THE KOLMOGOROV-SMIRNOV TESTS
(Chemical Rubber Co., 1974}
CRITICAL VALUES Of D IN THE KOLMOGOROV-SMIRNOV TWO-SAMPLE TEST
(Large aamplei: two-tailed tort)
Level of aignificancQ
,10
.05
.025
.01
.005
.001
Value of D so large at to call for rejection
of Ht at the indicated level of Bjnificance,
where D m maximum JP.,(X) — f.,(JQ!
1 M K+"«
11 *l*t
i an /"J,,,±B?
\ "UN
I tS f11"1""*
\ Hi»«
. ._ pMu
\ miM
/". + »•
>( »ifH
,.„ JUi
\ «»f
CRITICAL VALUES FOR THE K0LMOGOROV-SMIRNOV TEST OF H,: P,(x) -
I Sample aim n,
1
2
3
4
5
6
7
8
9
10
12
15
Sample sire m
1
*
2
•
.
3
Reject R, if
D - max \fm
exceeds the ti
The upper va
at moat .05 ai
value give* a
4
3/4
*
5
*
*
*
12/15
*
16/20
8
4/5
4/5
6
*
*
*
5/6
*
9/12
10/12
20/30
25/30
4/6
5/6
7
.
*
18/21
*
21/28
24/28
25/35
30/35
29/42
35/42
5/7
5/7
ibulftted value.
tue gives a level
ad the lower
level »t matt .01.
8
*
7/8
*
18/24
*
8/8
7/8
27/40
32/40
16/24
18/24
35/58
42/56
5/8
6/8
9
*
*
16/18
*
7/8
8/9
27/36
32/36
31/45
36/45
12/18
14/18
40/63
47/63
45/72
54/72
5/9
6/9
10
;
9/10
*
14/20
16/20
7/10
8/10
19/30
22/30
43/70
53/70
23/40
28/40
52/90
62/90
6/10
7/10
12
9/12
11/12
8/12
10/12
7/12
9/12
14/24
16/24
20/38
24/36
6/12
7/12
15
10/15
11/15
15/30
19/30
30/60
38/60
7/15
8/15
Note 1; Where * appear*, do not reject IT, at the green leveL
Note 2: For large value* of nt sad n* tho following approximate formula* may be used:
a - .05;
a - .01:
1.38
1.63
177
-------
where
r2 = coefficient of determination
SSE = sums of squares of the residuals (deviations
in the y-direction from the best fit line)
SST = (n-1) times the sample variance of the dependent
y observations
n = total number of x, y points
The coefficient of determination, r^, can take on values between 0 and 1 .
Values closer to 1 indicate that the points are closer to the best fit line.
In fact the value of r^ may be thought of as the fraction of the total
variability in y that is explained by the linear relationship. It should be
noted that, for simple bivariate (x,y) regression, r^ is identically the
square of the Pearson product-moment correlation coefficient. For multiple
regression (more than one independent variable), however, this is not the
case (Fisher, 1981).
There are some special considerations for applying regression techniques
to the problem of comparing two time-series. Yevjevich (1972b) states that
the method of least squares only gives reliable estimates of a and 3 if two
conditions are met. First, the residuals must be independent and, second,
the variance of the residuals must not be a function of the independent
variable x.
The first assumption is equivalent to saying that no time dependent or
serial correlation exists among the residuals* This can easily be checked
by the method of constructing a serial correlogram using the ressiduals.
The second assumption is referred to as homoscedasticity. If the
scatter of the x, y points around the regression line tends to increase as
the values of x increase, then the assumption is violated. The easiest way
of dealing with this problem is to transform the data so that the variance
is approximately equal along the regression line. The regression analysis
is then performed on the transformed data.
No further assumptions are required for assuming that the estimates
of a and (3 are unbiased. For the purposes of making inferences about the
regression coefficients, however, the distribution of the residuals around
the regression line should be normal. Bhattacharyya and Johnson (1977)
state that a moderate deviation from normality does not impair inference
especially when the data set is large.
Given that these conditions are met, we can test a and 0 to see whether
they meet the requirements for a good fit of the simulated to observed
data.
178
-------
6.2.2.1 Significance tests for a and ft
To test the hypothesis that 3 equals unity, the t statistic is computed:
b-1
t = (6.19)
sb
where b = S^/S (the estimate of 0 ) (6.20)
Sb = / MSE/SXX
Sxx = I 2
Syy - I (y±-y)2
MSB = (Syy - sJy/Sxx)/(n-2)
The value of t can be compared to values of critical t at some probability
level with n-2 degrees of freedom.
The parameter a = 0 can be tested by:
(6.22)
where a = y - b x
. /I + X2
with n-2 degrees of freedom.
n sxx
6.2.2,2 Example regression analysis
An example of the above procedures is provided below using the data
from the Arroyo Colorado "(Table 5.5). The plotted data are shown in Figure
6.8. The grouping of most of the data at the 90-160 ft3/sec level with
only a few points at higher flows, however, may lead to some problems that
will be discussed later. Notice that there is one point (x=1410, y=2465.2)
that does not appear on the graph. Prom the least squares we derive the
parameter estimates:
179
-------
500-
CO 400
IL
O
S 300
2
W
200-
100 -
I
100
T
200
I
300
400
500
OBSERVED FLOW (CF3)
FIGURE 6.8. Regression analysis of observed and simulated
flows for the Arroyo Colorado.
180
-------
a = 94.06
b = 1 .71
The r2 value is 0.92, It should be pointed out here that spurious (inflated
estimates of correlation as evidenced by high values of 'r1) correlation
can arise out of the type of situation represented in Figure 6.8 where most
of the data are clustered except for a few outlying points. In fact, the
reason that the slope is so large is due to the influence of the (x,y) pair
(1410, 2465). If this point were eliminated, the r^ value would drop
drastically-but the slope may or may not be closer to unity. Haan (1977)
provides a more in-depth discussion of spurious correlation.
Once the least squares line is defined it can be plotted on the scatter-
gram and visually compared to the line of perfect agreement. These are
also plotted in Figure 6,8.
The residuals are tabulated in Table 6.8. An autocorrelogram of the
residuals was shown in Figure 6.6. The correlogram indicates that although
one or two of the coefficients lie outside the 95% confidence band, on the
whole the correlogram is well contained indicating an independent series.
A test for normality of the residuals is done as follows. Compute the
frequency histogram of the residuals as shown in Table 6.9. Then compute
the mean and variance of the residuals and compute the standard normal
deviate value of the upper end of the frequency class. The standard normal
deviate is computed by:
ey - ey
(6.23)
where 6y = value of the upper limit of a frequency class
ey = mean of the residuals
SQ = standard deviation of the residuals
In this case se = 79.296. Once the standard normal deviates (SND) are found,
the cumulative area under the normal curve lying to the left of the SND can
be found in Table 6.10. Subtracting the cumulative probabilities, one can
find the individual cell probabilities (column 4). Multiplication by the
number of observations (73) gives the expected cell frequency.
The test for goodness-of-fit is Pearson's x^« B¥ using the formula
below, the x^ statistic can be founds
181
-------
TABLE 6.8. RESIDUAL OF REGRESSION OP ARROYO COLORADO SIMULATED
AND OBSERVED STREAMELOW (S-(a + K>)
Data
Point #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Residual
3.26
32.65
93.07
50.39
76.42
45.14
-27.99
1 .39
75.45
26.26
-6.85
42.17
-33.22
-72.59
-4.99
-72.10
-106.59
-87.27
-69.49
-52.13
-34.70
-24.92
-242.76
-33.48
-42.98
Data
Point #
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
48
48
49
50
Residual
-13.10
-78.11
5.09
-34.41
11.33
-19.83
-1.30
154.84
39.66
40.87
31.95
14.64
68.66
-4.69
76.27
13.91
33.32
-49.19
29.58
-14.49
-35.83
-21 .20
-5.59
144.95
91.97
Data
Point #
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
Residual
-275.02
-123.43
3.65
-38.30
-46.36
-68.27
-24.82
-24.45
-14.54
3.50
; 50.24
16.92
-29.39
322.54
-18.66
-69.77
58.65
5.80
-18.51
41 .32
45.05
89.54
50.56
182
-------
TABLE 6.9* COMPUTATION OF THE X STATISTIC FOR THE TEST OF NORMALITY
OF RESIDUALS
Cell (to. 8mnge
1
2
3
£ *
w
5
6
7
S
9
10
- oo to -100.
-100 to -75,
-75 to -50.
-50 to -25.
-25 to 0
0 to 25
25 to 50
50 to 75
75 to 100
100
Observed
Frequency
4
2
6
11
15
10
10
5
7
3
Standard
Normal Deviate
-1.261
-.8458
-.6305
-.3154
0
.3154
.6305
.9458
1.261
Cumulative
Normal Prob .
.1038
.1736
.2643
.3783
.5
.6217
.7357
.8264
.8962
1.000
Cell
Probability
0.1038
0.0698
0.0907
0.1140
0.1217
0.1217
0.1140
0.0907
0.0698
0.1038
Expected
Frequency
7.577
5.095
6.621
6.322
8.88
8.88
8.322
6.621
5.095
7.577
(0-E)a
I
1.689
1.883
0.058
0.861
4.497
0.138
0.336
0.394
0.715
2.744
-------
TABLE 6.10. STANDARD NORMAL PROBABILITIES
z
-3.5
-3.4
-33
-3.2
-3.1
-3.0
-2.9
-2.8
-2.7
-2.6
-2.5
-2.4
-23
-22
-2.1
-2.0
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-13
-1.2
-1.1
-1.0
-.9
-.8
^^ • /
-.6
-.5
-.4
-3
-2
-.1
-.0
.00
.0002
.0003
.0005
.0007
.0010
.0013
.0019
.0026
.0035
.0047
.0062
.0082
.0107
.013$
.0179
.0228
.0287
.0359
.0446
.0548
.0668
.0808
.0968
.1151
.1357
.1587
.1841
.2119
.2420
.2743
3085
3446
3821
.4207
.4602
.5000
.01
.0002
.0003
.0005
.0007
.0009
.0013
.0018
.0025
.0034
.0045
.0060
.0080
.0104
.0136
.0174
.0222
.0281
.0351
.0436
.0537
.0655
.0793
.0951
.1131
.1335
.1562
.1814
.2090
.2389
.2709
3050
3409
3783
.4168
.4562
.4960
.02
.0002
.0003
.0005
.0006
.0009
.0013
.0018
.0024
.0033
.0044
.0059
.0078
.0102
.0132
.0170
.0217
.0274
.0344
.0427
.0526
.0643
.0778
.0934
.1112
.1314
.1539
.1788
.2061
.2358
.2676
3015
3372
3745
.4129
.4522
.4920
.03
.0002
.0003
.0004
.0006
.0009
.0012
.0017
.0023
.0032
.0043
.0057
.0075
.0099
.0129
.0166
.0212
.0268
.0336
.0418
.0516
.0630
.0764
.0918
.1093
.1292
.1515
.1762
.2033
.2327
.2643
.2981
3336
3707
.4090
.4483
.4880
.04
.0002
.0003
.0004
.0006
.0008
.0012
.0016
.0023
.0031
.0041
.0055
.0073
.0096
.0125
.0162
.0207
.0262
.0329
.0409
.0505
.0618
.0749
.0901
.1075
.1271
.1492
.1736
.2005
.2297
.2611
.2946
3300
3669
.4052
.4443
.4840
.05
.0002
.0003
.0004
.0006
.000?
.0011
.0016
.0022
.0030
.0040
.0054
.0071
.0094
.0122
.0158
.0202
.0256
.0322
.0401
.0495
.0606
.0735
.0885
.1056
.1251
.1469
.1711
.1977
.2266
.2578
.2912
3264
3632
.4013
.4404
.4801
.06
.0002
.0003
.0004
.0006
.0008
.0011
.0015
.0021
.0029
.0039
.0052
.0069
.0091
.0119
.0154
.0197
.0250
.0314
.0392
.0485
.0594
.0721
.0869
.1038
.1230
.1446
.1685
.1949
.2236
.2546
.2877
3228
3594
3974
.4364
.4761
P(7. <
~tri
.07
.0002
.0003
.0004
.0005
.0008
.0011
.0015
.0021
.0028
.0038
.0051
.0068
.0089
.0116
.0150
.0192
.0244
.0307
.0384
.0475
.0582
.0708
.0853
.1020
.1210
.1423
.1660
.1922
.2206
.2514
.2843
3192
3557
3936
.4325
.4721
*A
X
.08
.0002
.0003
.0004
.0005
.0007
.0010
.0014
.0020
.0027
.0037
.0049
.0066
.0087
.0113
.0146
.0188
.0239
.0301
.0375
.0465
.0571
.0694
'.0838
.1003
.1190
.1401
.1635
.1894
.2177
.2483
.2810
3156
3520
3897
.4286
.4681
X.
0
.09
.0002
.0002
.0003
.0005
.0007
.0010
.0014
.0019
.0026
.0036
.0048
.0064
.0084
.0110
.0143
.0183
.0233
.0294
.0367
.0455
.0559
.0681
.0823
.0985
.1170
.1379
.1611
.1867
.2148
.2451
.2776
3121
.3483
3859
.4247
.4641
184
-------
TABLE 6.10 (Cont'd). STANDARD NORMAL PROBABILITIES
z
.0
.1
2
3
A
3
.6
.7
.8
.9
1.0
I.I
12
13
1.4
1.5
1.6
1.7
1.8
K9
2.0
2.1
12.
2.3
2.4
23
2.6
2.7
2.8
2.9
3.0
3.1
32
33
3.4
33
.00
.5000
3398
3793
.6179
.6554
.6915
.7257
.7580
.7881
.8159
.8413
.8643
.8849
.9032
.9192
.9332
.9452
.9554
.9641
.9713
.9772
.9821
.9861
.9893
.9918
.9938
.9953
.9965
.9974
.9981
.9987
.9990
.9993
.9995
.9997
.9998
.01 .02 .03 .04 .05 .06 .07 .08 .09
3040 3080 .5120 3160 3199 3239 3279 .5319 .5359
3438 .5478 .5517 3557 3596 3636 3675 3714 .5753
3832 .5871 3910 3948 3987 .6026 .6064 .6103 .6141
.6217 .6255 .6293 .6331 .6368 .6406 .6443 .6480 .6517
.6591 .6628 .6664 .6700 .6736 .6772 .6808 .6844 .6879
.6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224
.7291 .7324 .7357 .7389 .7422 .7454 .7486 .7517 .7549
.7611 .7642 .7673 .7703 .7734 .7764 .7794 .7823 .7852
.7910 .7939 .7967 .7995 .8023 .8051 .8078 .8106 .8133
.8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389
.8438 .8461 .8485 .8508 .8531 .8554 .8577 .8599 .8621
.8665 .8686 .8708 .8729 .8749 .8770 .8790 .8810 .8830
.8869 .8888 .8907 .8925 .8944 .8962 .8980 .8997 .9015
.9049 .9066 .9082 .9099 .9115 .9131 .9147 .9162 .9177
.9207 .9222 ,9236 .9251 .9265 .9279 .9292 .9306 .9319
.9345 .9357 .9370 .9382 .9394 .9406 .9418 .9429 .9441
.9463 .9474 .9484 .9495 .9505 .9515 .9525 .9535 .9545
.9564 .9573 .9582 .9591 .9599 .9608 .9616 .9625 .9633
.9649 .9656 .9664 .9671 .9678 .9686 .9693 .9699 .9706
.9719 .9726 .9732 .9738 .9744 .9750 .9756 .9761 .9767
.9778 .9783 .9788 .9793 .9798 .9803 .9808 .9812 .9817
.9826 .9830 .9834 .9838 .9842 .9846 .9850 .9854 .9857
.9864 .9868 .9871 .9875 .9878 .9881 .9884 .9887 .9890
.9896 .9898 .9901 .9904 .9906 .9909 .9911 .9913 .9916
.9920 .9922 .9925 .9927 .9929 .9931 .9932 .9934 .9936
.9940 .9941 .9943 .9945 .9946 .9948 .9949 .9951 .9952
.9955 .9956 .9957 .9959 .9960 .9961 .9962 .9963 .9964
.9966 .9967 .9968 .9969 .9970 .9971 .9972 .9973 .9974
.9975 .9976 .9977 .9977 .9978 .9979 .9979 .9980 .9981
.9982 .9982 .9983 .9984 .9984 .9985 .9985 .9986 .9986
.9987 .9987 .9988 .9988 .9989 .9989 .9989 .9990 .9990
.9991 .9991 .9991 .9992 .9992 .9992 .9992 .9993 .9993
.9993 .9994 .9994 .9994 .9994 .9994 .9995 .9995 .9995
.9995 .9995 .9996 .9996 .9996 .9996 .9996 .9996 .9997
.9997 .9997 J997 .9997 .9997 .9997 .9997 .9997 .9998
.9998 .9998 .9998 .9998 J998 .9998 .9998 .9998 .9998
185
-------
k (0 ~ E)2
2 = I ---------- (6.24)
where O^ = observed cell frequency
Ej_ » expected cell frequency
k = number of cells
The x2 statistic is shown in the table also. From Table 6.11, which shows
the percentage points of the x2 distribution, for k-1 degrees off freedom
and 0.05 probability, the x2 value is 16.919. Therefore we cannot reject
the hypothesis that the distribution of residuals is non-normal because
since our x^ of 13.3 does not exceed the x g Q 05 value of 16.9.
Since we have established that the residuals are independent and
normally distributed, we can perform the t-test to infer whether 3 differs
from unity and a differs from zero. From the computations, we find that
the t for 3 is 11.14 and the t for a is -6.75. From the t tables (Table
6.12), we can find critical values of t at the 0.05 probability level with
n-2 (73) degrees of freedom. Since this value should be <1 .6, we can
conclude that the g is different from unity and the a is different from
zero. Thus the point-to-point correlation between our model and the observed
data is imperfect.
6.3 MODEL APPLICATION AND SENSITIVITY TESTING
The successful integration of site-specific information and model
calibration is the first step in performing exposure assessments of pesticide
runoff under different hydrologic responses. '
The concept of risk reflects the probability of causing an effect and
implies that an organism must first have been exposed to the pesticide for
sufficient time and at a high enough concentration to inflict damage. The
use of continuous simulation models to generate data to derive probability
statements about hydrological events is an accepted technique, simulation
models have been used to estimate probabilities of environmental exposure
expressed as cumulative frequency distributions.
Frequency distributions of the mass of pesticide lost from runoff or
expressed as concentrations in stream channels appear to be valuable tools
to assist in assigning risk to pesticide use.
The many climatologic, hydrologic, agronomic, and pesticide charac-
teristics create numerous and diverse scenarios that may have to be investi-
gated when simulating pesticide runoff. The use of sensitivity analysis can,
186
-------
TABLE 6.11. PERCENTAGE POINTS OF X2 DISTRIBUTIONS
H_ ^
d.fX.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
50
60
70
80
90
100
.995
392704 X 10- I0
.0100251
.0717212
.206990
.411740
.675727
.989265
1.344419
1.734926
2.15585
2.60321
3.07382
3.56503
4.07468
4.60094
5.14224
5.69724
6.26481
6.84398
7.43386
8.03366
8.64272
9.26042
9.88623
10.5197
11.1603
11.8076
12.4613
13.1211
13.7867
20.7065
27.9907
35.5346
43.2752
51.1720
59.1963
67.3276
.990
157088 XJO-9
.0201007
.114832
297110
.554300
.872085
1.239043
1.646482
2.087912
2.55821
3.05347
3.57056
4.10691
4.66043
5.22935
5.81221
6.40776
7.01491
7.63273
8.26040
8.89720
9.54249
10.19567
10.8564
11.5240
12.1981
12.8786
13.5648
14.2565
14.9535
22.1643
29.7067
37.4848
45.4418
53.5400
61.7541
70.0648
.975
982069X10"9
.0506356
.215795
.484419
.831211
1.237347
1.68987
2.17973
2.70039
3.24697
3.81575
4.40379
5.00874
5.62872
6.26214
6.90766
7.56418
8.23075
8.90655
9.59083
10.28293
10.9823
11.6885
12.4011
13.1197
13.8439
14.5733
15.3079
16.0471
16.7908
24.4331
32.3574
40.4817
48.7576
57.1532
65.6466
74.2219
.950
393214 XlO'8
.102587
.351846
.710721
1.145476
1.63539
2.16735
2.73264
3.32511
3.94030
4.57481
5.22603
5.89186
6.57063
7.26094
7.96164
8.67176
9J9046
10.1170
10.8508
11.5913
12.3380
13.0905
13.8484
14.6114
15.3791
16.1513
16.9279
17.7083
18.4926
26.5093
34.7642
43.1879
51.7393
603915
69.1260
77.9295
.050
3.84146
5.99147
7.81473
9.48773
11.0705
12.5916
14.0671
15.5073
16.9190
18.3070
19.6751
21.0261
22.3621
23.6848
24.9958
26.2962
27.5871
28.8693
30.1435
31.4104
32.6705
33.9244
35.1725
36.4151
37.6525
38.8852
40.1133
41.3372
42.5569
43.7729
55.7585
67.5048
79.0819
90.5312
101.879
113.145
124J42
/
.025
5.02389
737776
9.34840
11.1433
12.8325
14.4494
16.0128
17.5346
19.0228
20.4831
21.9200
23.3367
24.7356
26.1190
27.4884
28.8454
30.1910
31.5264
32.8523
34.1696
35.4789
36.7807
38.0757
39.3641
40.6465
41.9232
43.1944
44.4607
45.7222
46.9792
593417
71.4202
83.2976
95.0231
106.629
118.136
129,561
^\
.010
6.63490
921034
113449
132767
15.0863
16.8119
18.4753
20.0902
21.6660
23.2093
24.7250
26.2170
27.6883
29.1413
303779
31.9999
33.4087
34.8053
36.1908
37.5662
38.9321
40.2894
41.6384
42.9798
44.3141
45.6417
46.9630
482782
493879
50.8922
63.6907
76.1539
883794
100.425
112329
124.116
135.807
S»a
E9»^
V*
Xa
.005
7,87944
10.5966
12.8381
14.8602
16.7496
18.5476
202777
21.9550
23.5893
25.1882
26.7569
28.2995
29.8194
31.3193
32.8013
34.2672
35.7185
37.1564
38.5822
39.9968
41.4010
42.7956
44.1813
45.5585
46.9278
482899
49.6449
50.9933
523356
53.6720
66.7659
79.4900
91.9517
104215
116.321
128299
140.169
From "Biometrika Tables for Statisticians," Vol. 1, (3rd Edition) Cambridge University Press (1966); Edited by
E. S. Pearson and H. O. Hartley.
187
-------
TABLE 6.12. PERCENTAGE POINTS OF t DISTRIBUTIONS
d.f.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
40
60
120
oo
.25
1.000
.816
.765
.741
.727
.718
.711
.706
.703
.700
.697
.695
.694
.692
.691
.690
.689
.688
.688
.687
.686
.686
.685
.685
.684
.684
.684
.683
.683
.683
.681
.679
.677
.674
.1
3.078
1.886
1.638
1.533
1.476
1.440
1.415
1.397
1.383
1.372
1.363
1.356
1.350
1.345
1.341
1337
1.333
1330
1328
1325
1.323
1321
1.319
1318
1316
1.315
1314
1.313
1.311
1310
1.303
1.296
1.289
1.282
.05
6314
2.920
2.353
2.132
Z015
.943
.895
. .860.
.833
.812-
.796
.782
1.771
1.761
1.753
1.746
1.740
1.734
1.729
1.725
1.721
1.717
1.714
1.711
1.708
1.706
1.703
1.701
1.699
1.697
1.684
1.671
1.658
1.645
a
.025
12.706
4303
3.182
2.776
2.571
2.447
2365
2306
2.262
2.228
2.201
2.179
2.160
2.145
2.131
2.120
2.110
2.101
2.093
2.086
2.080
2.074
2.069
2.064
2.060
2.056
2.052
2.048
2.045
2.042
2.021
2.000
1.980
1.960
.01
31.821
6.965
4.541
3.747
3.365
3.143
2.998
2.896
2.821
2.764
2.718
2.681
2.650
2.624
2.602
2.583
2.567
2.552
2.539
2.528
2.518
2.508
2.500
2.492
2.485
2.479
2.473
2.467
2.462
2.457
2.423
2390
2358
2326
0 /„
.005
63.657
9.925
5.841
4.604
4.032
3.707
3.499
3355
3.250
3.169
3.106
3.055
3.012
2.977
2.947
2.921
2.898
2.878
2.861
2.845
2.831
2.819
2.807
2.797
2.787
2.779
2.771
2.763
2.756
2.750
2.704
2.660
2.617
2.576
188
-------
however, reduce the number of simulations substantially. Pertinent dis-
cussions are provided by Donigian et al« (1983), Carsel et al. (1984), and
USDA (1980). Sensitivity testing assists in identifying the parameters
that must be investigated to obtain a range of pesticide exposure.
The information required to describe the use of models in conducting
exposure assessments is outside the intent of this manual. The use of
models for conducting exposure assessments are described by Onishi et al. ,
1982? Mulkey and Falco (19835; and Donigian et al. , 1985.
6.4 EXTRAPOLATION OF SITE SPECIFIC DATA TO OTHER FIELD SITES
Extrapolating observed field hydrologic and chemical characteristics
data from site to site or region to region is possible. The development of
this capability requires the assessment of paired field data. The require-
ments for these types of extrapolations are two-fold: (1) the distributions
{e.g., standard deviation, coefficient of variation) of the hydrologic
characteristics (soil, water, crop information) have to be known; (2) the
distributions of necessary pesticide characteristics," such as soil/water
distribution coefficient (Kp) and degradation rate also have to be known*
Determination of these distributions will enable detailed sensitivity testing
to be conducted to estimate the range, or distribution of runoff both from
site-to-site and from year-to-year.
6.5 REFERENCES FOR SECTION 6
Aitken, A.P. 1973. Assessing Systematic Errors in Rainfall Runoff Models.
Journal of Hydrology. 20;121-136.
Anderson, R.L. 1942. Distribution of the Serial Correlation Coefficients.
Annals of Mathematical Statistics. .§.(1): 1-13.
Austin, T.A. and B.J. Claborn. 1971. Statistical Model of Short Duration
Precipitation Events. Proceedings of the Symposium on Statistical
Hydrology, Tucson, Arizona, 31 August-2 September 1971. U.S» Department
of Agriculture, Washington, DC. Miscellaneous Publication No. 1275.
Bhattacharyya, G.K. and R.A. Johnson. 1977. Statistical Concepts and
Methods. John L. Wiley and Sons, New York, NY.
Carsel, R.F., C.N. Smith, L.A. Mulkey, J.D. Dean, and P.P. Jowise. 1984.
User's Manual for the Pesticide Root Zone Model (PRZM): Release I.
U.S. Environmental Protection Agency, Athens, GA. EPA-600/3-84-109.
Chen, C.W., S.A. Ghereni, J.D. Dean, R.J.M. Hudson, and R.A. Goldstein.
1984. Development and Calibration of the Integrated Lake-Watershed
Acidification Study Model. In: Modeling of Total Acid Precipitation
Impacts. J.L. Schnoor (Ed.). Acid Precipitation Series, Vol. 9.
189
-------
J.I. Teasley (Ed.)» Butterworth Publishers.
Dean, J.D. and L.A. Mulkey. 1982. Stochastic Deterministic Modeling:
The Effects of Supplemental Irrigation on Watershed Hydrology and
Transport. In: Statistical Analysis of Rainfall and Runoff. V.P.
Singh (Ed.). Water Resources Publications, Littleton, CO.
Dean, J.D., D.W. Meier, B.R. Bicknell, and A.S. Donigian. 1984.
Simulation of DDT Transport and Pate in the Arroyo Colorado Watershed,
Texas. Draft Report. Prepared for U.S. EPA, Athens, GA.
Donigian, A.S. Jr. 1982. Field Validation and Error Analysis of Chemical
Fate Models. In: Modeling the Fate of Chemicals in the Aquatic
Environment. K.L. Dickson, A.W. Maki, and J. Cairns (Eds;.). Ann
Arbor Science, Ann Arbor, MI. pp. 303-323.
Donigian, A.S., Jr., D.W. Meier, and p.p. Jowise. 1985. stream Transport
and Agricultural Runoff of Pesticides for Exposure Assessment (STREAM):
A Methodology. U.S. Environmental Protection Agency, Athems, GA.
(In preparation).
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell, J.L. Baker, D.A. Haith,
and M.F. Walter. "Application of Hydrologic Simulation Program—«~
FORTRAN (HSPF) in Iowa Agricultural Watersheds." U.S. Environmental
Protection Agency, AThens, GA. Report No. EPA-600/S3-83-069. Nov.
1983.
Egbuniwe, N. 1975. Generation of Hourly Rainfall. Water Resources
Bulletin. 11(4);706-71 3.
Fisher, W.D. 1981. Statistics Economized. University Press of America.
Washington, DC.
Grace, R.A. and P.S. Eagleson. 1966. Synthesis of Short Duration Time
Increment Rainfall Sequences. Massachusetts Institute of Technology.
Cambridge, MA. Report No. 91.
Haan C.T. 1977. Statistical Methods in Hydrology. Iowa State University
Press, Ames, IA.
Hillel, D. 1980. Fundamentals of Soil Hydraulics, Academic Press, New
York, NY. •
Hjelmfelt, A.T. 1980. Time Distribution of Clock Hour Rainfall. In:
CREAMS: A Field Scale Model for Chemicals, Runoff and Erosion from
Agricultural Management Systems. W.G. Knisel (Ed.). U.S., Depart-
ment of Agriculture, Washington, DC. Research Report No. 26. 640
PP«
Jenkins, G.M. and D.G. Watts. 1969. Spectral Analysis and Its Applications.
Holden Day Co,, San Fransisco, CA.
190
-------
McCuen, R.H. and W.M. Snyder. 1975. A Proposed index for Computing
Hydrographs. Water Resources Research. 11(6);1021-1024.
Mulkey, L.A. and J.W. Falco. 1983. "Methodology for Predicting Exposure
and Fate of Pesticides in Aquatic Environments," Ins "Agricultural
Management and Water Quality", F.W. Schaler, G.W. Bailey (Eds.). Iowa
State University Press, Ames, IA.
Onishi, Y., S.M. Brown, A.R. Olsen, M.A. Parkhurst, S.E. Wise, and W.H.
Walters. 1982. "Methodology for Overland Flow and Instream Migration
and Risk Assessment of Pesticides.™ U.S. Environmental Protection
Agency, Athens, GA. Report No. EPA-600/3-82-024. -May.
Siegel, S. 1954. Nonparametric Statistics for the Behavioral Sciences.
