EPA-600/2-76-151
May 1976
Environmental Protection Technology Series
LOADING FUNCTIONS FOR ASSESSMENT OF
WATER POLLUTION FROM NONPOINT SOURCES
Office of Air, Land and Water Use
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20460
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EPA-600/2-76-151
May 1976
LOADING FUNCTIONS FOR ASSESSMENT
OF
WATER POLLUTION FROM NONPOINT SOURCES
BY
A. D. McElroy
S. Y. Chiu
J. W. Nebgen
A. Aleti
F. W. Bennett
Midwest Research Institute
Kansas City, Missouri 64110
Contract #68-01-2293
Program Element #1HB617
Project Officer
Paul R. Heitzenrater
Agriculture and Nonpoint Sources Management Division
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20460
U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
WASHINGTON, D.C. 20460
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DISCLAIMER
This report has been reviewed by the Office of Research and
Development, U.S. Environmental Protection Agency, and approved
for publication. Approval does not signify that the contents
necessarily reflect the views and policies of the U.S. Environ-
mental Protection Agency, nor does mention of trade names or
commercial products constitute endorsement or recommendation
for use.
11
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CONTENTS
Page
1.0 Introduction 1
2.0 Guidelines for Use of the Handbook 5
2.1 Introduction 5
2.2 Terminology, Symbols and Formulas 6
2.3 Procedure for Use of the Handbook 6
2.4 Summary of Loading Functions 7
2.4.1 Sediment from Sheet and Rill Erosion 7
2.4.2 Nutrients and Organic Matter 10
2.4.3 Pesticides 14
2.4.4 Salinity in Irrigation Return Flow 15
2.4.5 Acid Mine Drainage 17
2.4.6 Heavy Metals and Radiation 19
2.4.7 Urban and Related Sources 21
2.4.8 Livestock in Confinement 23
2.4.9 Terrestrial Disposal 23
2.4.10 Background Emissions of Pollutants 24
2.5 Limitations and Accuracies 25
3.0 Sediment from Soil Erosion 29
3.1 Introduction 29
3.2 Sediment Loading from Surface Erosion 30
3.2.1 Overview 30
3.2.2 Sediment Loading Function for Surface Erosion . 35
3.2.3 Procedure for Use of the Sediment Loading
Function 37
3.2.4 Example of Assessing Sediment Loading from
Surface Erosion 39
iii
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CONTENTS (continued)
Page
3.2.5 Determination of Source Characteristic Factors . . 43
3.2.6 Source Characteristic Factors for Predicting
Maximum and Minimum Sediment Loadings 71
3.2.7 Source Area! Data 74
3.3 Sediment Loadings from Other Sources: Gullies,
Streambanks, and Mass Soil Movement 82
3.3.1 Overview 82
3.3.2 Methods for Quantifying Sediment Loading from
Gullies, Streambanks, and Mass Soil Movement . . 85
References 86
4.0 Nutrients and Organic Matter 90
4.1 Introduction 90
4.2 Nitrogen 91
4.2.1 Introduction 91
4.2.2 Precipitation 92
4.2.3 Nitrogen Loading Function 94
4.2.4 Evaluation of Parameters in the Nitrogen Loading
Function 96
4.3 Phosphorus 102
• 4.3.1 Introduction 102
4.3.2 Phosphorus Loading Function 104
4.3.3 Evaluation of Parameters in Phosphorus Loading
Function 104
4.4 Organic Matter 107
4.4.1 Organic Matter Loading Function 107
4.4.2 Evaluation of Parameters in the Organic Matter
Loading Function 107
IV
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CONTENTS (continued)
Page
4.5 Accuracy of Loading Functions 107
4.6 Example of Loading Computation 108
4.6.1 Nitrogen Loading 109
4.6.2 Phosphorus Loading 110
4.6.3 Organic Matter Loading 110
References 112
5.0 Pesticides 114
5.1 Introduction 114
5.2 Pesticide Loading Functions 116
5.2.1 Case 1: Insoluble Pesticides, Average Soil
Concentrations Known 116
5.2.2 Case 2: Water Insoluble Pesticides, Current
Area-Specific Data Available 117
5.2.3 Case 3: Water Soluble and Water Insoluble
Pesticides, Stream to Source Approach 118
5.3 General Information 119
5.3.1 Pesticide Solubility 119
5.3.2 Pesticide Persistence 119
5.4 Load Calculation: Examples 120
5.5 Limitations in Use 120
References 123
Bibliography 124
6.0 Salinity in Irrigation Return Flow 125
6.1 Introduction 125
6.2 Option I: Source to Stream Approach 126
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CONTENTS (continued)
6.2.1 Load Estimation Equation and Information Needs . . 126
6.2.2 Load Calculation - Irrigation Return Flow .... 127
6.3 Option II: Stream to Source Approach 129
6.3.1 Loading Equation and Information Needs 129
6.3.2 Option II Load Calculation 131
6.4 Option III: Loading Values for Salinity Loads in
Irrigation Return Flow 133
6.5 Estimated Range of Accuracy 133
References 139
7.0 Acid Mine Drainage 140
7.1 Introduction 140
7.2 Option I: Source to Stream Approach 141
7.2.1 Loading Function and Information Needs 141
7.2.2 Constants K& and Kj., in Option I Loading
Function 142
7.2.3 Load Index Factors for Option I Loading Function . 144
7.2.4 Background Alkalinity Term for Option I Loading
Function 146
7.2.5 Procedure for Using Option I Loading Function . . 146
7.2.6 Examples of Option I Loading Function Utilization. 148
7.3 Option II: Stream to Source Approach 151
7.3.1 Loading Function and Information Needs 151
7.3.2 Procedure for Using Option II Mine Drainage
Loading Function 152
7.3.3 Example of Option II Loading Function for Mine
Drainage 154
7.4 Estimated Range of Accuracy 154
References 158
VI
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CONTENTS (continued)
Page
8.0 Heavy Metals and Radioactivity 159
8.1 Introduction 159
8.2 Option I: Source to Stream Approach 161
8.2.1 Information Requirements for Loading Value
Equation 161
8.2.2 Procedure for Using Option I Loading Value
Equation 161
8.2.3 Example of Option I Source to Stream Approach . . 163
8.3 Option II: Stream to Source Approach 165
8.3.1 Loading Value Equations and Information Needs . . 165
8.3.2 Estimation of Heavy Metal and Radioactivity
Emissions from Background 166
8.3.3 Procedure for Using Option II Loading Value
Equations 175
8.3.4 Example of Option II Stream to Source Approach . . 175
8.4 Expected Accuracy of Methods 177
8.5 Heavy Metals Attached to Sediment 179
8.5.1 Loading Function 179
8.5.2 Information Needs 180
8.5.3 Relationship between Heavy Metals in Soils and in
Surface Waters 180
8.5.4 Reliability of the Procedure 181
Reference 185
9.0 Urban and Related Sources 186
9.1 Pollutants from Urban Runoff 186
9.1.1 Loading Functions 187
9.1.2 Procedure for Loading Calculations 190
9.1.3 Street Length and Land Use Data for Urban Areas . 193
9.1.4 Example 194
9.1.5 Techniques for Assessing Urban Runoff Pollution
Characteristics 198
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CONTENTS (continued)
9.2 Pollutants from Motor Vehicular Traffic on Roadways ... 199
9.2.1 Sources of Roadway Traffic Data 202
9.2.2 Example 202
9.3 Street and Highway Deicing Salts 202
9.3.1 Loading Functions 202
9.3.2 Sources of Required Data 204
References 205
10.0 Livestock in Confinement 206
10.1 Introduction 206
10.2 Loading Function for Livestock Operations 207
10.3 Feedlot Runoff Evaluation 208
10.3.1 Factors in Runoff Estimation 208
10.3.2 Precipitation Data Analysis 209
10.3.3 Estimation of Runoff from Feedlots 211
10.4 Pollutant Concentration in Feedlot Runoff 222
10.5 Pollutant Delivery Ratio, FLd 225
10.6 Feedlot Area, A 225
10.7 Methods for Developing Feedlot Statistics 227
10.8 Accuracy of Prediction 232
10.9 Procedure for Computing Pollutant Loading 232
10.10 Example 233
References 234
11.0 Terrestrial Disposal 236
11.1 Introduction 236
11.2 Loading Function for Landfills 238
11.3 Procedure for Computing Landfill Pollutant Loadings. . . 241
Vlll
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^ CONTENTS (continued)
Page
11.3.1 Landfill Characteristics including Number, Size,
Location, Age, and Surface Area 241
11.3.2 Percolation and Leachate Data 242
11.3.3 Pollutant Concentration Data 242
11.3.4 Leachate Delivery Ratio 242
11.4 Accuracy of Predicted Loads 243
11.5 Example 244
References 246
12.0 Background Pollutant Load Estimation Procedures 247
12.1 Introduction 247
12.2 Stream to Source Methods 247
12.2.1 Options Available . 247
12.2.2 Information Needs for Background Loading Value
Equations 248
12.2.3 Loading Value Equations and Definition of
Conversion Factors 249
12.2.4 Estimation of Background Pollutant
Concentrations 251
12.2.5 Procedure for Using Loading Value Eqs. (12-1) or
(12-2) 251
12.2.6 Examples of Using Loading Value Equations . . . 254
12.2.7 Estimated Ranges of Accuracy for Stream to
Source Options for Background Pollutant Loads. 255
12.3 Source to Stream Option 259
12.3.1 Description of Source to Stream Option 259
12.3.2 Estimated Ranges of Accuracy for the Stream to
Source (USLE-Sediment) Option for Background
Pollutant Loads 262
12.4 Iso-Pollutant Maps for Estimating Background Pollutant
Loads 262
References 286
Glossary 287
Smybols 293
ix
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CONTENTS (concluded)
Appendix A - Monthly Distribution of Rainfall Erosivity Factor R . . 298
Appendix B - Methods for Predicting Soil Erodibility Index K . . . . 316
Appendix C - Topographic Factor LS for Irregular Slopes 324
Appendix D - K • LS Indexes for Land Resource Areas East of the
Continental Divide 328
Appendix E - Estimated Soil Losses from Selected Cropping Systems in
Areas West of the Continental Divide (From 1972 SCS
Survey) 347
Appendix F - Reproduction of "Pesticide Residue Levels in Soils,
FY.1969 - National Soils Monitoring Program" 389
Appendix G - Pesticide Properties: Persistence, Solubility,
Leachability, Runoff 425
Appendix H - Statistics of Deicing Salt Application on Highway
and Tollways in the United States 439
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FIGURES
No.
3-1 Flow Diagram for Calculating Sediment Loading From
Surface Erosion 38
3-2 Mean Annual Values of Erosion Index (in English units)
for the Eastern United States 44
3-3 Mean Annual Values of Erosion Index (in English units)
for Hawaii 46
3-4 Soil Moisture - Soil Temperature Regimes of the
Western United States 48
3-5 Relationships Between Annual Average Rainfall Erosivity
Index and the 2-Year, 6-hr Rainfall Depth for Three
Rainfall Types in the Western United States 49
3-6 Storm Distribution Regions in the Western United
States 50
3-7 Slope Effect Chart Applicable to Areas A-l in
Washington, Oregon, and Idaho and all of A-3 53
3-8 Slope—Effect Chart for Areas Where Figure 3-7 is Not
Applicable 54
3-9 Slope Effect Chart for Irregular Slopes 56
3-10 Sediment Delivery Ratio for Relatively Homogeneous
Basins 66
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FIGURES (continued)
3-11 Projected Variation of Soil Erosion for Lands with
Constant Cover Factor, in Parts of Michigan, Missouri,
Illinois, Indiana, and Ohio
3-12 Projected Variation of Soil Erosion on Continuous Corn
Lands in Central Indiana ................ '->
4-1 Percent Nitrogen (N) in Surface Foot of Soil ....... 98
4-2 Soil Nitrogen vs Humidity Factor and Temperature ..... 100
4-3 Nomograph for Humidity Factor, H ............ 101
4-4 Nitrogen (NH^-N and N03-N) in Precipitation ....... 103
5-5 Phosphorus Content in the Top 1 ft of Soil ........ 105
7-1 Background Alkalinity Concentrations (ppm CaCOg) .... 147
7-2 Background Sulfate Concentrations (ppm) ......... 153
8-1 Background Total Heavy Metals (ppb) ........... 168
8-2 Background Iron + Manganese (ppb) ............ 169
8-3 Background Arsenic + Copper + Lead + Zinc (ppb) ..... 170
8-4 Background Miscellaneous Heavy Metals (ppb) ....... 171
8-5 Background Radioactivity (picocuries/liter) ....... 172
8-6 Background Alpha Radioactivity (picocuries/liter) .... 173
8-7 Background Beta Radioactivity (picocuries/liter) ..... 174
9-1 Climate Zone for the Cities from which Data Are Available
and Used in the URS Study ................ 192
9-2 Correlation Between Population Density and Curb Length
Density ........................ 195
Xll
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FIGURES (concluded)
No.