McGraw-Hill Book Co., New York, NY.
U.S. Army Corps of Engineers. 1956. Snow Hydrology. Pacific Northwest
Division, Portland, OR.
U.S. Department of Agriculture, 1980. CREAMS: A Field-Scale Model for
Chemicals, Runoff, and Erosion from Agricultural Management Systems.
W.G. Knisel (Ed.). U.S. Department of Agriculture, Washington, DC.
Conservation Research Report No. 26.
Valencia, D. and J.C. Schaake. 1973. Disaggregation Processes in Stochastic
Hydrology. Water Resource Research. 9{3):580-585.
Yevjevich, V. 1972a. Stochastic Processes in Hydrology. Water Resources
Publications, Fort Collins, CO.
Yevjevich, V. 1972b. Probability and Statistics in Hydrology. Water
Resources Publications, Fort Collins, CO.
Young, G.K. and C.L. Alward. 1983. Calibration and Testing of Nutrient
and Pesticide Transport Models. In: Agricultural Management and
Water Quality. F.W. Schaller and G.W. Bailey (Eds.). Iowa state
University Press, Ames, IA.
191
-------
APPENDIX A
Table A.1. USEFUL CONVERSION FACTORS FOR ENVIRONMENTAL DATA BASES
Temperature
°C = (°F - 32)
°K » °C -f 273
°R = °F 4- 460
(5/9)
Pressure
Psi
1
0.491
14.696
0.0193
14.504
14.223
1 .450x10-4
Mass
lbm
1
2.205
Volume
in3
1
1728
231 .0
61.023
61023.7
Density
lb/ft3
1
7.4806
62.428
0.624
in.HG
2.036
1
29.921
0.0393
29.530
28.959
2.953x10-4
Ma
0.454
1
ft3
5.787x10-4
1
0.1337
0.0353
35.315
Ib/gal
0.1337
1
8.345
0.0083
atm
0.068
0.033
1
0.0013
0.987
0.968
9.869x10-2
gal
4.329x1
7.4806
1
0.2642
264.17
g/cm3
0.0160
0.1198
1
0.001
nunHg
51.715
25.40
760.0
1
750.06
735.56
0.
0.
1 .
0.
1
0.
bar
0689
0339
0132
0013
9806
0.0075 1x10-5
liter
0-3 o
28
3
1
1000
kg/m3
16
119
10000
1
.0164
.317
.785
*
kg/cm^
0.0703
0.0345
1 .033
0.0013
1 .0197
1
1 . 0197x1 0~5
Newton/m^
6894.8
3386.4
101 ,325
133.32
1 X 105
98,066
1
meter3
1.
0.
0.
0.
1
639x10-5
0283
00378
001
(g/liter)
.018
.827
*
192
-------
APPENDIX B
Table B.I. RANDOM UNIT TABLES*
Use of Table. If one wishes to select a random sample of N items
from a universe of M items, the following procedure may be applied. (M >
N.)
1. Decide upon some arbitrary scheme of selecting entries from the
table. For example, one may decide to use the entries in the first line,
second column; second line, third column; third line, fourth column; etc.
2. Assign numbers to each of the items in the universe from 1 to M.
Thus, if M = 500, the items would be numbered from 001 to 500, and
therefore, each designated item is associated with a three digit number.
3. Decide upon some arbitrary scheme of selecting positional digits
from each entry chosen according to Step 1. Thus, if M = 500, one may
decide to use the first, third, and fourth digit of each entry selected,
and as a consequence a three digit number is created for each entry
choice.
4. If the number formed is _<_ M, the correspondingly designated
item in the universe is chosen for the random sample of N items. If a
number formed is > M or is a repeated number of one already chosen, it is
passed over and the next desirable number is taken. This process is
continued until the random sample of N items is selected.
*Handbook of tables for probability and statistics, second edition,
W.H. Beyer (Ed.). Chemical Rubber Co., Cleveland, Ohio, 1974.
193
-------
A TABLE OF 14,000 RANDOM UNITS
Line/Col.
1
2
3
4
5
0
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
28
24
26
26
27
28
20
SO
31
32
33
34
35
36
37
38
39
40
41
12
43
44
45
49
47
48
48
GO
(1)
10480
22368
24130
42167
37570
77921
99562
9«301
89579
85475
28918
63553
09429
10365
07119
51085
02368
01011
62162
07056
48663
54164
32639
29334
02488
81525
29676
00742
05386
91921
00582
00725
690H
25976
09763
91567
17955
46603
92157
14577
98427
34914
70060
53976
70072
90725
64364
08982
95012
15664
(2)
15011
46573
48360
93093
39975
08907
72905
91977
14342
36867
69578
40961
93969
61129
97336
12765
21382
64092
53916
97628
91245
58492
32363
27001
33062
72295
20591
57392
04213
28418
04711
69884
65797
57948
83473
42595
56349
18584
89834
62766
07523
63976
28277
54914
29515
52210
67412
00358
68379
10493
(3)
01536
25595
22527
06243
81837
11008
56420
05463
63661
43342
88231
48236
62636
87529
71048
51821
62404
33362
46369
33787
85828
22421
05697
87637
28834
04839
68086
39064
25669
64117
87917
62797
95876
29888
73677
27958
90999
18845
94824
35605
33362
88720
39475
08990
40980
83974
33339
31662
93526
20492
W
02011
85393
97265
61680
16656
42751
69994
07972
10281
53988
33276
03427
92737
85689
08178
612S9
60268
94904
58586
09998
14346
74103
24200
87308
07351
96423
26432
66432
26422
94305
77341
56170
55293
88604
12908
30134
49127
49618
78171
81283
64270
82765
48473
67246
07391
29992
31926
25388
70765
38391
(5)
81647
30995
76393
07866
06121
27766
88872
18876
17453
53060
70997
49626
88974
48237
77233
77452
89368
31273
23216
42698
09172
47070
13363
58731
19731
24878
46901
84673
44407
26766
42206
86324
18988
67917
30883
04024
20044
02304
84610
39667
01638
34476
23219
68350
58745
65831
14883
61642
10593
91132
(8)
91846
89198
64809
16376
91782
53498
31016
20922
18103
59533
79936
69445
33488
52267
13916
16308
19885
04146
14513
06691
30168
25306
38005
00268
92420
82651
20849
40027
44048
25940
35126
88072
27354
48708
18317
86385
59931
51038
82834
47358
92477
17032
53416
82948
26774
38857
24413
34072
04542
21999
cn
69179
27982
15179
39440
60468
18602
71194
94595
67740
38867
56865
18683
36320
67689
47584
60756
55322
18594
83149
76988
90229
76468
94342
45834
60952
66566
89788
32832
37937
39972
74087
76222
26575
18912
28290
29880
06115
20665
09922
58873
86969
87589
94970
11398
22987
50490
59744
81249
70483
59516
(8)
14194
53402
24830
53537
81305
70659
18738
56869
84378
62300
05859
72695
17617
93394
81056
92144
44819
29852
98736
13602
04734
26384
28728
15398
61280
14778
81536
61362
63904
22209
99547
38086
08625
82271
35797
99730
20542
58727
26417
58307
98420
40836
25832
42878
80059
83765
92351
35648
54328
81652
(»)
62690
93965
49340
71341
49684
90665
44013
69014
25331
08158
90106
52180
30015
01611
97735
49442
01188
71585
23495
61861
59193
68151
35806
48557
50001
76797
86845
98947
45766
71500
81817
84637
40801
65424
06998
55536
18069
28188
44137
81607
04880
32427
89975
80287
39911
55657
97473
58891
02349
27195
(10)
38207
34095
32081
57004
60672
15053
48840
60046
12666
17983
31596
20847
08272
26368
85977
53900
66256
86030
64360
46104
22178
06646
06912
41135
67658
14780
12669
98067
66134
64568
42807
93161
69920
69774
41688
84855
02008
15475
48413
49518
45586
70002
94884
88267
96189
14361
89286
69352
17247
48223
(ID
20989
52686
30680
00849
14110
21918
63213
18425
58678
16439
01647
12234
84115
85104
29372
70960
64835
51132
94738
88916
30421
21524
17012
10367
32686
13300
92259
64760
75470
91402
43808
76038
29841
33611
34952
29080
73708
56942
25666
89656
46566
70863
19861
47363
41151
31720
35931
48373
28885
46751
(12)
Si70
10174
19655
74917
06.927
S1825
21069
84903
44947
11458
86590
90511
27156
20285
74461
03990
44919
01915
17752
19609
81866
16227
04161
07884
86879
87074
07102
84584
88520
42116
76355
06355
80160
54262
37S88
09250
83517
£3389
21S48
20103
04102
88363
72828
4M34
14022
57375
04110
45578
14777
22123
(13)
91291
39615
63348
97768
01263
44394
10634
42508
05585
18593
91810
33703
30613
29975
28651
75601
06944
92747
35156
25626
98904
96909
182S6
36188
60720
79666
80428
96096
34693
07844
62028
77919
12777
86963
38917
79666
36103
20562
35509
77490
48880
77775
00102
08541
60697
56228
23726
78547
82730
32281
(14)
90700
99505
58829
16378
.54613
42880
129S2
32307
56941
64952
78188
90322
74962
89868
90707
40719
561S7
64951
35749
58104
32812
44582
22861
18510
94953
96728
26280
982S3
90449
69818
76830
88006
48501
03547
88050
73211
42791
87338
20488
18062
45709
69348
86791
97809
59583
41646
51900
81788
92277
86863
194
-------
A TABLE OF 14,000 RANDOM UNITS
line/Col,
51
52
53
54
55
58
57
68
69
60
61
62
63
64
65
66
67
88
69
70
71
72
73
74
76
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
.98
99
100
CD
16408
18629
73115
67491
30406
16631
96773
38936
31624
78919
03931
74426
09068
42238
16153
21457
21581
65612
44657
91340
91227
50001
66300
27604
37189
11508
37449
46515
30986
63798
82486
21885
60336
43937
97666
03299
79626
86636
18039
08362
79566
92608
23982
09916
50937
42488
46784
03237
86591
38534
(2)
81899
81963
36101
16703
83946
35006
20206
64202
76384
19474
33309
33278
00903
12426
08002
40742
57802
78095
66999
84979
21199
38140
05224
96131
94861
70225
30362
70331
81223
64995
84846
32906
98782
46891
63175
01221
06486
68335
14367
15656
29068
82674
25836
96308
33300
78077
86273
45430
81482
01716
(3)
04163
06520
47498
23167
23792
85900
42559
14349
17403
23632
57047
43972
20795
87025
26504
29820
02050
83197
99324
46949
31935
66321
72958
83944
39117
51111
06694
85922
42416
46583
99254
92431
07408
24010
89303
05418
03574
47639
81337
60627
04142
27072
40055
05908
26695
69882
63003
56417
52667
94964
(4)
53381
91962
87637
49323
14422
98276
78985
82874
53363
27889
74211
10119
95452
14267
41744
98783
89728
33732
51281
81973
27022
19924
28609
41575
89632
38351
54690
38329
58353
09765
87832
09060
53458
25560
16275
38982
17688
03129
08177
36478
16268
32634
67006
97901
62247
61657
93017
63282
61583
87288
(5)
79401
04739
99016
45021
15059
32388
05300
08523
44167
47914
63445
89917
92648
20979
81U5U
29400
17937
05810
84463
37949
84067
72163
81408
10573
00959
19444
04052
57015
21532
44180
43218
64297
13564
86355
07100
55758
07785
65651
12143
85648
15387
17075
12293
28395
69927
34136
31204
90816
14972
65680
(«)
21438
13092
71060
33132
45799
52390
22164
44133
84486
02584
17361
15665
45454
04508
65642
21840
37621
24813
60583
61023
05462
09538
39147
08619
16487
66499
53115
15765
30502
78128
60076
51674
69089
33941
92083
92237
76020
11977
46609
16764
12856
27698
02753
14186
76123
79180
36692
17349
90053
43772
(7)
83035
97662
88824
12544
22716
16815
24369
00697
64758
37680
62825
52872
09552
64535
74240
15035
47076
86902
79312
43997
35216
12151
25549
64482
65536
71945
62757
97161
32305
83091
21361
64126
26445
25786
21942
26759
79924
02510
32989
53412
66227
98204
14827
00821
50842
97526
40202
88298
89534
39560
(8)
92350
24822
71013
41035
19792
69298
54224
35552
75368
20801
39908
73823
88815
31355
56302
34537
42080
60397
93454
15263
14486
06878
48542
73923
49071
05422
95348
17869
86482
42865
64816
62570
29789
54990
18611
86367
26651
26113
74014
09013
38358
63863
22235
80703
43834
43092
35275
90183
76036
12918
(9)
36693
04730
18735
80780
09983
82732
35083
35970
78554
72152
05607
73144
16553
86064
00033
33310
07403
16489
68876
80644
29891
91903
42627
36152
39782
13442
78662
45349
05174
92520
51202
28123
85205
71899
47348
21216
83325
99447
64708
07832
22478
11951
35071
70426
86654
04098
57308
26600
49199
£6537
(10)
31238
06496
20286
45393
74353
38480
19687
19124
31801
39339
91284
88662
51125
29472
67107
06116
48626
03284
25471
43942
68807
18749
45233
05184
17095
78675
11183
61796
07901
83531
88124
05155
41001
15475
20203
98442
88428
88645
00533
41574
73373
34648
99704
76847
70969
73571
55543
78406
43716
62738
(ID
59649
35090
23153
44812
6868S
73817
11052
63318
12614
34806
68833
88970
79375
47689
77510
95240
68895
88525
93911
89203
41867
34405
57202
94142
02330
84081
81651
86345
54339
80377
41870
59194
12535
95434
18534
08303
85076
34327
35398
17639
88732
88022
37543
76310
79725
80799
53203
06218
97548
19638
(12)
91754
04822
72924
12515
30429
32523
91491
29686
33072
08930
25570
74492
97596
05974
70625
15957
43805
42786
25650
71795
14951
56087
94617
25299
74301
66938
50245
81073
58861
35909
52689
52799
12133
98227
03882
66613
72811
15152
58408
82163
09443
56148
11601
88717
93872
76636
18098
95787
04379
51132
(13)
72772
86772
36166
98931
70735
41981
60383
03387
80332
85001
38818
51806
16296
62468
28725
16572
33386
05269
12682
99533
91696
82790
23772
84387
00275
93654
34971
49108
74818
81250
51275
28225
14645
21824
78095
91511
22717
55230
13261
60859
82558
34925
35503
37890
28117
71265
47625
42579
46370
25739
(14)
02338
98289
43040
91202
25499
44437
19746
59846
92325
87820
46920
99378
86092
16834
34101
08004
21597
92532
73572
60501
86065
70925
07896
34925
48280
59894
52924
79880
48942
54238
83556
85762
23541
195S5
50136
75928
50585
93448
47908
75567
06250
57031
85171
40129
19233
64239
88684
90730
28672
56947
195
-------
A TABLE OF 14,000 RANDOM UNITS
Line/Col.
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
U)
13284
21224
99052
00199
60578
91240
97458
35249
38980
10750
36247
70994
99638
72055
24038
74976
35553
35676
74815
45246
76509
19689
42751
11946
96518
35726
39737
97025
62814
25578
68763
17900
71944
54684
25946
01353
99083
62021
78755
25282
1.159
Hi 44
063k7
7628o
55322
78017
44768
25100
83612
41347
(2)
16834
00370
47887
50993
06483
18312
14229
38646
46600
82745
27850
66986
94702
15774
65541
14631
71628
12797
67523
88048
47069
90332
35318
22681
48688
58643
42750
66492
08075
229SO
68576
00813
60227
93691
27623
39318
88191
45400
47744
69106
94202
13792
97912
75714
07589
80928
43342
19336
46623
81666
(3)
74151
30420
81086
98603
28733
17441
12063
34475
11759
38749
73958
99744
11463
43857
85788
35908
70189
51434
72985
65173
86378
04315
97513
45045
20996
76869
48968
56177
09788
15227
88991
64361
63551
85132
11258
44961
27662
37945
43776
59180
02743
98190
68110
89585
39600
90220
20696
14605
62876
82961
(4)
92027
03883
64933
38452
37867
01929
59611
72417
11900
87365
20673
72438
18148
99805
55835
28221
26436
82976
23183
50989
41797
21358
61537
13964
11090
84622
70536
04049
56350
83291
49662
60725
71109
64399
65204
44972
99113
75234
83098
16257
86847
01424
59812
99296
60866
82503
26331
86603
85197
60413
(5)
24670
96648
66279
87890
07936
18163
32249
60514
46743"
58959
37800
01174
81386
10419
38835
39470
63407
42010
02446
91060
11910
97248
54955
57517
48396
39098
84864
80312
76787
41737
46704
88974
05624
29182
52832
91766
57174
24327
03225
22810
79725
30078
95448
52640
63007
83375
43140
51680
07824
71020
(6)
36665
89428
80432
94624
98710
69201
90466
69257
27860
53731
63835
42159
80431
76939
59399
9154S
91178
26344
63594
89894
49672
11188
08159
59419
57177
36083
64952
48028
51591
79599
63362
61005
43836
44324
50880
90262
35571
86978
14281
43609
51811
28197
43244
46518
20007
26986
69744
97678
91392
83658
(7)
00770
41683
65793
69721
98539
31211
33216
12489
77940
89295
71051
11392
90628
25993
13790
12854
90348
92920
9S924
36063
88575
39062
00337
58045
83867
72505
38404
26408
54509
96191
56625
99709
58254
14491
22273
56073
99884
22644
83637
12224
12998
55583
31262
55486
66819
74399
82928
24261
58317
02415
(8)
22878
17664
83287
67484
27186
54288
19358
51924
39298
59062
84724
20724
52506
03544
35112
30166
55359
92155
20633
32819
97966
63312
80778
44067
86464
92265
94317
43591
49295
71845
00481
30666
26160
55226
05554
06606
13951
87779
55984
25643
76844
05197
88880
90754
84164
30885
24988
02464
37726
33322
(9)
02179
27395
34142
67501
31237
39296
02591
86871
97838
39404
52492
54322
02016
21560
01324
09073
80392
58807
58842
68559
32466
52496
27507
58716
14342
23107
65402
75528
85830
86899
73323
26451
32116
78793
99521
51826
71057
23753
13300
89884
05320
47714
13040
88932
61131
88567
94237
86563
84628
66036
(10)
51602
63904
13241
77638
80612
37318
54263
92446
05145
13198
22342
36923
85151
83471
39520
75887
41012
54644
85961
99221
10083
07349
95478
58840
21545
60278
13589
6S341
59860
70694
91427
11528
63403
34107
73791
18893
53961
99026
52212
31149
54236
68440
16458
19937
81429
'29169
46138
74812
42221
98712
(U)
07270
41648
30590
44331
44488
65724
88449
36607
32378
59960
78071
70009
88598
43989
76210
36782
36270
58581
07648
49475
54728
79178
21252
45557
46717
05822
01055
49044
30883
24290
15264
44323
35404
30374
85744
83448
61448
63898
58781
85423
53891
22016
43813
57119
60676
72816
77426
60069
10268
46796
(12)
76517
49197
97760
11257
97819
90401
01912
11458
68038
70408
17456
23233
47821
90770
22467
00268
77786
95331
70164
50558
81972
33692
12746
96345
73364
,4'W60
79044
95495
89660
01551
W969
34778
57146
43429
29276
31915
74909
54886
14905
3K581
70226
70204
81)416
23251
42807
53357
31)039
711674
20692
HS308
(13)
97275
82277
35848
71131
70401
79017
07436
30440
89351
29812
96104
65438
00265
22965
83275
97121
89578
78629
34994
34698
58975
57352
37554
33271
86954
44294
19308
81256
96142
80092
57048
60342
10909
51376
70326
97764
07322
18051
46502
34374
38632
06862
42482
55619
78286
15428
55596
15478
16690
28413
(14)
45960
24120
91983
11059
95419
62077
50813
S2639
37005
83126
18327
59685
82625
44247
32288
57676
21059
73344
67662
71800
30761
72862
97775
53464
55580
07672
83823
53214
18354
82118
54149
60388
07346
09559
60251
75091
80960
96314
04472
70873
84776
94451
33939
23679
29015
86932
12655
47642
29167
05417
196
-------
A TABLE OF 14,000 RANDOM UNITS
Line/Col.
161
152
163
154
155
156
157
158
159
160
1B1
162
163
164
165
166
167
168
169
170
m
172
173
174
176
176
177
178
179
ISO
181
182
183
184
185
186
187
188
189
190
191
192
193
194
19S
196
197
198
199
200
(1)
38128
60960
90524
49897
18494
65373
40653
51638
69742
68012
18348
59614
75688
13941
96666
03363
70386
47870
79504
46987
14558
12440
32293
10640
47615
16948
21258
15072
99164
08759
67323
09255
36304
15884
18745
72934
17626
27117
93995
67392
04910
81453
19480
21456
89406
09866
86S41
10414
49942
23995
(2)
51178
00455
17320
18278
99209
72984
12843
22238
99303
74072
19855
09193
28630
77802
86420
82042
08390
36605
77606
74841
50769
25057
29938
21875
23169
11128
61092
48853
57412
61089
57839
13986
74712
67429
32031
40086
02944
61399
18678
89421
12261
20283
75790
13162
20912
07414
24681
96941
06683
68882
(3)
75096
73254
29832
67160
81060
30171
04213
56344
62578
67488
42887
58084
39210
69101
96475
15942
69155
12927,
22761
50923
35444
01132
68853
72462
39571
71624
66634
15178
09858
23706
61114
84834
00374
86612
35303
88292
20910
50967
90012
09623
37566
79929
48539
74608
46189
55977
23421
06205
41479
42291
(4)
13609
96067
96118
39408
19488
37741
70925
44587
83575
74580
08279
29086
52897
70061
86458
14549
25496
16043
30518
15339
59030
38611
10497
77981
56972
72754
70335
30730
65671
32994
62192
20764
10107
47367
08134
85728
57662
41399
63645
80725
80016
59839
23703
81011
76376
16419
13521
72222
58982
23374
(5)
16110
50717
75792
97056
65596
70203
95360
83231
30337
47992
43206
44385
62748
35460
54463
38324
13240
53257
28373
37755
87516
28135
98919
56550
20628
49084
92448
474S1
70655
35426
47547
72206
85061
10242
33925
38300
80181
81636
85701
62620
21245
23875
15537
55512
25538
01101
28000
57167
56288
24299
(6)
73533
13878
25326
43517
59787
94094
55774
50317
07488
69482
47077
45740
72858
34576
96419
87094
57407
93798
73898
98995
48193
68089
46587
55999
21788
96303
17354
48490
71479
36606
58023
89393
69228
44880
03044
42323
38579
16663
85269
84162
69377
13245
48885
07481
87212
69343
94917
83902
42853
27024
(7)
42564
03216
22940
84426
47939
87261
76439
74541
51941
58624
42637
70752
98059
15412
55417
19069
91407
52721
30550
40162
02945
10954
77701
87310
51736
27830
83432
41436
63520
63988
64630
34548
81969
12060
59929
64068
24580
15634
62263
87368
50420
46808
02861
93551
20748
13305
07423
07460
92196
67460
(8)
59870
78274
24904
59650
91225
30056
61768
07719
84316
17106
45606
05663
67202
81304
41375
67590
49160
73120
76684
89561
00922
10097
99119
69643
33133
45817
49608
25015
31357
9S844
34886
93438
92216
44309
95418
98373
90529
79717
68331
29560
85658
74124
86587
72189
12831
94302
57523
69507
20632
94783
(9)
29399
65863
80523
20247
98768
58124
52817
25472
42067
47538
00011
49081
72789
58757
76886
11087
07379
48025
77366
69199
48189
54243
93165
45124
72696
67867
66520
49932
56968
37533
98777
88730
03568
46629
04917
48971
52303
94696
00389
00519
55263
74703
74539
76261
57166
80703
97234
10600
62045
40937
(10)
67834
37011
38928
19293
43688
70133
81151
41602
49692
13452
20662
28960
01869
35498
19008
68570
34444
76074
32276
42257
04724
06480
67788
00349
32605
18062
06442
20474
06729
08269
75442
61805
39630
55105
57596
09049
50436
59240
72571
84545
68667
35769
65227
91206
35026
57910
63951
08858
78812
16961
(10
91055
91283
91374
02019
00438
18936
52188
77318
28616
22620
14642
57454
13496
94830
66877
22591
94567
95605
04690
11647
21263
50856
17638
25748
41569
87453
59664
53S21
34465
27021
95592
789S5
81869
66793
24878
59943
29401
25543
15210
08004
78770
95588
90799
89941
16817
36933
42876
07685
35895
26053
(12)
89917
33914
55597
14790
05548
02138
31940
15145
29101
24260
49984
99264
14663
75521
35934
65232
66035
67422
61667
47603
20892
65435
23097
00844
76148
17226
20420
5101S
70685
45886
06141
18952
52824
93173
61733
36538
57824
97989
20769
24526
04533
21014
58789
15132
79121
57771
46829
44472
51851
78749
(13)
51096
91303
97567
02852
09443
59372
54273
57515
03013
40155
94509
24142
87645
00603
59801
85915
38918
41646
64798
48779
92955
79377
21468
96831
91544
72904
39201
79841
04184
22835
45096
46436
50937
00480
92834
05976
86039
63306
44686
41252
14513
37078
96257
37738
18929
42546
09781
64220
83534
46704
(14)
89011
49326
38914
05819
82897
09075
49032
07633
73449
74716
56380
74648
89713
97701
00497
91499
65708
14557
66276
97907
90251
53890
36992
30651
21121
71474
69549
32405
25250
78451
73117
58740
27954
13311
64454
82118
81062
90946
96176
14521
18099
39170
02708
59284
40628
03003
58160
27040
10689
21983
197
-------
APPENDIX C. STUDENT'S t-DISTRIBUTION (Chemical Rubber Co., 1974*)
PERCENTAGE POINTS, STUDENTS ^-DISTRIBUTION
Ftf)
/« _/n + l
'(-r
.VSr(=
\F
n\
I
2
3
4
6
0
7
8
fl
10
11
12
13
14
15
10
17
18
10
20
21
22
23
24
25
26
27
28
29
30
40
60
120
GO
.60
,325
.289
.277
.271
.267
.265
.263
.262
.261
,260
.260
.259
.259
.258
.258
.258
.257
.257
.257
.257
.257
.256
.256
.256
'.256
.256
.256
.256
.256
.256
.255
.254
.254
.253
.75
1.000
.816
.765
.741
.727
.718
.711
.706
-703
.700
.697
.695
.694
.692
.691
.690
.689
.688
.688
.687
.686
.886
.685
.685
.684
.684
.684
.683
.683
.683
.681
.679
.677
.674
.90
3.078
1.886
1.638
1.533
1.476
1.440
1.415
1.397
1,383
1.372
1.363
1.358
1.350
1.345
. 1.341
1.337
1.333
1.330
1.328
1.32S
1.323
1.321
1.319
1.318
1.316
1.315
1.314
1.313
1.311
1.310
1.303
1.296
1.289
1.282
.95
6.314
2.920
2.353
2.132
2.015
1.943
1.895
1.860
1.833
1.812
1.796
1.782
1.771
1.761
1.753
1.746
1.740
1.734
1.729
1.725
1.721
1.717
1.714
1.711
1.708
1.706
1.703
1.701
1.699
1.697
1.684
1.671
1.6S8
1.645
.975
12.706
4.303
3.182
2.776
2.571
2.447
2.365
2.306
2.262
2.228
2.201
2.179
2.160
2 . 145
2.131
2.120
2.110
2.101
2.093
2.086 •
2.080
2.074
2.069
2.064
2.060
2.056
2.052
2.048
2.045
2.042
2.021
2.000
1.980
1.960
•!»
31.821
6.965
4.541
3.747
3.365
3.143
2.998
2.896
2.821
2.764
2.718
2.681
2.650
2.624
2.602
2.583
2.567
2.552
2.539
2.528
2.518
2.508
2.500
2.492
2.485
2.479
2.473
2.467
2.462
2.457
2.423
2.390
2.358
2.326
.995
63.657
9.925
5.841
4.604
4.032
3.707
3.499
3.355
3.250
3.169
3.106
3.055
3.012
2.977
2.947
2.921
2.898
2.878
2.861
2.845
2.831
2.819
2.807
2.797
2.787
2.779
2.771
2.763
2.756
2.750
2.704
2.660
2.617
2.576
*' .9995
636.619
31.598
12.924
8.610
6.869
5.959
5.408
5.041
4.781
4.587
4.437
4.318
4.221
4.140
4.073
4.015
3.965
3.922
3.883
• 3.850
3.819
3.792
3.767
3.745
3.725
3.7*07
3.690
3.674
3.659
3.646
3.551
3.460
3.373
3.291
*Handbook of tables for probability and statistics, second edition,
W.H« Beyer, Ed. Chemical Rubber Company, Cleveland, Ohio. 1974.
198
-------
APPENDIX D: SOIL NAMES AND HYDROLOGIC CLASSIFICATIONS
1,2
AABERG
AASTAD ;
ABAC
ABAJO
ABBOTT
ABBOTTSTOWN
ABCAL
ABEGG
ABELA
ABELL
ABERDEEN
ABES
ABILENE
ABINGTON
ABIQUA
ABO
ABOR
ABRA
ABRAHAM
ABSAROKEB
ABSCOTA
ABSHER
ABSTED
ACACIO
ACADEMY
ACADIA
ACANA
ACASCO
ACEITUNAS
ACEL
ACKER
ACKMSN
ACME
ACO
ACOLITA
ACOMA
ACOVE
ACREE
ACRELANE
ACTON
ACUFF
ASHORTH
ACY
ADA
ADAIR
ADAMS
C
B
D
C
D
C
D
B
B
B
D
D
C
B
C
B/C
D
C
B
C
B
D
D
C
C
D
D
D
B
D
B
B
C
B
B
C
C
C
C
B
B
B
C
B
D
A
ADAHSON
ADAMSTOWN
ADAMSVILLE
ADATON
ADAVEN
ADDIELQU
ADDISON
ADDY
ADE
ADEL
ADELAIDE
ADELAHTO
ADELINO
ADELPHIA
AD EN A
ADGER
ADILIS
ADIRONDACK
AD IV
ADJUNTAS
ADKINS
ADLER
ADOLPH
ADRIAN
AENEAS
AETNA
AFTON
AQAR
AGASSIZ
AGATE
AGAHAM
AGENCY
AGER
AGNER
AGNEW
AGNOS
AGUA
AGUADILLA
AGUA DULCE
AGUA FRIA
AGUALT
AGUEDA
AGUILITA
AGUIRRE
AGUSTIN
AHATONS
B
C
D
D
C
D
C
A
A
D
B
B
C
C
D
A
B
C
B
C
D
A/D
B
B
D
B
D
D
B
C
D
B
B/C
B
B
A
C
B
B
B
B
D
B
D
AHL
AHLSTROM
AHHEEK
AHOLT
ANTANUM
AHWAHNEE
AIBONITO
AIKEN
AIKMAN
AILEY
AINAXEA
AIRMONT
AIROTSA
AIRPORT
AITS
AJO
AKAKA
AKASKA
AKELA
ALADDIN
ALAE
ALAELOA
ALAGA
ALAKAI
ALAMA
ALAMAHCE
ALAMO
ALAMOSA
ALAPAHA
ALAPAI
ALBAN
ALBANO
ALBANY
ALBATON
ALBEE
ALBEMARLE
ALBERTVILLE
ALB I A
ALBION
ALBRIGHTS
ALCALDE
AI£ESTER
ALCOA
ALCONA
ALCOVA
ALDA
C
C
B
D
C
C
C
B/C
D
B
B
C
B
D
B
C
A
B
C
B
A
B
A
D
B
B
D
C
D
A
B
D
C
D
C
B
C
C
B
C
C
B
B
B
B
C
ALDAX
ALDEN
ALDER
ALDERDALE
ALDERWOOD
ALDINO
ALDWELL
ALEKNAGIK
ALEMEDA
ALEX
ALEXANDRIA
ALEXIS
ALFORD
ALGANSEE
ALGERITA
ALGIERS
ALGOMA
ALHAMBRA
ALICE
ALICEL
ALICIA
ALIDA
ALIKCHI
ALINE
ALKO
ALLAGASH
AJULARD
ALLEGHENY
ALLEMANDS
ALLEN
ALLENDALE
ALLENS PARK
ALLENSVILLE
ALLENTINE
ALLENWOOD
ALLESSIO
ALLEY
ALLIANCE
ALLIGATOR
ALLIS
ALLISON
ALLOUEZ
ALLOW AY
ALHAC
ALHENA
ALMONT
D
D
B
C
C
C
C
B
C
B
C
B
B
B
B
C/D
B/D
B
A
BV
B
B
B
A
D
B
B.
B
D
B
C
B
C
D
B
B
C
B
D
D
C
C
B
C
D
'ALMY
ALOHA
ALONSD
ALOVAR
ALPENA
ALPHA
ALPUN
ALPOWA
ALPS
ALSEA
ALSPAUGH
ALSTAD
ALSTOWN
ALTAMONT
ALTAVISTA
ALTOORF
ALTMAR
ALTO
ALTOGA
ALTON
ALTUS
ALTVAN
ALUM
ALUSA
ALVIN
ALVIRA
ALVISO
ALVOR
AMADOR
AMAGON
AMALU
AHANA
AMARGOSA
AHARILLO
AMASA
AMBERSON
AMBOY
AMBRAW
AMEDEE
AMELIA
AMENIA
AMERICUS
AMES
AMESHA
AMHERST
AMITY
B
C
B
C
B
C
B
B
C
B
' C
B
B
D
C
D
B
C
C
B
B
B
B
D
B
C
D
C
D
D
D
B
D
B
B
C
C
A
B
B
A
C
B
C
C
Conservation Service. 1972. Hydrology. Section 4, SCS National
Engineering Handbook. U.S. Department of Agriculture, Washington DC.
NEH-Notice 4-102.
2NOTE: A blank hydrologic group indicates the soil group has not been deter-
mined. Two soil groups such as B/C indicates the drained/undrained situation.
199
-------
AHMON
AMOLE
AHOR
AMOS
AHSDEH
AMSTERDAM
AHTOre
AHY
ANACAPA
ANAHUAC
ANAMITE
ANAPRA
ANASAZI
AHATONE
ANAVERDE
AKAHALT
AHCHO
ANCHORAGE
ANCHOR BAY
ANCHOR POINT
AHOLOTE
AHCO
ANDERLY
ANDERS
ADERSOH
ANDES
ANDORINIA
AHOOVER
ANDREEN
ANDREESON
ANDRES
ANDRES
AN ED
AHETH
ANGELICA
ANGELINA
ANGELO
ANGIE
ANGLE
ANGLEN
ANGOLA
ANGOSTORA
ANRALT
ANIAK
ANITA
AHKENY
ANLAUF
ANNABELLA
ANN AND ALE
ANNXSTON
ANGXA
ANONES
AHSARI
ANSEL
ANSELHO
AHSON
ANTELOPE SP
ANTERO
ANT FIAT
AKTHO
ANTHONY
ANTIGO
ANTILON
ANTIOCH
ANTLER
ANTOINE
ANTROBUS
AHTX
B
C
B
C
B
B
D
D
B
D
D
B
B
D
B
D
B
A
D
D
D
C
C
C
B
C
C
D
B
C
B
C
D
A
D
B/D
C
C
A
B
C
B
D
D
D
A
C
B
C
C
A
C
D
B
A
B
C
C
C
B
B
B
B
D
C
C
B
B
ANVIK
ANHAY
ANZA
ANZIANO
APACHE
APAXDIE
APISHAPA
APISON
APOPKA
APPIAN
APPLEGATE
APPLETON
APPLING
APRON
APT
APTAKISIC
ARABY
ARADA
ARANSAS
ARAPIEN
ARAVE
ARAVETON
ARBELA
ARBONE
ARBOR
ARBUCKLE
ARCATA
ARCH
ARCHABAL
ARCHER
ARCHIN
ARCO
ARCOLA
ARD
ARDEN
ARDENVOIR
ARDILLA
AREDALE
ARENA
ARENRLES
ARENDTSVILLE
ARENOSA
ARENZVILLE
ARGONAUT
ARGUELLO
ARGYLE
ARIEL
ARIZO
ARKABUTLA
ARKPORT
ARLAND
ARLE
ARLING
ARLINGTON
ARLOVAL
ARMAGH
ARMIJO
ARHINGTON
ARMO
ARMOUR
ARMSTER
ARMSTRONG
ARMJOHEE
ARNEGARD
ARNHART
ARNHEIH
ARNO
ARNOLD
B
B
B
C
D
A
C
B
A
C
C
C
B
B
C
B
C
D
C
O
B
O
B
B
B
B
B
B
C
C
B
C
C
B
B
C
B
C
B
A
B
D
B
B
C
A
C
B
B
B
D
C
C
D
D
D
B
B
C
D
D
B
C
C
D
B
ARNOT
ARNY
AROOSTOOK
ARDSA
ARP
ARRINGTON
ARRITOLA
ARROLIME
ARRON
ARROW
ARROWSMITH
ARROYO SECO
ARTA
ARTOIS
ARVADA
ARVANA
ARVESON
ARVILLA
ARZELL
ASA
ASBHRY
ASCALON
ASCHOPP
ASHBY
ASHCROFT
ASHDALE
ASHE
ASKKUH
ASHLAR
ASHLEY
ASH SPRINGS
ASHTON
ASHUE
ASHUELOT
ASHWOOD
ASKEW
ASO
ASOTIN
ASPEN
ASPERMONT
ASSINNIBOINE
ASSUlttTION
ASTATULA
ASTOR
ASTORIA
ATASCADERO
AT AS COS A
ATCO
ATENCIO
ATEPIC
ATHELWOLL
ATHENA
ATHENS
ATHERLY
ATHERTON
ATHMAR
ATHOL
ATKINSON
ATLAS
ATLEE
ATHORE
ATOKA
ATON
ATRYPA
ATS ION
ATTERBERRY
ATTEWAN
ATTICA
C/D
A
C
C
B
D
C
D
B
B
B
C
C
D
C
D
B
C
B
B
B
B
C
B
B
B
C
B
A
C
B
B
C
C
C
C
C
B
B
B
B
A
A/D
B
C
D
B
B
D
B
B
B
B
B/D
C
B
B
0
C
B/D
C
B
C
C
B
.A
B
ATTLEBORO
ATHATER
ATM ELL
ATWOOD
AUBBEENAUBBEE
AUBERRY
AUBURN
AUBURN DALE
AUDI AN
AU GRES
AUGSBURG
AUGUSTA
AULD
AURA
AURORA
AUSTIN
AUSTHELL
AUXVASSE
AUZQUI
AVA
AVALANCHE
AVALON
AVERY
AVON
AVONBURG
AVONDALE
AWBREX
AXTELL
AYAR
AYCOCK
AYON
AYR
AYRES
AYRSHIRE
AYSEES
AZAAR
AZARMAN
AZELTINE
AZFIELD
AZTALAN
AZTEC
AZOLE
AZHELL
BABB
BABBINGTON
BABCOCK
BABYLON
BACA
BACH
BACKUS
BACKBONE
BACULAN
BADENAUGH
BADGER
BADGER TON
BADQ
BADUS
BAGARD
BAGDAD
BAGGOTT
BAGLEY
BAREM
BAILE
BAINVILLE
BAIRD HOLLOW
BAJURA
BAKEOVEN
B
C/D
B
B
B
C/D
D
B
C
B
C
D
B
C
C
D
D
B
C
B
B
B
C
D
E
D
D
D
B
B
B
D
C
B
C
C
B
B
B
B
C
B
A
B
C
A
C
D
C
A
A
B
C
B
D
C
C
B
D
B
B
D
C
C
D
D
BAKER
BAKER PASS
BALAAM
BALCH
BALCOM
BALD
BALDER
BALDOCK
BALDWIN
EALDY
BALE
BALLARD
BALLER
BALLINGER
BALM
B&LMAN
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200
-------
BARROWS
BARRY
BARS TOW
EARTH
BARTINE
BARTLE
BARTL.EY
BARTON
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B AS COM
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201
-------
BOTTOM
BOKDAY
BOH
BON ACCORD
BONAPARTE
BOND
BOHDRANCH
BOHDURANT
BONHAH
BON IF AY
80NILLA
BONITA
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202
-------
CALICO
CALIFOH
CALIMUS
CALITA
CALIZA
CALKINS
CALLABO
CALLAHAN
CALLEGUAS
CALLINGS
GALLOWAY
CALHAR
CALNEVA
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CIRRI LLOS
CERRO
CHACRA
CHAFFEE
CHAGRIN
CHAIX
CHALFONT
CHALMERS
CHAMA
CHAMBER
CHAMBERING
CHAMISE
CHAMOKANE
CHAMPION
CHANCE
CHANDLER
CHANEY
CHAHNAHON
CHANNING
CHAMTA
CHANTIER
CHAP IN
CHAPMAN
CHAPPELL
CHARD
CHARGO
CKARITON
CHARITY
CHARLEBOIS
CHARLESTON
CHARLEVOIX
CHARLOS
CHARLOTTE
CHARLTON
CHASE
CHASE BURG
CHASEVILLE
C HA SKA
CHASTAIN
CHATBURN
CHATFIELD
CHATHAM
CHATSWORTH
CHAUNCEY
GRAVIES
CHAHANAKEE
CHEADLE
CHEOCETT
CHEDEHAP
CHEEK TOW AG A
B
D
B
B
C/D
B
D
C
A/D
D
B
C
B
B
D
B
B
A
B
C
C
C
B
B
C
C
B
C
C
B
B
B
B/D
B
C
B
B
B
D
C
B
B
D
D
D
C
C
B
A
A/D
B
C
B
A
C
D
B
C
B
D
C
B
C
C
D
B
D
CHEESEMAN
CHEHALEM
CHEHALIS
CHEHULPUM
CHILAN
CHELSEA
CHEMAWA
CHEMUNG
CHEN
CHENA
CHENANGO
CHENEY
CHEDNEBY
CHENOWETH
CHEQUEST
CHEREETE
CHERIONI
CHEROKEE
CHERRY
CHERRYHILL
CHERRY SPRINGS
CHESAW
CHESHIRE
CHESHNINA
CHESNIMNUS
CHESTER
CHESTERTON
CHETCO
CHETEK
CHEVELON
CHEWACLA
CHEWELAH
CHEYENNE
CHIARA
CHICKASHA
CHICOPEE
CHICOTE
^CHISLEY
"CHILCOTT
CHILGREN
CHILHOMIE
CHILI
CHILKAT
CHILLICOTHE
CHILLISQUAQUE
CHILLUM
CHILMARK
CHILO
CHILOfiUIN
CHILOS
CHILSON
CHILTON
CHIMAYO
CHIMNEY
CHINA CREEK
CHINCHALLO
CHINIAK
CHINO
CHINOOK
CHIPETA
CHIPLEY
CHIPMAN
CHIPPENY
CHIPPEWA
CHIQUITO
CHIRICAHUA
CHISPA
CHITIHA
C
C
B
D
B
A
B
D
A
A
B
C
B
C
A
D
D
C
B
C
A
B
C
B
B
C
D
B
C
C
B
B
D
B
B
D
C
D
C
C
B
C
C
B
B
B/D
B
B'
D
' B
D
B
B
B/D
A
B/C
B
D
C
D
D
B/D
C/D
D
B
B
203
-------
CUITTENDEN
CH'TWOOD
CHIVATO
CHIWAHA
CHO
CHOBEE
CHOCK
CHOCOLODDO
CHOPAKA
CHOPTAKK
CHOPTIE
CHORALMONI
CHOSKA
CHOTEAU
CHRISTIAN
CHRISTIANA
CHRISTIANBURG
CHRISTY
CHROME
CHUALAR
CHUBBS
CHUCKAHALLA
CHUGTER
CHULITNA
CHUMMY
CHUMSTICK
CHUPADERA
CHURCH
CHURCHILL
CHURCHVILLE
CHURN
CHURHDASHER
CHUTE
CIALES
CIBEQUE
CXBO
CIBOLA
CICERO
CIDRAL
CIENEBA
CIHA
CZHARRON
CINCINNATI
CIHCO
CINDERCONE
CINEBAR
CINTRONA
CIPRIANO
CIRCLE
CIRCLEVILLE
CISNE
CISFUS
CITICO
CLACKAHAS
CLAIBORNE
CLAIRE
CLAIREHONT
CLALLAM
CLAM GULCH
CIAHO
CLANTON
CLAPPER
CLAREHORE
CLARENCE
CLARESON
CLAREVILLE
CLARINDA
CLARION
C
C
D
B
C
D
B/D
B
C
A
D
B
B
C
C
B
D
B
C
B
C
B
B
B
C/D
C
C
D
D
D
B
B
A
D
B
D
B
D
C
C
C
C
C
A
B
B
D
D
C
C
D
A
B
C
B
A
B
C
D
C
C
B
D
D
C
C
D
B
CLARITA
CLARK
CLARK FORK
CLARKSBURG
CLARKSDALB
CLARKSON
CLARKSVILLE
CLARNO
CLARY
CLATO
CLATSOP
CLAVERACK
CLAWSON
CLAYBURN
CLAYSPRINGS
CLAYTON
CLEARFIELD
CLEAR LAKE
CLEEK
CLE ELUH
CLEGG
CLEMAN
CLEMS
CLEMVILLE
CLEORA
CLERP
CLERMONT
CLEVERLY
CLICK
CLIFFDOWN
CLIFFHOUSE
CLIFFORD
CLIFFHOOD
CLIFTERSON
CLIFTON
CLIFTY
CL IMARA
CLIMAX
CLIME
CLINTON
CLIPPER
CLODINE
CLONTARF
CLOQUALLUH
CLOQUATO
CLOOUET
CLOUD
CLOUDCROFT
CLOUD PEAK
CLOUD RIM
C LOUGH
CLOVERDALE
CLOVER SPRINGS
CLOVIS
CLUFF
CLUNIE
CLURDE
CLURO
CLYDE
CLYMER
COACHELLA
COAD
COAL CREEK
COALMONT
COAMO
COARSEGOLD
COATICOOK
COATSBURG
D
B
A
C
C
B
B
B
B
B
D
C
C
B
D
B
C
D
C
B
B
B
B
B
B
C
D
B
A
B
C
B
C
B
C
B
D
D
C
B
B/C
D
B
C
B
B
D
D
C
B
D
D
B
B
C
D
C
C
D
B
B
B
D
C
C
B/C
C
D
COBB
COB EN
COBEY
COBURG
COCHETOPA •
COCOA
COCOLALLA
CODORUS
CODY
COE
COEBURN
COEROCK
COFF
COFFEEK
COGGON
COGSWELL
COHASSET
COHOCTAH
COHOE
COIT
COLLINSTON
COLLINSVILLE
COLMA
COLMOR
COLD
COLOCKUM
COLOMA
COLOMBO
COLONA
COLONIE
COLORADO
COLOROCK
COLOSO
COLOSSE
COLP
COLRAIN
COLTON
COLTS NECK
COLUMBIA
COLUMBINE
COLUSA
COLVILLE
COLVIN
COLWOOD
COLY
COLYER
COMER
COMERID
COMETA
COHFREY
COMITAS
COMLY
COMMERCE
COMO
COMODORE
COMORO
COMPTOHE
COMPTON
COMSTOCK
COMUS
CONALB
CONANT
CONASAUGA
CONATA
CONBOY
CONOHAS
COHCHO
CONCONULLY
B
D
B
C
C
A
C
C
A
A
C
D
D
B
B
C
B
D
B
C
C
C
B
B
B
B
A
B
C
A
B
D
D
A
D
B
A
B
B
A
C
B/C
C
B/D
B
C/D
B
B
D
C
A
C
C
A
B
B
B
C
C
B
B
C
C
D
D
C
C
B
CONCORD
CONCREEK
CONDA
CONDIT
CONDON
CONE
CONEJO
CONES TOGA
CONESUS
CONGAREE
CONGER
CON I
CONKLIN
CONLEN
COKEDALE
COKEL
COKER
COKES BURY
COKEVILLE
COLBATH
COLBERT
COLBURN
COLBY
COLCHESTER
COLDCREEK
GOLDEN
COLD SPRINGS
COLE
COLEBROOK
COLEMAN
COLEMANTOWN
COLETO
COLFAX
COLIBRO
COLINAS
COLLAMER
COLLARD
COLLBRAN
COLLEEN
COLLEGIATE
COLLETT
COLLIER
COLLINGTON
COLLINS
CONLEY
CONNEAUT
CONNECTICUT
CONNERTON
CONOTTON
CONOVER
CONOHINGO
CONRAD
CONROE
CONSER
CONSTABLE
CONSTANCIA
CONSUHO
CONTEE
CONTINE
CONTINENTAL
CONTRA COSTA
CONVENT
COOK
COOKPORT
COOLBRITH
COOLIDGE
COOLVILLE
COOMBS
D
B
C
D
C
A
C
B
B
B
B
D
B
B
B/C
B
D
D
B
C/D
D
B
B
B
B
D
C
B/C
B
C
D
A
C
B
B
C
B
C
C
C
C
A
B
C
C
C
B
B
B
C
B
B
C/D
A
D
B
D
C
C
C
C
D
C
B
B
C
B
COONEY
COOPER
COOTER
COPAKE
COPALIS
COPELAND
COPITA
COPLAY
COPPER RIVER
COPPERTON
COPPOOC
COPSEY
COQUILLE
CORA
CORAL
CORBETT
CORBIN
CORCEQA
CORD
CORDES
CORDOVA
CORINTH
CORKINDALE
CORLENA
CORLETT
CORLEY
CORMANT
CORNHILL
CORNING
CORNISH
CORNUTT
CORNVILLE
COROZAL
CORPENING
CORRALITOS
CORK ECO
CORRERA
CORSON
CORTADA
CORTEZ
CORTINA
CORUNNA
CORVALLIS
CORWIN
CORY
CORYDON
COSAD
COSH
COSHOCTON
CO SKI
COSSAYUNA
COSTILLA
COTACO
:COTATI
COTITO
GOTO
COTOPAXI
COTT
COTTER
COTTKKAL
COTTIER
COTTONWOOD
COTTRELL
COUCH
COUGAR
COULSTONE
COUNTS
COUPEVILLE
B
C
C
B
B
B/D
B
D
B
B
D
C/D
D
C
B
B
C
C
B
C
C
B
A
B
C
C
B
D
B
C
B
C
D
A
C
D
C
B
D
A
D
B
B
C
C
C
C
C
B
C
A
C
C
C
C
A
B
B
B
B
C
C
C
D
B
C
C
204
-------
COURT
COURTHOUSE
COURTLAWD
COURTNEY
COURTROCK
COUSE
COUSHATTA
COVE
COVIILO
COVE LAND
COVELLO
COVENTRY
COVEYTOWN
COVINGTON
COWAH
COW ARTS
COWDEN
COHDREY
COWEEMAH
COWERS
COWETA
COWICHE
COWOOD
COX
COXVILLE
COY
COYATA
COZAD
CRABTON
CR&DDOCIC
CRADLEBAUGH
CRAFTON
CRAGO
CRAGO LA
CRAIG
CRAXGMO1IT
CRAIGSVILLE
CRAMER
CRANE
CRANSTON
CRARY
CRATER LAKE
CRAVEN
CRAWFORD
CREAL
CREDDIN
CREDO
CREEDMAI1
CRBEDMOOR
CREIGHTON
CRELDON
CRBSBARD
CRESCENT
CRESCO
CRBSPIH
CREST
CRKSTLIKE
CRESTMORE
CRESTOH
CRBSHBLJ.
CRETE
CRBVA
CREVASSE
CREWS
CRIDER
GRIM
CRISFIELD
CRITCHELL
B
D
B
D
B
C
B
D
B
C
B/C
B
C
D
A
C
D
C
D
B
C
B
C
D
D
D
C
B
B
B
D
C
B
D
C
C
A
D
B
B
C
B
C
D
D
C
C
DS
C
B
B
C
B
C
C
C
B
A
C
D
D
A
D
B
B
B
B
CRIVITZ
CROCKER
CROCKETT
CROESUS
CROFTON
CROC HAN
CROOK1D
CROOKED CREEK
CROOKS TON
CROOM
CROPLEY
CROSBY
CROSS
CROSSVILLE
CROSWELL
CROT
CROTON
CROUCH
CROW
CROW CREEK
CROWFOOT
CROWHEART
CROW HEART
CROW HILL
GROWL BY
CROWN
CROWSHAW
CROZIER
CROCKS
CROCKTON
CRUICKSHANK
GRUME
CRUMP
CRUTCH
CRUTCHER
CRUZE
CRYSTAL LAKE
CRYSTAL SPRINGS
CRYSTOLA
CUBA
CUBERANT
CUCHILLAS
CUDAHY
CUERO
CUEVA
CUEVITAS
CULBERTSOt)
CULLEH
CULLEOKA
COLLO
CULPEPER
CULVERS
CUMBERLAND
CUMLEY
CIMMINGS
CUHDIYO
CUNICO
CUPPER
CURANT
CURDLI
CDRBCANTI
CURHOLLOW
CURLEW
CURRAN
CURTIS CREEK
CURTIS SIDING
GUSHING
CUSHHAN
A
A
D
C
B
B
C
D
B
B
D
C
D
B
A
D
D
B
C
B
B
0
D
C
D
B
B
C
0
B
C
B
D
B
D
C
B
D
B
B
B
D
D
B
D
D
B
C
B
C
C
C
B
C
B/D
B
C
B
B
C
B
D
C
C
D
A
B
C
COSTER
CUTTER
CUTZ
CUYAMA
CUYON
CYAN
CYLINDER
CYNTHIANA
CYPREMORT
CYRIL
DABOB
OACONO
DACOSTA
DADE
D AFTER
DAGFLAT
DAGGETT
DAGLUH
DAGOR
DAGUAO
DAGUEY
DAHLQUIST
DAIGLE
DAILEY
DAKOTA
DALBO
DALBY
DALCAN
DALE
DALHART
DALIAN
D ALLAH
D ALTON
DALUPE
DAMASCUS
DAMON
DANA
D ANBURY
DANBY
D ANDREA
DANDRIDGE
DANGBERG
DAN 1C
DANIELS
DAHKO
DAMLEY
DANNEMORA
DANSKIN
DANT
DANVERS
DANVILLE
DANZ
DARCO
DARGOL
DARIEN
DARLING
DARNELL
DARKEN
D&RR
D.XRRET
DARROCH
DARROUZETT
Da*T
DARVADA
DiVRWIN
DASSEL
DAST
C
D
D
B
A
D
B
C/D
C
B
B
C
D
A
B
C
A
D
B
C
C
B
C
A
B
B
D
C
B
B
B
B
C
B
0
D
B
C
C
D
D
C
B
D
C
D
B
D
C
C
B
A
D
C
B
C
B
A
C
C
C
A
D
D
D
C
DATEMAN
DATING
DATWYLER
DAULTON
DAUPHIN
DftVEY
DAVIDSON
DAVIS
DAVISON
DJWTONE
DAWES
DAWHOO
DAWSON
DAXTX
DAY
DAYBELL
DAYTON
DAYVILLE
DAZE
DEACON
DEADFALL
DEAMA
DEAN
DEAN LAKE
DEARDURFF
DEARY
DEARYTON
DEATMAN
DEAVER
DEBENGER
DEBORAH
DECAN
DBCATHON
DECATUR
DECCA
DECKER
DECKERVILLE
DBCLO
DKXIRRA
DBCROSS
DEB
DEEPWATER
DEER CREEK
DBERFIBLD
DEERFORD
DEERING
DSERLODGE
DEER PARK
DEERTON
DEISRTRAIL
DEFIANCE
DEFORD
DBGARMO
DEGNER
DE GREY
DEJARNET
DSKALB
DEKOVEN
DELA
DEI.AKE
DELANCO
DELANEY
DELANO
DELECO
D ELENA
DELFINA
DELHI
DELICIAS
C
C
C
D
A
B
B
B
B
C
B/D
D
C
D
A
D
B/C
D
B
B
C
C
C
B
C
B
C
C
C
D
D
D
B
B
C
C
B
8
B
C
C
C
B
D
B
D
A
B
C
D
D
B/C
C
D
B
C
D
B
B
C
A
B/C
D
D
B
A
B
DEUCS
DELL
DELLEKER
DELLO
DELLROSE
DELM
DELMAR
DELNITA
DELMONT
DELMORTE
DELPHI
DELPHILL
DELPIEDRA
DELPINE
DELRAY
DEL REY
DEL RIO
DELSON
DELTA
D ELTON
DELWIN
DELYKDIA
DEMAST
DE MASTERS
DE MAYA
DEMERS
DEKKY
DEMONA
DEMOPOLIS
DEMPSEY
DEMPSTER
DEKAY
DENHAWKEN
D EDISON
DENMARK
DENNIS
DENNY
DEKROCK
DENTON
DENVER
DEODAR
DEPEH
DEPOE
DEPORT
DERA
DERINDA
DERR
DERRICK
DESAN
DESART
DESCALABRADO
DESCHUTES
DESERET
DESERTER
DESHA
DESHLER
DESOLATION
DESPAIN
DETER
DETLOR
DETOUR
DETRA
DETROIT
DEV
DEVILS DIVE
DEVOE
DEVOIGNES
DEVOL
B/D
C
B
A/C
B
D
D
C
B
C
B
C
C
D
A/1
C
B
C
C
B
A
A
B
B
C
D
D
C
C
B
B
B
D
C
D
C
D
D
0
C
D
C
D
D
B
C
C
B
A
C
D
C
C
B
D
C
C
B
C
C
C
B
C
B
D
D
C/
B
205
-------
DEVON
DEVORE
DEVOY
DBfARD
DEHEY
DEWILLE
DEXTER
DIA
DIABLO
DIAMOND
DIAMOND SPRINGS
DIAHONDVILLB
DIANEV
DIAHOLA
DIAZ
DIBBLE
DICK
DICKEY
DICKINSON
DICKSOH
DIGBY
DIGGER
DIGHTON
DILI.
DILIARD
DILLDOKN
DILLINGER
DILLON
DILLHYN
OILMAN
DILTS
DXHCRfH
DIHAL
DIMXAW
DINGLE
DIHGLISHKA
DIKXELHAN
DINKEY
DINHEH
DINSDALE
DINUBA
DIHZBR
DIOXICE
DIWAN
DIQOE
DISABEL
DISAUTEL
DISCO
DISHHSR
DISTERHKFF
DITCHCAMP
DXTHOD
DIVERS
DIVIDE
DIX
DIXIE
DIXMOHT
DIXMORE
DIXOHVILLE
DIXVILLE
DOAK
DOBBS
DOBEL
DOBROH
DOB*
DOCAS
DOCXBRY
OOCT
B
B
D
B
B
B
C
D
D
C
C
C
D
C
C
A
A
C
C
C
C
B
B
C
B
D
A
C
D
D
D
C
B
D
B
A
B
B
B/C
B
B
D
B
D
B
B
D
C
C
C
B
B
A
C
C
B
C
A
B
C
D
D
D
B
C
B
DODGE
DODGEVILLE
DODSON
DOGER
DOGUB
DOLAND
DOLE
DOLLAR
DOLLARD
DOLORES
DOLPH
DUHEZ
DOMINGO
DOHIHGUEZ
DOMIMIC
DOMINO
DOHIHSON
DOHA AHA
DONAHUE
DONALD
DONAVAH
DONEGAL
DOHERAIL
DOHEY
DOMICA
DOHLONTON
DONNA
DONNAN
DONHAROO
DOHNYBROOK
DONOVAN
DQOL1Y
DOOHE
DOOR
DORA
DORAN
DORCHESTER
DOROSHIN
DOROTHEA
DOR OVA N
DORS
DORSET
DOS CABEZAS
DOSS
DUSSHAN
DOTEH
DOTHAN
DOTTA
DOTY
DOUBLETOP
DOUDS
DOUGHERTY
DOUGHTY
DOUGLAS
DOtffiO
DOVER
DOVRAY
DOW
DOHAGIAC
DOIDEN
DOHELLTON
DOWER
DONKEY
DOWS
DOXIE
DOTCE
DOYLE
DOYLESTOWN
B
B
C
A
C
B
C
B
C
B
C
C
C
C
A
C
A
B
C
B
B
C
C
A
C
D
C
B
D
B
A
B
B
D
C
B
D
C
D
B
B
C
C
B
D
B
B
B
B
B
A
A
B
B
B
D
B
B
C
D
B
B
B
C
C
A
D
DOXN
DRA
DRACUT
DRAGE
DRAGOON
DRAGSTON
DRAMA?