11-1 Leachate Flow Through Path Through Zones Where Attenua-
tion May be Effected 237
11-2 Average Annual Percolation 240
12-1 The National Hydrologic Benchmark Network 266
12-2 Background Suspended Sediment (ppm) 267
12-3 Background Nitrate Concentrations (ppm as N) 268
12-4 Background Total Phosphorus Concentrations (ppm as P) . . 269
12-5 Background BOD Concentrations (ppm) 270
12-6 Background Total Coliform Count (MPN/100 ml) 271
12-7 Background Conductivity (micromhos) 272
12-8 Background pH (standard units) 273
12-9 Background Total Dissolved Solids (ppm) 274
12-10 Background Alkalinity (ppm CaC03) 275
12-11 Background Hardness (ppm as CaCO^) 276
12-12 Background Chloride Concentrations (ppm) 277
12-13 Background Sulfate Concentrations (ppm) 278
12-14 Background Total Heavy Metal Concentrations (ppb) .... 279
12-15 Background Iron + Manganese (ppb) 280
12-16 Background Arsenic + Copper + Lead + Zinc (ppb) 281
12-17 Background Miscellaneous Heavy Metals (ppb) 282
12'18 Background Total Radioactivity (picocuries/liter) .... 283
12-19 Background Alpha Radioactivity (picocuries/liter) .... 284
12-20 Background Beta Radioactivity (picocuries/liter) 285
xiii
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TABLES
2-1 Source - Pollutant Matrix
3-1 Some Reported Quantitative Effect of Man's Activities on
Surface Erosion ...................... 32
3-2 Applicability of Rj. and RS Factors in the Areas West of the
Rocky Mountains ...................... 47
3-3 Relative Protection of Ground Cover Against Erosion ..... 59
3-4 "C" Values for Permanent Pasture, Rangeland, and Idle Land . 61
3-5 "C" Factors for Woodland .................. 62
3-6 "C" Factors for Construction Sites ............. 63
3-7 "P" Values for Erosion Control Practices on Croplands. ... 64
3-8 Typical Values of Drainage Density ............. 68
3-9 Summary of Applicability of Characteristic Factors ..... 69
3-10 Estimated Range of Accuracy of Sediment Loads from Surface
Erosion. . . . „ ..................... 71
3-11 Land Use and Treatment Needs Categories of the Conservation
Needs Inventory ........ „ ............. 78
xiv
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TABLES (Continued)
No. Page
4-1 Nutrient and Sediment Losses 96
4-2 Effect of Clear-Cutting and Fertilization on Nutrient
Output in Douglas Fir Forests 97
4-3 Probable Range of Loading Values for Nutrients and Organic
Matter 108
4-4 Sediment Yield in Example 109
4-5 Available Nitrogen Loading, Y(NA)E , in Example 110
4-6 Available Phosphorus Loading, Y(PA)E , in Example 110
4-7 Organic Matter Loadings in Example « . . . Ill
5-1 Estimates of Accuracy for Pesticides. . 122
6-1 Comparison of Salinity Loads Obtained with Option I Load
Estimation Equation with Reported Salinity Loads in
the Grand Valley, Colorado 128
6-2 Comparison of Salinity Loads Estimated by Option II Methods
with Those Reported by EPA 132
6-3 Salt Yields from Irrigation in Green River Subbasin .... 134
6-4 Salt Yields from Irrigation in Upper Colorado Main Stem
Subbasin 134
6-5 Salt Yields from Irrigation in San Juan River Subbasin. . . 135
6-6 Salt Yields from Irrigation in Lower Colorado River Basin . 135
6-7 Salt Yields from Irrigation for Selected Areas in
California 136
xv
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TABLES (Continued)
No.
6-8 Estimated Range of Accuracy for Option I (Source to
Stream) Procedure for Estimating Salinity from
Irrigation Return Flow 136
6-9 Estimated Range of Accuracy for Option II (Stream to
Source) Procedure for Estimating Salinity from
Irrigation Return Flow 137
7-1 Values of Ka and Kb for Acid Mine Drainage Option I
Loading Function » « 143
7-2 Load Index Values for Active and Inactive Surface and
Underground Mines 143
7-3 Example of Determination of Load Index Values 145
7-4 Conversion Factors a to be Used for Option II Mine
Drainage Loading Function ..... „ . „ 152
7-5 Estimated Mine Drainage Emissions from Tioga and Janiata
River Basins Using Option II Loading Function 155
7-6 Estimated Range of Loads for Option I (Source to Stream)
Acid Mine Drainage Loading Functions 156
7-7 Estimated Range of Loads for Option II (Stream to Source)
Acid Mine Drainage Loading Functions 157
8-1 Conversion Factors a to be Used for Option I Loading
Value Equations 162
8-2 Nonpoint Heavy Metal Emissions Estimates from Some Inactive
Mines in the Coeur d'Alene Valley, Idaho, Using Option
I Methods 164
8-3 Conversion Factors "a" to be Used for Option II Loading
Value Equation 167
8-4 Heavy Metal Pollutant Emissions from Several Streams in
Clear Creek County, Colorado 176
xvi
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TABLES (continued)
No.
8-5 Expected Accuracy of Option I (Source to Stream) Method
for Heavy Metals 178
8-6 Expected Accuracy of Option II (Stream to Source) Method
for Heavy Metals 178
8-7 Heavy Metal Concentrations in Surficial Materials in the
United States 182
8-8 Expected Accuracy of Heavy Metal Loads Delivered with
Sediment 184
9-1 Solid Loading Rates and Compositions — Nationwide Means and
Substitutions of the Nationwide Means at 8070 Confidence
Level 188
9-2 Mean Concentrations of Mercury and Chlorinated Hydrocarbons
in Street Dirt from Nine U.S. Cities 189
9-3 Equivalent Curb-Length per Unit Area of Street Surface,
Arranged by Land Use Types 196
9-4 General Land Consumption Rates for Various Land Uses . . . 196
9-5 Deposition Rates of Traffic-Related Materials 200
10-1 Stations for Which Local Climatological Data are Issued,
as of 1 January 1974 210
10-2 Hourly Precipitation 212
10-3 Local Climatological Data 213
10-4 Daily Precipitation 214
10-5 Seasonal Rainfall Limits for Various Antecedent Moisture
Conditions 216
10-6 Runoff (in Inches) from Feedlot Sufaces for Various
Antecedent Moisture Conditions 216
xvn
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TABLES (continued)
No.
10-7
10-8
10-9
10-10
10-11
10-12
10-13
11-1
11-2
11-3
12-1
Daily Precipitation Data (Inches) for Kansas City,
Missouri - 1974
Estimated Runoff (Inches) for Kansas City, Missouri - 1974.
Runoff and Rainfall Relationships on Beef Cattle Feedlots .
Beef Cattle Feedlot Runoff Characteristics
Runoff Characteristics from Cattle Feedlots in Kansas . . .
Number of Cattle Feedlots and fed Cattle Marketed—in Small
Lots, by States (1974)
Estimated Range of Accuracy for Predicting Pollutant Loads
from Feedlots
Chemical Characteristics of Leachates
Estimated Range of Predicted Loads for Various Pollutants
in Leachates in Landfills
Pollutant Loading Rates in Example
Conversion Factors "a" to be Used for Options I and III
ra.&
218
220
221
223
224
226
232
239
244
245
Loading Value Equation: Flow as Direct Runoff, Q(R) . . 250
12-2 Conversion Factors "a" to be Used for Options II and IV
Loading Value Equation: Flow as Streamflow, Q(str) . . . 252
12-3 Listing of Background Isopollutant Maps 253
12-4 Expected Accuracy of Background Pollutant Loads Calculated
Using Stream to Source Methods 256
12-5 Expected Accuracy of Background Pollutant Loads Calculated
Using Stream to Source Methods 257
12-6 Expected Accuracy of Background Pollutant Loads Calculated
Using Stream to Source Methods 258
12-7 "C" Values for Permanent Pasture, Rangeland, and Idle Land. 260
xvi 11
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TABLES (Concluded)
No. Page
12-8 "C" Factors for Woodland. 261
12-9 Expected Accuracy of Background Pollutant Loads Cal-
culated Using the Source to Stream (USLE-Sediment
Option 262
12-10 Location of Hydrologic Benchmark Stations ... 264
xix
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ACKNOWLEDGEMENT
We are pleased to acknowledge the extensive and useful support and
cooperation given by numerous agencies, associations, and individ-
uals. These include the Agricultural Research Service, the Soil
Conservation Service, the Economic Research Service, the Forest
Service, the Agricultural Stabilization and Conservation Service,
and regional offices of the USDA; the U.S. Geological Survey, the
Bureau of Mines, the Bureau of Land Management, and the Bureau of
Reclamation of the U.S. Department of the Interior; the Water Re-
sources Council; the Tennessee Valley Authority; the Appalachian Re-
gional Commission; several land grant universities; the U.S. Army
Corps of Engineers; Environmental Protection Agency Environmental
Research Centers and Laboratories; the National Forest Products
Association, the Society of American Foresters, the National
Association of State and Private Foresters; and the American
Public Works Association.
Grateful acknowledgement is extended to Mr. Paul Heitzenrater,
Project Officer, U.S. Environmental Protection Agency, and the
Project Advisory Committee, composed of representatives from
many offices of EPA and USDA, for their helpful comments and
suggestions during our research and manuscript preparation.
The Midwest Research Institute project staff included Dr. A. D.
McElroy, Program Manager; Dr. S. Y. Chiu, Principal Investigator,
Dr. J. W. Nebgen, Dr. A. Aleti, and Mr. F. W. Bennett, senior
staff; and Mr. J. Edwards, Mr. E. Trompeter, Mr. R. Ward, and
Mr. M. Valentine, and supporting staff.
xx
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SECTION 1.0
INTRODUCTION
The rates and magnitudes of discharges of pollutants from nonpoint sources
do not relate simply to source characteristics or source-related param-
eters. Evaluation of 'the severity of this problem is hampered by the lack
of tools to quantify pollutant loads, and scanty and imprecise data on the
interrelationships between control measures and pollutant loads are a de-
terrent to formulation of control or regulatory strategies. This User's
Handbook is the result of a program which had as one objective the develop-
ment of nonpoint pollution loading functions for significant sources and
significant pollutants.
The Handbook has two basic functions. First, it presents loading functions
together with the methodologies for their use. Second, it presents some of
the needed data, provides references to other sources of data, and suggests
approaches for generation of data when available data are inadequate. A
corollary function consists of assessments of the adequacies of functions
and their supporting inventories of data, and an assessment as well of the
extent to which pollutants and nonpoint sources are adequately covered.
A loading function, as the term is used here, is a mathematical expression
which one uses to calculate the emission of a pollutant from a nonpoint
source and discharge of the pollutant into surface waterways. For pur-
poses of this Handbook, a substance becomes a pollutant only when it is
deposited in surface waters. For example, the movement of sediment and
nutrients from a corn field to the edge of the field does not qualify as
pollutant discharge, even though the transport process may be an impor-
tant part of the overall pollutant emission mechanism.