DRAIN
DRAKE
DRANYON
DRAPER
DRESDEN
DRESSLER
DRBHS
DREXEL
DRIFTON
DRIGGS
DRUM
DRUMMER
DRUHMOND
DRURY
DRYAD
DRYBORG
DRY CREEK
DRYDEN
DRY LAKE
DUANE
DUART
DUDAXELLA
DUBAY
DOBBS
DUBOIS
DUBUQUE
DICEY
DUCHESNE
DUCKBTT
DUCOR
DUDA
DUDLEY
DUEL
DOELM
DUFFAU
DOFFER
DUPFIELD
DUTFSON
DUFFY
DUFUR
DUG6IHS
DUGOUT
DUOHAY
DUKES
DULAC
DUMAS
DUHECO
DtlMONT
DUNBAR
DUNBARTON
DDNBRIDGE
DUNCAN
DUNCANNON
DUNCOH
DDNDAS
DUNDAY
DUNDEE
DUN ELLEN
DUNE SAND
DUNGEKESS
DON GLEN
C
C
C
B
B
C
D
D
B
B
C
B
C
B
B
C
B
C
B
D
B
C
B
C
B
C
B
C
C
D
B
C
B
B
B
C
D
A
D
B
C
B
D
B
B
B
B
D
D
D
A
C
B
C
B
D
C
B
D
B
D
C
A
C
B
A
B
C
DUNKINSVILLE
DUNKIRK
DUN LAP
DUNHORE
DUNNING
DUNPHY
DUNUL
DUNVILLE
DU PAGE
DUPEE
DUPLIN
DDPO
DUPONT
DUPREE
DURALDE
DURAND
DURANT
DURELLE
DURHAM
DURKEE
DUROC
DURRSTEIN
DUSTON
DUTCHESS
DUTSON
DUTTON
DUVAL
DZEL
DWIGHT
DWYER
DYE
DYER
DYKE
DYRENG
EAC HUSTON
BAD
EAGAR
EAGLECONE
EAKIN
EDDS
EDDY
EDEN
ED EN TON
EDENVALE
EDGAR
EDGECUMBE
EDGELEY
EDOEHONT
EDGEWATER
EDGBHICK
EDGEWOOD
EDGINGTON
EDINA
EDINBURG
EDISON
EDISTO
EDITH
EDLOE
EDMONDS
EDMORE
EDMUND
EDNA
EDNEYVILLE
EDOM
EDROY
EDSON
B
B
B
B
D
D
A
B
B
C
C
C
D
D
C
B
D
B
B
C
B
D
B
B
D
D
B
B
D
A
D
B
D
D
C
B
B
B
B
C
C
C
D
B
B
C
B
C
B
A
C
D
C
B
C
A
B
D
D
C
D
B
C
D
C
EDWARDS
BEL
BFFINGTON
EFHUN
EGAH
SGAN
EANES
EARLE
EARLMONT
BARP
IZASLEY
BAST FORK
SAST LAKE
EASTLAND
:EASTON
EASTONVILLE
5'JlST PARK
32ASTPORT
12ATONTOHN
;BAUGALI.IE
12BA
BBBERI
I-;BBS
BBENEZER
TCCLES
19CHARD
IDCHLER
13CKERT
EQCLEY
KCKMKS
BCKRANT
IKTOR
!X>ALGO
EGBERT
EGELAHD
EGGLESTON
EIGNAR
KICKS
EIFORT
BtCAH
EKALAKA
ELAH
ELBERT
ELBURN
ELCO
EIiD
ELDER
ELDER HOLLOW
ELDERON
E:LDON
ELDORADO
ELDRIDGE
ELEPHANT
ELEROY
ELFRIDA
ELIJAH
ELIOAK
ELK
ELKADER
EK.KCREEK
EUC HOLLOM
ELKHORK
EUCINS
EKJCINSVILLB
ELKMOUHD
ECJC MOUNTAIN
EF..KOL
EUCTON
B/D
C
D
A
C
B
B
D
B/C
B
D
C
A
C
C
A
D
A
B/D
C
D
B
C
B
C
B
D
B
B
D
D
C
B/C
B
B
C
C
C
C
B
A
D
B
B
B
B
D
B
B
C
C
D
B
B
C
C
B
B
C
B
B
D
B
C
B
D
D
206
-------
ELLABELLE
ELL EDGE
ELLERY
ELLETT
ELLIBER
ELLICOTT
ELLINGTON
ELLINOR
ELLIOTT
ELLIS
ELLISFORDE
ELLISON
ELLOAM
ELLSBERRY
ELLSWORTH
ELL OH
ELMA
ELMDALE
ELMEHDORF
ELMIRA
ELMO
ELMQNT
ELHORE
ELM WOOD
ELNORA
ELOIKA
ELPAN
EL PECO
EL RANCHO
ELRED
ELROSE
ELS
ELSAH
ELSINBORO
ELS MORE
ELSMERE
ELSO
EL SOLYO
ELSTON
ELTOPIA
ELTREE
ELTSAC
ELWHA
ELHOOD
ELY
ELYS I AN
BLZINGA
EMBDEN ;
EHBRY
EMBUDO
EMDENT
EMIR
EMERALD
EMERSON
EMIDA.
EMIGRANT
EMIGRATION
EMILY
EHLIN
EMMA
EMMERT
EMMET
EHMONS
EMORY
EMPEDRADO
EKPEY
EMPEYVILLE
EMPIRE
B/D
C
D
D
i.
A
B
C
C
D
C
B
D
C
C
C
B
B
D
A
C
B
B
C
B
B
D
C
B
B/D
B
A
B
B
A
A
D
C
B
B
B
D
B
C
B
B
B
B
B
B
C
C
B
B
D
B
D
B
B
C
A
B
C
B
C
B
C
C
EMRICK
EMCE
ENCIERRO
EHCIHA
ENDERS
ENDERSBY
ENDICOTT
ENET
ENFIELD
ENGLE
BUGLES ID1
EH6LEMOOD
ENGLUND
ENNIS
ENOCHVILLE
ENOLA
EHON
ENOREE
ENOS
ENOS BURG
ENSENADA
ENSIGN
ENSLEY
ENSTROH
ENTENTE
ENTERPRISE
ENTIAT
EKUMCLAW
EPHRAIM
EPHEATA
EPLEY
EPOUFETTE
EPPING
EPSIE
ERA
ERAM
ERBER
ERIC
ERIE
ERIN
ERNEST
ERNO
ERRAMOUSPE
ESCABOSA
ESCAL
ESCALANTE
ESCAMBIA
ESCONDIDO
ESMOND
ESPARTO
ESPIL
EPINAL
ESP LIN
ESPY
ESQUATZEL
ESS
ESSEN
ESSEX
ESSEXVILLE
ESTACADO
ESTELLINE
ESTER
ESTER BROOK
ESTHERVILLE
ESTIVE
ESTO
ESTRELLA
ETHAN
B
B
D
B
C
B
C
B
B
B
B
C
D
B
B/D
B
C
D
B
D
B
D
D
B
B
B
D
C
C
B
B
D
D
D
B
C
C
B
C
B
C
B
C
C
B
B
C
C
B
B
D
A
D
C
B
B
C
C
D
B
B
D
B
B
C
B
B
B
ETHETE
ETHRIDGE
ETIL
ETNA
ETOE
ETOWAH
ETOHN
ETSEL
ETTA
ETTER
ETTERSBURG
ETTRICK
EUBANKS
EUDORA
EUFAULA
EUREKA
EUSTIS
EUTAH
EVANGELINE
EVANS
EVANSTON
EVARO
EVART
EVENDALE
EVERETT
EVERGLADES
EVEELY
EVERMAN
EVERSON
EVESBORO
EHA
EMAIL
Ell ALL
EHINGSVILLE
EXCELSIOR
EXCHEQUER
EXETER
EXLINE
EXRAY
EX DM
EYERBOW
EYRE
FABIUS
PACEVILLE
FAHIY
FAIM
FAINES
FAIRBANKS
FAIRDALE
FAIRFAX
FAIR FIELD
FAIRHAVEN
FAIRMODNT
FAIRPORT
FAIRYDELL
FAJARDO
FALAYA
FALCON
FALFA
FALFURRIAS
FALK
FALKNER
FALL
FALLBROOK
FALLON
FALLS BURG
FALLSINGTON
B
C
A
B
B
B
D
C
B
B
D
B
B
A
D
A
D
C
B
B
A
D
C
B
A/D
B
C
D
A
B
A
A
B
B
D
C/D
D
D
C
D
B
B
B
B
C
A
B
B
B
B
B
D
C
C
C
C
D
C
A
B
C
B
B/C
C
C
D
FANCHER
FANG
F ANN IN
FANNO
FANU
FARADAY
FARALLONE
FARAWAY
FARB
FARGO
PA8ISITA
FARLAND
FARMINGTON
FARNHAM
FARNHAMTON
FAIN OF
FARHUM
FARRAGUT
FARRAR
FARRELL
FARKENBURG
FARROT
FARSON
FARWELL
FASKIN
FATIMA
FATTIG
PAUHCE
FAUOUIER
FAUSSE
FAWCETT
FAWN
FAXON
FAYAL
FAYZTTE
FAYETTBVILLE
PAYHDOD
FE
FEDORA
FELAN
FELDA
FELIDA
FELKER
FELLOWSHIP
FELT
PELTA
FELTHAM
FELTON
FELTONIA
FENCE
FENDALL
PENHOOD
FERA
FERDELFORD
FERDIG
FERDINAND
FERGUS
FERGUSON
FERNANDO
FERN CLIFF
FERNDALE
FERNLEY
FERNOH
FERNPOINT
FERRELO
FERRIS
FERRON
FERTALINE
C
B
B
C
C
B
B
D
D
D
C
B
C/D
B
B/C
B
B
C
B
B
B
C
B
C
B
B
C
A
C
D
C
B
D
C
B
B
C
D
B
A
B/D
B
D
D
B
C
A
B
B
B
C
B
C
C
C
C
B
B
B
B
B
C
B
C
B
D
D
D
FESTINA
FETT
FETTIC
FIANDER
FIBEA
FIDALGO
FIDDLETOWN
FIDDYMENT
FIELDING
FIELDON
FIBLDSOM
FIFE
FIFER
FILLHORE
FINCASTLE
FINGAL
FINLEY
FIRESTEEL
FIRGRELL
FIRM AGE
FIRO
FIRTH
FISH CREEK
FISHERS
FISHHOOK
FISHKILL
FITCH
FITCHVILLE
FITZGERALD
FITZHUGH
FIVE DOT
FIVEMILE
FIVES
FLAGG
FLAGSTAFF
FLAK
FLAMING
FLAMINGO
FLANAGAN
FLANDREAU
FLASHER
FLATHEAD
FLAT HORN
FLATTOP
FLATHILLOM
FLAXTON
FLEAK
FLECHADO
FLEER
FLEETWOOD
FLEISCHMANN
FLEMING
FLETCHER
FLOKE
FLOM
FLOMATION
FLOHOT
FLORENCE
FLORESVILLE
FLORIDANA
FLORISSANT
FLOM ELL
FLOWEREE
FLOYD
FLOETSCH
FLUSHING
FLUVANNA '
FLYGARE
B
D
D
C
D
C
C
C
B
B
A
B
D
D
C
C
B
B
B
B
D
B/C
B
B
D
A
C
B
B
B
B
B
B
C
B
B
D
B
B
A
A
B
D
B
A
A
B
D
D
C
B
D
C
A
B
C
C
B/D
C
C
B
B
C
C
B
207
-------
FLYHH
FQARD
FOGSLSVILLE
FOLA
FOLEY
FONDA
FONDIS
FONTAL
POHTREBN
FOP1ANO
FORBES
FORD
FORDNEY
FORDTRAN
FORDVILLE
POJRE
FORELAND
FORELLE
PORES HAH
FORESTDALE
FORESTER
FORESTON
FORGAY
FQRHAN
FORNEY
FORREST
FORSEY
FORSGREN
FORT COLLINS
FORT DRUH
FORT LYON
FOR? HEWS
FORT HQTO
FORT PIERCE
FORT ROCK
FORTUNA
FQRTHINGATE
FORWARD
FOSHOHE
FOSSUH
FOSTER
FOSTORIA
FOUNTAIN
FOURLOG
FOURHILB
FOUR STAR
FOOTS
FOX
FOXCR2BK
FQXHOUNT
FOXOL
FOX PARK
FOX PARK
FOXTON
FRAILEY
FRAN
FRANCIS
FRAHCITAS
FRANK
FRANKFORT
FRAKKIRK
FRANKLIN
FRANKS TOWN
FRAHKTOMH
FRANKVILLE
FRATERNIOAD
FRAZER*
FRED
D
D
B
B
D
D
C
D
B
D
B
D
A
C
B
D
D
B
B
D
C
C
A
B
D
C
C
C
B
C
B
A
A
C
C
D
C
C
B
B
B/C
B
D
D
B
B/C
B
B
B/D
C
D
D
D
C
B
B
A
D
D
D
C
B
B
D
B
D
C
C
FREDENSBORG
FREDERICK
FREDON
FRZDONIA
FREORIOCSON
FREEBURG
FREECE
FREEDOM
FREEHOLD
FREEL
FREEMAN
FREEHANVILLE
FREEOK
FREER
FREESTONE
FREEZENER
FREMONT
FRENCH
FRENCHTOWN
FRENEAU
FRESNO
FRIANA
FRIANT
FRIDLO
FRIEDMAN
FRIENDS
FRIES
FRINDLE
FRIO
FRIZZELL
FROBERG
FROHMAH
FRONDORF
FRONHOFER
FRONTON
FROST
FROITA
FRUITLAND
FRYE
FUEGO
FUERA
FUGAWEE
FULCHER
FULDA
FULLERTON
FOLMER
FULSHEAR
FULTON
FUQUAY
FURNISS
FURY
FUSULINA
GAASTRA
GABALDON
GABBS
GABEL
GABICA
GACEY
GACHADO
GADDES
GADES
GADSDEN
GAGE
GAGEBY
GAGETOWN
GAHEE
GAINES
C
B
C
C
C
C
D
C
B
B
C
B
B
C
C
C
C
C
D
C
C/D
D
D
C
B
D
D
B
B
C
D
C
C
C
D
D
B
B
C
C
C
B
C
C
B
B/D
C
0
B
B/D
B/D
C
C
B
D
C
D
D
D
C
G
D
B
C
B
C
GAINESVILLE
GALATA
GALE
GALEN
GALENA
GALEFFI
GALESTOWN
GAL ETON
GALEY
GALISTED
GALLAGHER
GALLATIN
GALL EGOS
GALLINA
GALLION
GALVA
GALVESTON
GALVEZ
GALVIN
GAUfAY
GAMBLER
GAMBOA
GANNETT
GANSNER
GAPO
GAPPMAYER
GARA
GABBER
GAR BUTT
GARCENO
CARD EL LA
GARDENA
GARDINER
A
D
B
B
C
C
A
D
B
C
B
A
B
C
B
B
A
C
C
B
A
B
D
D
D
B
B
A
B
C
D
B
A
GARDNER'S FORK B
GARDHERVILLE
GARDONE
GAREY
GARFIELD
GARITA
GARLAND
GARLET
GARLOCK
GARMON
GARMORE
GARNER
GARO
GARR
GARRARD
GARRETSON
GARRET T
GARRISON
CARTON
GARHIN
GASCONADE
GAS CREEK
GASKELL
GASS
GASSET
GATES BURG
GATESON
GATEVIEW
GATEWAY
GATEWOOD
GAULDY
GAVINS
GAVIOTA
GAY
GAYLORD
D
A
C
C
C
B
A
C
C
B
D
D
D
B
B
B
B
C
C
D
C
C
D
D
A
C
B
C
D
B
C
D
D
B
GAYNOR
GAYVILLE
GAZELLE
GAZOS
GEARHART
GEARY
GEE
GEEBURG
GEER
GEFO
GELKIE
GEM
GEMID
GEMSON
GENESEE
GENEVA
GENOA
GENOLA
GEORGEVILLE
GEORGIA
GERALD
GERBER
GERIG
GERING
GERLAND
GERMANIA
GERMANY
GERRARD
GESTRIN
GETTA
GETTYS
GEYSEN
GHENT
GIBBLER
GIBBON
GIBBS
GIBBS TOWN
GIFFIN
GIFFORD
GILA
GILBY
GILCBRIST
GILCREST
GILEAD
GILES
GILFORD
GILHOULY
GILISPIE
GILLIAN
GILLIGAN
GILLS
GILLSBURG
OILMAN
GILMORE
GILPIN
GILROY
GILSON
GILT EDGE
GINAT
GINGER
GINI
GINSER
GIRARDOT
GIRD
GIVEN
GLADDEN
GLADE PARK
GLADSTONE
C
B
D
B
A
B
B
C
C
A
B
C
C
C
B
C
D
B
B
B
D
D
B
B
C
B
D
B
C
C
D
C
C
B
D
A
C
C
B
B
B
B
C
B
B/D
B
C
C
B
C
C
B
C
C
C
B
D
D
C
B
C
0
A
C
A
C
B
'3 LAD WIN
SLAMIS
'3LANN
iJLASGOW
GLEAN
OLEASON
ijLEN
I1LENBAR
GLENBERG
GLENBROOK
GLENCOE
GLENDALE
r.LENDIVE
GLENDORA
I3LENELG
(JLESFIELD
(ILENFORD
GLEN HALL
CLKNHAM
6LEHMORA
CLENNALLEN
CiLENOMA
6LENROSE
('LENSTKD
GLENTON
GLENVIEM
CLENVILLE
GLIDE
CLIKON
GLORIA
CILO 1C ESTER
GLOVER
CILYHDON
0LYNN
COBLE
CiODDARD
CODDE
GODBCKE
GODFREY
GODWIN
COBGLEIN
CIOESSEL
GOFF
CiOGEBIC
GOLBIN
CiOLCONDA
COLD CREEK
COLD END ALE
GOLDFXELD
GOLDHILL
GOLDMAN
COLDRIDGE
GOLDRUN
GOLDSBORO
GOLDSTON
GOLDSTREAM
GOLD VALE
GOLD VEIN
GOLIAD
GOLLAHER
GOLTRY
GOMEZ
GOHM
GONVICK
GOOCH
GOODALE
GOOD ING
GOODINGTON
A
C
B/C
C
B
C
B
B
B
D
D
B
B
D
B
D
C
B
B
C
C
B
B
D
B
B
C
B
B
C
A
C/D
B
C
C
B
D
D
C
D
C
D
C
B
C
D
D
B
B
8
C
B
A
C
C
D
C
C
C
A
A
B
D
B
D
C
C
C
208
-------
GOODLOW
GOODMAN
GOODRICH
GOODSPRINGS
GOOSE CREEK
GOOSE LAKE
GOOSMUS
GORDO
GORDON
GORE
GORGONIO
GORHAM
GORIN
GORING
GORMAN '
GORUS
GORZELL
GOSHEM
GOSHOTE
GOSPORT
GOTHAM
GOTHARD
GOTHIC
GOTHO
GOULDING
GOVAN
GOVE
GCNEM
GRABE
GRABLE
GRAGEMONT
GRACEVILLE
GRADY
GRAFEN
GRAFTON
GRAHAM
GRAIL
GRAMM
GRANATH
GRANBY
GRANDE ROHDE
GRAMDPIBLD
GRAHDVIEH
GRANER
GRANGER
GRANGEVILLE
GRANILE
GRANO
GRANT
GRANTS EORG
GRANTSDALE
GRAMVILLE
GRAPEVINE
GRASNERE
GRASSN&
GRASSY BUTTE
GRATZ
GRAVDEM
GRAVE
GRAVITY
GRAYCAIM
GRAYFORD
GRAYLING
GRAYLOOC
GRAYPOINT
GRAYS
GREAT BEND
GREELEY
B
B
B
D
B
D
B
B
D
D
A
B
C
C
B
A
B
B
D
C
A
D
C
C
D
C
B
B
B
B
B
B
D
B
B
D
C
B
B
A/D
D
B
C
C
C
B/C
B
D
B
C
A
B
C
B
B
A
C
C
B
C
A
B
A
B
B
B
B
B
GREEN BLUFF
GREENBRAE
GREEN CANYON
GREENCREHC
GREENDALE
GREENFIELD
GREENHORN
GREENLEAF
GREENOUGH
GREENPORT
GREEN RIVER
GREENSBORO
GREENSON
GREEN TON
GREENVILLE
GREENWATER
GREENWICH
GREENWOOD
GREER
GREGORY
GREHALEM
GRELL
GRENADA
GRENVILLE
GRESHAM
GREWINGK
GREYBACK
GREYBULL
GREYCLIFF
GREYS
GRIFFY
GRIGSTON
GRIMSTAD
GRISHOLD
GRITNEY
GRIVER
GRIZZLY
GROGAN
GROSECLOSE
GROSS
GROTON
GROVE
GROVBLAND
GROVER
GROVETON
CROWD EN
GROWLER
GRUBBS
GRULLA
GRUKMIT
GRUNDY
GRUVER
GRYGLA
GUADALUPE
GUAJE
GUALALA
GUAMAMI
GUANABANO
GOAHAJIBO
GUANICA
GUAYABO
GUAYABOTA
GUAYAHA
GUBEN
GUCKEEN
GUELPH
GUENOC
GUERNSEY
B
C
B
B
B
B
B
B
C
B
C
C
B
A
B
D
C
A
B
D
C
B
C
D
B
C
C
B
B
B
B
B
C
C
C
B
C
C
A
A
B
B
B
B
B
D
D
D
C
C
C
B
A
D
B
B
C
D
B
D
D
B
C
B
C
C
GUERRERO
GUEST
GUIN
GULER
GULKANA
GUHBOOT
GUNBARREL
GUHH
GUMMOK
GUHSIGHT
GUNTER
GURABO
GURNEY
GUSTAVUS
GHSTIN
GUTHRIE
GUYTON
GWIN
GWINNETT
GYMER
GYPSTRUM
HACCKE
HACIENDA
HACK
HACKERS
HACKETTSTOHH
HADAR
HADES
HADLEY
HAG EN
HftGENBARTH
HAGENER
HAGER
HAGERH&M
HAGERSTOHN
HAGGA
HAGGERTY
HAGSTADT
HAGUE
ttAIG
HAIKU
HAILMAN
HAINES
HAIRE
HALAHA
HALDER
HALE
HALEDON
HALEIWA
HALEY
HALF MOON
HALFORD
HALFWAY
HALGAITOH
HALII
HAIiI D4AILE
HALIS
HALOL
HALLEOC
HALL RANCH
HALLVILLE
HALSEY
HAMACER
HAMAKUAPOKO
HAHAN
HAMAR
C
D
A
B
B
C
A
B
C
B
A
D
C
D
C
D
D
D
B
C
B
C
D
B
B
B
A
C
B
B
B
A
C
C
C
B
B
C
A
C
B
B
B/C
C
B
C
B
C
B
B
B
A
D
B
B
B
B
B
B
C
B
D
A
B
B
B
HAMBLEN
HAMBRIGHT
HAMBURG
HAHBY
HAMEL
HAMERLY
HAMILTON
HAMLET
HAMLIN
HAMMONTON
HAMPDEN
HAMPSHIRE
HAMPTON
HAHTAH
HANA
HAN ALE I
HANAMAOU)
HANCEVILLE
HANCO
HAND
HANDRAN
HANDS BORO
HANDY
HANEY
HAHPORD
HANGAARD
HANGER
HANIPOE
HANK INS
HANKS
HANLY
BANNA
HAMNUH
HANOVER
HANS
HANSEL
HANSKA
HANSON
HANTHO
HANTZ
HAP
HAP GOOD
HAPNEY
HARBORO
HARBOURTON
HARCO
HARDEMAN
HARDESTY
HARDING
HARDSCRABBLE
HARDY
HARGREAVE
BARKERS
HARKEY
HARLAN
HARLEM
HARLESTON
HARLINGEN
HARMEHL
HARMONY
HARNEY
HARPER
HARPETH
HARPS
HARPSTER
HARPT
HARQUA
HARRIET
C
D
B
C
C
C
A
B
B
C
C
C
C
A
C
A
B
D
B
C
D
D
B
B
C
B
B
C
B
A
B
D
C
C
C
C
A
B
D
B
B
C
B
B
B
B
D
B
D
B
C
B
B
C
C
D
C
C
C
D
B
B
C
B
C
D
HARRIMAti
HARRIS
HARRISBURG
HARRISON
HARRISVILLE
HARSTEKE
HARSTINB
HART
HART CAMP
HARTFORD
HARTIG
HARTLAND
HARTLETON
MARTLINE
HARTSBURG
HARTS ELLS
HARTSHORN
HARVARD
HARVEL
HARVEY
HARMOOD
HA SKI
HASKILL
HA SKINS
HASSELL
HASTINGS
HAT
HATBORO
HATCH
HATCHERY
HATFIELD
HATHAWAY
HAITI E
HATTON
HAUBSTADT
HADGAN
HAUSER
HAVANA
HAVEN
HAVERLY
RAVERSON
HAVILLAH
HAVINGDON
HAVRE
HAVRELON
HAH
HAWES
HAHI
HAWKEYE
HAWK SELL
HAWKSPRINGS
HAXTUH
HAYBOURNE
HAYBRO
HAYDEN
HAYESTON
HAYESVILLE
HAYFIELD
HAYFORD
HAYHOND
HAYNESS
HAYNIE
HAYPRESS
HAYSPUR
RAYTBR
HAYTI
HAYHOOD
HAZEL
B
D
D
C
C
B
C
D
C
A
B
B
B
B
B
B
B
B
B
C
C
B
A
C
C
B
D
D
C
C
C
B
C
C
C
B
D
B
B
B
B
C
D
B
B
B
A
B
A
A
B
A
B
C
B
B
B
B
C
B
B
B
A
B/I
B
D
B
C
209
-------
KA2EIAIR
HAZEH
HAZLEHURST
HAZLESON
HAZTOH
HEADLEY
HEADQUARTERS
HEAKE
HEATH
HEATLY
HEBESOKVILLE
HEBER
HEBERT
HEDGEN
HEBO
HEBROH
HBCHT
HEOCI
HECLA
HECTOR
BEDDEH
HEORXCK
HEDVILLE
HEGNE
HEIDEH
HEIDTOAH
HBZL
HEIKDAL
HEISETON
HEISLER
HEIST
HSITT
HSXT2
HEIZER
HELOT
HELEHANO
HELENA
HEWER
HELVETIA
HELY
HEHBRE
HEHHI
IIEWFIELD
HEKPSTEAD
IIEHCRATT
HENDERSON
HENDRICKS
HENEPER
HENKXH
HEKLEY
HEHLINS
HEHMBCE
HEHHEPIN
HENHINGSEN
HENRY
HENSEL
HEHSHAW
HENSLEY
HEPLER
HERBERT
HEREFORD
HERKXMER
HERLONG
HERHISTON
HERHON
HERNDON
HERO
HERRERA
D
B
C
B
D
B
B
D
C
A
B
B
C
A
D
C
C
C
B
D
C
B
D
D
D
C
D
B
B
B
B
C
D
D
C
C
C
C
C
B
B
C
C
B
B
B
C
B
C
C
D
B
C
D
B
C
D
D
B
B
B
D
B
A
B
B
A
HERRICK
HERHON
HERSH
HERS HAL
HESCH
HESPER
HESPERIA
HESPERUS
HESSE
HESSEL
HESSELBERG
HESSELTINE
HESSLAN
WESSON
HETTINGER
HEXT
HEZEL
HIALEAH
HIAWATHA
HI BEARD
HIBBING
HIBERHIA
HICKORY
HICKS
HIDALGO
HIDEAWAY
HXDEWOOD
HIERRO
HIGHAHS
HIGHFIELD
HIGH GAP
HIGHLAND
HIGHHQRE
HIGH PARK
HIHIMAKU
HIIBNER
HIKO PEAK
HIKO SPRINGS
HILDRETH
HILEA
HILES
HILGER
HILGRAVE
HILLEMAKN
HILLERY
HILLET
HI1LF1ELD
HILLGATE
HILMARD
HILLON
HILLS BORO
HILLSDALE
HILHAR
HILO
HILT
HILTON
HINCKJt,EY
HINDES
HIKES BURG
HINKLE
HINMAH
HINSDALE
HINTZE
HIPPLE
HISLE
HITT
HI VISTA
HIWASSEE
C
B
A
B/D
B
C
B
B
C
D
D
B
C
C
D
B
B
D
A
D
C
C
C
B
B
D
C
C
D
B
C
B
B
B
A
C
B
D
D
D
B
B
B
C
D
D
B
D
B
B
B
B
C/D
" A
B
B
A
C
C
D
C
D
C
D
B
C
B
HIWOOD
HIXTON
HOBACKER
HOBAH
HOBBS
HOBOG
HOBSON
HOCHBIM
HOCKIHG
HOCKINSON
HOOCLEY
HODGE
HODGINS
HODGSON
HOEBE
HOELZLE
HOFFMAN
HOF?HANVILLE
HOGANSBURG
HOG ELAND
HOGG
HOGRIS
HOH
HOHMANN
HOKO
HOLBROOK
HOLCONB
HOLDAHAY
HOLD EN
HOLDER
HOLDERMAN
HOLD ERN ESS
HOLDREGE
HOLLAND
HOLLINGER
HOLLIS
HOLLISfER
HOLLOHAN
HOLLOWAY
HOLLY
HOLLY SPRINGS
HOLi.vwnnn
HOLM3EL
HOLMES
HOLOMUA
HOLOPAW
HOLROYD
HOLSINE
HOLST
HOLSTON
HOLT
HOLTLE
HOLTVILLE
HOLYOKE
HOMA
HOME CAMP
HOHELAKE
HOMER
HOMESTAKE
HOMESTEAD
HONAUNACI
HONCUT
KONDALE
HONDO
HONDOHO
HONEOYE
HONEY
HONEYGROVE
A
B
B
C
B
D
C
B
B
C
C
B
C
C
B
C
C
C
B
B
C
B
B
C
C
B
D
D
A
B
C
C
B
B
B
C/D
D
C
A
D
D
13
C
B
B
B/D
B
B
B
B
B
B
C
C/D
C
C
B
C
D
B
C
B
D
C
B
B
D
C
HONEYVILLB
HONK
HONOKAA
HONOLUA
HONOMANU
HONOULIULI
HONUAULU
HOOD
HOODLE
HOODSPORT
HOODVIEW
HOOKTON
HOOLEHUA
HOOPAL
HOOPER
HOOPESTON
HOOSIC
HOOT
HOOTEN
HOOVER
HOPEKA
HOPETON
HOPEWELL
HOPGOOD
HOPKINS
HOPLEY
HOPPER
HOQUIAM
HORATIO
HORD
HOREB
BORNE
HORNELL
HORNING
HRONITOS
HORROCKS
HORSESHOE
HORTON
HORTONVILLE
HOSKIN
HOSKINNINI
HOSLEV
HOSMER
HOTAW
HOT LAKE
HOUDEK
HOUGHTON
HOUR
HOULKA
HOULTON
HOUNDBY
HOURGLASS
HOUSATONIC
HOUSEMOUNTAIN
HOUSEVILLE
HOUSTON
HOUSTON BLAC3C
HOYDE
HOVEN
HOVE NW EBP
HOVERT
HOVEY
HOWARD
H DWELL
ROWLAND
HOVE
HOYLETON
HOYPUS
C
B
A
B
B
D
A
B
B
C
B
C
B
D
D
B
A
D
D
B
D
C
C
B
B
B
B
D
B
B
D
D
A
D
B
B
B
B
C
D
D
C
C
C
B
A/D
C
D
C/D
D
B
D
D
C
D
D
A/C
D
C
D
C
B
C
C
B
C
A
IIOYTVILLE
HUBBARD
HUBERLY
HUBERT
HUBLERSBURG
HUCKLEBERRY
HUDSON
I1UECO
HUEL
HUENEHE
1IUERHUERO
HOEY
HUFFINE
MUGGINS
HUGHES
UUGHESVILLE
HUGO
HUICHICA
OUIKAU
BULETT
lilULLS
nULLT
JTOLUA
HUM
HUMACAO
EWMATAS
BUIBARGER
HUMBIRD
HUHBOLDT
H HMD UN
HUME
HUMES TON
HtMMINGTON
HUMPHREYS
HUMPTULIPS
HUNSAKER
HUNTERS
HUNTING
HONTINGTON
HUHTSVILLE
HUPP
KURDS
HURLEY
HURON
HURST
HURWAL
HOSE
HUSSA
HUSSMAN
HUTCHINSON
HUTSON
HUXLEY
HYAM
HXAT
HYATTVILLE
HYDABURG
HYDE
HYDRO
HYMAS
HYRUM
HYSHAM
IAO
IBERIA
ICENE
IDA
IDABEL
IDAK
D
A
D
B
C
C
C
C
A
B/C
D
D
A
C
B
B
B
C/D
A
B
C
B
D
B
B
C
B
C
D
B
C
C
C
B
B
B/C
B
C
B
B
B
B
D •
C
D
B
C
B/D
D
C
B
D
D
A
C
D
D
C
D
B
D
D
C
B
B
B
210
-------
IDANA
IDEON
IDMON
IGNACIO
IGO
IGUALDAD
IHLEN
I JAM
ILDEFONSO
ILKA,
ILLION
IHA
IMBLER
IMLAY
IMMOKALEE
IMPERIAL
IN AVAL E
INDART
INDIAHOMA
INDIAN
INDIAN CREEK
JNDIANO
INDIANOLA
INDIO
INGA
INGALLS
INGARD
INGENIO
INGRAM
INKLER
INKS
IMHACHUK
INHAN
INHO
INNESVALE
INSKIP
INVERNESS
INVILLE
INHOOD
IO
IOLA
IOLEAU
IONA
IONIA
IOSCO
IPAVA
IRA
IREDELL
IRETEBA
IRIM
IROCK
IRON BLOSSOM
IRION MOUNTAIN
IRON RIVER
IRONTON
IRRIGFON
IRVINGTON
IRWIN
ISAAC
ISAAQUAH
ISAN
ISANTI
I SHELL
ISHAM
ISHI PISHI
ISLAND
ISOM
ISSAQUAH
C
D
B
C
D
D
D
D
B
B
B/D
B
B
C
B/D
D
A
B
D
D
C
A
B
B
B
B
C
D
B
D
D
C
A
D
C
D
B
C
a
A
C
B
B
B
B
C
D
C
C
B
D
D
B
C
C
C
D
C
B/C
D
D
C
C
C
B
B
B/C
ISTOKPOGA
ITCA
IUKA
IVA
IVAN
IVES
I VIE
IVINS
IZAGORA
IZEE
JABO
JACAGUAS
JACANA
JACINTO
JACK CREEK
JACKLIN
JACKHIFE
JACKPORT
JACKS
JACKSON
JACKSONVILLE
JACOB
JACOBS EN
JACOBY
JACQUES
JACQUITH
JACWIH
JAFFREY
JAGUEYES
JAL
JALMAR
JAMES CANYON
JAMESTOWN
JANE
JANISE
JANS EN
JARAB
JARBOE
JARITA
JARRE
JARVIS
JASPER
JAOCAS
JAVA
JAY
JAYEM
JAYSON
JEAN
JEANERETTE
JEAN LAKE
JEDD
JEDDO
JEFFERSON
JEKLEY
JELM
JENA
JENKINS
JENKINSON
JENNESS
JENNINGS
JENNY
JERAULD
JERICHO
JEROME
JERRY
JESBEL
D
D
C
C
B
B
A
C
C
C
C
B
D
B
A
B
C
D
C
B
C
D
D
C
C
C
B
A
B
B
A
B/C
C
C
C
A
D
C
C
B
B
B
A
B
C
B
D
A
D
B
C
D
B
C
D
B
B
D
B
C
D
D
C
C
C
D
JESSE CAMP
JESSUP
JETT
JIGGS
JIM
JIMENEZ
JIMTOWN
JOB
JOBOS
JOCITY
JOCKO
JODERO
JOEL
JOES
JOHNS
JOHNSBURG
JOHNSON
JOHNSTON
JOHNSHDOD
JO ICE
JOLAN
JOLIST
JONESVILLE
JONUS
JOPLIN
JOPPA
JORDAN
JORGE
JORNADA
JORY
JOSE
JOSEPHINE
JOS IE
JOY
JUASA DIAZ
JUBILEE
JUDD
JUDITH
JUDKINS
JUDSON
JUDY
JUG1T
JUGHANDLE
JULES
JULESBURG
JOLIAETTA
JUMPE
JUNCAL
JOHCOS
JUNCTION
JUNEAU
JUNIATA
JOMIPERO
JUNIUS
JUNO
JUNQUITOS
JURA
JUVA
JUVRN
KAALUALU
KACHEMAX
KADAKE
KADASHAN
KADE
KADIN
KADOKA
KAENA
C
C
B
C
C
C
C
C
C
B
A
B
B
B
C
D
B
B/D
B
D
C
C
A
B
B
B
D
B
C
C
C
B
B
B
B
C
D
B
C
B
C
D
B
B
A
B
B
C
D
B
B
B
C
B
C
C
B
D
A
B
D
B
C
B
B
D
KAHALUU
1CAHANA
KAHANUI
KAHLER
KAHOLA
KAH SHEETS
KAHUA
KAIKLI
KAILUA
KAIMU
KAINALIU
KAIPOIOI
KAIWIKI
KALAE
1CALALOCH
1CALAMA
1CALAMAZOO
KALAPA
KALAUPAPA
1CALIFONSKY
KALIHI
KALISPELL
KALKASKA
KALMIA
KALOKO
KALOLOCH
KALSIN
KAMACK
KAHAKOA
KAMAOA
KAKAOLE
KAMAY
KAMIE
KAMRAR
KANABEC
KANAKA
KAN AP A HA
KANDIK
KANE
KANEOHE
KANEPUU
KAN IMA
KANLEE
KANOSH
KANZA
KAPAA
KAPAPALA
KAPOD
KAPOWSIN
KAPIIHIKANI
KARAMIN
KARDE
KARHEEN
KARLAN
KARLIN
KARLO
KARLUK
KARKAK
KARNES
KARRO
KARS
KARSHNER
KARTA
KARTAR
KASCHMIT
KASHNITNA
KASILOF
KASKI
D
B
B
B
B
D
D
D
A
A
A
B
A
B
B
C
B
B
D
D
D
A
A
B
D
B
D
B
A
B
B
D
B
B
B
B
A/D
B
B
B
B
C
B
C
D
A
B
B
C
D
B
B
D
C
A
D
D
D
B
B
A
D
C
B
D
B
A
B
KASOTA
KASSLER
KASSON
KATAMA
KATEMCY
KATO
KATRINE
KATULA
KATY
KAUFMAN
KAOPO
KAVETT
KAWAIHAE
KAWAIHAPAI
KAHBAWGAM
KAWICH
KANXAWLIN
KEAAU
KEAHUA
KEALAKEKUA
KEALIA
KEANSBURG
KEARNS
KEATING
KEAUKAHA
KEAWAKAPU
KEBLER
KECH
KECKO
KEDRON
KEEPERS
K1EGAN
KEEI
KEFKEE
KEELDAR
KEENE
KEENO
KEESE
KEG
KEHENA
KEIGLEY
KilSER
KEITH
KEKAHA
KEKAKE
KELLER
KELLY
KELN
KELSEY
KELSO
KELTNER
KELVIN
KEMMERER
KEMOO
KEMPSVILLE
KEMP TON
KEN A I
KENANSVILLE
KENDAIA
KENDALL
KENDALLVILLE
KENESAW
KENMOOR
KENNALLY
KENNAN
KENNEBEC
KENNEDY
KENNER
C
A
C
B
C
C
B
B
C
D
A
D
C
B
C
A
C
D
B
C
D
D
B
C
D
B
B
D
B
C
C
D
B
B
C
C
D
B
C
C
B
B
B
D
C
D
C
D
C
B
C
C
B
B
B
C
A
C
B
B
B
B
B
B
B
B/C
D
211
-------
KENNEHICX
KENHEY
KSHHtY LAKE
KEHO
KEHOMA
KENSAL
KENSPUR
KENT
KENYON
KEO
KEOLDAR
KEOHAH
KEOTA
Ksams
KEPLER
KERBY
KERHEL
KERHIT
KBRMO
KERR
KBRRICK
KERRTOHN
KERSHAW
KERSICK
KERSTQH
KERT
KERW1K
KESSLER
KESHICK
KEfCHLY
KETTLE
KSTTLEMAN
KECTNER
KEVIN
KEWAUNEE
KEWEENAH
KEYA
KEYES
KEYNER
KEYPOKT
KEYSTONE
KEYTESVILLE
KEZAR
K I AW AH
KIBBIE
KICKBRVILLE
KIDD
KIDHAH
KIEHL
KIETZXE
KIEV
KIKONI
KILARC
KILAUEA
KILBOURNE
KILBURN
KXLCNIS
K1LDOR
KILGOR2
KILKENNY
KILLBOCK
KILLEY
KILLXHGHORTH
KILLPACK
KIIHBRQUE
KILN
KILO A
KXLOHANA
B
A
C
D
D
B
A
D
C
B
B
C
C
D
C
B
B
A
A
B
B
A
D
A/D
C
C
C
D
B
B
B
C
C
C
A
B
D
D
C
A
D
B
C
B
B
D
B
A
D
B
B
D
B
A
B
D
C
B/D
B
C/D
D
C
C
D
A
A
KILHINHING
KIM
KIHANA
KIMBALL
KIMBERLY
KB4BROUGH
KIHHERLING
KDIMONS
KIMO
KIKA
KINCO
KINESAVE
KINGFISHER
KINGHURST
KINGMAN
KIKGS
KINGSBURY
KINGSLEY
KIKGS RIVER
KINGSTON
KINGSVILLE
KINKEAD
KIHKEL
KINKORA
KINMAN
KIKKEAR
KINNEY
KINNIGX
KINREAD
KINROSS
KINSTOH
KINTA
KINTON
KINZEL
KIOHATIA
KIONA
KIPLING
KIPP
KIPPEN
KIPSON
KIRK
KIRKHAM
KIRK LAND
KIRJCTON
KIHCVIM.1
KIRHBX
KIRVIN
KISRING
KISSICK
KISTLER
KfTCHELL
KITCHEN CREEK
KITSAP
KITTANNIHG
KITTITAS
KITTREDGE
KITTSON
KIUP
KIVA
KIHAHIS
KIZHUYAK
KJAR
KLABER
KLAMATH
KLAUS
KLAWASI
KLEJ
KLIQCER
C
B
B
C
B
D
D
C
C
D
A
C
B
B
D
C/D
D
B
C
B
C
C
B
D
C
B
B
C
D
D
D
D
C
B
A
B
D
C
A
C
B/D
C
D
B'
C
C
C
D
D
C/D
B
B
C
D
C
C
B
B
A
B
D
C
B/D
A
D
B
C
KLICKITAT
KLINE
KLINESyiLLE
KLINGER
KLONKIKE
KLONE
KLOOCHMAN
KLOTEN
KLUTINA
KNAPPA
KNEELAND
KNIFFIN
KNIGHT
KNIK
KNIPPA
KNOB HILL
KNONLES
KNOX
KNULL
KNUTSEN
KOBAR
KOBEH
KOCH
KODAK
KODIAK
KOEHLER
KOELE
KOEPKE
KOERLIMG
KOGISH
KOHALA
KOKEE
KOKERNOT
KOKO
KOKOKAHI
KOKOHO
KOLBERG
KOLEKOLE
KOLLS
KOLLUTUK
KOLOA
KOLOB
KOLOKOLO
KONA
KONAWA
KONNER
KONOKTI
KOOLAD
KOOSKIA
KOOTENAI
KOPIAH
KOPP
KOPPES
KORCHEA
KORNMAN
KOSHOS
KOSSE
KOSTSR
KOSZTA s
KOTEDO
KOUTS
KOVICH
KOYEN
KOYUKUK
KRADE
KRANZBURG
KRATKA
KRAUSE
C
B
C/D
B
D
B
C
B
B
B
C
C
C
B
D
B
B
B
C
B
C
B
C
C
B
C
B
B
B
D
A
B
C
B
D
B/D
B
C
D
D
C
C
B
D
B
D
C
C
C
A
D
B
B
B
B
D
D
C
B
D
B
D
B
B
B
B
C
A
KREAMER
KREMLIN
KRENTZ
KRESSON
KRUM
KRUSE
KRUZOr
KOBE
KUBLER
KUBLI
KUCERA
KUCK
KUGRUG
KUHL
KUKAIAU
KULA
KULAKALA
KULLIT
KUMA
KONIA
KUNUWETA
KUPREANOF
KUREB
KURD
KUSKOKHIM
KUSLINA
KUTCH
KUTZTOHN
KVICHAK
KNETHLUK
KYLE
KXLER
LA BARGE
LABETTE
LABISH
LABOU
LABOUKTY
LA BOUNTY
LA BRIER
LABSHAFT
LACAMAS
LA CASA
LACITA
LACKAWANNA
LACONA
LACOTA
LACX
LADD
LADDER
LADELLE
LADOGA
LADUE
LADYSMITH
LA FAROE
LAFE
LAFITE
LA FONDA
LAFONT
LAGLORIA
LAGONDA
LA GRANDE
LAGRANGE
LAHAINA
LA HOGUE
LAHONTAN
LAHRITY
LAIDIG
C
B
C
C
D
B
B
B
C
C
B
C
D
D
A
B
B/C
B
B
B
C
B
A
D
D
D
D
B
B
A
D
D
B
C
D
D
C
C
C
D
C/D
C
B
C
C
D
D
B
D
B
C
B
D
B
D
D
B
B
B
C
C
D
B
B
D
A
C
LAIDLAW
LAIL
LAIRD SVILLE
LAIREP
LAJARA
LAKE
LAKE CHARLES
LAKE CREEK
LAKEHELEN
LAKEHURST
LAKE JANEE
LAKELAND
LAKEMONT
LAKEPORT
LAKESHORE
LAKESOL
LAKETON
LAKEVIEW
LAKEHIN
LAKEHDOD
LAKI
LAKIN
LAKOHA
LALAAU
LA LANDE
LALLIE
LAM
LAMAR
LAKARTINE
LAMBERT
LAMBETH
L AH BORN
LAMINGTOM
LAMO
LAHONI
LAHONT
LAMONTA
LAMOURE
LAHPHIER
LAMPSHIRE
LAMSON
LANARK
LANCASTER
LANCE
LAND
LANDES
LAND IS BURG
LAND LOW
LANDOUSKY
LANE
LANEY
LANG
LANGFORD
LANGHEI
LANGLEY
LAKGLOIS
LANGOLA
LANGRSLL
LANGSTOH
LANIER
LANIGER
LAKKBUSH
LANKIN
LANKTREE
LANOAK
LANSDALE
LANSDOHNE
LANSING
B
C
D
D
D
A
D
C
B
A
B
A
D
B
D
B
B
C
B
A
B
A
D
A
B
D
B/D
B
B
B
C
D
D
B
D
A
D
C
B
D
D
B
B
C
D
B
C
C
D
C
C
B/D
C
B
C
D
B
B
C
B
B
B
C
C
B
B
C
B
212
-------
IANTIS
LANTON
LANTONIA
LANTZ
LAP
LA PALMA
LAPEER
LAP IKE
LAPLATTA
LAP ON
LAPORTE
LA POSTA
LA PRAIRIE
LARABEE
LARAND
LARCHMOUNT
LARD ELL
LAREDO
LARES
L ARGENT
LARGO
LARIM
LARIMER
LARK IN
LARKSON
LA ROSE
LARRY
LARSON
LARUE
LARVIE
LAS
LAS AN MAS
LASAUSES
LAS FLORES
LASHLEY
LASIL
LAS LUCAS
LAS POSAS
LASSEN
LASTANCE
LAS VEGAS
LATAH
LATAHCO
LATANG
LATANIER
LATENE
LATHAM
LATHROP
LATINA
LATOM
LATONIA
LATTY
LAUDERDALS
LAUGENOUR
LAUGHLIN
LAUMAIA
LAUREL
LAURELHURST
LAURELWOOD
LAUREN
LAVALEE
LAVATE
LAVEEN
LAVELDO
LAVERKIN
LA VERKIN
LAVINA
B
D
B
D
D
C
B
A
C
D
C
A
B
B
B
B
C
B
C
D
B
A
B
B
C
B
D
D
A
D
C
C
C
D
D
C
C
D
B
D
C
C
B
D
B
D
C
D
D
B
D
B
B/D
B
B
C
C
B
B
B
B
B
D
C
C
C
LAWAI
LAWET
LAHLER
LAWRENCE
LAWRENCEVILLE
LAWSHE
LAMSON
LAWTHER
LAWTON
LAX
LAXAL
LAYCOCK
LAYTON
LAZEAR
LEA
LEADER
LEADPOINT
LEAD VALE
LEADVILLE
LEAF
LEAHY
LEAL
LEAPS
LEATHAM
LEAVENWORTH
LEAVITT
LBAVITTVILLE
LEBANON
LEBAR
LE BAR
LEBEC
LEBO
LEBSACK
LECX KILL
LEDBEDER
LEDGEFORK
LEDGER
LEDRU
LEDY
LEE
LEEDS
LEEFIELD
LEELANAU
LEEPER
LEESVILLE
LEETON
LEETONIA
LEFOR
LBGLER
LEGORE
LEHEW
LEHIGH
LEHMAN S
LEHR
LEICESTER
LEILEHUA
LELA
L ELAND
LEMETA
LEMING
LEMH
LEHONEX
LEMPSTER
LEN
LENA
TENAPAH
LENAWEE
B
C
B
C
C
C
B
D
C
C
B
B
A
D
C
B
B
C
B
D
C
B
C
C
B
B
B
C
B
B
B
C
C
B
B
A
D
D
D
C
C
A
D
B/C
C
C
B
B
B
C
C
D
B
C
B
D
D
D
C
B
D
C/D
C
A
D
B/D
LENHEP
LESOIR
LENOX
LENZ
LEO
LEON
LEONARD
LEONARDO
LEONARDTOWN
LEON I DAS
LEOTA
LEPLEY
LERDAL
LEROY
LESA0E
LESHARA
LESHO
LESLIE
LESTER
LE SUEUR
LETA
L ETCHER
LETHA
LETHENT
LETORT
LETTERBOX
LEVAN
LEVASY
L EVERETT
LEVIATHAN
LEVIS
LEWIS
LEWISBERRY
LEWISBORG
LEWISTON
LEWISVILLE
LEX
LEXINGTON
LHAZ
LIBBINGS
LIBBY
LIBE6
LIBERAL
LIBERTY
LIBORY
LIBRARY
LIBUTTE
LICK
LICK CREEK
LICKDALE
LICKING
LICKSKILLET
LIDDELL
LIEBERMAN
LIEN
LIGGET
LIGHTNING
LIGNtW
LIGON
LIHEN
LIHUE
LIKES
LILAH
LILLIWAUP
LIMA
LIMANI
LIMBER
D
D
B
B
B
A/D
C
B
D
B
C
D
C
B
B
B
C
D
B
B
C
D
D
C
B
B
A
C
C
B
C
D
B
C
C
C
B
B
B
D
B
A
D
C
A
D
D
B
D
D
C
D
D
C
D
8
D
C
D
A
B
A
A
A
B
B
B
LIMERICK
LIMON
LIMONES
LIWIA
LINGO
LINCOLN
LINCROFT
LINDLEY
LINDSEY
LIHDSIDE
LINDSTROM
LINDY
LINEVILLE
LINGANORE
LINKER
LINKVILLE
LINNE
LINNET
LINNEUS
LINO
LIHOYER
LINSLAM
LINT
LINTON
LINVILLE
LINirOOD
LIPAN
LIPPINCOTT
LIRIOS
LIRRET
LISADE
LISAM
LISBON
LISMAS
LISMORE
LITCHFIELD
LITHGOH
L1THIA
LITIMBER
LITLE
LXTTLEBEAR
LXTTLEFIELD
LITTLE HORN
LITTLE POLE
LITTLETON
LITTLE MOOD
LXTZ
LIV
LIVERHORE
LIVIA
LIVINGSTON
LIVONA
LIZE
LIZZANT
LLANOS
LOBDELL
LOBELVILLE
LOBERG
LOBERT
LOBITOS
LOCANE
LOCSY
LOCHSA
LOCKE
LOCKERBY
LOOCHARD
LOCK HART
C
C
B
C
B
A
A
C
D
C
B
C
C
B
B
B
C
D
B
C
B
D
B
B
B
A/D
D
B/D
B
D
B
D
B
0
B
A
C
c
C
C
A
D
C
D
B
B
C
C
A
D
D
A
C
B
C
C
C
B
B
C
D
C
B
B
C
a
B
LOCKPORT
LOCKWOOD
LOCUST
LODAR
LODEMA
LODI
LODO
LOFFTUS
LOFTON
LOGAN
LOGDELL
LOGGERT
LOGHOUSE
LOGY
LOHLER
LOHMILLER
LOHNES
LOIRE
LOLAK
LOLALITA
LOLEKAA
LOLETA
LOLO
LOLON
LOMA
LONALTA
LOMAX
LOUISA
LOMITAS
LONDO
LONE
LONE PINE
LONE RIDGE
LONE ROCK
LONETREE
LONGFORD
LOHGLO1S
LONGMARE
LONGMONT
LONGRIE
LONGVAL
LONG VALLEY
LONGVIEM
LONOKE
LONTI
LOOKOUT
LOON
LOPER
LOPEZ
LORADALE
LORAIN
LORDSTOWN
LOREAUVILLE
LORELLA
LORENZO
LORETTO
LORING
LOS ALAMOS
LOS BANDS
LOSEE
LOS GATOS
LOS GUINEOS
LOSHMAN
LOS OSOS
LOS ROBLES
LOS TANOS
LOST CREEK
D
B
C
D
A
C
D
C
D
D
D
A
B
B
C
C
A
B
D
B
B
C/D
A
A
C
D
B
B
D
C
C
C
B
A
A
C
B
D
C
C
B
B
C
B
C
C
B
B
D
C
C/D
C
C
D
A
B
C
B
C
B
B/C
C
0
C
B
B
B
213
-------
LOST HILLS
LOS TRANCQS
LOSTHELLS
LOTHA1R
torus
LOUDON
LOUDOHVILLE
LOUIE
LOUISA
LOUXSBORG
LOOP
LOURDES
LOUVIERS
LOVBTOY
LOVELANB
LOVELL
LOVELOCK
LOWELL
LOWRY
LOWVILLE
LOYAL
LOYALTON
LOYSVILLB
LOZANO
LOZIER
LUALUALEI
LUBBOQC
LUBRECHT
LUCAS
LUCE
LUC ED ALE
LUCERNE
LUCIEN
LUCILE
LUCILETON
LUCXENBAGH
LUCKY
LUCKY STAR
LUCY
LUDDEH
LUDLOW
LUEDERS
LUFKIN
LUHQK
LUJANE
LUKIN
LULA
LUXiXNG
LUMBEE
IiUNMX
LUH
TJH3&
iMJflA
LUNCH
LUHDMO
LUKDY
LUNT
LUPPINO
LOFTON
LDRA
LURAY
LOTS
LOTH
LUTHER
LUTIE
LUTOH
LOVBRNE
LUXOR
LUZEHA
C
D
B
C
B
C
C
C
B
B
D
C
D
C
C
C
C/D
C
B
B
B
D
D
B
D
D
C
C
C
C
B
B
C
D
B
C
B
B
A
D
C
C
D
B
C
c
B
0
D
B/C
**f *•*
r-
C
C
D
C
C
D
D
C/
D
C
B
B
D
C
D
D
LYCAN
LYCOHING
LYDA
L YD IOC
LYFORD
LYLES
LYMAN
LYMANSON
LYNCH
LYNCHBORG
LYNDEN
LYNNDH,
LYNH HAVEN
LYNNVILLE
LYNX
LYOHttN
LYONS
LYONSVILLE
LYSINE
LYSTAIR
LYTELL
HABANK
MABEH
MABI
MA BRAY
HACAR
MACEDONIA
HACFARLAHE
MACHETE
MACHIAS
HACHUELO
HACK
MACKEN
MACK IN AC
MACKS BURG
MACOMB
MACCHBER
MACON
MACY
MADALIN
MADAHASKA
HADDOCK
MADDOX
HADELIA
MADELINE
KADERA
MADISON
MADONNA
MADRAS
MADRID
MADRONE
HADUREZ
MAFURT
HAGALLON
HAG ENS
MAGGIE
MAGINNIS
HAGKA
MAGNOLIA
MAGHUS
HAGOTSU
HAGUAYO
HAHAFPEY
HAHALA
HAHALASVILLE
HAHANA
B
C
D
B
C
B
C/D
C
D
B/D
A
A
B/D
C
B
C
D
B
D
B
B
D
C
D
D
B
C
B
C
B
D
C
D
B
B
B
B
B
B
D
B
A
C
D
D
B
C
C
B
C
B
B
B
B
D
C
D
B
C
D
D
C/D
C
B/D
B
MAHASKA
MAKER
MAHONING
MAHUKONA
MAIDEN
MAIEE
MAINSTAY
MAJADA
MAKAALAE
HAKALAPA
MAXAPILI
MAKAWAO
MAKAWELI
MAKENA
HAKIKI
MAKLAK
MftKOTI
MAL
MALA
MALABAR
MALA BON
MALACHY
MALAGA
MALAMA
MALAYA
MALBIS
MALCOLM
MAL EOT
MALEZA
MALIBU
MALIN
MALJAMAR
MALLOT
MALM
MALO
HALONE
MALOTERRE
MALPAIS
MALPOSA
KALVERN
MAMALA
MAMOU
MANAUAA
HANALAPAN
MAHAHA
MANASSA
MANASSAS
MANASTASH
MANATEE
MAHAHA
MANCKLONA
MANCHESTER
MANDAN
HANDERFIELD
MANDEVILLE
MANFRED
MANGUH
MANHATTAN
MANHEIM
MAN I
MANILA
MAHISTEE
HAHITOU
MAULEY
MAMLIDS
MANLOVE
HMO) ING
MANOGUE
B
C
D
B
B
A
D
B
B
D
A
B
B
B
B
A
C
B
B
A/D
C
B
B
A
D
B
B
C
B
D
C/D
B
A
C
B
B
D
C
C
C
D
C
C
C
C
B
C
B/D
C
A
A
B
B
B
D
D
A
C
C
C
B
C
B
C
B
B
D
MANOR
MANSFIELD
MANS 1C
MANSKER
HANTACHIE
MANTEO
RANTER
MAN TON
MANTZ
HAND
MANVEL
MANHOOD
MANZANITA
MANZANO
MANZANOLA
MATES
MAPLE MOUNTAIN
MAPLETON
MARAGUEZ
MARATHON
MARBLE
MARBLEMOUNT
MARCELXNAS
MARCETTA
MARCIAL
HARCUM
MARCUS
MARCUSE
MARCY
HARDEN
HARDIN
MARENGO
MARESUA
MARGERDM
MARGUERITE
MARIA
MARIANA
MARIAS
MARICAO
MARICOPA
MARIETTA
MARILLA
MARINA
MARION
MARIPOSA
MARISSA
MARKES
MARKEY
MARKHAM
HARKLAND
MARKSBORO
MARLA
MARLBORO
MARLEAN
MARLETTE
MARL BY
MARLIN
MARLOW
MARLTON
MARMARTH
MARNA
MARPA
MARPLEEN
MARQUETTE
MARR
MARRIOTT
MARSDEH
MARSELL
MARSHALL
B
D
B
B
C
C/D
B
B
B
C
C
D
C
C
C
C
B
C/0
B
B
A
B
D
A
D
B
C
D
D
C
C
C/I)
B
B
B
B/C
C
D
B
B
C
C
A
D
C
C
D
D
C
C
C
A
B
B
B
C
D
C
C
B
0
B
D
A
B
E
C
B
B
MARS HAN
HARSHOALE
MARSHPIELD
MARS IMG
MART
MARTELLA
MARTIN
MARTINA
MARTINECK
MARTINEZ
MARTINI
MARTINSBURG
MARTINSDALE
MARTINSON
MARTIN SVILLE
MARTINTON
MARTY
MARVAN
MARVELL
MARVIN
MARY
MARYDEL
MARYSLAND
MASADA
MASCAMP
MASCHETAH
MASCOTTE
MASHEL
MASHULAVILLE
MASON
MASONVILLE
MASSACK
MASSENA
HASSILLON
MASTERSON
MATAGORDA
MATAMOROS
MATANUSKA
MATANZAS
MATAFEAKE
MATAHAN
MATCHER
MATPIELD
MATHERS
MATHERTON
MMATHESON
MATHEWS
MATHIS
MATHISTON
MATLOCK
MAXMON
MATT APEX
MATTOLE
MAD
MA DDE
MAUGHAN
MADKEY
MAOHEE
MADNABO
MAUPIN
MAUREPAS
MAURICE
MAORINE
MAORY
MAVERICK
MAVIE
MAWAE
MAX
MAXEY
D
C
C
B
C
B
C
A
D
D
B
B
B
D
B
C
B
D
B
C
C
B
D
C
D
B
D
C
B/D
B
C
B
C
B
B
D
C
C
B
B
C
A
C
B
B
B
A
C
D
D
C
C
D
B
C
C
A/D
D
C
D
A
D
B
C
D
A
B
C
214
-------
MAXFIELD
KAXSON
MAXTON
HAXVILLE
MAXWELL
MAY
MAYBERRY
MAYBESO
MAY DAY
MAYER
HAYES
MAYFIELD
MAYFLOWER
MAYHEM
MAYLAND
MAYHEN
MAYHARD LAKE
MAYO
MAYODAH
MAYOWORTH
HAYSDORF
MAYSVILLE
MAYRTOWN
MAYVILLE
MAYHOOD
MAZEPPA
MAZOH
MAZUHA
MCAFE
MCALLEN
MCALLISTER
MCALPIN
MCBEE
MCBETH
MCBRIDE
MCCABE
HCCAFFERY
MCCAIN
MCCALEB
MCCALLY
MCCAMMOli
MCCANN
MCCARRAN
MCCARTHY
MCCLAVE
MCCLBARY
MCCLELLAN
HCCLOUD
MCCOIN
MCCOLL
HCCONNEL
MCCOOK
MCCORNICK
MCCOY
MCCREE
MCCRORY
MCCROSKIE
MCCULLOUGH
MCCULLY
MCCUNE
HCCUTCHEN
MCDOLE
MCDONALD
MCDONALD SVILLE
MCEWEN
MCFADDEM
MCFAUL
C
A
B
A
D
B
C
D
D
D
D
B
C
D
C
D
B
B
B
C
B
C
B
B
B
C
C
'c
B
C
C
B
D
B
B
A
C
B
D
D
C
D
B
C
C
B
C
D
D
B
B
C
C
B
D
0
C
C
D
C
B
B
C
B
B
C
MCGAFFEY
MCGARR
MCGARY
MOGEHEE
MCGILVERY
MCGINTY
MCGIRK
MCGOHAN
MOGRATH
MCGREW
MCHENRY
MCILHAINE
MCIOTOSH
MCINTYRE
HCKAMIE
MCKAY
MCKENNA
MCKENZIE
HCKIKLEY
MCKINNEY
MCLAIN
MCLAURIN
MCLEAN
MCLEOD
MCMAHON
MCHEEH
MCMULLIN
MCHURDIE
MC MURPHY
MCMURRAY
HCNARY
MCPAUL
MCPHERSON
MCPHIE
MCQUARRIE
MCQUEEN
MCRAE
MCTAGGART
MCVICKERS
MEAD
MEADIN
KEADOWVILLE
MEADVILLE
MEANDER
MECAM
MECCA
MECKESVILLE
MECKLENBURG
MEDA
MEOANO
MEDARY
MEDFORD
MEDFRA
MEDICINE LODGE
MEDINA
MEDLEY
MEDWAY
MEEKS
MEETEETSE
MEGGETT
HEGON
MEBL
MEHLHORN
MEIG3
MBIKLE
MJEISS
MELBOURNE
MELBY
B
C
C
C
D
B
C
B
B
A
B
A
B
B
D
D
C/D
D
B
D
C
B
C
B
C
C
D
C
B
D
D
B
C
B
D
C
B
B
C
D
A
B
C
D
B
B
C
C
B
C
C
B
D
B
B
B
B
A
D
D
C
C
C
D
D
B
C
MKLITA
MELLENTHIN
HELLO R
MELLOTT
MELOLAND
MELROSE
MELSTONE
MELTON
MELVILLE
MBLVIN
MEMALOOSE
MEMPHIS
MENAHQA
MEN AN
MENARD
MENCH
MENDEBOUR*;
MENDOCINO
MENDON
MEN DOT A
MENEFEE
MENFRO
MENO
MENOKEN
MENOMINEB
MENTO
MENTOR
MEQUON
MERCED
MERCEDES
MERCER
MERCEY
MEREDITH
MERETA
M ERG EL
MERIDIAN
MERINO
MERKEL
MERLIN
MERMILL
MERNA
MEROS
MERRIFIELD
MERRILL
MERRILLAN
MERRIMAC
MERRITT
HER ROUGE
MERTON
MERTZ
MESA
MESCAL
MESCALERO
MESITA
MESKILL
MESMAN
MESPUN
MESSER
MET
METALINE
METAMORA
METEA
METHOW
METIGOSHE
METOLIUS
METRE
METZ
MEXICO
B
D
D
B
C
c .