A source is a land area devoted reasonably exclusively to a specific use,
which therefore can be treated as a unit with respect to land use practices
and potential for pollutant discharges. A cornfield, a field of soybeans,
a highway under construction, a mine, a forest, and a landfill are sources.
Similarly, a group of cornfields is considered to be a source if practices
from field to field are sufficiently uniform that an average set of data
adequately describes the entire acreage.
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A load is defined as the quantity of pollutant discharged to surface
waters from the source per unit of time: load = kilogram BOD per source
per day, etc. The loading function is the expression or equation which
permits calculation of the load. The function has provisions for calcu-
lation of a load on a per hectare basis (or other suitable unit dimension),
and total source load is calculated by multiplying the unit load by source
size. Finally, all sources within an area of interest, such as a watershed
or river basin, can be summed up to obtain the load of a particular pollu-
tant discharged to the surface waters from all identified sources.
A tremendous variety and quantity of data are necessary for productive
use of the loading functions. A small fraction of that body of data is
included in this handbook, primarily in the appencices. The user is re-
ferred to other sources of data ranging from systematic compilations to
the knowledge and judgment of local experts. The importance of the latter
can hardly be overemphasized. These sources are delineated in succeeding
sections as specific data needs arise for individual loading functions.
Essentially three categories of data are needed. One category is the in-
formation which describes the areal.characteristics of a source: its lo-
cation within a county, basin, state; its sizes, perhaps its dimensions;
and its basic land use, i.e., row crops, construction of residences, solid
waste disposal, and strip mining.
A second category of data is that which is characteristic of a source or
area, independently of land use. This category includes data which de-
scribe agricultural productivity, water permeability, erodibility, and
similar properties of soils; topographic features of the land; rainfall
and runoff; and stream miles, locations, and stream densities.
The third category is the data which are needed to describe how a source
is used. Examples are tillage methods and conservation practices, crop-
ping patterns, quantities and schedule of pesticide use, irrigation flows,
and population densities.
It may perhaps be construed from the above discussion that the loading
functions are straightforward expressions or equation, matched by pre-
cise, well-documented data, and that calculations can be made by routine
procedures with perhaps little discriminatory inputs of judgment by the
user. This seldom is the case. A substantial fraction of the presenta-
tions of the following sections is devoted to procedural descriptions
which should assist the user in using his or other local judgments and
inputs, and instruct the user in the limits of applicability of the
functions.
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Emphasis is given to loading functions or estimating procedures which are
generally useful from the standpoint of the depth, quality, and quantity
of available data or information. For this reason the functions are in
the main relatively simple and basic concepts, as opposed to theoreti-
cally oriented descriptions of physical, chemical, mechanical, and bio-
logical processes. Indeed, where necessary and appropriate, estimates
and the rule of thumb approach are preferred to more rigid theoretical
functions which suffer from the lack of key data.
The loading functions cover the following sources and pollutants:
Sources
Agriculture: cropland, pasture and rangeland, irrigated land, wood-
land, and feedlots
Silviculture: growing stock, logging, road building
Construction: urban development and highway construction
. Mining: surface mining and underground mines
Terrestrial disposal: landfill and dumps
Utility maintenance: highways and streets, and deicing
Urban runoff
Precipitation
Background sources: native forests, prairie land, etc.
Pollutants
Nutrients: nitrogen and phosphorus
Sediment
Biodegradable organics
Pesticides
Salinity
, Radioactivity
Mine drainage
Metals
Microorganisms
The definition of the term pollutant used in this document takes some
liberties with current legal definitions (specifically various interpreta-
tions of the law) and with philosophical interpretations of what substances
under what circumstances should be viewed as pollutants. Basically, a
substance is termed a pollutant if it has been observed in nonbeneficial
quantities or concentrations. The task of determining when a specific
substance is present in nonbeneficial quantities is left to the user and
to official interpretations of water quality regulations. Sediment ex-
emplifies the "pollutant" which serves a very useful purpose at some
optimum levels
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The concept of natural background is both quite important and difficult
to describe in universally acceptable terms. Furthermore, background
levels of the polluting substances cannot be readily and precisely deter-
mined by current methods which treat this subject and the approaches
proposed in this document are admittedly the object of controversy with
regard to both the philosophy of the approach and the technical methodology.
The importance attributed to natural background comes from the following:
(a) background is often thought to represent the ideal environmental quality,
and thus to represent the goal which we should strive to achieve, and (b)
background accordingly is often thought to be a fundamental criterion for
assessing the reasonableness of control measures and for evaluating the
cost of control in relation to benefits. These are not necessarily self-
evident truths. A notable case in point is the salinity in much of our
natural waters at levels above those suitable for certain beneficial uses.
Background is nevertheless a useful concept in that it serves as a point
of reference for determining what might be reasonably achieved in water
quality management, for establishing goals and objectives, and for iden-
tifying conditions or sources over which we may have little or no control,
such as precipitation-borne nitrogeneous compounds. The use of background
at such a point of reference is legitimate, as long as ones interpretation
of background is not used as an excuse to indiscrimately set aside or
ignore certain problems.
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SECTION 2.0
GUIDELINES FOR USE OF THE HANDBOOK
2.1 INTRODUCTION
This handbook has been developed for use with data or information on
record and accessible to the user. Some exceptions occur; that is,
certain loading functions assume the capability on the part of the
user to procure data by field and laboratory analysis or other on-site
data procurement methods. Field sampling and analysis is suggested
when data on record are inadequate, perhaps not in existence at all.
The handbook user should obtain and use the best data he can find.
The best data usually are those which have been measured or developed
locally. Such data can supplement the data or data sources recom-
mended throughout the handbook, or it can be used in a complementary
fashion, i.e., to help arrive at a range of values appropriate for
the specific user area.
The user is encouraged to use functions which are more area specific
than those presented in the handbook, to use research models if he is
so inclined, and to adapt suggested methodologies so that they directly
represent his area.
The information regarding loading functions and their use is presented
in Sections 3.0 through 12.0 and in Appendices A through H. The texts
of Sections 3.0 through 12.0 are devoted chiefly to descriptions of the
functions themselves, of the terms within the functions, and of proce-
dures for use of the loading functions. These sections contain certain
tables and figures which either present data needed in the loading func-
tions or which describe procedures for use of the loading functions.
Lengthy compilations of data are for the most part presented in the
appendices. In addition, the appendices contain certain types of back-
ground information and presentations of specific procedures which are
essential but which do not fit conveniently in the discussions in Sec-
tions 3 through 12.0.
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The following subsections present information on terminology, symbols,
formulas, and procedures for use of the handbook. The loading functions
themselves are presented together vith definitions of terms in tabular
form. The material presented in the remaining parts of this section
should be consulted by the user to help him define his specific prob-
lem and.to guide him to those parts of the handbook which he will use
in calculating the emissions of nonpoint pollutants from his sources.
2.2 TERMINOLOGY, SYMBOLS AND FORMULAS
The terminology and symbols conform with some exceptions to standard
symbols and terms. A broad range of subject matter is covered, and
the symbols normally used in one area or discipline overlap or are in
conflict with those of another. These conflicts were sometimes re-
solved; other times the best course was to keep the old and familiar
terminology.
Equations and formulas for the most part avoid the abbreviated notation
and symbolism typical of engineering or physical science equations, in
favor of more cumbersome but more readily interpreted terms.
The term Y universally denoted pollutant load for a source. The usual
symbols for basic parameters have been used almost without exception: Q
for volume rate of water flow, runoff or streamflow; P for precipitation;
C for concentration, etc. S uniformly denotes sediments. The majority
of the terms and symbols used in the handbook are defined in the summary
of loading functions presented in Section 2.4, and in the "Glossary" and
"Symbols" given in the last part of the handbook. Miscellaneous symbols
are defined as they occur throughout the text of Sections 3.0 through 12.0
and the appendices.
The general format of the equations or loading functions is shown in
Section 2.4. Note that multiplication of one term by another is to
be performed only if the two terms are separated by the dot (•) symbol.
The parenthesis is not used _to denote multiplication; it has been re-
served for the function of separating and defining terms or symbols.
Thus, C(HM)BG denotes "background concentration of heavy metal," and
Y(RAD) "load of radiation."
2.3 PROCEDURE FOR USE OF THE HANDBOOK
Several basic steps are involved in estimation of pollutant loads.
These are:
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1. Establish the boundaries of the area under consideration, which will
usually be a political or physical entity: an urban area, a watershed,
a minor basin, a state, etc. Define the general character of the area:
agriculture, silviculture, mining, urban, etc.
2. Identify nonpoint sources in the area, in appropriate detail. Iden-
tify pollutants to be evaluated for each source. The source-pollutant
matrix, Table 2-1, will assist in defining sources and pollutants.
3. Identify loading function options: Section 2.4 and Sections 3.0
through 12.0.
4. Identify data needs, determine availability of data for possible op-
tions; assess quality and depth of coverage of available data: Sections
3.0 through 12.0 and Appendices A through H.
5. Select loading functions which best match the problem with quality
and depth of data.
6. Procure necessary data for all sources/pollutants.
7. Calculate pollutant loads (see Sections 3.0 through 12.0) for indi-
vidual sources, and sum to obtain total loads.
2.4 SUMMARY OF LOADING FUNCTIONS
In this section approaches to calculation of pollutants are summarized.
Limitations to their use are presented as well, in summary fashion. Pol-
lutants and sources which must be treated by approximate methods which
require much discretion in use are delineated.
The summary cites no references. These are cited in Sections 3.0 through
12.0 and the appendices. References to tables, figures, and equations in
Sections 3.0 through 12.0 are provided to facilitate location of methods
and procedures, together with detailed discussion of dimensional units.
2.4.1 Sediment From Sheet and Rill Erosion
The basic tool for estimation of sediment from sheet and rill erosion is
the Universal Soil Loss Equation (USLE). The loading function based on
the USLE is:
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a/
Table 2-1. SOURCE - POLLUTANT MATRIX-'
Source
Pollutant
Agriculture
Irrigation return flow
Silviculture
Mining
i
00
i
Construction
Urban runoff
Highways
Feedlots
Terrestrial disposal
Background
3
3
3
9
9
10
12
4 4
9 9
9 9
10 10
11^ 11
12 12
Organic
Nutrient matter Pesticides Salinity Heavy
Sediment jj, P BOD H,I,F TDS metals Radioactivity Coliform Other
3445 8
8
8 8
8
9 9
9
9
9
11
12
11
12
10
Acid mine
drainage - 7
Suspended
solids - 10
12
12
a/ Numbers in table indicate section numbers.
t>/ Nitrogen only.
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Y(S) = A-(R'K'L'S'C-P'Sd) (3-1)
E
where Y(S)E = sediment loading
A = source area
R = the rainfall factor; Figures 3-2 and 3-3, or methods
presented in Section 3.2
K = the soil erodibility factor; USDA K factor lists,
Appendices B, D, and E
L = the slope length factor; Figures 3-7, 3-8, and 3-9,
Appendices C, D, and E
S = the slope gradient factor; Figures 3-7, 3-8, and 3-9,
Appendices C, D, and E
C = the cover factor; USDA C factor lists, Tables 3-3 to
3-6
P = the practice factor, Table 3-7
Sd = sediment delivery ratio, Eq. (3-2), Figure 3-10
The USLE was developed primarily for agriculture, and has been used
chiefly east of the Rocky Mountains. The factors are best defined for
these areas of use, and methods for use in silviculture, construction,
mining, and other sources outside agriculture are not well developed.
For the latter sources the USLE can serve as the basis for estimations
by personnel skilled in soil science and hydrology. The USLE is quite
useful in areas outside agriculture for estimating the probably impact
of control measures (dikes, vegetation, slope modification), even though
it may be inaccurate for estimation of absolute values for sediment
yields, especially from small subwatersheds .
2.4.1.1 Streambank and gully erosion (see Section 3.0) -
Streambank and gully erosion are not treated by the USLE, and landslides
are similarly not treated. Calculation of sediment yields from these
sources requires examination of experience and data in the area of in-
terest, by local personnel.
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2.4.1.2 Sediment from urban runoff -
Methods for estimating sediment in urban runoff are presented in Section
9.0 (see 2.4.7). The basic method involves the use of values for various
urban areas developed by analysis of sediment loads in many urban areas.
2.4.1.3 Sediment from feedlots (see Section 10.0) -
The Universal Soil Loss Equation is not used for feedlots. Measured data
on feedlot runoff are used as the basis for prediction. Feedlot loading
functions are synopsized in Section 2.4.8.
2.4.2 Nutrients and Organic Matter (see Section 4.0)
The principal method of estimating nutrient and organic matter loads con-
sist of first calculating sediment yields, and multiplying sediment yields
by factors which denote concentrations of these substances in the soil and
enrichment in the erosion process.
2.4.2.1 Nitrogen -
Yields of total nitrogen (NT, all forms of nitrogen) are estimated by mul-
tiplying sediment yields by concentrations of total nitrogen in soil and
by an enrichment factor. In addition to sediment-carried nitrogen, nitro-
gen carried in rainfall is included in the loading function. Available
nitrogen is the sum of precipitation-borne nitrogen and a fraction of the
sediment-borne nitrogen.
Y(NT)£ = a-Y(S)E.Cs(NT).rN (4_1}
Jpr'b (4.3)
Y(NT) = Y(NT)E + Y(N)pr
Y(NA) = Y(NT)E-fN + Y(N)pr (4_4)
where Y(S>E = sediment load
Y(NT)E = total nitrogen from erosion
Y(N)pr = nitrogen from rainfall, discharged to streams
NT = sum of nitrogen of all chemical forms
A = area of source
iVr = enrichment factor
10
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NA = available (mineralized) nitrogen
% = ratio of NA to NT in sediment
a = dimensional constant
b = attenuation factor
Npr = rate of deposition of nitrogen from the atmosphere in
precipitation
Cg(NT) = concentration of nitrogen in soil
Q(OR) = overland runoff
Q(Pr) = total precipitation
"Available nitrogen" is the forms of nitrogen which are readily available
for plant nutrition: nitrate, ammonia, and simple amines. Essentially
all of the nitrogen in rainfall is in the available form.
The loading functions for nitrogen based on the USLE and presented in
Section 4.0 do not apply to nitrogen from certain specific sources.
Nitrogen from terrestrial disposal operations, presented in Section 11.0
(see 2.4.9) is estimated by procedures for estimating leachate volumes,
pollutant concentrations in leachates, and delivery ratios for leachates.
Nitrogen from feedlots, presented in Section 10.0 (see 2.4.8) is esti-
mated from runoff volumes and a range of observed nitrogen concentrations,
Nitrogen in urban runoff, presented in Section 9.0 (see 2.4.7) is esti-
mated from data on urban runoff characteristics.
The loading functions for nitrogen do not encompass soluble nitrogen
forms, principally nitrate, which are leached into subsurface waters
and eventually reach surface waters in groundwater or drainage flows.
Methods for treating such situations via a generalized function are
not available, and local experience, data and expertise must be relied
upon.
The loading functions for nitrogen based on sediment as a carrier become
increasingly inadequate as sediment yields diminish. This inadequacy
will be most evident in situations where erosion is minimal and mineral-
ized nitrogen is abundant. A newly harvested forest temporarily devoid
11
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of growing timber may have a temporary excess of mineralized nitrogen
which is susceptible to both leaching and transport overland in sedi-
ments and in solution. Nitrogen emissions from terraced fields may be
higher in mineralized nitrogen than emissions from fields with less con-
trol of runoff and erosion.
2.4.2.2 Phosphorus -
Functions for phosphorus are presented in:
Section 4.0, Nutrients and Organic Matter;
Section 9.0, Urban Runoff; and
Section 10.0, Livestock in Confinement.
Refer to Sections 2.4.7 and 2.4.8 for a summary of functions for phos-
phorus in urban runoff and feedlot runoff. Functions for sediment-borne
nutrients from other sources are presented below.
Phosphorus is carried almost entirely on sediment. In situations where
erosion can be predicted, the loading function for phosphorus can be ex-
pressed as the product of sediment yield times phosphorus concentration
in sediment. The concentration of phosphorus is taken to be the concen-
tration in the eroding soil times an enrichment factor.
The load of available phosphorus is calculated by multiplying the load
of total phosphorus by the ratio of available phosphorus to total phos-
phorus .
Y(PT) = a-Y(S)E-Cs(PT).rp (4-8)
Y(PA) = Y(PT)-fp (4_9)
where Y(PT) = yield of total phosphorus
a = dimensional constant
CS(PT) - concentration of phosphorus in soil
j-
P = enrichment factor
Y(PA) = yield of available phosphorus
P = ratio of available phosphorus to total phosphorus
12
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2.4.2.3 Organic matter -
Functions for organic matter (BOD) are presented in:
Section 4.0, Nutrients and Organic Matter;
Section 9.0, Urban Runoff (see Section 2.4.7); and
Section 10.0, Livestock in Confinement (see Section 2.4.8).
Essentially all nonpoint emissions of organic matter are sediment borne.
Organic matter loading functions are expressed as a function of sediment
yields, with the exception of feed lot runoff, where a range of concen-
trations in runoff is used to calculate loads. For other nonurban sedi-
ment and for urban sediments, the yield of organic matter is a product
of sediment yield and organic matter concentration in sediment; an en-
richment factor is needed to account for preferential erosion of organic-
rich sediments in nonurban sources.
Y(OM)E = a-Cs(OM)-Y(S)E«roM (4-12)
where Y(OM)E = organic matter loading
Y(S)g = total sediment loading from surface erosion (see Eq.
(3-1))
~Y*
OM = enrichment factor
a = dimensional constant
Cg(OM) = organic matter content of soil
Loads of organic matter calculated by procedures in the handbook cover
major known sources, except for special cases which are not amenable to
treatment by generalized functions. Some of these are:
Direct or wind-blown deposition in streams of leaves from forests.
Capture of hay and other vegetative debris by floodwaters.
Irresponsible dumping of livestock wastes or other organic wastes
in sites susceptible to washout or erosion (including improper
field spreading of manure).
13
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2.4.3 Pesticides
Loading functions for pesticides are among the least satisfactory of
those presented in the handbook.
In orae option (Case I Method, Section 5.0) national historical data
on concentrations of pesticides in soils are the basis for estimation.
These concentrations are multiplied by sediment yields to calculate
pesticide loads. The method is restricted to insoluble pesticides.
A major drawback of the approach is its insensitivity to peak loads
which may occur during the pesticide use season at times of high run-
off. The method also suffers (presently) from a relative scarcity of
data on soil concentrations. It is useful primarily for large areas
(states, major basins) where average yields may be adequate, and small
watershed specific loads are not required.
2.4.3.1 Insoluble pesticides, Case I and Case II methods -
Y(HIF) = Y(S)E'Cs(HIF)-rHIF-10"6 (5-1)
where Y(HIF) = total pesticide loading for source
Y(S)E = sediment loading, Eq. (3-1)
CC(HIF) = concentration of pesticide in soil
b
enrichment ratio
The adequacy and applicability of the above method may be increased sub-
stantially through inputs of local/regional data and experience on pesti-
cide usage, levels in soils, rainfall and runoff patterns in relation to
pesticide use, and data on persistencies (life times) of pesticides in
the use environment. The Case II method for insoluble pesticides is
based on losses in sediments, as in the Case I method, with liberal in-
puts of local data.
2.4.3.2 Soluble plus insoluble pesticides (Case III method) -
A Case III method is presented, in Section 5.0, for both soluble and in-
soluble pesticides, which requires that local data be obtained on runoff
and on pesticide concentrations in runoff. If historical data of this
type are available it may be used for predictive purposes. If none are
available, data accumulated in a sampling program in the area or region
of concern will serve as the basis for development of a predictive capa-
ability.
14
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Y(HIF) = ^QiCi'a (5-2)
i
where Q = runoff volumes
C = concentration in runoff
i = storm event
a = dimensional constant
2.4.4 Salinity in Irrigation Return Flow
Three optional methods are presented in Section 6.0 for estimating salin-
ity in irrigation return flow. Each of the options is valid in principle
but has drawbacks due to one or more of the following reasons: (a) data
inputs are not readily accessible; (b) the quality of existing data inputs
varies widely; and (c) a good deal of insight about specific cases is re-
quired on the part of the user. At the present time, a good bit of effort
is underway to develop mathematical models for salinity in irrigation re-
turn flow. It is reasonable to expect that outputs from these models will
yield loading functions which will supercede the three methods presented
in this handbook.
The three methods are:
Option I; Calculation of Water and Salt Balances About the Irrigation
Site
Y(TDS)iRp = a-A-C(TDS)GW-[IRR + Pr - CU] (6-1)
where Y(TDS)iRp = salinity load in irrigation return flow
A = irrigated area
IRR = irrigation water added to crop root annually
Pr = annual precipitation
CU = annual water consumptive use
15
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CCTDS)™ = concentration of total dissolved solids in ground-
water contributing to subsurface return
a = conversion factor
Option II; Salt Balances in Streamflov
Y(TDS)IRF - a.[Q(Str)B.C(TDS)B - Q(Str)A'C(TDS)A3 - Y(TDS)BG - Y(TDS)pT (6-4)
where Y(TDS) = salinity load contribution from irrigation
IRF
Y(TDS) = salinity load contribution of background
BG
Y(TDS)pT = salinity load contribution of point sources
Q(Str)B = streamflow below irrigated areas
Q(Str)A - streamflow above irrigated areas
C(TDS)B = total dissolved solids concentration below irrigated
area
CCTDS)^ = total dissolved solids concentration above irrigated
area
a = conversion constant
Option III: Estimation From Historical Data
Loads from several irrigated areas have been quantitatively assessed over
a period of years. These data, synopsized in Tables 6-3 through 6-7, and
other data available to the user, can serve as guidelines for current es-
timations of salinity in irrigation return flows.
Option I requires specific information concerning how much water is de-
livered, how much is used consumptively, and the concentration of salt
in groundwater where applied irrigation water may be lost by deep perco-
lation. Uncertainty in any of these input data will affect the accuracy
of the procedure. Option I is most realistic in cases where the ground-
water dissolved solids are several times higher than dissolved solids in
applied irrigation water, and is recommended for use in those areas meet-
ing such a specification. Furthermore, the procedure is not recommended
for sprinkler irrigation systems where evaporative losses in the delivered
water may be excessive.
16
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Option II has been the method traditionally used to estimate salinity
from irrigation return flow. Basically, this method consists of mea-
suring salinity loads in streams above and below irrigated areas. Dif-
ferences in salinity are thus attributed to irrigation. The principle
uncertainty in the Option II method lies in the contribution of back-
ground to the salinity load. Definition of background salinity will re-
quire knowledge and insight about the particular area being considered.
Option Ill — loading values—is perhaps the most reliable method. How-
ever, loading values must have been determined for the area of interest
or for like areas in order for the method to be useful. Such values are
not available in many irrigated areas. The measurement of salinity loads
requires extensive monitoring and analysis, which are beyond the resources
of many irrigation projects. As mathematical models are developed for
predicting salinity from irrigation return flow, it is likely that their
outputs can be used to obtain valid estimates of salinity loading values
from specific areas.
2.4.5 Acid Mine Drainage
Two options are presented for acid mine drainage emissions to surface
waters—source to stream approach, and stream to source approach. The
loading functions are discussed in Section 7.0 of the handbook.