A
B
B
D
D
B
A
C
B
C
C
B
B
B
D
B
C
C
B
C
B
C
C/D
D
C
C
B
C
B
B
D
B
D
B/D
D
A
B
C
C
A
B/C
B
B
B
B
B
C
C
C
C
A
C
D
B
B
B
B
A
B
D
A
D
MHOON
MIAMI
MIAMIAN
MICCO
MICHELSON
MICHIGAMME
MICK
MIDAS
MIDDLE
MIDDLE BURY
MIDESSA
MIDLAND
MIDNIGHT
MIDVALE
MIDWAY
MIPFLIN
MIFFLINBURG
MIGUEL
MIKE
MIKESELL
MILACA
MILAN
MILES
MILFORD
MILHAM
MILHEIM
MILL
MILLARD
MILLBORO
MILLBROOK
MILLBORNE
MILLCREEK
MILLER
MILLERLUX
MILLERTON
MILLETT
MILLGROVE
MILL HOLLOW
MILLICH
MILL I KEN
MILLINGTON
WILLIS
MILLRACE
MILLSAP
UILLSDALE
MILLSHOLM
MILLVILLE
MILLWOOD
MILNSR
MILPITAS
MILROY
I«LTON
MIHBRES
MIMOSA
HI MA
MINAM
MINATARE
MINCHEY
MINCO
MINDALE
MINDEGO
HINDEMAN
iUNDEH
MINE
MINEOLA
MINER
MINERAL
MINERAL MO
MINERVA
D
B
O
A/D
B
C
B
D
C
B
B
D
D
C
D
B
B
D
D
C
B
B
B
C
C
C
B
B
D
B
B
B
D
D
D
B
B/D
B
D
C
B
C
B
C
B/D"
C
B
D
C
C
D
C
C
C
c
B
D
B
B
B
B
B
C
B
D
A
C
B
MING
MINGO
MINIDOKA
MINNEISKA
MINNEOSA
MINNEQUA
MINNETONKA
MINNEWAUKAN
MINNIECE
MINOA
MINORA
MINTO
MINU
MIKVALE
MIRA
MIRABAL
MIRACLE
MIRAHAR
MIRANDA
MIRES
MIRROR
MIRROR LAKE
MISSION
MITCH
MITCHELL
MITIWANGA
MITRE
MIZEL
MIZPAH
MOANO
MOAPA
MOAULA
HOBEETIE
MOCA
HOC HO
HODA
MODALE
MODEL
MODENA
MODESTO
MODOC
MOENKOPIE
MOEPITZ
MOFFAT
MOGOLLON
MOGUL
MOHALL
MOHAVE
MOHAWK
MOIRA
MOKELUMNE
MOKENA
MOKIAK
MOKULEIA
MOLAND
MOLCAL
MOLENA
MOLINOS
MOLLVILLE
MOLLY
MOLOKAI
MOLSON
MOLYNEUX
MONAD
MONAHAN
MONAHANS
MONARDA
MONCLOVA
MONDAMIN
B
B
C
C
B
B
D
B
D
C
C
C
D
B
D
C
B
B
D
B
B
A
B
B
B
C
C
D
C
D
D
A
B
D
B
D
C
C
B
C
C
D
B
B
B
B
B
B
B
C
D
C
B
B
B
B
A
B
D
B
B
B
B
A
D
B
D
B
C
215
-------
HQHDOVI
HOMES
H08ICO
HDHXDA
HONITEAU
MONHOUTH
MONO
HOKOLITH
H080NA
MONONGAHSLA
MONROE
HDNTOYA
HOHTPIELLIER
HOHTROSB
HONTVALE
HONTVERDE
HONTWSL
HONUE
MOODY
HONROEVILLE
MONSE
KONSERATE
MONTAGUE
MONTALTO
HONTARJk
MOHTAUK
HONTCALM
MONTE
MONTE CRISTO
HONTEGRANDE
MOMTELL
MONTELLO
HONTEOLA
MQNTEROSA
MOHTEVALLO
MONTGOMERY
MOBTICBLLQ
MONTIETH
HONTMORENCI
HONTOSO
HONTOOR
MOOHOO
MOOSE RIVER
MORA
HORADO
MORALES
HORD
KOREAU
HOREHEAD
HO REHOUSE
HORELAHD
MORELANDTON
MORET
MORE?
HORFITT
HORGANPIELD
HORGHEC
MORIAETY
HORICAL
MORLEY
MORMOH MESA
MOROCCO
MOROHX
MOROP
HORRILL
MORRIS
MORRISON
MORROW
MORSE
B
D
B
B
D
C
D
C
B
C
B
D
C
B
D
A/D
C
8
B
C/D
B
C
D
C
D
C
A
B
D
D
D
C
D
D
D
D
B
A
B
B
D
B
D
B
C
D
C
D
C
C
D
A
D
D
B
B
D
C
C
D
A/C
D
C
B
C
B
C
D
MORTENSON
MORTON
MORVAL
MOSBY
HOSCA
MOSCOW
HOSEL
MOSHANNON
MOSHER
MOSHERVILLE
HOSIDA
MOSOUET
MOSSYROQC
HOT*
MOTLEY
HOTOQUA
MOTTSVILLE
HOULTON
HOUND
MOUNTAINBURG
HOUNTAINVIEW
MOUNTAINVILLE
MOUNT JURY
MOUNT CARROLL
MOUNT HOME
MOUNT HOOD
MOUNT LUCAS
MOUNT OLIVE
MOUNOTIEW
MOVILLE
MOW ATA
HOMER
MOYERSON
MOYINA
MUCARA
HUCET
MUDRAY
MUD SPRINGS
HUGHOUSE
HUIR
HU IRK IRK
MUKILTEO
HULDROW
MULKEY
MULL INS
HULLINVILLE
MULT
MULTORPOR
M DWORD
HUNDELEIN
MUNDOS
HUNISING
HUNK
HUNSON
HUNUSCONG
HURDO
HURDOCK
MUREN
MURRILL
KURVILLE
MUSCATINE
MUSE
HUSELLA
HUSICX
MUSINIA
HUSKING UM
HUSKOGEE
MDSQOIZ
MUSSEL
C
B
B
C
A
C
C
D
D
C
B
D
B
B
B
D
A
B/D
C
D
B/D
B
A
B
B
B
C
D
B
C
D
C
D
D
D
C
D
C
C
B
B
D
D
C
D
B
C
A
B
B
B
B
C
D
D
B
C
B
B
D
B
C
B
B
B
C
C
C
B
MUSSELSHELL
MUSSEY
MUSTANG
MUTNALA
MUTUAL
MYAKKA
HYATT
MYERS
HYERSVILLE
MYLREA
MYRICK
MYRTLE
MYSTEN
MYSTIC
MYTON
HAALEHU
NEBESNA
NACEVILLE
NACHES
NACIMIENTO
NACOGDOCHES
NADEAN
NAOINA
NAFF
NAGEESI
NAGITSY
NAGLE
NAGOS
NAHATCHE
NAHMA
N&HUNTA
NAIWA
NAKAI
NAKNEK
NALDO
NAHBE
NAMON
NANAHCIN
NANCY
NANNY
NANNYTON
NANSENE
NANTUCKET
NANUK
NAPA
NAPAISHAK
NAFAVINE
NAPIER
NAPLENE
NAPLES
NAP PANE E
NAP TOWN E
NARANJITO
NARANJO
NARCISSE
NARD
NARLON
NARON
NARRAGANSETT
NARROWS
NASER
NASH
NASHUA
NASHVILLE
NASON
NASSAU
NASSET
NATALIE
B
D
A/D
B
B
A/D
B/D
D
B
B
D
B
A
D
B
B
D
C
B
C
B
B
D
B
B
C
B
D
C
C
C
B
B
D
B
B
C
A
B
B
B
B
C
C
D
D
B
B
B
B
D
B
C
C
B
B
C
B
B
D
B
B
A
B
C
C/D
B
C
NATCHEZ
NATHROP
NATIONAL
NATRONA
NATROY
NATURITA
NAUKATI
NAUMBURG
NAVAJO
NAVAN
NAVARRO
NAVESINK
BAYLOR
MAYPED
NAZ
N-HAR
NEAPOLIS
NEBEKER
NEBGEN
NEBISH
NEBO
HECHE
NEDERLAND
NEEDHAM
NEEDLE PEAK
NEEDHORE
NEELEY
NEESOPAH
NEGITA
NEGLEY
NEHALEM
NEHAR
NEILTON
NEISSON
NEKIA
N ELLIS
NELKAN
NELSCOTT
NELSON
NEMAH
N EMOTE
NENARA
NENNO
NEOLA
NEOTOKA
NEPAL TO
NEPESTA
NEPHI
NEPPEL
NEPTUNE
NERESON
NESDA
NESHAMINY
NESIKA
NESKAHI
NESKOHIN
NESPELEM
NESS
NESSEL
NESSOPAH
NESTER
NESTUCCA
NETARTS
NETCONG
NETO
NETTLETON
NEUBERT
NEUNS
NEUSKE
B
B
B
B
D
B
D
C
D
D
B
C
B
B
B/D
C
D
B
C
B
D
C
C
B
C
B
B
B
B
A
B
C
B
B
B
B
C
A
B
B
D
B
A
C
B
B
A
B
A
B
B
B
C
B
D
B
B
C
C
A
B
B
C
B
B
B
NEVADOR
NEVILLE
NEVIN
NEVINE
NEVKA
NEVOYER
NEVTAH
NEVU
NEWARK
NEW ART
NEWAYGO
NEHBERG
NEWBERRY
; NEHBY
NEW CAMBRIA
NEWCASTLE
NEWCOMB
NEWDALE
NEWELL
NEWELLTON
: NEWFANE
NEW PORK
NEWKIRK
NEWLANDS
NEWLIN
NEWMARKET
NEWPORT
NEWRUSS
NEWRY
NEWSKAH
NEWSTEAD
NEWTON
NEWTON I A
NEWTOWN
NEWVILLE
NE2 PERCE
NIAGARA
NIART
NIBLEY
NICHOLSON
NICHOLVILLE
HTrVPT
nXVo^olj
NICODEMUS
NICOLAUS
NICOLLET
.NIELSEN
NIGHTHAWK
WTWTf T
N XflXJulj
NIKABUNA
NIKEIf
NXKXSHKA
NIKLASON
N IKOLA I
KXIiAHD
ft OlKtSl
NIMROD
NINCH
NINEMILE
NINEVEH
NINIGRET
NININGER
N INN ESC AH
NIOBELL
NIOTA
NIPS
NIPPERS IHK
NIPPT
NIPSUH
C
B
C
B
C
D
C
D
C
B
B
B
C
B
C
B
A
B
B
D
D
D
B
B
B
C
B
B
B
D
A/D
B
c
c
c
c
B
c
c
C
B
C
B
D
B
B
D
B
B
c
c
D
B
B
B
B
C
D
B
B
A
C
216
-------
NIRA
NISHNA
NISHON
NISO.UALLY
NISSHA
NIU
NIULII
NIVLOC
NIWOT
NIXA
NIXON
NIXONTON
NIZINA
NOBE
NOBLE
NOBSCOTT
NOCKEN
NODAWAY
NOEL
NOHILI
NOKASIPPI
NOKAY
NOKOMIS
NOLAM
NOLICHUOCY
NOLIN
NOLO
NOME
NONDALTON
NONOPAHU
NOOKACHAMPS
NOOKSACK
NOONAN
NORA
NORAO
NORBERT
NORBOURNE
NORBY
NORD
NORDBY
NORDEN
NORDNESS
NORFOLK
NORGE
NORKA
NORMA
NORMANGEE
NORREST
NORRIS
NORRISTON
NORTE
NORTHDALE
NORTHFIELD
NORTHMORE
NORTHPORT
NORTH POWDER
NORTHUMBERLAND
NORTON
NORTONVILLE
NORTUNE
NORWALK
NORWAY FLAT
NORHELL
NORWICH
NORWOOD
NOTI
NOTUS
NOUQUE
B
C
D
A
B
B
C
D
C
C
B
B
A
D
B
A
C
B
D
D
D
C
B
B
B
B
B
D
B
D
C/D
B
D
B
B
D
B
B
B
B
B
B
B
B
B
B/C
D
C
C
B
B
C
B
C
C
C/D
C
C
D
B
B
C
D
B
D
A/C
D
NOVARA
NOVARY
NOWOOD
NOYO
NOYSON
NUBY
NUCKOLLS
NUCLA
NUECES
NUGENT
NUGGET
NUMA
NUNDA
NUNICA
NUNN
NUSS
NUTLEY
NUTRAS
NUTRIOSO
NUVALDE
NYALA
NYMORE
NYSSA
NYSSATON
NYSTROM
OAHE
OAKDALE
OAKDEN
OAKFORD
OAK GLEN
OAK GROVE
OAK LAKE
OAKLAND
OAKS RIDGE
OAKVILLE
OAKWOOD
OANAPUKA
OASIS
OATRAN
OBAN
OBARC
OBEN
OBRAST
OBRAY
OBURN
OCALA
OCEANET
OCEANO
OCHEYEDAN
OCHLOCXONEE
OCHO
OCHOCO
OCHOPEE
OCILLA
OCKLEY
OCDEE
OCONEE
OCONTO
OCOSTA
OCQUEOC
OCTAGON
ODEE
ODELL
ODEM
ODERMOTT
ODESSA
ODIN
B
B
C
C
'c
C/D
C
B
C
A
C
C
c
c
c
D
C
C
B
C
D
A
C
B
C
B
B
D
B
B
C
B
C
C
A
D
B
B
B
C
B
C
D
D
D
D
, D
A
B
B
D
C
B/D
C
B
A/D
C
B
D
B
B
D
B
A
C
D
C
ODNE
O'FALLON
OGDEN
OGEECHEE
OGEHAW
OGILVIE
OGLALA
OGLE
OKAYS I
OHIA
OJAI
OJATA
OKANOGAN
OKAW
OKAY
OKEBCHOBEE
OKEELANTA
OKEHAH
OKLARED
OKLAWAHA
OKMOK
OKO
OKOBOJI
OKOLONA
OKREEK
OKTIBBEHA
OLA
OLAA
OLALLA
OLANTA
OLATHE
OLD CAMP
OLD HAM
OLDS
OLDSMAR
OLDWICK
OLELO
OLENA
OLEQUA
OLETE
OLEX
OLGA
OLI
OLIAGA
OLINDA
OLIPHANT
OLIVENHAIN
OLIVER
OLIVIER
OLJETO
OLMITO
OLMITZ
OLMOS
OLMSTFD
OLNEY
OLOKUI
OLPE
OLSON
OLTON
OLUSTEE
OLYIC
OL5MPIC
OMADI
OMAHA
OMAK
OMEGA
OMENA
OMNI
C
D
D
C
C
C
B
B
D
A
B
D
B
D
B
A/D
A/D
C
B
A/D
B
D
C
D
D
D
C
A
C
B
C
D
C
D
B/D
B
B
B
B
C
B
C
B
B/D
B
B
D
B
C
A
D
B
C
B/D
B
D
C
D
C
B/D
B
B
B
B
C
A
B
C
ONA
ONALASKA
ONAMIA
ONARGA
ONAWA
ONAWAY
ONDAWA
ONEIDA
O'NEILL
ONEONTA
ONITRA
ONITE
ONOTA
ONOVA
ONRAY
ONSLOH
ONTARIO
ONTKO
ONTONAGON
ONYX
OOKALA
OPAL
OPEQUON
OPHIR
OPIHIKAO
OPPIO
OQUAGA
ORA
ORAN
ORANGE
ORANGEBURG
ORCAS
ORCHARD
ORD
ORDNANCE
ORDWAY
ORELIA
ORELLA
OREM
ORESTIMBA
ORFORD
ORIDIA
ORIF
ORIO
ORION
ORITA
ORLAND
ORLANDO
ORMAN
ORMSBY
ORODELL
ORO FIND
ORO GRANDE
ORONO
OROVADA
ORPHANT
ORR
ORVILLE
ORSA
ORSINO
ORTELLO
ORTIGALITA
ORTING
ORTIZ
ORTLEY
ORWET
ORWOOD
OSAGE
A/D
B
B
B
D
B
B
B
B
B
C
B
C
D
C
B
B
B/D
D
B
A
D
C/D
C
D
D
C
C
B
D
B
D
B
A
C
D
D
D
A
C
C
c
A
C
B
B
B
A
C
B/C
C
B
C
D
C
D
C
C
A
A
A
C
C
C
B
A
B
D
OSAKIS
OSCAR
OSCURA
OSGOOD
OSHA
OSHAWA
O'SHEA
OSHKOSH
OSHTEMO
OSIER
OSKA
OSMUND
OSO
OSOBB
OSORIDGE
OSOTE
OSSIAN
OST
OSTRANDER
OTERO
OTHELLO
OTIS
OTISCO
OTISVILLE
OTLEY
OTSEGO
OTTER
OTTERBEIN
OTTERHOLT
OTTOKEE
OTWAY
ORTWELL
OUACHITA
OURAY
OUTLET
OVALL
OVERGAARD
OVERLAND
OVERLY
OVERTON
OVID
OVINA
OWEGO
OWEN CREEK
OWENS
OWHI
OWOSSO
OWYHEE
OXALIS
OXBOW
OXERINE
OXFORD
OZAMIS
OZAN
OZAUKEE
PAAIKI
PAALOA
PAAUHAU
PACK AP PA
PACHECO
PACK
PACKARD
PACKER
PACXHAM
PACKS ADDLE
PACKWOOD
PACOLET
B
D
C
B
B
D
C
C
B
B/D
C
B
B
D
D
B
C
B
B
B
D
C
A
A
B
C
B/D
C
B
A
D
C
C
A
C
C
C
c
c
D
c
B
D
C
D
B
B
B
C
C
C
D
B/D
D
C
B
B
A
B
B/C
C
B
C
B
B
D
B
217
-------
PACTOLUS
PAD EM
PADRONI
PADUCAH
PADUS
PAESL
PAGST
PAGODA
PAHRANAGAT
PAHREAH
PAHROC
PAIA
PAICE
PAINESVILLE
PAINTROQC
PAIT
PA3ARITO
PAJARO
PAKA
PAKALA
PAKIHI
PALA
PAIACIO
PALAPALAI
PALATINE
PALESTINE
PALISADE
PALHA
PALHAREJO
PALM BEACH
PALMER
PALMER CANYON
PALMICH
PALMS
PALMYRA
PALO
PALDOURO
P ALOHAS
PALOHXSO
PALOS VERDES
PALOOSE
PALSGROVE
PAHLICO
PAHOA
PAHSDEL
PAKUNKEY
PA11A
PANACA
PANAEHA
PANASOFFKSE
1'AHCHERI
PAHCHUELA
PANDO
PAHDOAB
PANDORA
PANDURA
PAHB
PANGUITCH
PANHILL
PANIOGUE
PANKY
PANOCHE
PANOLA
PANSEY
PANTEGO
PANTHER
PAN TON
PAOLA
C
C
B
B
B
B
B
C
C
D
D
C
C
C
C
B
B
C
B
B
B
B
B
B
B
B
B
B
C
A
D
B
B
D
B
B
B
B
D
B
B
B
D
C
D
C
B
D
D
D
B
C
B
C
D
D
B
B
B
B
C
B
D
D
D
D
D
A
PAOLI
PAONIA
PAPAA
PAPAI
PAPAKATING
PAPOOSE
PARADISE
PARADOX
PARALOMA
PARAMORE
PARASOL
PARCELAS
PARDEE
PAREHAT
PARENT
PARIETTE
PARIS
PARIS HVILLE
PARKAY
PARKDALE
PARKE
PARKER
PARKFIELD
PARKHILL
PARKHURST
PARKINSON
PARKVIEW
PARKVILLE
PARKHOOD
PARLEYS
PARLIN
PARLO
PARMA
PARK ELL
PARR
PARRAN
PARRISH
PARSHALL
PARSIPPANY
PARSONS
PARTRI
PASAGSHAK
PASCO
PASO SECO
PASQUETTI
PASQUOTANK
PASSAR
PASS CANYON
PASSCREEK
PASTURA
PATAHS
PATENT
PATILLAS
PATILO
PATIT CREEK
PATNA
PATOUTVILLE
PATRICIA
PATRICK
PATROL E
PATTANI
PATTENBURG
PATTER
PATTERSON
PATTON
PATWAY
PAUL
PAULDING
B
C
D
A
D
C
C
B
C
D
B
D
D
C
C
c
c
B
B
B
B
C
D
B
B
C
A/D
B
C
B
C
D
B
D
C
B
D
D
C
D
B/C
D
C/D
B/D
C
D
C
D
B
C
B
C
B
B
C
B
B
C
D
B
C
C
B/D
C
B
D
PAULINA
PAULS ELL
PAULSON
PAULVILLE
PAUMALU
PAUNSAUGUNT
PAUSANT
PAWELA
PAVANT
PAVILLION
PAVOHROO
PAWCATUCK
PAWLET
PAWNEE
PAXTON
PAXVILLI
PAYETTE
PAYMASTER
PAYNE
PAYSON
PEACHAM
PEARL HARBOR
PEARMAN
PEARSOLL
PEAVINE
PECATONICA
PECOS
PEDEE
PEDERNALES
PEDIGO
PEDLAR
PEDOLI
PEDRICK
PEBBLES
PEEL
PEELER
PEEVER
PEGLER
PEGRAM
PEKIN
PELHAM
PELIC
PELLA
PCLLEJAS
PELONA
PELBK
P EMBERTON
PEMBINA
PEMBROKE
PENA
PENCE
PENDEN
PEND OREILLE
.PENDROY
PENELAS
PENINSULA
PENXSTAJA
PENITENTS
PEN LAW
PENN
PBNNEL
PENNXNGTON
PENO
PESOYER
PENROSE
PENSORE
PENTHOUSE
PENTZ
D
D
B
B
B
D
B
B
D
B
B
D
B
D
C
D
B
B
C
D
D
D
D
C
B
D
C
C
B/C
D
C
B
C
C
B
C
D
B
C
B/D
D
D
B
C
D
A
C
B
B
A
B
B
D
D
c
B
B
C
c
c
B
C
C
D
D
D
D
PENHELL
PENHOOD
PEOGA
PEOH
PEONE
PEORIA
PEOTONE
PEP DON
PEQUEA
PERCHAS
PERCIVAL
PERELLA
PERHAM
PERICO
PERITSA
PERKINS
PERKS
PERLA
PERMA
PERMANENTE
PERRIN
PERRINE
PERROT
PERRY
PERRYPARK
PERRYVILLE
PERSANTI
PERSAYO
PERSHING
PERSIS
PERT
PERU
PESCADERO
PESET
PEHASTIN
PESO
PETEETNEET
PETER BORO
PETERS
PETOSKEY
PBTRIE
PETROLIA
PETTONS
PEW AMD
PEYTON
PFEIFFER
PHAGE
PHANTOM
PHARO
PHAROLIO
PHEBA
PHEENEY
PHELAN
P HELPS
PHIFERSON
PHILBON
PHILIPS BURG
PHILLIPS
PHILO
PHILOMATH
PHIPPS
PHOEBE
PHOENIX
PIASA
PICACHO
PICAYUNE
PICKAWAY
PICKENS
A
A
C
C
B/C
D
C
B
C
D
C
C
C
B
C
C
A
C
A
C
B
D
D
D
B
B
C
D
C
B
D
C
C/D
C
B
C
D
B
D
D
D
C
B/D
B
B
B
C
B
D
C
B
B :
B
B
B/D
B
C
B
D
C
B
D
D
C
B
C
D
1 PICKETT
PICKFORD
PICKRELL
PICKWICK
PICO
PICOSA
PICTOU
, PIE CREEK
PIERIAN
PIERPONT
PIERRE
PIERSONTE
PIIHONUA
PIKE
PILCHUCK
PILGRIM
PILOT
PILOT ROOC
PIMA
PIMER
FINAL
FINAL END
PINAMT
PINATA
PINAVETES
PINCHER
PINCKNEY
PINCONNING
PINCUSHION
PINEDA
PINED ALE
PINEGUEST
PINELLOS
PINETOP
PINEVILLE
PINEY
PINICON
PINKEL
PINKHAM
PINKS TON
PINNACLES
PINO
PINOLA
PINOLE
PINON
PINONES
PINTAS
PINTLAR
PIN-TO
PINTURA
PINTHATER
PIOCHE
PIOPOLIS
PIPER
PIROUETTE
PIRIM
PISGAH
PISHKUN
PISTAKEE
PIT
PITTMAN
PIWSFIELD
PITTS TOWN
PITTWOOD
PITZER
PIOTE
PLACEDO
PLACKKTIA
B
D
D
B
B
C
B
D
A
C
D
B
A
B
A
B
B
C
B
B
D
B
B
C
A
C
C
D
B
B/D
B
B
A/D
C
B
C
B
C
B
B
C
C
C
B
C
D
D
A
C
A
D
D
D
B/C
D
B
C
B
B
D
D
B
C
B
C
D
D
D
218
-------
PLACBRITOS
PLACID
PLACK
PLAINFIELD
PLAINVIEH
FLAISTED
PLANO
PLASKETT
PLATA
PLATEA
PLATEAU
PLATNER
PLATO
PLATORO
PLATTE
PLATTVILLE
PLAZA
PLEASANT
PLEASANT GROVE
PLEASANTON
PLEASANT VALE
PLEASANT VIEW
PLEDGER
PLEEK
PLEINE
PLEVNA
PLOME
PLOVER
PLUHAS
PLDMHER
PLUSH
PLUTH
PLUTOS
PLYMOUTH
POALL
POARCH
POCALLA
POCATELLO
POCKER
POCOMOKE
PODO
PODDNK
FOE
POEVILLE
POGAL
POGANEAB
POGUE
POBAKUPU
POINDEXTER
POINSETT
POIHT
POINT ISABEL
POJOAQ0E
POKEGEMA
POKEMAN
POKER
POLAND
POLAR
POLATIS
POLE
POLEBAR
POLELIHE
POLEO
POLEY
POLICH
POLLARD
POLLASKY
POLLY
C
A/D
D
A
C
C
B
0
B
C
B
C
C
B
D
B
B/C
C
B
B
B
B
D
C
D
D
B
B
B
B/D
B
B
C
A
C
B
A
B
0
0
0
B
B/C
"I **
0
B
0
B
A
C
B
B
C
B
B
B
C
B
B
C
A
C
B
C
B
C
C
B
POLO
POLSON
POLVADERA
POMAT
POMELLO
POMPANO
POHPONIO
POMPTON
POMROY
PONCA
PONCENA
PONCHA
POND
POND CREEK
PON0ILLA
PONIL
POHfOTOC
PONZER
POOKU
POOLS
POOLER
POORMA
POPE
POPPLETON
POQUONOCK
PORRETT
PORT
PORTAGEVILLE
PORTALES
PORTALTO
PORT BYRON
PORTERS
PORTE RVILLE
PORTHILL
PORTING
PORTLAND
PORTNEOF
PROTOLA
PORTSMOUTH
PORUM
POSANT
POSEY
POSITAS
POSKIN
POSOS
POSE
POTAMO
POTH
POTLATCH
POTRATZ
POTSDAM
POTTER
POTTS
POUDRE
POULTNEY
POUNCEY
POVERTY
PfttftgiD
« UMUEixv
POWDERHORN
POWELL
OflMFt}
K\MKP.