Source to stream approach - The source to stream loading function has
been developed based upon statistical analysis of acid mine drainage
data in the Monongahela River Basin. The loading function describes
the potential acid formation from a "typical" mine, and allows for the
neutralization of part of the acid by background alkalinity. Defini-
tion of the typical mine was established by regression analysis. The
source to stream loading function is:
Y(AMD) = N[Ka-(IAU + IITJ + 1^ + IIS) - Kb-Q(R)-C(Alk)BG] (7-1)
where Y(AMD) = acid mine drainage load
N = total number of sources which are potential emitters
of mine drainage
•''' Z' -"-' I = load index values> Table 7'2
AS
Ka> Kb = constants determined from regression analysis,
Table 7-1
17
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Q(R) = flow as annual average runoff
C(Alk)BG = concentration of background alkalinity, Figure 7-1
This loading function depends upon knowledge of the numbers of mines
in various categories (underground and surface, active and inactive)
and upon the neutralization potential of background. It should be ap-
plicable to all mines associated with pyritic wastes whether they be
coal or metal ore mines. There is a good deal of uncertainty in its
application to metal mines, since the function was developed for the
Appalachian coal regions of the United States, and becomes less ac-
curate as one moves westward into coal areas of the midwest and west.
Stream to source approach - The stream to source loading function for
acid mine drainage is based upon comparison of sulfate loadings in
streams to sulfate contributions from background and from point sources.
The rationale for this approach lies in the fact that sulfate is a prin-
cipal product of acid mine drainage. The loading function is:
Y(AMD) = a-A-Q(R)[c(S04) - C(SC>4)BG - C(S04)PT] (7-8)
or Y(AMD) = a-Q(Str)[C(S04) - C(S04)BG - (XSO^^J
where Y(AMD) = acid mine drainage
A = area containing mine drainage sources
Q(R) = flow as annual average runoff
Q(Str) = flow as streamflow
C(S04) = sulfate concentration in surface waters
= sulfate concentration in surface waters attribu-
table to background, Figure 7-2
= sulfate concentration from point sources
a = dimensional constant
The stream to source approach does not allow for neutralization of acid
mine drainage between the point where it is formed and the point where
it is discharged. Thus, high values may be estimated in some cases.
18
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The main uncertainty in the function is contribution of sulfate from
background and point sources. Knowledge of the area under considera-
tion is essential for accuracy. If the function is used in primarily
rural areas, the point source sulfate contribution term can be ignored.
2.4.6 Heavy Metals and Radiation (see Sections 8.0, 9.0 and 11.0)
Nonpoint sources of heavy metals and radiation include sediment, abandoned
mine sites, chat piles, tailings piles, urban runoff, and landfill.
Methods for estimation of emissions of heavy metals from urban runoff
and landfill are presented in Sections 9.0 and 11.0 (see 2.4.7 and
2.4.9), respectively. Methods for estimation of loads from mines and
mining refuse are presented in Section 8.0. The latter methods are
summarized below.
In general, methods for calculating loads of heavy metals and radio-
activity are relatively crude. Their principal usefulness likely is
limited to pinpointing of problem areas, so that needs for analysis
in greater depth can be determined.
One option for estimation of loads assumes that data on individual
sources are available, and can be summed to a total load (Option I
below). A second option assumes no source data are available and
historical data (or handbook user-generated data) on streamflow or
runoff and on concentrations of the pollutants in the flows are com-
pared with information on background levels of the pollutants (Option
II below).
The special case of heavy metals associated with sediment emissions is
considered in Section 8.5.
Option I: Summation of Loads From Individual Sources
Y(HM, PAD) = a°EQn°C(HM, RAD)n (8-1) and (8-2)
where Y(HM, RAD) = heavy metal (HM) load or radioactivity (RAD) load
C(HM, RAD)n = heavy metal or radioactivity concentration emitted
from the ntn source
Qn = flow from the ntn source
a = conversion factor, Table 8-1
19
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Option II: Estimation From Data on Runoff or Streamflow
Y(HM, RAD) = a.A.Q(R)-[C(HM, RAD) - C(HM, RAD)BG] (8-3) and (8-5)
or Y(HM5 RAD = a-Q(Str)[c(HM, RAD) - C(HM, RAD)BG] (8-4) and (8-6)
where Y(HM, RAD) = heavy metal (HM) load or radioactivity (RAD) load
C(HM, RAD) = concentration of heavy metal or radioactivity in
runoff or streams
C(HM, RAD)ng = heavy metal or radioactivity concentration emitted
from background, Figures 8-1 through 8-7
A = area containing nonpoint sources
Q(R) = flow as average annual runoff
Q(Str) = flow as streamflow
a = conversion constant, Table 8-3
Heavy Metals Attached to Sediment
The special case of heavy metals in the soil matrix carried into surface
waters with sediment is treated by the following method. The method is
discussed in detail in Section 8.5.
Y(HM)s = a.Cs(HM)-Y(S)E (8-7)
where Y(HM)g = yield of heavy metals in sediment
Cg(HM) = concentration of heavy metals in the eroded soil
Y(S)E = sediment yield as defined by Eq. (3-1).
a = conversion factor
20
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2.4.7 Urban and Related Sources (see Section 9.0)
An extensive amount of data have been assembled and evaluated in several
recent studies. These data comprise loading values for pollutants in ur-
ban and highway runoff. Pollutants documented include solids (sediment),
BOD, COD, phosphorus, nitrate, ammonia, coliforms, organic nitrogen, and
heavy metals.
The loading functions are summarized below. The data on which the func-
tions are based have been analyzed for standard error, which usually is
relatively low (< + 5070) . Discretion must be exercised in extrapolations
to urban areas for which no data exist.
2.4.7.1 Urban runoff -
Solids
Y(S)U = L(S)-Lst (9-1)
where Y(S) = solid loading from urban nonpoint sources
L(S) = solid loading rate, Table 9-1
Lgt = street curb-length
Other pollutants
= a-Y(S)u-C(i)u (9-2)
where Y(i)T - loading of pollutant i from urban nonpoint sources
a = dimensional constant
Y(s)n = urban solid loading
C(i)n = concentration of pollutant i in solids, Tables 9-1 and
9-2
21
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2.4.7.2 Road traffic -
= Y(i)-LH-TD.AX (9-4)
where Y(i)tr = loading of pollutant i from road traffic
Y(i) = deposition rate of pollutant i , Table 9-5
LH = length of highway
TD = traffic density
AX = average number of axles per vehicle
2.4.7.3 Street and highway deicing salts -
Y(DI) = a-b-DI (9-5)
where Y(DI) = salt loading
a = dimensional constant
b = attenuation factor
DI = amount of deicer applied, Appendix H
22
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2.4«8 Livestock in Confinement (see Section 10.0)
Data on ranges of concentrations of pollutants in feedlot runoff have
been combined with methods for estimating runoff quantities from feed-
lots. The overall methodology is crude, and differences between actual
and estimated loads may be great. The precision of the estimates is
also dependent on the accuracy of information on feedlot locations,
areas, and sizes. Feedlots with runoff control are excluded from the
nonpoint category,,
Pollutants covered are sediment, BOD, phosphorus, nitrogen, and coliforms,
Y(i)FL = a.Q(FL)-C(i)FI/FLd.A (10-1)
where Y(i-)FL = loading rate of pollutant i from a livestock facility
a = a constant
Q(FL) = direct runoff from feedlots
C(i)FT = concentration of pollutant i in runoff, Tables 10-1
and 10-2
FLj = delivery ratio, feedlots
A = area of livestock facility
2.4.9 Terrestrial Disposal
Leachates from wastes disposed on land vary widely in quantity and com-
position, and delivery of leachate-contained pollutants to surface waters
may range from 0 to 10070. The loading function for these pollutants is
thus very crude, and reasonably accurate results depend greatly on the
availability of site-specific information. The handbook presents synop-
ses of data on pollutant concentrations in leachates, and suggests a gen-
eral methodology which should be adapted to local or regional needs.
The loading function requires knowledge either of percolating or leach-
ate rates, information on pollutant concentration, and knowledge of site
characteristics which permit estimation of a delivery ratio.
Y(i)LF = a'C(i)LF-Q(LF)-LFd-A (11-1)
23
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where Y(i)LF = loading rate of pollutant i
a - dimensional factor
C(i) = concentration of pollutant i in leachate at site,
Table 11-1
Q(LF) = percolation rate (Figure 11-2)
= leachate delivery ratio
A = area of landfill
2.4.10 Background Emissions of Pollutants (see Section 12.0)
Stream to source approach - The background, or "natural" rates of pollu
tant emissions is a sensitive, controversial area. One approach to def
inition and estimation of background loads is based on the National Hy-
drologic Benchmark Network. Iso-pollutant maps for various pollutants
have been developed from the Network data. These may be used to deduce
probable "natural" in-stream pollutant concentrations, or to estimate
delivered loads.
= a«A.Q(R)-C(i)BG (12-1)
or Y(i)BG = a.Q(Str)°C(i)BG (12-2)
where Y(i)BG = load of background constituent i
a = conversion factor, Tables 12-1 and 12-2
A = watershed area
Q(R) = flow as average annual runoff
Q(Str) = flow as streamflow
C^BG = estimated concentration of background constituent i ,
Figures 12-1 through 12-18, Tables 13-1 and 13-2, and
Figures 13-1 and 13-2
The iso-pollutant maps will not adequately represent many local, site-
specific, problem areas. Background concentrations and loads should in
such cases be deduced from local, site-specific data.
24
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Source to stream approach - Where a "natural" condition can be defined,
it is in principal possible to calculate background loadings via use
of loading functions for a specific pollutant. In Section 12.3 is pre-
sented a method for calculating natural nonpoint emissions of sediment.
The method may be extended to phosphorus, total nitrogen, BOD, and heavy
metals. It consists of calculation of sediment yields from a natural site,
namely, land with a vegetative cover typical of that which existed before
man changed that condition.
Y(S) = Y(S) from Eq. (3-1) for natural conditions
o(j E
G = Cs(NT)BG-Y(S)BG-Va
rw> rp = enrichment ratios
CS(NT) = concentration of nitrogen in soil
Cg(PT) = concentration of phosphorus in soil
a = dimensional constant
2.5 LIMITATIONS AND ACCUEACIES
The estimation of nonpoint pollution is an approximate science, in its
present stage of development. In some instance the term science is not
appropriate. The loading functions presented in this handbook should
be adopted and used with this understanding. In not one case does a
function cover all possible variables and all possible situations. Par-
ticularly lacking is the capability to follow a dynamic, hour by hour
event or a day by day situation, and develop an integrated load
curve which reflects changes with time. In nearly all cases scien-
tific methods will permit reasonably accurate measurement of gross pro-
cesses in a dynamic event, e.g., a rainstorm with its accompanying over-
land runoff and transport of pollutants. It is not the purpose of this
handbook, however, to present the detail of measurement methodologies,
for the objective of the work has been to provide a methodology which
will permit estimation of nonpoint pollutant emissions with a minimum
of field measurement and will be dependent primarily on existing data
and information.
The lack of scientifically derived expressions (or even valid empirical
relationships) has led to the development of estimating procedures
based on averaged data. In only one case--soil loss—has the accumu-
lated data been developed into a currently useful load equation based
25
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on parameters which represent the physical phenomena involved in gener-
ation and transport of the pollutant. The Universal Soil Loss Equation
(USLE) represents long term average on an annual basis. It can be used
to predict an assumed single storm event or a series of storm events,
but factor values for single events or for seasonal events are not as
complete and available as "annual average" factors. If one is interested
in extremes over a period of years, i.e., the equivalent of 7 day-10 year
flow, factor values are essentially nonexistent.
The program which generated this handbook has "piggy-backed" the USLE
to formulate loading functions for nitrogen, phosphorus, organic matter,
and certain pesticides under certain conditions. These functions thus
are based on the well established USLE factors and on an extensive body
of data relating to the specific pollutants. Again, the functions are
better for average conditions than for extremes.
The above discussion illustrates a key point regarding the accuracies
and limits of usefulness of the loading functions presented in this
handbook. The technology is usually adequate to reasonably good for
predicting averaged pollutant loads. The spread or range of values
which make up that average is likely to be high, however, and an esti-
mated accuracy which includes the probable actual extreme loads about
the calculated average will therefore be much worse than the accuracy
in predicting the average. The estimates of accuracies presented in
Sections 3.0 to 12.0 for the most part are our estimates of the capa-
bility of the loading function to predict an average load, whether it
be an "annual average" or a "30 day-maximum average." The user should
recognize that any specific real year may be quite atypical with regard
to rainfall quantities, intensities, runoff, vegetative cover and other
factors, and that the actual load may be well outside the specified ac-
curacies.
It should be emphasized that sufficient data are simply not available
for statistically valid estimation of accuracies. The reported accur-
acies are generally best estimates based on characteristics of the func-
tion, its required data base, and the reported/observed ranges of loads
of various pollutants.
Worthy of special mention is the fact that good, area-specific input
data will give much better results than nonspecific or haphazardly
selected data.
Some nonpoint sources are not amenable to treatment by loading func-
tions, for one or more of several reasons: (1) the source may be so
irregular in occurrence that it can only be described by local personnel;
26
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(2) data on loads may be lacking; and (3) the source itself cannot be
described in terms which can be translated into rates of pollutant emis-
sion. A list of sources and pollutants which fall in this category fol-
lows :
Roadside erosion
Gully erosion
Landslide, creep
Streambank erosion
Improper manure spreading or dumping
Bacteria from nonurban areas, excepting feedlots
Direct deposition of vegetation in surface waters: leaf fall, wind
blown organic matter
Floodwater transport of floodplain debris
Floodwater scouring of floodplains
Salt leakage from oil fields
Drainage-borne pollutants: forests, wetlands, agricultural lands
Nutrients in irrigation return flow
Groundwater contamination with nitrates, metals, bacteria, pesticides
Direct deposition of fertilizers and pesticides in surface waters
Improper disposal of construction and demolition debris
Nonregulated, unauthorized dumping of domestic and industrial wastes
The loading functions and associated guidelines presented in the handbook
vary considerably in sophistication, overall adequacy, demands for data
collection, and requirements for local judgments, technical skills, and
other resources. Nothing really constructive can be gained by ranking
them by order of adequacy or by other yardsticks, and the limitations of
each have been pointed out throughout the test. It is appropriate to
point out a situation or two which currently present difficult challenges.
Perhaps the greatest void consists of the lack of a capability to sys-
tematically describe the movement of pollutants through the earth, from
surface soil into the root zone, to storage in soil and subsoil moisture,
into near surface and deep aquifers, and movement from thence to the sur-
face as drainage and baseflow in streams. The transport of pollutants
via these processes is little understood. Nitrate movement via subsur-
face routes is inadequately dealt with by current technology, and is es-
sentially excluded from the loading functions. Metals, salts, bacteria,
and soluble organic materials are treated generally with marginally ade-
quate procedures; landfill leachate movement is a case in point. Treat-
ment of irrigation return flow, and its load of salts, nutrients, etc.,
27
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is a particularly difficult problem; this problem is better described
than are like problems simply because it has been extensively and capably
studied and monitored.
28
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SECTION 3.0
SEDIMENT FROM SOIL EROSION
3.1 INTRODUCTION
The sediment produced by erosion of sloping lands, gullies, and streambanks,
and transported to surface water is generally recognized as the greatest
single pollutant from nonpoint sources. Sediment reduces water quality and
often degrades deposition areas. Sediment occupies space needed for water
storage in reservoirs, lakes, and ponds; restricts streams and drainageways;
alters aquatic life and reduces the recreational and consumptive use value
of water through turbidity. More importantly, sediment, particularly that
produced from eroded topsoil, also carries other water pollutants such as
nitrogen, phosphorus, organic matter, pesticides and pathogens.
Erosion of soil by water can take a variety of forms. Sheet erosion is the
uniform removal of a thin layer of soil, normally by the impact of falling
raindrops. Channel erosion exists as rill erosion, gully erosion, and
streambank erosion, caused by detachment and transportation of sediment by
flowing streams (channels) of water. Rill erosion is the result of soil
removal by small concentrations of surface water, such as that often found
between the rows of cultivated crops planted up and down slopes. Channels
formed in rill erosion are small enough to be smoothed completely by cul-
tivation methods.
Gully erosion, similar to rill erosion, is also caused by temporary con-
centration of surface runoff. However, erosion by gullying cuts, by defi-
nition, deeply enough into soil/subsoil that channels so formed cannot be
smoothed completely by ordinary tillage tools.
Streambank erosion refers to carrying off of the soil material on the
sides of a permanent streambed, including those with intermittent flow,
by the energy of moving water.
29
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Sediments are also produced from mass soil movement, which is the downs lope
movement of a portion of land surface under the effect of gravity. Such
movements may take the form of landslide, mudflow, or downward creep of an
entire hillside.
This section presents methods for assessing sediment loading from various
sources. Sheet erosion and rill erosion are treated together as surface
erosion in Section 3.2; the remaining are presented in Section 3.3.
3.2 SEDIMENT LOADING FROM SURFACE EROSION
3.2.1 Overview
In general, the most important contributor of sediment nationwide is sur-
face erosion. Erosion agents, including water, wind, and rain splash, work
continuously to break down the earth's surface to produce sediment from
cropland, forests, pastures, construction sites, mining sites, road rights-
of-way, etc.
The basic mechanisms of soil erosion by water consist of: (a) soil detach-
ment by raindrops; (b) transport by rainfall; (c) detachment by runoff; and
(d) transport by runoff.—' The damage caused by raindrops hitting the soil
at a high velocity is the first step in the erosion process. Raindrops
shatter the soil granules and clods, reducing them to smaller particles and
thereby reducing the infiltration capacity of soil. The force of the rain-
drops also carries the splashed soil, resulting in movement of soil down-
slope.
When the rate of rainfall exceeds the rate of infiltration, depressions on
the surface fill and overflow, causing runoff. Runoff water breaks sus-
pended soil particles into smaller sizes, which helps to keep them in sus-
pension.
3.2.1.1 Factors affecting surface erosion -
Factors which have been considered the most significant in affecting erosion
of topsoil consist of:
1. Rainfall characteristics,
2. Soil properties,
3. Slope factors,
30
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4. Land cover conditions, and
5. Conservation practices.
Rainfall characteristics define the ability of the rain to splash and erode
soil. Rainfall energy is determined by drop size, velocity, and intensity
characteristics of rainfall.
Soil properties affect both detachment and transport processes. Detachment
is related to soil stability, basically the size, shape, composition, and
strength of soil aggregates and clods. Transport is influenced by perme-
ability of soil to water, which determines infiltration capabilities and
drainage characteristics; by porosity, which affects storage and movement
of water; and by soil surface roughness, which creates a potential for tem-
porary detention of water.
Slope factors define the transport portion of the erosion process. Slope
gradient and slope length influence the flow and velocity of runoff.
Land cover conditions affect detachment and transportation of soil. Land
cover by plants and their residues provides protection from impact of rain-
drops. Vegetation protects the ground from excessive evaporation, keeps
the soil moist, and thus makes the soil aggregates less susceptible to de-
tachment. In addition, residues and stems of plants furnish resistance to
overland flow, slowing down runoff velocity and reducing erosion.
Conservation practices concern modification of the soil factor or the slope
factor, or both, as they affect the erosion sequence. Practices for ero-
sion control are designed to do one or more of the following: (a) dissipate
raindrop impact forces; (b) reduce quantity of runoff; (c) reduce runoff
velocity; and (d) manipulate soils to enhance the resistance to erosion.
3.2.1.2 Effect of man's activities on surface erosion -
Man alters surface erosion primarily by changing cover and altering the
hydraulic system through which the water and sediment are transported.
Activities which impact surface erosion can be categorized into four classes
cropping practices, silvicultural activities, mining activities, and con-
struction activities. Depending on the initial status of land and the na-
ture of activity, a wide range of impact can be expected. Table 3-1 lists
some reported values of the magnitude of the impact.
Surface erosion from croplands - Cropping practices change the soil cover
so that it favors one type of plant and discourages the growth of others.
The practices expose the soil and leave it loose and liable to erosion.
31
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Table 3-1. SOME REPORTED QUANTITATIVE EFFECTS OF
MAN'S ACTIVITIES ON SURFACE EROSION
Initial status
Forestland
Grassland
Forestland
Forestland
Forestland
Forestland
Row crop
Pastureland
Forestland
Type of
disturbance
Planting
row crops
Planting
row crops
Building
logging roads
Woodcutting
and skidding
Fire
Mining
Construction
Construction
Construction
Magnitude of
impact by the
specific disturbance^.'
100-1,000
20-100
220
1.6
7-1,500
1,000
10
200
2,000
Reference
Brown (2)
Brown (2)
Megahan (3)
Megahan (3)
Ralston and
Hatchell (4)
Collier et al. (5)
USDA/SCS (6)
USDA/SCS (6)
USDA/SCS (6)
a/ Relative magnitude of surface erosion from disturbed surface, assuming
"1" for the initial status. The first row of the table, for example,
indicates that transforming a forestland into row crops will increase
surface erosion 100 to 1,000 times.
32
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Soil erosion can be affected by cropping practices such as tillage, irriga-
tion, planting, fertilization, and residue disposition.
Tillage detaches soil and promotes oxidation of organic matter in soils.
These processes decrease aggregation and reduce the infiltration capacity.
Plowing creates a plow pan. Agricultural machinery compresses the soil,
reducing large-pore space and, consequently, its infiltration capacity.
All this results in higher runoff and erosion rates.
Crop planting varies in its effect on erosion, depending on the species,
the stand density, the distance between the rows, and the direction of the
rows with respect to the slope. The denser and the more nearly on the
contour the planting is made, the less erosion will result.
Fertilization helps to ensure stands, causes faster and heavier growth, and
is consequently a help in protecting the soil and in creating beneficial
residues. Manure can serve both as a fertilizer and a ground cover.
Crop residues help to protect soil from detachment by rainfall and runoff.
They also contribute to making up organic matter in soils and therefore
increase soil stability against water erosion.
Surface erosion from forestlands - Forestland generally can be character-
ized by: (a) a vegetative canopy above the ground surface; (b) a layer of
decayed and undecayed plant remains on the surface; and (c) a system of
living and dead roots within the soil body. These conditions insulate the
soil against the impact of rain, obstruct overland flow, and retard move-
ment of soil by water action. These conditions reduce erosion and sediment
production to a minimum.
Major causes of erosion on forestlands include:
1. Damage to cover from cutting, logging, and reforestation activities,
and construction of roads and fire lanes.
2. Damage to cover because of fire, grazing, and recreational activity.
3. Damage on land reverting to forest cover from other land use, such as
strip mines, and on which adequate cover conditions have not developed.
Surface erosion from pasturelands - The dense cover of grasses, legumes,
and other low growing plants is generally effective in protecting the soil
from erosion by rainfall and runoff. Consequently, the amount of erosion
from a well-managed pasture is small.
33
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Overgrazing is the major cause of accelerated erosion on pasturelands . The
grazing animals may eat the forage down to the ground, lessening the effec-
tiveness of plants in intercepting the raindrops. Open spots on pasturelands
can erode as rapidly as cultivated fields.
Surface erosion from construction sites and mining sites - Construction and
mining activities involve extensive earth-moving operations. In these di-
verse earth-moving activities, the natural protective ground cover is dis-
turbed; compacted soils are dislodged and redistributed; highly erosive
soils from the deeper horizons are exposed to the elements; shallower,
smoother terrain is recontoured to steeper slopes; and runoff is often in-
creased and accelerated.
Sediment production from construction sites differs from that caused by
other types of nonpoint sources in that it is generally of limited duration.
Agricultural operations continue to produce sediment-containing runoff year
after year, while intensive sediment yields from a construction project
typically last from a few weeks to a few years, during which time the areas
of exposed soils may be well stabilized by vegetation, chemical application,
or other control measures, either permanent or temporary.