POWHITE
POMLEY
POWWATKA
OI"\V
rUZ
POYGAN
POZO
POZO BLANCO
B
C
B
C
C
A/D
C/D
B
B
B
0
A
B/C
B
A
D
B
0
A
B/D
D
B
B
A
C
B/0
B
0
B
B
B
B
D
C
C
0
B
C
0
C
C
B
D
C
C
D
0
C
C
C
B
C
B
B
B
o
j^
C
C
B
C
0
C
D
C/D
B
PRAG
PRATHER
PRATLEY
PRATT
PREACHER
PREAKNESS
PREBISH
PREBLE
PRENTISS
PRESQUE ISLE
PRESTO
PRESTON
PREWITT
PREY
PRICE
PRIDA
PRIDHAM
PRIETA
PRIMEAUX
PRIMGHAR
PRINCETON
PRDJEVILLE
PRIMG
PRINS
PRITCHETT
PROCTOR
PROSRESSO
PROMISE
PROMO
PROMONTORY
PRONG
PROSPECT
PROSPER
PROSSER
PROTIVIN
PROOT
PROVIDENCE
PROVO
PROW BAY
PROWERS
PTARMIGAN
PUAULU
POCHYAN
PUDDLE
PUBRCO
PUERTA
PUETT
PUGET
PUGSLEY
PUHI
PUHIMAU
PULASKI
PULEHU
PULLMAN
PULS
PULSIPHER
PULTNEY
PUMEL
PUMPER
PUNA
PUNALUU
PUKOHO
PURDAN
PURDY
PURGATORY
PURSER
PURSLEY
PURVES
PUSTOI
C
B
C
A
B
D
D
C
C
B
A
A
B
0
C
D
D
D
C
B
B
C
B
C
C
B
C
D
D
B
C
B
B
C
C
C
C
D
0
B
B
A
A
O
D
0
D
B/C
B
A
D
B
B
0
0
0
C
C
C
A
D
A
C
0
D
D
B
D
A
PUTNAM
PUUKALA
PLTUONE
PBU OO
PUU OPAE
PUU PA
PUYALLUP
PYLE
PYLON
PYOTE
PYRAMID
PYRMONT
QUACKENBDSR
QUAKER
QUAKERTOHN
QUANBA
QUAMON
QUANAH
QUANDAHL
QUARLES
CUARTZBURG
QUATAMA
QUAY
QUAZO
QU1ALY
gUEBRADA
QUEENY
QUEETS
QUEMADO
QUENZER
QOICKSELL
QUIETUS
QUIGLEY
QUILCENE
QUILLLAYOTE
QUIMBY
QUINCY
QUINLAN
QDINN
QUIHNEY
QUINTON
QUITMAN
gUONSET
RABER
RABEY
RABIDEUX
RABUN
RACE
RACHERT
RACINE
RACOON
RAO
RAD ERS BURG
RADFORD
RADLEY
RADNOR
RAFAEL
RAGER
RAGLAN
RAGNAR
RAGO
RAGSDALE
RAGTOWN
RAHAL
RAIN
RAIL
RAINBOW
D
D
C
A
B
B
B
A
D
A
D
D
C
C
B
D
A
B
B
D
C
C
B
0
0
C
D
B
C
D
D
C
B
C
B
B
A
C
0
C
C
A
C
A
B
B
D
D
B
D
C
B
B
C
0
D
B
C
B
C
B/D
D
C
C
C/0
C
RAINBY
RAINS
RAINSBORO
RAKE
RALSEN
RAM ADA
RAMADERO
RAMBLER
RAMELLI
RAM! RES
RAMMEL
RAHO
RAMONA
RAMPART
RAMPARTAR
RAMPARTER
RAMSEY
RAMSHORN
RANGE
RANCHERIA
RAND
RAN DADO
RANDALL
RAN OMAN
RANDOLPH
RANDS
RANGER
RANIER
RANK IN
RAN TOOL
RANYHAN
RAPELJE
RAP HO
RAPIDAN
RAPLEE
RARDEN
RARICK
RARITAN
RASBAND
RASSET
RATAKE
RATHBUN
RATLIFF
RATON
RATTLER
RATTO
RAUB
RAUVILLE
RAUZI
RAVALLI
RAVENDALE
RAVENNA
RAVOLA
RAWAH
RAWHIDE
RAWSON
RAY
RAYADO
RAYENOUr
RAYMONDVILLE
RAYNE
RAYNESFORD
RAYNBAN
RAYNOR
RAZOR
RAZORT
READING
READINGTON
READLYN
B
B/D
C
0
B/C
C
B
B
C
D
C
C
B
B
A
A
D
B
C
B
B
C
D
D
0
C
D
C
C
D
B
C
B
B
C
C
B
C
B
B
C
C
B
D
B
D
B
D
B
C
D
C
B
B
D
B
B
C
B
D
B
B
C
D
C
B
C
C
B
219
-------
REAGAN
REWCOR
REAL
REAP
REAR0AN
RBAVILLE
REBA
REBEL
REBUCK
REGAL
RECLUSE
REDBAHK
RED BAY
RED BLUFF
RED BUTTS
REDBY
REDCHIEF
REDCLGUD
REDDICK
REDDING
REDFXELD
RED HILL
RED HOOK
REDLAKE
REDLANDS
REDLODGE
REDMANSON
REDHOHD
REDHUH
REDOLA
REDOHA
REDRIDGE
REDROB
RED ROCK
RED SPUR
REDSTOE
REDTHAYHE
REDTOH
RED VALE
REDVIEH
REE
RCEBEX
REED
REDDER
REEDPOINT
REEDY
REELFOor
REESER
REESVILLE
REEVES
REFUGE
REGAN
REGENT
REHM
REICHEL
R2IFP
RE1LL*
REIHACH
BEROP
RELAN
RELAY
RELIANCE
SELIZ
SBLSE
REMBERT
RENMIT
REHSEH
REHUDAR
REMUHDA
B
B
C
0
C
C
C
B
D
C
B
B
C
B
C
C
B
C
D
B
C
C
D
B
D
B
C
C
B
B
B
0
B
B
B
B
C
C
C
B
C
D
B
C
D
C
C
C
C
C
B
C
C
B
B
A
B
0
A
B
C
0
B
D
A
D
B
C
REN BAG
RENCALSON
REHCQT
RENFROW
RENICK
RENNIE
RENO
RENOHILL
RENOVA
RENOX
RENSHAH
RENSLOW
RENSSELAER
RENTIDE
REN TON
RENTSAC
REPARADA
REPP
REPPART
REPUBLIC
RESCUE
RESERVE
RESNER
RBT
RETRIEVER
RETSOF
RETSOK
REXBURG
REXFORD
REXOR
REYES
REYNOLDS
REYNOSA
REWAT
RHAHE
RHEA
RHINEBECK
RHOADES
RHOAME
RIB
RICCO
RICETON
~ RICEVILLE
RICHARDSON
RICHEAU
RICKEY
RICHFIELD
RICHFORD
RICHLIE
RICHMOND
RICHTER
RICHVALE
RICHVIEW
RICHWOOD
RICKHORE
RICKS
RICO
RICREST
RIDD
RIDGEBORY
RIDGECREST
RIDGEDALE
RIDG ELAND
RIDGELAWN
RIDGELY
RIDGEVILLE
RIDGEHAY
RIDIT
RIETBROCK
D
C
A
D
D
C/D
D
C
D
B
B
B
C
C
B/C
C
D
A
B
B
C
B
B
B/C
D
C
B
B
C
A
C/D
B
D
B
B
D
D
C
C
D
B
C
B
C
C
C
A
A
D
B
B
C
B
C
A
C
B
C
C
C
B
D
A
B
B
D
C
C
RIFFE
RIFLE
RIGA
RIGGINS
RIGLEY
RILEY
RILLA
RILLITO
RIMEI
RIMINI
RIMROCK
RIN
RINCON
RINCONADA
RINDGE
RINGLING
RINGO
RINGOLD
RINGWOOD
RIO
RIO ARRIBA
RIOCONCHO
RIO GRANDE
RIO KING
RIO LAJAS
RIO PIEDRAS
RIPLEY
RIPON
RIRIE
RISBECK
RISLEY
RISTA
RISUE
RITCHEY
RITNER
RITO
RITTER
RITTMAN
RITZ
RITZCAL
RITZVILLE
RIVERHEAD
RIVERSIDE
RIVERTON
RIVERVIEW
RIVRA
RIXIE
RIXON
RIZ
ROANOKE
ROBANA
ROBBINS
ROBBS
ROBERTS
ROBERTSDALE
ROBERTSVILLE,
ROBIN
ROBINSON
ROBINSONVILLE
ROBLEDO
ROB 1«3Y
ROBY
ROCA
ROCHE
ROCHELLE
ROCHEPORT
ROCKAHAY
ROCKCASTLE
ROCK CREEK
B
A/D
D
A
B
C
B
B
C
A
D
B
C
C
D
C
D
B
B
D
D
C
B
C
A
B
B
B
B
B
D
C
D
B
C
B
B
C
B/D
B
B
B
A
C
B
A
C
C
D
D
B
B
D
D
C
D.
B
D
B
D
C
C
D
C
C
C
C
D
D
ROCKFORD
ROCKHOUSE
ROCKINGHAM
ROCKLIN
ROCKLY
ROCKPORT
ROCK RIVER
ROCKTON
ROCKWELL
ROCKHOOD
ROCKY FORD
RODDY
RODMAN
ROE
ROEBUCK
RO ELLEN
ROEMER
ROESIGER
ROGERS
ROHNERVILLE
ROHRERSVILLE
ROIC
ROKEBY
ROLETTE
ROLFE
ROLISS
ROLLA
ROLLII
ROLQFF
ROMBERG
ROMBO
ROMEO
ROHNEY
ROMULUS
ROND
RONNEBY
RONSON
ROOSE
ROOTEL
ROSACHI
ROSAMOND
ROSANE
ROSANKY
ROSARIO
ROSCOE
ROSCOMMON
ROSEBERRY
ROSEBLOOM
ROSEBUD
ROSEBORG
ROSE CREEK
ROSEGLEN
ROSEHILL
ROS ELAND
ROSELLA
ROSELMS
' ROSEMOUNT
ROS END ALE
ROSE VALLEY
ROSEVILLE
ROSEMORTH
ROSHE SPRINGS
ROSITAS
ROSLYN
ROSMAN
ROSNEY
ROSS
ROSE FORK
ROSSI
B
A
C/D
C/D
D
C
B
B
B
B
B
A
B
D
D
C
B
D
B
C
D
D
C
C
D
C
D
C
B
C
C
C
D
C
B
B
B
D
C
B
C
C
C
D
D
B/D
D
B
B
C
B
D
D
D
D
B •'
B
C
B
C
D
A
B
B
C
B
C
C
ROSSMOYNE
ROSS VALLEY
ROTAN
ROTHIEMAY
ROTHSAY
ROTTULEE
ROUBIDEAU
ROUEN
ROUHD BUTTE
ROUNDLEY
ROUHDTOP
ROUNDUP
ROUND Y
ROUSSEAU
RODTON
ROOTT
ROVAL
ROME
ROWENA
ROWLAND
ROWLEY
ROXAL
R OX BURY
ROY
ROYAL
ROYALTON
ROYCE
ROYSTONE
ROZA
ROZELLVILLE
ROZETTA
ROZLEE
RUARK
ROBICOS
ROBIO
RUBY
RUBYHILL
RUCH
RUCKLES
RUCLICK
RUDD
RUDEEN
RUDOLPH
R0DYARD
RUELLA
RUGGLES
RUIDOSO
RWKO
RULE
RULICK
RUMBO
RUMFORD
RUMNEY
RUMPLE
ROM RIVER
RUNE
' RUNGE
RUNN1LLS
RUNNYMEDE
RBPERT
RUSCO
ROSE
RUSH
RUSHTOWN
RUSHVILLE
RUSS
RUSSELL
RUSSELLVILLE
RUSSLER
C
C
C
B
B
B
, C
C
D
C
C
C
C
A
D
C
D
D
C
C
B
D
B
B
B
C
B
B
D
B
B
C
C
A
C
B
C
B
D
C
D
B
C
D
B
B
C
D
B
C
C
B
C
C
C
C
B
C
B
A
C
D
C
A
D
B
B
C
C
220
-------
RUSTON
RUTLAND
RUTLEGE
RYAN
RYAN PARK
RYDE
RYDER
RYEGATE
RYELL
RYEPATCH
RYER
RYORP
RYUS
SABANA
SABANA SBC A
SABENYO
SABINA
SABIHE
SABLE
SAC
SACO
SACRAMENTO
SACUL
SADDLE
SADDLEBACK
SADER
SADIE
SADLER
SAFFELL
SAGA8ING
SAGE
SAGEHILL
SAGEMOQR
SAGERTON
SAGINAW
SAGO
SAGOUSPE
SAGUACHE
SAHALIE
SAINT HELENS
SAINT MARTIN
SALADO
SALADON
SALAL
SALAMATOF
SALAS
SALCHAKET
SALEM
SALEMSBURG
SALGA
SALIDA
SALINAS
SALISBURY
SAL IX
SALKUM
SALLISAW
SALLYANN
SALMON
SALOL
SALONIE
SALREE
SALTAIR
SALT CHUCK
S ALTER
SALTERY
SALT LAKE
SALUDA
SALUVIA
B
C
D
D
B
B/D
C
B
A
D
C
C
C
D
D
B
C
A
D
B
D
C/D
D
B
B
D
B
C
B
D
D
B
C
C
D
C
A
B
A
C
B
D
B
D
C
B
B
B
C
A
C
D
B
q
B
C
B
D
D
C/D
D
A
B
D
D
C
SALVISA
SALZER
SAMBA
SAMJSH
SAMMAMISH
SAMPSEL
SAW SON
SAMSIL
SAN ANDREAS
SAM ANTON
SAN ANTONIO
SAN ARCACIO
SAN BENITO
SANCHEZ
SAND ALL
SANDERSON
SANDLAKE
SANDLEE
SAN EL I
SAN EMIG'DIO
SANFORD
S ANGER
SAN GERMAN
SANGO
SANGREY
SANILAC
SAN ISABEL
SAN JOAQUIN
SAN JON
SAN JOSE
SAN JUAN
SAN LUIS
SAN MATED
SAN MIGUEL
SANPETE
SANPITCH
SAN FOIL
SAN SABA
SAN SEBASTIAN
SANTA
SANTA CLARA
SANTA FE
SANRTA ISABEL
SANTA LUCIA
SANTA MARTA
S ANT ANA
SANTAgUIN
SANTA YNEZ
SANTEE
SANTIAGO
SANTIAM
SAN TIMOTEO
SANTONI
SANTOS
SANTO TOMAS
SAN YSIDRO
SAPINERO i
SAPP
SAPPHIRE *
SAPPHO
SAPPINGTON
SARA
SARALEGUI
SARANAC
SARAPH
SARATOGA
SARATOU
SARBEN
SARCO
C
D
D
C/D
C
D
B
D '
C
B
C
B
B
D
C
B
C
A
D
B
A
B
D
C
A
C
B
D
C
B
A
B
B
C
B
C
B
D
B
C
C
D
D
C
C
C
A
C
D
B
C
C
D
C
B
D
'B
' D
B
B
B
C
B
D
D
B
B
A
B
SARDINIA
SARDO
SARGEANT
SARITA
SARKAR
SARPY
S ARTEL L
SASKA
SASPAMCO
SASSAFRAS
SASSER
SATAN KA
SATANTA
SATELLITE
SATT
SATTLEY
SATIRE
SATURN
SATUS
SAUCIER
SAUDE
SAUGATUCK
SAUGUS
SAUK
SAULICH
SAUH
SAUNDERS
SAUVIE
SAUVOLA
SAVAGE
SAVANNAH
SAVENAC
SAVO
SAVOIA
SAWABE
SAWATCH
SAWCREEK
SAWMILL
SAWYER
SAXBY
SAXON
SAYBROOK
SAYLESVILLE
SAYLOR
SCALA
SCAMHAN
SCAHDIA
SCANTIC
SCAR
SCARBORO
SCAVE
SCHAFFENAKEN
SCHAMBER
SCHAHP
SCHAPVILLE
SCHEBLY
SCHERRARD
SGHLEY
SCHMUTZ
SCHNEBLY
SCHNEIDER
SCHNOGRSON
SCHNORBUSH
SCHODACJC
SCHOOSON
SCHOFIELD
SCHOHARIE
SCHOLLE
SCHOOLEY
C
B
D
A
D
A
A
B
B
B
B
C
B
C
D
B
B
B
B
B
B
C
B
B
D
C
C
C/D
C
C
C
C
C
B
D
C
B
C
C
D
B
B
C
A
B
C
B
C
A
D
C
A
A
C
C
D
fa
B
B
D
C
B/D
C
C
B
C
B
C/D
SCHOONER
SCHRADER
SCHRAP
SCHRIER
SCHROCK
SCHUMACHER
SCHUYLKILL
SCIO
SCIOTOVILLE
SCISM
SCITUATE
SCOBEY
SCOOTENEY
SCORUP
SCOTT
SCOTT LAKE
SCOUT
SCOWL ALE
SCRAN TON
SCRAVO
SCRIBA
SCRIVER
SCROGGIN
SCULL IN
SEA BROOK
SEAMAN
SEAQUEST
SEARCHLIGHT
SEARING
SEARLA
SEARLES
S EATON
SEATTLE
SEA WILLOW
SEBAGO
SEBASTIAN
SEBASTOPOL
SEBEKA
SEBEWA
SEBREE
SEBRING
SEBUD
SEC AT A
SECCA
SECRET
SECRET CREEK
SEDAN
SEDILLO
SEDWELL
SEEDSKADEE
SEES
SEEWEE
SEGAL
SEGNO
SEHORN
SEITZ
SEJITA
SBCIL
SSKIU
SELAH
S ELD EN
SELEGNA
SELFRIDGE
SELKIRK
SELLE
SELLERS
SELMA
SEMIAHMOO
SEMIHMOO
D
D
D
B
B
B
B
B
C
B
C
C
B
C
D
B
B
C
B/D
A
C
B
C
C
C
C
C
C
B
B
C
B
D
B
D
D
C
D
B/D
D
D
B
C/D
C
C
B
B
C
D
C
B
D
C
D
C
b
C
D
C
C
D
C
D
B
A/D
B
D
D
SEMINARIO
SEMIX
SEN
SENEGAVILLE
SEQUATCHIE
SEQUIN
SEQUIN
SEQUOIA
SERENE
SERNA
SEROCO
SERPA
SERVOSS
SESAME
SESPE
SESSIONS
SESSUH
SETTERS
SETTLEMEYER
SEVAL
SEVERN
SEVILLE
SEVY
SEWARD
SEWELL
SEXTON
SEYMOUR
SHAAK
SHAD ELAND
SHAFFER
SHAKAN
SHAKESPEARE
SHAKOPEE
SHALCAR
SHALET
SHAM
SHAHBO
SHAMEL
SHANAHAN
SHAMDON
SHANE
SHAKO
SHANTA
SHAP LEIGH
SHARATIN
SHARKEY
SHARON
SHARPSBURG
SHARROTT
SHARVANA
SHASKIT
SHASTA
SHAVANO
SHAVER
SHAWA
SHAWANO
SHAWMUT ' ''
SHAY
SHEAR
S HECKLER
SHEDADO
SHEDD
SHEEGE
SHEEP CREEK
SHEEPHEAD
SHEEPROCK
SHEETIRON
SHEFFIELD
SHELBURNE
D
C
B
C
B
A
B
C
D
D
A
C/D
D
C
C
C
D
C
D
D
B
D
C
B
B
D
C
D
C
A
B
C
C
D
D
D
B
B
B
D
B
B
C/D
B
D
B
• B
D
C
B/C
A
B
B
B
A
B'
b .
c
c
B
c
D
C
C
A
B
D
C
221
-------
SHELBY
SHELBYVILLE
SHELDOH
SUELIKOF
SHELLABARGER
SHELLDRAKE
BHELLROQC
SHBLHADINE
SHELOCTA
SHELTOH
SHENA
SHEKAHDOAH
SHBP
SHEPPARD
SHERANDQ
SHERAR
SHERBURNE
SHERIDAN
SHERLOCK
SHERM
SHERRYL
SHERWOOD
EHIBLE
SHIELDS
SHIPFER
SHILOH
SHINAKU
SHINGLE
SHINGLETOHH
SHZNN
SHINROCX
SHIOCTON
SHIPLEY
SHIPROOC
SHIRAT
SHIRK
SHOALS
SHOEBAR
SHOEFFLER
SHOKKIN
SHOOFLIN
SHOOK
SHOREWOOD
SHOREV
SHORN
SHORT GREEK
SHOSHONF
SHOTHELL
SHOWS
SHOW ALTER
SHOHLOW
SHREWSBURY
SHRINE
SHROE
SHROUTS
SHUBUTA
STOLE
SHULLSBURG
SHUKWAY
SHUPRRT
SHUHAH
SI
SIBLEYVILLE
SIBYLEE
SICILY
SICJCLESTEETS
SIDELL
SIEANCIA
B
B
D
B
A
A
D
B
C
C
C
B
A
A
C
B
B
B
D
B
B
B
C
B
C
D
D
C
B
C
B
C
B
B
C
C
B
B
D
C
A
C
B
B
D
D
D
B
C
C
D
B
D
D
C
B
C
B
C
B
B
B
D
B
C
B
B
SIEBER
SIELO
SIEROCLIFF
SIERRA
SIERRAVILLE
SIESTA
SIFTON
SIGNAL
SIGURD
SIKESTOH
SILCOX
SILENT
SILER
SILERTON
SILI
SILSTID
SILVER
SILVERADO
SILVERBOW
SILVER CREEK
SILVERTON
SILVIES
SIHAS
SIMCOE
SIMEON
SIMHLER
SIMMQNT
SIMNER
SIMON
SIHONA
SIMOTE
SIMPERS
SIMPSON
SIMS
SINAI
SINCLAIR
SINE
SINGLETREE
SIN6SAAS
SINNIGAM
SINOHAX
SINTON
SINUK
SION
SIOUX
SIPPLE
SIRI
SISKIYOU
SISSETON
SISSOH
SITES
SITKA
SIXMILE
SIZEMORE
SIZER
SKAGGS
SKAGIT
SXAHA
SKALAH
SKAHANIA
SKAHOKAWA
SKANEE
SKELLOCK
SKERRY
SKIDHORE
SKILLET
SKINNER
SKIYOU
A
C
D
B
B
D
B
C
B
D
B
0
B
B
D
A
C
C
0
D
C
D
C
C
A
D
C
A
C
D
C
B
C
D
C
C
C
C
B
C
B
B
D
B
A
A
B
B
B
B
C
B
B
B
B
B
B/C
A
C
B
B
C
B
C
B
C
C
C
SKOKOMISH
SKQOKUMCHUCK
SKUMHEGAN
SKULL CREEK
SKIMP AH
SKUTOM
SKYBERG
SKYHAVEN
SKYKOMISH
SKYLICK.
SKYLINE
SKYHAY
SLAB
SLATE CREEK
SLAUGHTER
SLAV EN
SLAWSON
SLAYTON
SLEETH
SLETTEN
SLICKROCK
SLIGHTS
SLIGO
SLIKOK
SLIP
SLIPMAN
SLOAN
S LOCUM
SLODUG
SLOSS
SLUICE
SMARTS
SMITH CREEK
SHITHDALE
SMITHNECK
SHITHTON
S MO LAN
SHOOT
SNAG
SNAHOPISH
SNAKE
SNAKE HOLLOW
SNAKELUH
SNEAO
SHELL
SHELLING
SNOHOHISH
SNOQUAIMIE
SNOW
SNOHDEN
SNOHLIN
SNOWVILLE
SNOWY
SOAKPAK
SOAP LAKE
SO BOB A
SOBRANTE
SODA LAKE
SOD HOUSE
SODOS
SOELBERG
SOFIA
SOGN
SOGZIE
SOKOLOF
SOLANO
SOLDATNA
SOLDIER
SOL DUG
SOLDUC
B/C
B
B
D
D
C
C
D
B
C
D
B
D
C
C
D
B
D
C
D
B
D
B
D
B
B/C
**/ >•*
D
B
C
C
B
B
A
B
B
D
C
D
B
B
C
B
B
D
C
B
D
B
B
C
B
D
A
B
B
A
C
B
D
C
B
B
D
B
B
D
B
C
B
B
SOLLEKS
SOLLER
SOLOMON
SOLONA
SOMBRERO
SOMERS
SOMERSET
SOMERVELL
SQMSEN
SONOITA
SONOMA
SONTAG
SOPER
SOQUEL
SORDO
SORF
SORRENTO
SORTER
SOSA
SOTELLA
SOTIM
SOUTHFORK
SOUTHGATE
SOUTHWICK
SPAA
SPACE CITX
SPADE
SPALDING
SPAN
SPANAWAY
SPAN EL
SPARTA
SPEARFISH
SPEARMAN
SPEARVILLE
SPECK
OPC&'-lEfZt
SPEELYAI
jjmpTy^T E»
Oz^ISXUljKi
SPENAR0
SPENCER
SPENLO
SPERRY
SPICER
SPILLVILLE
SPINKS
SPXRES
SPIRIT
SPIRO
SPLENDORA
SPLITRO
SPOPPORD
SPOKANE
SPONSELLER
SPOON BUTTE
SPOONER
SPOTTSWOOD
SPRAGUE
SPRECKELS
SPRING
SPRING CREEK
SPRINGDALE
SPRINGER
SPRINGERVILLE
SPRINGFIELP
SPRINGMEYER
SPRINGTOWN
SPROOL
SPUR
SPURLOCK
C
D
D
B
D
B
D
B
C
B
D
D
B/C
B
C
C
B
B/D
C
C
B
D
D
C
D
A
B
D
D
B
D
A
B
C
C
D
0
C
g
D
Q
B
C
C
B
a
0
B
B
C
D
C
B
B
0
C
B
B/C
C
C/0
C
B
B
D
D
C
C
D
B
B
SftUALICUM
SpUAW
SQUILLCHUCK
SQUIMER
SO.UIRES
ST. ALB AN S
ST. CHARLES
ST. CLAIR
ST. ELMO
ST. GEORGE
ST. HELENS
ST. IGNACE
ST. JOE
ST. JOHNS
ST. LUCIE
ST. MARTIN
ST. MARYS
ST. NICHOLAS
ST. PAUL
ST. THOMAS
STAATSBURG
STABLER
STACY
STADY
STAFFORD
STAGECOACH
STAHL
STALE?