3.2.1.3 Sediment delivery ratio -
Sediment loadings to surface waters are dependent on erosion processes at
the sediment sources and on the transport of eroded material to the recep-
tor water. Only a part of the material eroded from upland areas in a water-
shed is carried to streams or lakes. Varying proportions of the eroded
materials are deposited at the base of slopes, in swales, or on flood plains.
The portion of sediment delivered from the erosion source to the receptor
water is expressed by the delivery ratio.
Factors affecting sediment delivery ratio - Many factors influence the sedi-
ment delivery ratio. Variations in delivery ratio may be dependent on some
or all of the following factors and others not identified. The reader is
referred to References 7 and 8 for more detailed discussion of the subject.
Proximity of sediment sources to the receptor water--e.g., channel-
type erosion produces sediment that is immediately available to the
stream transport system, and therefore has a high delivery ratio.
Materials derived from surface erosion, however, often move only short
distances and may lodge in areas remote from the stream, and therefore
have a low delivery ratio.
Size and density of sediment sources--when the amount of sediment
available for transport exceeds the capability of the runoff transport
system, deposition occurs and the sediment delivery ratio is decreased.
34
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Characteristics of transport system—runoff resulting from rainfall
and snowmelt is the chief agent for transporting eroded material.
The ability to transport sediment is dependent on the velocity and
volume of water discharge.
Texture of eroded material—in general, delivery ratio is higher for
silt or clay soils than for coarse textured soils.
Availability of deposition areas—deposition of eroded material mostly
occurs at the foot of upland slopes, along the edges of valleys and in
valley flats.
Relief and length of watershed slopes—the relief ratio of a watershed
has been found to be a significant factor influencing the sediment-
delivery ratio. The relief ratio is defined as the ratio between the
relief of watershed between the minimum and maximum elevation, and the
maximum length of watershed.
3.2.2 Sediment Loading Function for Surface Erosion
Sediment loading is defined in this handbook as the quantity of soil mate-
rial that is eroded and transported into the watercourse. Sediment loading
is dependent on (a) on-site erosion, and (b) delivery, or the ability of
runoff to carry the eroded material into the receptor water.
The sediment loading function is based on concepts of the mechanisms of
gross erosion and sediment delivery. The Universal Soil Loss Equation—'
(USLE) is chosen to predict the on-site surface (including sheet and rill)
erosion, for the following reasons:
1. This equation is applicable to a wide variety of land uses and climatic
conditions.
2. It predicts erosion rates by storm event and season, in addition to
annual averages.
3. An extensive nationwide collection of. data has been made for factors
included in the equation.
The sediment loading function has the form:
Y(S)E =_S [Ai-(R.K.L.S.C-P.Jd)i] (3-1)
35
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where Y(S) = sediment loading from surface erosion, tons/year
J_i
n = number of subareas in the area
Source areal factor:
A- = acreage of subarea i, acres
Source characteristic factors:
R = the rainfall factor, expressing the erosion potential of
average annual rainfall in the locality, is a summation
of the individual storm products of the kinetic energy
of rainfall, in hundreds of foot-tons per acre, and the
maximum 30-min rainfall intensity, in inches per hour,
for all significant storms, on an average annual basis
K = the soil-erodibility factor, commonly expressed in tons
per acre per R unit
L = the slope-length factor, dimensionless ratio
S = the slope-steepness factor, dimensionless ratio
C = the cover factor, dimensionless ratio
P = the erosion control practice factor, dimensionless ratio
S^ = the sediment delivery ratio, dimensionless
The R factor in the above equation can be expressed in metric units
[(hundreds of meter-metric tons/ha-cm) times (maximum 30-min intensity,
cm/hr)] by multiplying the English R values by 1.735. The factor for
direct conversion of K to metric-tons per hectare per metric R unit is
1.292.I2/
Equation (3-1) can be used to predict sediment loading resulting from
sheet and rill erosion from noncroplands as well as croplands. Param-
eter values for silviculture, construction, and mining are less well
documented than for agriculture, however. The user will thus find it
relatively easy to use Eq. (3-1) for agriculture, and substantially
more difficult for other sources. It does not predict sediment con-
tributions from gully erosion, streambank erosion, or mass soil move-
ment .
36
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In Sections 3.2.3 and 3.2.4 below, procedures and an example will be
presented for estimating sediment loadings based on the above described
loading function. Section 3.2.5 presents data and data sources of source
characteristic factors. Methodology for predicting minimum and maximum
erosion rates is presented in Section 3.2.6. Section 3.2.7 presents data
sources for source areal factors.
3.2.3 Procedure for Use of the Sediment Loading Function
The following procedure is to be used to calculate sediment loading from a
designated area based on the loading function in Eq. (3-1). The terminology
applies to agricultural lands, but the procedure is applicable to other non-
point sources. This procedure is shown as a flow diagram in Figure 3-1.
Estimation of surface erosion should be made for each land-use type. For
a land-use type, if 9070 or more of the area is made up of one soil type,
one may calculate soil loss for the land use based on that soil type. If
there is less than 90% of one soil type, one should calculate soil loss for
each soil type that makes up at least 10% of the land use, and then obtain
ll/
a weighted average for the entire land-use area.—'
Obtain basic land data -
Total area, and land use acres in the area: cropland, pastureland, and
woodland, etc.
Soil characteristic information including soil name, soil texture, etc.,
for each land use.
Information about canopy and ground cover condition for each land use.
Topographic information, such as slope gradient and slope length of the
land.
Information about the type and extent of conservation practices.
Determine factor values -
Determine R: Use the appropriate isoerodent map (see Figure 3-2 and 3-3),
or procedures described in the Section 3.2.5.1 for the western United States.
Obtain K: Obtain the K values of the named soils from published lists of
SCS, or determine K values on nomographs (Figures B-l and B-2 in Appendix B)
from soil properties.
Determine LS: Refer to Figures 3-7 or 3-8 for uniform slopes, or Figure 3-9
for irregular slopes.
37
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Land Use Acreage, Aj's
i
Soil Names,
i
Land Use Types,
Dates of Cropstages,
Types of
LO
OO
Slope Lengths,
Local Drainage
Density, Soil
Texture, and
Figure 3 - 10,
or Eq. (3-2 T
T
Sediment
Delivery
Ratio S
-------
Obtain C: Refer to the appropriate table for the crop or ground cover con-
dition for C value in Section 3.2.5.4.
Obtain P: Refer to Table 3-7.
Determine sediment delivery ratio, S^: Obtain from local sources or from
Figure 3-10 by using drainage density and soil texture for homogeneous
watersheds .
Calculations -
Multiply R, K. LS, C, and P- and S^ to obtain sediment loadings for crop-
land, pasture, and woodland in annual yields per unit area of source.
Multiply loading rates by source sizes (total hectares or acres) for crop-
land, pastureland, and woodland to obtain total loading per source.
Sum source loadings calculated in the item above to obtain total loading
from land uses (total loading in the watershed will require summation of
other sources within the watershed).
3.2.4 Example of Assessing Sediment Loading from Surface Erosion
Assume a watershed area of 830 acres in Parke County, Indiana (west central).
Compute sediment loading from the watershed from sheet and rill erosion in
terms of average daily loading, maximum daily loading during a 30-consecutive-
day period, and minimum during a 30-consecutive-day period.
Basic information -
Land use types:
Cropland
Pasture
Woodland
Delivery ratio: 607o.
Land information:
Cropland - 180 acres
Continuous corn
Conventional tillage, average yield ~ 40 to 45 bu
Cornstalks are left after harvest
Contour strip-cropped
39
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Soil - Fayette silt loam
Slope - 67,
Slope length - 250 ft
Pasture - 220 acres
No appreciable canopy
Cover at surface - grass and grass like plants
Percent of surface or ground cover - 80%
Soil - Fayette silt loam
Slope - 6%
Slope length - 200 ft
Woodland - 430 acres
Medium stocked
Percent of area covered by tree canopy - 507,
Percent of area covered by litter - 807o
Undergrowth - managed
Soil - Bates silt loam
Slope - 127>
Slope length - 150 ft
Maximum and minimum rates - The ratios between 30-day maximum and average
daily rates, and 30-day minimum and average daily rates for continuous corn,
pasture, and woodland for this area are evaluated in Section 3.2.6. They
are:
Continuous corn: Ratio--30.-day maximum/average daily = 3.2
Ratio--30-day minimum/average daily = 0.25
Pasture and woodland: Ratio--30-day maximum/average daily = 2.5
Ratio--30-day minimum/average daily = 0.25
Calculations of loading per acre -
Cropland:
R = 200 (Figure 3-2)
K = 0.37 (USDA-SCS)
LS = 1.08 (Figure 3-8)
C = 0.49 (Section 3.2.5.4)
40
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P = 0.25 (Table 3-7)
Sd = 0.60
Calculate average annual loading per acre.
Y(S) . = 200 x 0.37 x 1.08 x 0.49 x 0.25 x 0.6
annua1
= 5.87 tons/acre/year
Calculate average daily loading per acre.
Y^^avg. daily = 5.87 tons/acre/year v 365 days
= 0.016 .tons/acre/day = 32 Ib/acre/day
Calculate maximum loading per acre during a 30-consecutive-day period.
Y(S)3Q_day max = 0.016 tons/acre/day x 3.2
= 0.052 tons/acre/day = 104 Ib/acre/day
Calculate minimum loading per acre during a 30-consecutive-day period,
Y(S)™ j • = °-016 tons/acre/day x 0.25
30-day mm
= 0.004 tons/acre/day = 8 Ib/acre/day
Pasture:
R = 200
K = 0.37
LS = 0.95
C = 0.013 (Table 3-4)
P = 1.0
Sd = 0.60
Y(S)annual = 20° x °*37 X °'95 X °'°13 X 1>0 X °*6
= 0.548 tons/acre/year = 1,100 Ib/acre/year
Y^S)avg. daily = °'548 tons/acre/year v 365 days
= 0.0015 tons/acre/day = 3 Ib/acre/day
41
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A = 0.0015 tons/acre/day x 2.5
-day max = Oi003g tons/acre/day = 7.6 Ib/acre/day
Y(S)on A . = 0.0015 tons/acre/day x 0.25
day min = 0.0004 tons/acre/day = 0.8 Ib/acre/day
Woodland :
R = 200
K = 0.32
LS = 2.75
C = 0.003 (Table 3-5)
P = 1.0
Sd = 0.60
Y(S)annual = 200 x 0.32 x 2.75 x 0.003 x 1.0 x 0.60
= 0.3168 tons/acre/year
Y(S)avg> daiiy = 0.3168 tons/acre/year 4 365 days
= 0.0009 tons/acre/day = 1.8 Ib/acre/day
Y(S)on j = 0.0009 tons/acre/day x 2.5
30-day max , , J
= 0.0022 tons/acre/day = 4.4 Ib/acre/day
Y(S)on , . = 0.0009 tons/acre/day x 0.25
v '30-day mxn , , J
= 0.0002 tons/acre/day = 0.4 Ib/acre/day
Calculations of gross loading -
Average daily:
Cropland - 180 acres x 0.016 tons/acre/day = 2.88 tons/day
Pasture - 220 acres x 0.0015 tons/acre/day = 0.33 tons/day
Woodland -430 acres x 0.0009 tons/acre/day = 0.39 tons/day
Total Y(S)avg> total = 3.60 tons/day
42
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30-day maximum:
Cropland - 180 acres x 0.052 tons/acre/day = 9.36 tons/day
Pasture - 220 acres x 0.0038 tons/acre/day = 0.84 tons/day
Woodland -430 acres x 0.0022 tons/acre/day = 0.95 tons/day
Total Y(S)30-.nax total = 11.15 tons/day
30-day minimum:
Cropland - 180 acres x 0.004 tons/acre/day = 0.72 tons/day
Pasture - 220 acres x 0.0004 tons/acre/day = 0.09 tons/day
Woodland -430 acres x 0.0022 tons/acre/day = 0.09 tons/day
Total Y(s)30-day min total = °'90 tons/day
3.2.5 Determination of Source Characteristic Factors
3.2.5.1 The rainfall factor (R) -
R is a factor expressing tne erosion potential of precipitation in a lo-
cality. It is also called index of erosivity, erosion index, etc. It is
the summation of the individual storm products of the kinetic energy of
rainfall (denoted by E) , and the maximum 30-min rainfall intensity (de-
noted by I) for all significant storms within the period under consideration.