STAMBAUGH
STAMFORD
STAMPEDE
STAN
STANDISH
STANEY
STANFIELD
STANLEY
STANSBURY
STAKTON
STAPLETON
SI AR BUCK
STARGO
S1ARICHKOF
STORKS
STARLEY
STARR
STASER
STATE
STATEN
SI'ATLER
STAVE
STAfTRON
STEAMBOAT
STEARNS
STECUM
S1EED
STSEDHAN
S1EEKEE
STEELE
STEESE
STEFF
STEGALL
STEIGER
STEINAUER
STEINBECK
STEINMETZ
S1EINSBURG
STEOTER
STELLAR
STEHILT
B
B
B
B
B
B
B
D
A
C
A
C
B/D
B/D
A
C
B
D
B
D
B
B
B
C
B
C
C
B
D
D
B
C/D
D
C
C
D
D
B
D
B
D
C
D
B
B
B
D
B
D
D
D
D
A
A
D
C
B
C
C
C
A
B
B
D
C
C
C
C
222
-------
STENDAL
STEPHEN
STEPHENSBURG
STEPHENVILLE
STERLING
STERLING-TON
STETSON
STETTER
STEOBEN
STEVENS
STEVENSON
STEWART
STICKNEY
STIDHAM
STIGLER
STILLMAN
STILLWATER
STILSON
STIMSON
STINGAL
STINSON
STIRK
STIRtM
STISSING
STIVERSVILLE
STOCKBRIDGE
STOOC.LAND
STOCKPEN
STOcacroN
STODICK
STOKES
STOHAR
S TONER
STONEWALL
STONO
STONYFORD
STOOKEY
STORDEK
STORLA
STORMITT
STORM KING
STORY
STOSSEL
STOUGH
STOWELL
oqvw
o t,\J I
STRAIGHT
STRAIN
STRASBURG
STRATFORD
STRAUSS
STRAW
STRAWN
STREATOR
STROLE
STRONGHURST
STRONTIA
STROUPE
STRYKER
STUBBS
STUCKCREEK
STUKEL
STUKE5T
STUMBLE
STUHPP
STUMP SPRINGS
STUNNER
STUTTGART
STUTZHAN
C
C
B
B
B
B
B
D
B
B
B
D
C
A
C
A
D
B
B/C
B
C
D
B
C
B
B
B
D
D
D
D
C
B
A
B/D
D
B
B
B
B
D
C
C
D
C
B
C
B
C
B
B
C
B
B
B
C
B
C
B
D
B
A
D
B
B
D
C
STUTZVILLE
SUBLETTE
SUDBURY
SUDDUTH
SUFFIELD
SUGARLOAF
SUISUN
SULA
SULM
SULPHURA
SULTAN
SUHAS
SUM} OH
SUM HA
SUMMERFIELD
SUMMERS
SUMMERVILLE
SUMHIT
SUMMITVILLE
SUHTER
SUN
SUNBURST
SUNBURY
SUNCOOK
SUND
SUNDELL
SUNDERLAND
SUNDOWN
SUNFIELD
SUNNILAND
SUHNYKAY
SUNNYSIDE
SUNNYVALE
SUNRAY
SUNRISE
SUNSET
SUNSHINE
SUNSWEET
SUNUP
SUPAN
SUPERIOR
SUPERSTITION
SUPERVISOR
SUPPLEE
SUR
SURGEM
SURGH
SURPRISE
SURRENCY
SURVYA
SUSIE CREEK
SUSITNA
SUSgUEHANNA
SUTHER
SUTHERLIN
SUTLEW
SQTPHEN
SUTTLER
BUTTON
SVEA
SVERDRUP
SVOLD
SWAGER
SWAKANE
GU& U
OMAN
SWANBOY
SWANNER
SHANSON
SWAN TON
B/C
B
B
C
C
B
D
B
B
D
B
B/C
D
B
C
B
C
C
B
C
D
C
B
A
C
C
C/D
B
B
C
D
B
C
B
D
B
C
C
D
B
C
A
C
B
B
C
B
B
B/D
C
D
B
D
C
C
B/C
D
B
B
B
B
C
C
C
C
D
D
B
B/D
SWANTOWN
SWAPPS
SWARTSWOOD
SWART2
SWASEY
SWASTIKA
SWATARA
SWAUK
SWAWILLIA
SWEATMAN
SWEDE
SWEDEN
SWEEN
SWEENEY
SHEET
SWEETGRASS
SWEETWATER
SWENODA
SWIFTCREEK
SWXFTON
SWIMS
SWINGLES
SWINK
SHISOOD
SWITCHBACK
SWITZERLAND
SHDPE
SWYGERT
SYCAMORE
SYCAN
SYLACAUGA
CVT Xf&U
QIl*Y«M
<* VM i?BfinN
O *H ISIv LWSt
SYNAREP
SYRACUSE
SYRENE
SYRETT
TABERNASH
TABIONA
TABLE MOUNTAIN
TABLER
TABOR
TACAN
TACOMA
TACOQSH
TAFT
TAGGERT
TAHOMA
TAHQUAMENON
TAHQUATS
TAINTOR
TAJO
TAKEUCHI
TAKILHA
TAKOTNA
TALAG
TALANTE
TALAPQS
TALBOTT
TALCOT
TALIHINA
TAIJCEETNA
TALLAC
TALLADEGA
TALLAPOOSA
TALLEYVILLE
TAU.S
C
C
C
D
D
C
A
C
A
C
B
B
C
B
C
B
D
B
B
A
A
C
D
D
C
B
C
C
B/C
A
B/D
B
B
p
c
B
B
B
D
D
B
D
D
C
C
B
D
C
C
c
c
B
B
D
C
B
C
C
D
C
B
C
C
B
B
TALLULA
TALLY
TALHAGE
TALMO
TALOKA
TALPA
TAMA
TAMAHA
TAMALCO
TAMBA
TAMELY
TAMMANY CREEK
TAMMANY RIDGE
TAMMS
TAMP ICO
TANAMA
TANBERG
TANDY
TAN BUM
TANEY
TANGAIR
TANNA
TANNER
TANS EM
TANTALUS
TANWAX
TAOPI
TAOS
TAP I A
TAPPEN
TARA
T ARK 10
TARKLIN
TARPO
TARRANT
TARRETE
TARRYALL
TASCOSA
TASSEL
TATE
TATIYEE
TAT0
TATOM
TAUNTON
TAVARES
TAWAS
TAWCAW
TAYLOR
TAYLOR CREEK
TAYLORS FLAT
TAYLORSVILLE
TAYSOM
TAZLINA
TEAL
TEALSON
TEALWHIT
TEANAWAY
TEAPO
TEAS
TEAS DALE
TEBO
TECHICK
TECOLOTE
TECUMSAH
TEDROW
TEEL
TEHACHAPI
TEHAMA
TBJA
B
B
A
B
D
D
B
C
D
C/D
B
B
C
B
D
D
C
C
C
C
C
c
B
A
D
C
D
C
D
B
D
C
C
D
D
B
B
D
B
C
C
C
C
A
A/D
C
C
D
D
C
B
A
D
C
C
c
B
C
B
B
B
B
B
B
B
D
C
D
TEJON
TEKOA
TEIA
TELEFONO
TELEPHONE
TELPER
TELFERNER
TELIDA
TELL
TELLER
TELLICO
TELLMAN
TELSTAD
TEMESCAL
TEMPLE
TEMVIK
TENABQ
TENAHA
TEN AS
TENCEE
TENERIFFE
TEN EX
TENIBAC
TEKINO
TENNO
TENORIO
TENOT
TEN RAG
TEKSAS
TENSED
TENSLEEP
TEOCULLI
TEPEE
TEPETE
TERBIES
TERESA
TERINO
TERMINAL
TEROUGE
TERRA CEIA
TERRAD
TERRERA
TERR ETON
TERRIC
TERRY
TERWILLIGER
TESAJO
TESCOTT
TESUQUE
TETON
TETONIA
TETONKA
TETOTUM
TEH
TEX
TEXLINE
TEZUMA
THACKERY
THADER
THAGE
THANYON
THATCHER
THATUHA
THAYNE
THEBES
THEBO
THEDALUND
THEN AS
THEO
B
C
B
C
0
A
D
D
B
B
B
B
B
D
B/C
B
0
B
C
0
C
A
B
B
D
B
C
g
0
c
B
B
D
B/D
C
C
D
D
D
A/D
1 D
C
C
B
B
C
A
C
B
A
B
C
C
B/D
B
B
C
B
C
C
A
B
C
B
B
D
C
c
c
223
-------
THERESA
THERIOT
THERMAL
THERHOPOLXS
THESS
THETFORD
THIEL
THIOKOL
T«OE8Y
THOMAS
TMORNDALE
THORNDIKE
THORNOCX
THORNTON
THQ8HSOOD
THOROUGHFARE
THORP
THORR
THORREL
THOW
THREE MILE
SHROCK
THUHOERBIRD
THURBER
THURLONI
THURLOH
THURMAN
THURMONT
THURSTOH
TXAGOS
TIAK
TIBAH
TIBBITTS
TICA
TICE
TXCHIGAM
TICKHOR
TICKAPOO
TIOCASOH
TXOWELL
TIERRA
TIETON
TIFFANY
TIFTQN
TIGER CREEK
TXGER0N
TIGWON
TIGRETT
TXGUA
TI01ERAS
TILfORD
TIM, EDA
TILLICUH
TILLHAIJ
TXIMA
TILSIT
TILTON
TIMBERS
TIMBERLY
TXMBLIN
TBU5HWA
TXKXEN
TIHHERHAN
TXMHOHS
TIHPAHUTE
TIMPAHOGOS
TXKPBR
TXWOONEKE
TIMULA
TINA
A
B
D
B
B
B
B
C
C
C
B
C
B
B
D
C
D
B
A
A
C
D
D
D
C/D
D
D
B
B
C
B
B
B
D
C
D
C
C
C
A
B
B
B
C
B
B
0
C
C
D
D
B
D
D
B
C
B
B
D
B
D
B
B
D
B
D
B
B
C
TINDAHAY
TINE
TINGEY
TINSLEY
TINTON
TIHYTOWN
TIOCANO
TIOGA
TIPPAH
TIPPECANOE
TIPPER
TIPPERARY
TIPPIPAH
TIPPO
TIPTOH
TIPTONVILLE
TIRO
TISBHRY
TISCH
TISH TANG
TITUSVILLE
TIVERTON
TIVOLI
TIVY
TOA
TOBICO
TOBIN
TOBISH
TOBLER
TOBOSA
TOBY
TOCCOA
TODD
TODDLER
TODDVILLE
TOEHEAD
TOEJA
TO EM
TOGO
TOGUS
TOHONA
TOINE
TOISNOT
TOIYABE
TOKEEN
TOKUL
TOLBY
TOLEDO
TOLICHA
TOLKB
TOLL
TOLLGATE
TOLLHOUSE
TOLMAH
TOLNA
TOLO
TOLSONA
TOLSTOI
TOLT
TOIiTEC
TOLUCA
TOLVAR
TOMAH
TOMAS
TOHAST
TONE
TOM EL
TOMERA
TOWCHI
TONOKA
A
A
B
A
A
B
D
B
C
B
A
A
D
C
B
B
C
B
C
B
C
A
A
C
C
D
B
C
B
D
B
B
B
B
B
C
C
C
B
D
C
C
D
C
B
C
A
D
D
B
A
B
D
D
B
B
D
D
D
C
B
B
C
B
C
B
D
D
A
A/D
TONASKET
TONATA
TONAHANDA
TONEY
TOHGUE RIVER
TONINI
TONKA
TONKEY
TONKIN
TONKS
TONOPAH
TONOR
TONRA
TONSINA
TONUCO
TOOLE
TOOHES
TOP
TOP I A
TOPPENISH
TOPTON
TOQUERVILLE
TOQUOP
TORBOY
TORCHLIGHT
TORDIA
TORHUNTA
TORNING
TORODA
TORONTO
TORPEDO LAKE
TORREON
TORRES
TORRINGTON
TORRO
TORSIDO
TORTUGAS
TOSTON
TOTELAKE
TOTEM
TOTTEN
TOUCHET
TOOHEY
TOOLON
TOORN
TOURNQOIST
TOURS
TOOTLE
TOWER
TOMHEE
T OWNER
TOHNLEY
TOtmSBORY
TOWNSEND
TOHSON
TOXAWAY ,
TOY
TOYAH
TOZE
TRABOCO
TRACK
TRACY
TRAER
TRAIL
TRAIL CREEK
TRAM
TRANSYLVANIA
TRAPPER
TRAPPIST
B
C
C
D
C
B
C
D
C
B/D
B
C
A
B
C
D
C
C
D
B/C
TRAP PS
TRASK
TRAVELERS
TRAVER
TRAVESSILLA
TRAVIS
TRAHICK
TRAY
TREADWAY
TREASURE
TREBLOC
TREGO
TRELONA
TREMANT
TREMBLES
TREMPE
TREMPEALEAU
TRENARY
TRENT
TRENTON.
TREP
TRES HERHANOS
C
A
B
C
D
C
B
B
C
D
C
B
B
C
D
D
D
A
B
B
B
B
B
C
B
B
A
D
D
B
C
B
C
B
D
D
B
B
C
B/C
B
C
A
B
B
B
A
C
TRETTEN
TREVINO
TREXLER
TRIAMI
TR I ASS 1C
TRICON
TRIDELL
TRIDENT
TRIGO
TRIMBLE
TRIMMER
TRINCHERA
TRINITY
TRIOMAS
TRIP IT
TRIPLEN
TRIPOLI
TRIPP
TRITON
TRIX
TROJAN
TROMMALD
TROMP
TRONSEN
TROOK
TROPAL
TROSI
T80UP
TROUT CREEK
TROUTDALE
TROUT LAKE
TROUT RIVER
TROOTVILLE
TROXEL
TROY
TRUCE
TRUCKEE
TRUCKTON
TRUEFISSURE
TRUESDALB
TRULL
TRULON
TRIMAN
TRUMBULL
TRUMP
TRYON
TSCHICOMA
TUB
C
D
B
C
B
C
B
C
B
B
D
C
C
B
D
D
A
C
B
C
A
B
B
C
C
C
B
A
C
C
B
B
D
D
D
B
C
B
C
D
B/C
D
C
B
C
D
B
D
C
D
B
B
A
B
B
B
D
B
B
C
D
C
C
C
B
D
C
B
B
'TUBAC
' TUCANNON
TUCKERHAN
TUCSON
TIKUMCARI
TUFFIT
TUGHILL
TUJUNGA
TUKEY
TUKWILA
TOLA
TULANA
TULARE
TULAROSA
TULIA
TULLAHASSEE
TULLER
TULLOCK
TULLY
TULUKSAK
TUMBEZ
TOMEY
TDMITAS
TUMWATER
TUNEHEAN
TUMICA
TUNIS
TUNITAS
TUNKHANNOCK
TUNNEL
TUPELO
TUPUKHUK
TUQUE
TURBEVILLE
TURBOTVILLE
TORBYFILL
TURIN
TURK
TURKEYSPRINGS
IURLEY
TURLIN
TURNBOW
TURNER
TORNERVItLE
TURNEY
TURRAH
TURRET
TURRIA
TURSON
TUSCAN
•CUSCARAWAS
TUSCARORA
TUSCOLA
TUSCUMBIA
eras EL
TUSKEEGO
WUSLER
VDSQOITEE
CTSTIN
TUSTUMENA
5?DTHILL
TWCNI
1'UTMILER
TUXEDO
TUXEKAN
1'WIN CREEK
TWINING
THXSP
1'WO DOT
C
' C
D
B
B
D
D
A
C
B
C
C/D
C/D
B
B
C
D
B
C
D
D
0
B
A
D
D
D
B
A
B
D
D
B
C
C
B
B
D
C
C
B
C
B
B
B
D
B
C
B/C
D
C
C
'B
D
C
C
B
B
B
B
B
B
B
B
B
C
B
C
224
-------
TVBO
TYEE
TYGART
TYLER
TYNDALL
TYNER
TYRONE
TYSON
UANA
UBAR
UBLY
UCOLA
UCOLO
UCOPIA
UDEL
UDOLPHO
OFFEMS
UGAK
UHLAND
UHLIG
UINTA
URIAH
ULEN
OLLOA
HIM
ULRICHER
ULUPALAKUA
ULY
ULYSSES
UMA
OMAPINE
UMIAT
UMIKOA
UM1L
UMNAK
UMPA
UMPQOA
UNA
UNADILLA
UNAWEEP
ONCOM
UNCOMPAHGRE
UNEEDA
UNGERS
ONION
UNIONTOWH
UNIONVILLE
UHISUN
UPDIKE
UPSAL
UPSATA
UPSHUR
UPTON
URACCA
URBAN A
URBO ' .'
URICH
URNE .
URSINE
URTAH
URWIL
USAL
USHAR
USINE
USKA
OTALINE
UTE
UTICA
D
D
D
D
B/C
A
C
C
D
C
B
D
C
B
D
C
D
D
B
B
B
C
B
B
B
B
B
B
B
A
B/C
D
B
D
B
B
B
D
B
B
B
D
B
B
C
B
C
C
D
C
A
C
C
B
C
D
D
B
D
C
D
B
B
B
D
B
C
A
UTLEY
UTUADO
UVADA
OVALDE
UWALA
VAGHERIE
VADER
VADO
VAIDEN
VAILTON
VALBY
VALCO
VALOEZ
VALE
VALENCIA
VALENT
VALENTINE
VALERA
VALKARIA
VALLAN
VALLBCITOS
VALLEONO
VALLERS
VALMONT
VALMY
VALOIS
VAMER
VANAJO
VANANDA
VAN BOREH
VANCE
VANDA
VANDALIA
VANDERDASSON
VANDERGRIFT
VANDERHOFF
VANDERLIP
VAN DUSEN
VANET
VANS
VAKHORN
VAN NOSTERN
VANNOY
VANOSS
VANTAGE.
VAN WAGONER
VARCO
VARELIM
VARICK
VARINA
VARNA
VARRO
VARYSBURG
VASHTI
VASQUEZ
VASSALBORO
VASSAR
VASTINE
VAUCLUSE
VAUGHN SVILLE
VAYAS
VEAL
VEAZIE
VEBAR
VECONT
VEGA
VAGA ALTA
B
B
D
C
B
C
B
B
D
B
C
C
B/C
B
B
A
A
C
B/D
D
C/D
B
C
C
B
B
D
D
D
C
D
C
D
C
D
A
B
D
B
B
B
B
B
C
D
C
C
D
C
C
BV
B
C
B
D
B
, C
C
C
D
B
B
B
D
C
C
VEGA BAJA -
VEKOL
VELDA
VELHA
VELVA •
VENA
VEHANGO
VENATOR
VENETA
VENEZIA
VENICE
VENIiO
VENUS
VERBOORT
VERDE
VERDEL
VERDELLA
VERDICO
VERDIGRIS
VERDUN
VERGENNES
VERHALEN
VERHEJO
VERNAL
VERNALIS
VERNIA
VERNON
VERONA ,
VESS1R
VESTON
VETAL
VETERAN
VEYO
VIA
VIAS
VIBORAS
VIBORG
• VICKERY •
VICKSBURG
VICTOR
VIC?TORIA
VICTORY
VICU
VIDA
VIDRINE
VIEJA
VIENNA
VIEQUES
VIEW
VIGAR
VIGO
VIGOS
VIKING
VIL
VILAS
VILLA GROVE
VILLARS
VIL1.Y
VINA
VINCENNES
VINCENT
VINEYARD
VINGO
VINING
VINITA
VINLAND
VINSAD
VINT
C
. D
B
B
B
C
C
D
C
D
D
D
B
D
C
D
D
D
B
D
D
D
D
B
B
A
D
C
C
D
A
B
D
B
B
D
B
C
B
A
D
B
D
B
C
D
B
B
C
C
D
C
D
D
A
B
B
D
B
C
C
C
B
C
C
C
C
B
VINTON
VIRA
VIRATON
VIRDEN
VIRGIL
VIRGIN PEAK
VIRGIN RIVER
VIRTUE
VISALIA
VISTA
VIVES
VIVI
VLASATY
VOCA
VODERMAIER
VOLADORA
VOLCO
VOLENT1
VOLGA
VOLIN
VOLINIA
VOLKE
VOUCH AR
VOMER
VOLNEY
VOLPERIE
VOLTAIRE
VOLOSIA
VONA
VORE
VROOHAN
VULCAN
VYLACH
HABANICA
KABASH
WABASHA
HABASSA
WABEX
WACA
WACOTA
MACOUSTA
W ADAMS
WADDELL
WAD DO UPS
WADELL
WAD EN A
HADESBORO
WADLEIGH
HADMALAW
HADSHORTH
WAGES
WAGNER
NAQRAH
WAHA
HAHEE
HAMIAWA
WAMIKULI
WAHKEENA
WAHKIACUS
HAHLUKE
WAHMONIE
WAHPETON
WAHTIGUP
HAHTUH
WAIAHA
WAIAKOA
WAIALEALE
B
C
C
C
B
D
D
C
B
C
B
B
C
C
B
B
D
C
D
B
B
C
B
D
B
C
D
C
B
B
B
C
D
D
D
D
B/D
B
C
B
C
B
B
B
B
B
B
D
D
C
B
D
A
C
D
B
B
B
B
B
D
C
C
D
D
C
D
HAIALUA
WAIAWA
HAIHUNA
WAIKALOA
WAI KANE
WAIKAPU
WAIKOHO
HAILUKU
WAIMEA
WAINEE
HAINOLA
WAIPAHU
WAI SKA
WAITS
WAKE
WAKEEN
WAKEFIELD
HAKELAND
WAKONDA
HAKULLA
WALCOW
WALDECK
WALDO
WALDRON
WALDROUP
HALES
WALFORD
WALKE
HALL
WALLACE
WALLA WALLA
WALLER
W ARLINGTON
HALLIS
HAIJJCILL
HALLHAN
WALLOHA
HALLPACK
WALLROQC
HALLS BURG
HALLSON
HALPOLE
WALSH
HALSHVILLE
HALTERS
HALTON
WALUH
HALVAN
WAMBA
HA MIC
W AMP SVILLE
WANATAH
WANBLEE
WAN DO
WANETTA
WANILLA
WANN
HAPAL
WAPATO
WAPELLO
HAPINITIA
HAPPING
HAPS IE
HARBA
HARD
WARDBORO
WARDELL '
HARDEN
HARDSELL
B
D
' -D
B
B
B
D
B
B
'B
A
C
B
B
D
B
B
B/D
C
A
B
C
• D
D
D
B
C
C
B
B
B
B/D
C
B
C/D
C
C
C
B/C
D
B
C
B
D
A
C
B
B
B/C
B
B'
B
D
A
A
C
A
B
C/D
B
B
B
" B
B
D
A
D
B
C
225
-------
WARE
WAREHAM
WARHAH
MARK SPRINGS
WARMERS
t/ARREN
HARRENXOH
WARRIOR
WARSAW
WARSING
WARWICK
HASATCH
HASEPI
XASHBURN
WASHINGTON
HASHOE
HA3HOUGAL
HASHTENAH
WASHTEMAW
NASILLA
HASIOJA
HASSAIC
HAXAB
HATAUGA
HATCHAUG
HATCHUHG
HATERDORO
HATERBURY
HATER INO
HATERS
UATKINS
HATKINS RIDGE
tfATO
HATOPA
WATROUS
HATSEXA
WATSON
WATSONIA
HATSONVILLE
VXtT
RA4 *
WATT0N
NAUBAY
VAUB&EK
HAUBONSIE
HADCHULA
HAUCOMA
WAUCONDA
HAUKEE
WAUKEGAH
HAUKZHA
HAOKOH
KAUMBEK
HAURIKA
HAOSEOM
WAVERLY
HAWAXA
HAYCUP
HAVDEN
WAYLAND
WAYWC
WAYNES BORO
WAXSIDE
NBA
HEAVER
WEBB
WEBER
WEBSTER
WEDDCIND
WEDERTZ
B
C
D
C
A/D
B/D
B
B
A
A
B
B
C
B
C/D
C/D
D
B
B
C
B
B
D
D
C
C
B
B
B
B
B
C
C
D
D
0
C
B
B
B
B/D
B
B
B
B
D
B
B
D
B/D
B/D
C
B
D
C/D
B
B
B
C
C
B
C
D
C
WEDGE
WEDOWEE
NEED
WEEDING
WEE DMA RK
WEEKSVILLE
WEEPON
WEHADKEE
WEIKERT
WEDtER
WEINBACH
WEIR
WEIRMAN
WEISER
WEISHAUPT
WEISS
WEITCHPEC
WELAKA
WELBY
WELCH
WELD
WELDA
WELDON
WELDONA
HELLER
WELLINGTON
WELLHAH
WELLNER
WELLS BORO
WELLS TON
WELLSVILLE
WELRING
WEKPLE
HEN AS
WENATCHEE
WEHDEL
WEN HAM
WENONA
WENTWORTH
WERLOW
WERHER
WESO
WESSEL
WESTBROOK
WEST BURY
WESTCREEK
WESTERV1LLE
WESTPALL
WESTFIELD
WESTFORD
WESTLAND
WESTMINSTER
WESTHORE
WESTMORELAND
WESTON
WESTPHALIA
WESTPLAIN
WESTPORT
WESTVILLE
WETHERSFIEIiD
WETHEY
WETTERHORN
WETZEL
WEXMOUTH
WHAKANA
WHALAN
WHARTON
WHATCOH
WHATEL*
WHEATLEY
A
D
B
A/e
B
B/D
D
D
C/D
D
C
D
B
C
D
A
B
A
B
C
C
C
D
B
C
D
B
B
C
B
B
D
B
B/C
C
B/C
C
B
C
B
C
B
D
C
B
C
C
B/D
C/D
B
B
D
B
C
A
B
C
B/C
C
D
B
B
B
C
C
D
D
WHEATRIDGE
WHEATVILLE
WHEELER
WHEELING
WHEELON
WHELCHEL
WHETSTOHE
WHIDBE*
WHIPPAN*
WHIPSTOCK
WHIRLO
WHIT
WHITAKER
WHITCOMB
WHITE BIRD
WHITECAP
WHITEPISH
WHITEFORD
WHITEHORSE
WHITE HOUSE
WHITELAKE
WHITELAW
WHITEMAN
WHITEROCK
WHITESBURG
WHITE STORE
WHITE SWAN
WHITEWATER
WHITEWOOD
WHITLEY
WHITLOCK
WHITMAN
WHITNEY
WHITORE
WHITSOL
WHITSON
WHITWELL
WHOLAN
WIBAUX
WICHITA
WICHUP
WICKERSHAM
WICKETT
WICKHAM
WICKIUP
WICKLIFFE
WICKS BURG
WIDTSOE
WIEHL
HIEN
WIGGLETON
WIGTON
WILBRAHAH
WILBUR
WILCO
WILCOX
WILCOXSON
WILDCAT
WILDER
WILDERNESS
WILDROSE
WILDWOOD
WILEY
WILKES
WILKESON
WILKINS
WILL
WILLACY
WILLAKENZIE
WILLAMAR
C
B
B
B
D
B
B
C
C
C
B
B
C
C
C
D
B
B
B
C
B
B
D
D
C
D
C
B
C
B
B
D
B
A
B
D
C
C
C
C
D
B
C
B
C
D
B
C
C
D .
B
A
C
C
C
D
C
D
B
C
D
D
C
C
C
D
D
B
C
D
WILLAMETTE
WILLAPA
WILLARD
WILLETTE
WILLHAND
WILLIAMS
WILLIAMS BURG
WILLIAMSON
WILLIS
WILLITS
WILLOUGHBY
WILLOW CREEK
WILLOWDALE
WILLOWS
WILLWOOD
WILMER
WILPAR
WILSON
WILTSHIRE
WINANS
WINBE8RY
WINCHESTER
WINCH OCK
WINDER
WINDHAM
WINDMILL
WINDOM
WIND RIVER
WINDSOR
WINDTHORST
WINDY
WINEG
WIN EM A
WINETTI
WINPIELD
WING
WIN GATE
WINGER
WINGVILLE
WINIFRED
WINK
WINKEL
WINKLEHAN
WINKLER
WINLO
WINLOCK
WINN
WINNEBAGO
WINNEKJCCA
WINNESHIEK
WINNETT
WINONA
WINOOSKI
WINSTON
WINTERS
WINTERSBORG
WINTERS ET
WINTHROP
WINfONER
WIND
WINZ
WZOTA
WISHARD
WISHEYLU
WISHKAH
WISKAH
WISHER
WITBECK
WITCH
WITHAM
B
C
B
A/D
B
B
B
C
C
B
B
B
B
D
A
C
D
D
C
B/C
D
A
C
B/D
B
B
B
B
A
C
C
C
C
B
C
D
B
C
B/D
C
B
D
C
A
D
C
C
B
B
B
D
D
B
A
C
C
C
A
C
C
C
B
A
C
C
C
D
D
D
D
MITHEE
'WITT
•ttlTZEL
•HODEN
HODSKOW
WOLCOfTSBORG
MOLD ALE
WOLF
WOLFESEN
WOLFESON
WOLFORD
WOLF POINT
WOLFTEVER
WOLVERINE
WOODBINE
WOODBRIDGE
WOODBURN
WOODBORY
WOODCOCK
WOODENVILLE
WOODGLEN
WOODHALL
WOODHORST
WODDINVILLE
WOODLY
WOODLYN
WOODMANSIE
WOODMERE
WOOD RIVER
WOODROCK
WOODROW
WOODSCROSS
WOQDSFIELD
WOODSIDE
WOODSON
WOODSTOCK
WOODSTOWN
WOODWARD
WOOLMAN
WOOLPER
WOOLSEY
WOOSLEY
WOOSTER
WOOSTERN
. WOOTEN
WORCESTER
WORP
WORK
.WORLAND
WORLEY
WORMS ER
WOROCK
WORSHAH
WORTH
WORTHEN
WORTHING
WORTHINGTON
WORTMAN
' WRENTHAH
WRIGHT
WRIGHTMAN
WRIGHTPSVILLE
WONOE5T
WORTSBORO
WYALUSING
WYARD
WYARNO
^ WYATT
WYEAST
WlfEVILLE
C
B
D
B
B/C
C/D
B
C
C
B
D
C
A
B
C
C
D
B
C
D
B
A
C/D
B
C/D
B
B
D
C
C
D
C
A
D
C/D
C
B
B
C
C
C
C
B
A
B
D
C
B
C
C
B
D
C
B
D
C
C
C
C
C
D
B
C
D
B
B
C
C
C
226
-------
SYGA8T
WYKOPP
WYMAN
WYMORE
KYNN
WYNQOSE
WYO
WYOCENA
XAVIER
YACOLT
YAHARA
YAHOIA
YAK I
YAK IMA
YAK US
YALLANI
YALMER
YAMAC
YAMHILL
YAMPA
YAMSAY
YANA
YANCY
YARDLEY
YATES
YAUCO
YAWDIM
YANKEY
YAXON
YEARY
YEATES HOtLOW
YEGEN
YELM
YENRA1
YEOMAN
YESUM
YETULL
YODER
YOKOHL
YOLLABOLLY
YOLO
YOLOGO
YOMBA
YOMONT
YONCAIXA
YONGES
YOJMA
YORDY
YORK
YORKVILiE •
YOST
YOUGA
YOCWAN
YOUNGSTON
YOURAME
YOVIMPA
YSIDORA
YTRURBIDE
YUBA
YUKO
YUKON
YUNES
YUNQUE
ZAAR
ZACA
ZACHARIAS
ZACHARY
C
B
B
C
B
D
B
B
B
B
B
B
D
B
D
B
B
B
C
C
D
B
C
C
D
C
D
C
B
C
C
B
B
A
B
B
A
B
D
D
B
D
C
B
C
0
B/D
B
C
D
C
B
C
B
A
D
D
A
D
C
D
D
C
D
D
B
D
ZAFRA
Z AH ILL
ZAHL
ZALESKI
ZALLA
ZAMORA
ZANE
ZANEIS
ZANESVILLE
ZANONE
ZAPATA
ZAVALA
ZAVCO
ZEB
ZEESIX
ZELL
ZEN
ZENDA
ZENIA
ZEHIFF
ZEONA
ZIEGLER
ZIGHEID
ZILLAH
ZIM
ZIMMERMAN
ZING
ZINZER
ZION
ZIPP
ZITA
ZOAR
ZOATE
ZOHNER
ZOOK
ZORRAVISTA
ZOFELT
ZUKAN
ZDMBRO
2UWHALT
ZUNDELL
ZUNHALL
ZUNI
ZURICH
ZWINGLE
B
B
B
C
A
B
C
B
C
C
C
B
C
B
C
B
C
C
B
B
A
C
B
B/C
D
A
C
B
C
C/D
B
C
D
B/D
C
A
B/D
D
B
C
B/C
B/C
D
B
D
227
-------
APPENDIX E
HANDLE,
O.SCM
1_
CYLINDER-
PLAN
1
1 ,1
1
1
1
1
1 1
EL.EV;
,
1
VTTION
l!
«
H
I
OS
*l°
"f.
J
)
5
)
FIGURE E.1 .
Surface soil sampler design with transfer funnel
specifications.
228
-------
c
CUTTING BLADE
ELEVATION
0
h-
•
W
25.5 CM
FIGURE E.1 (Cont'd).
PLAN
Surface soil sampler design with transfer funnel
specifications.
229
-------
ISOMETRIC VIEW SHOWING FUNNEL SUPPORT
NOT TO QCAL.E
•-8.1CM-*
FUNNEL. - ELEVATION VIEW
FIGURE E.1 (Cont'd). Surface soil sampler design with transfer funnel
specifications.
230
« U.S.OOVERNME«TPRIOTlKOOfFCE1»eS- 559-111/20603
-------
-------
.y ii.
POSTAUt & FEES PAlU
EPA
PERMIT No. G-35
Official Business
Penalty for Private Use, $300
Please make all necessary changes on the above label,
detach or copy, and return to the address in the upper
left-hand corner.
If you do not wish to receive these reports CHECK HERE a;
detach, or copy this cover, and return to the address in the
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EPA/600/3-85/043
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