The product El reflects the combined potential of raindrop impact and run-
off turbulence to transport dislodged soil particles from the site.9.'
Values of average annual rainfall-erosivity index, R, are shown in Figure
3-2 for the continental U.S. and Figure 3-3 for islands of Hawaii. On
these maps, the lines joining points with the same erosion index value
are called isoerodents. Points lying between the indicated isoerodents
may be approximated by linear interpolation.
Interpolation for values of R factors in the mountainous areas, particu-
larly those west of the 104th meridian may not be appropriate because of
the sporadic rainfall pattern. Values of the erosion index at specific
areas can be computed from local recording rain gage records with the
help of a rainfall-energy table and the computation procedure presented
by Wischmeier and Smith.-=•£'
ARS recently recommended that 350 be the maximum used in the Gulf and
southeastern states, shown in Figure 3-2, until further research can val-
idate values higher than 350.
43
-------
35
3
Figure 3-2. Average annual values of the rainfall-erosivity factor, R—'
a/ Source:
"Control of Water Pollution from Cropland, Volume I - A Manual for Guideline
Development," Agricultural Research Service, USDA (Report No. ARS-H-5-1),
and Office of Research and Development, EPA (Report No. EPA-600/2-75-026a) ,
Washington, D.C., November 1975.
-------
MOLOKAI
OAHU
LANAI
0_J_? 3_1 5 M,|«
,90.
l
-P-
Ln
I
KAUAI
HAWAII
MAUI
Figure 3-3. Mean annual values of erosion index (in English units) for Hawaii—'
-------
In the northwestern United States, runoff from snowmelt contributes sig-
nificantly to surface erosion. The annual index of R for some portions of
this region is the combined effect of rainfall and snowmelt designated by
Rr and Rs, respectively. The snowmelt factor (Rs) is important in Areas
A-l, B-l, and C on Figure 3-4 (also refer to Table 3-2). The map values
in the shaded region of the Northwest (see Figure 3-2) represent values for
the rainfall effect (Rr) only, and must be added with appropriate Rs values
to account for the effect of runoff from thaw and snowmelt.
Interim procedures for calculating annual R values, which include both Rr
and Rs, for the northwestern U.S. are described in Conservation Agronomy
Technical Note No. 32, USDA/SCS, Portland, Oregon, September 1974,II/ and
are briefly presented below.
Annual Rr factor: The annual Rr factor is obtained by using as base the
2-year, 6-hr rainfall (2-6 rainfall). Relationships between Rr and 2-6 rain-
fall vary to conform to specific local climatic characteristics. These re-
lationships are designated as Type I, Type IA, and Type II, and are shown
in Figure 3-5. Specific areas applicable to these curves are shown in Fig-
ure 3-6. Type I curve is for the central valley and coastal mountains and
valleys of southern California. Type IA curve applies to the coastal side
of the Cascades in Oregon and Washington, the coastal side of the Sierra
Nevada Mountains in northern California, and the coastal regions of Alaska.
Type II curve applies to the remainder of the region. For 2-6 rainfall data,
refer to Technical Paper No. 40, U.S. Department of Commerce, Weather Bureau,
Washington, B.C., May 1961, or other suitable rainfall frequency analysis
reports.
Annual Rs factor: To obtain the annual Rs factor for a given location, ob-
tain the average annual total precipitation by snowfall (in inches of water
depth) and multiply it by the constant 1.5 to give annaul Rs.
Sources of snowfall data: The 1941 Yearbook of Agriculture, USDA, Washington,
B.C.; "Climates of the States," Water Information Center, Inc., Port
Washington, New York, 1974; data resulting from the Western Federal-State-
Private Cooperative Snow Surveys, coordinated by SCS/USBA, Portland, Oregon;
or other equally suitable precipitation records.
Bata on snow density is necessary to convert depth of snow to depth of melt-
water. Snow at the time of fall may have a density as low as 0.01 and as
high as 0.15 g/ml. The average snow density for the United States is taken
to be O.lO.lA/ If snowfall is recorded as inches of precipitation, no con-
version is required.
Annual R factor: The annual R factor for the western United States is the
summation of effect of rainfall, Rr, and snowmelt, Rg. Where Rg is not sig-
nificant, values of R and Rr are the same.
46
-------
Figure 3-4. Soil moisture - soil temperature regimes
of the western United Stateail/
47
-------
Table 3-2. APPLICABILITY OF Rr AND Rs FACTORS IN THE AREAS
WEST OF THE ROCKY MOUNTAINS!!/
Areas
(see Figure 3-3)
A-l
Typical locations
Washington, Idaho, Nevada,
Rr
xa/
RS
X
A-2
A-3
A-4
B-l
California, western Utah
Cascades, Sierra, Tetons of
Idaho, Wasatch Mountains
West of Cascades, San Joaquin
Valley, west of Sierras
Areas of southern California,
east of Santa Annas, southern
Nevada, intermountain Nevada,
Salt Lake area, Utah
Western Montana, Colorado,
eastern Utah, high elevations
of Arizona
X
b/
B-2
Great plains area of eastern
Montana, Wyoming, Colorado
(includes gently sloping
mesas and upland at lower
elevations of Monticello,
Utah area)
Rainfall during summer is
high; high elevations
£/ X needed.
b/ - Not needed.
48
-------
2.0
3.0
4.0
2-YEAR, 6-HOUR RAINFALL, cm
5.0 6.0 7.0 8.0 9.0
10.0
11.0 12.0
0.5 1.0 1.5 2.0 2.5 3.0 3.5
2-YEAR, 6-HOUR RAINFALL, Inches
4.0
4.5
5.0
Figure 3-5. Relationships between annual average rainfall erosivity index and the
2-year, 6-hr rainfall depth for three rainfall types in the
western United States^/
-------
LEGEND - Storm Distribution
Figure 3-6. Storm distribution regions in the western United States
50
-------
Monthly distribution of R factor -'The monthly distribution of the erosion
index for the 37 states east of the Rocky Mountains has been reported in
USDA-ARS Agriculture Handbook No. 282.-' The erosion index distribution
curves are reproduced and shown in Appendix A. Average monthly erosion in-
dex values are expressed as percentages of average annual values and plotted
cumulatively against time.
The monthly distribution of erosion index for the islands of Hawaii also has
been developed.— These curves are shown in Appendix A.
For the areas west of the Rockies in the continental United States, the
monthly distribution of erosion index R is the summation of R and Rg.
Where Rg values are not needed, the R and Rr curves are the same.
As of June 1974, the monthly R distribution curves for portions of the area
-I O /
had been made available.—' The reader should contact the state Soil
Conservation Service for such information. Procedures suggested by SCS for
computing and plotting monthly R distribution curves for the western United
States are described in Appendix A.
3.2.5.2 The soil-erodibility factor (K) -
K factor is a quantitative measure of the rate at which a soil will erode,
expressed as the soil loss (tons) per acre per unit of R, for a plot with
97o slope, 72.6 ft long, under continuous cultivated fallow.
K factors for topsoils, as well as subsoils, for most soil series have been
developed. Values of K for soils studied thus far vary from 0.12 to 0.70
tons/acre/unit R.
The K values for named soils at different locations of the nation can be
obtained from the regional or state offices of the Soil Conservation Service.
K values of soils can be predicted from soil properties. In Appendix B of
this handbook, two nomographs are presented from which K values may be de-
termined for topsoils and subsoils when the governing soil properties are
known.
3.2.5.3 The topographic factor (LS) -
Soil loss is affected by both length (L) and steepness of slope (S). These
factors affect the capability of runoff to detach and transport soil mate-
rial.
51
-------
The slope length factor is the ratio of soil loss from a specific length
of slope to that length (72.6 ft) specified for the K factor in the equa-
tion. Slope length is defined as the distance from the point of origin of
overland flow to either of the following, whichever is limiting, for the
major part of the area under consideration: the point where the slope de-
creases to the extent that deposition begins; or the point where runoff
enters a well-defined channel that may be part of a drainage network, or
a constructed channel that may be part of a drainage network, or a con-
structed channel such as a terrace or diversion. Slope length can be de-
termined accurately by on-site inspection of a field, or by measurements
from aerial photographs, or topographic maps. When the land is terraced,
the terrace spacing should be used. All slope lengths are compared to a
slope length of 72.6 ft, which has a factor value of 1.
The slope gradient or percent slope factor is the ratio of soil loss from
a specific percent slope to that slope (970) specified for the K value in
the ULSE. A 9% slope has a factor value of 1. Slope data may be obtained
from topographic maps, engineering or land level surveys, and other sources.
A widely used method is to estimate slope from soil survey maps in which
the soils have been mapped by slope range.
The slope length (L) and slope gradient (S) are combined in the USLE into
a single dimensionless topographic factor, LS, which can be evaluated using
a slope-effect chart.
Slope-effect charts for uniform slopes - The slope-effect chart in Figure
3-7 is designed for the following areas shown in Figure 3-4: A-l in
Washington, Oregon, and Idaho; and all of A-3.il/
For the remainder of the U.S., the slope-effect chart, Figure 3-8, is to
be used.!3-/
Slope-effect charts in Figure 3-7 and 3-8 can be used when uniform slopes
are assumed. The following steps are to be used for obtaining LS values
from these charts:
1. Enter the chart on the horizontal axis with the appropriate value of
slope length.
2. Follow the vertical line for that slope length to where it intersects
the curve for the appropriate percent slope.
52
-------
Slope Length, Meters
20 30 40 60 '80 100 150200 300 400 600 800
- 1 1 1 1—i 1 1 1 1 1 1—
40.0
20.0
10.0
6.0
4.0
1.0
0.6
0.4
0.2
0.1
(Slope%)
^ 60
50
^ 45
^ 40
*<* 35
^- 30
^- 25
20
-- 18
^ 16
^ 14
„- 12
.- 10
.- 6
— — 2
1
0.5
_J I
70 100 200 400 600 1000 2000
Slope Length, Feet
Figure 3-7. Slope effect chart applicable to Areas A-l in Washington,
Oregon, and Idaho and all of A-!?13'a'b/
a/ See Figure 3-4.
b/ Dashed lines are extensions of LS formulae beyond values tested in
studies.
53
-------
20.0
3.5 6.0 10
Slope Length, Meters
20 40 60 100
200 400 600
10
40 60 100 200 400 600 1000
Slope Length, Feet
2000
Figure 3-8. Slope—effect chart for areas where Figure 3-7 is not
applicable ~*>a'
a/ The dashed lines represent estimates for slope dimensions beyond
the range of lengths and steepnesses for which data are available.
54
-------
3. Read across the point of intersection to the vertical axis. The number
on the vertical axis is the LS value.
Slope-effect charts for irregular slopes - An irregular slope should be
divided into a series of segments such that the slope gradient within each
segment can be treated as uniform. The slope segments need not be of
equal length. The total soil loss from the entire slope is calculated
based on the effective LS value for the entire length of the irregular
slope.
A family of curves shown in Figure 3-9 was designed to facilitate the de-
termination of the LS factor for the irregular slopes ranging from 2 to
20%. The quantity plotted on the vertical scale is designated by the sym-
bol U. Slope lengths, designated by \, are plotted on the horizontal scale.
Assume an irregular slope with n segments illustrated as follows:
where X^ = distance from the top of the entire slope (the point at which
overland flow begins) to the lower end of the jth segment
X-i = length of entire slope above segment j
X = overall slope length
S^j = the slope gradient of segment j, in percent
The steps taken for calculating LS for irregular slopes using Figure 3-8
are :ii'
1. Enter on the horizontal axis with the value of ^j-1 (the slope length
above segment j).
2. Move vertically to the curve with the appropriate percent slope for
segment j.
55
-------
3.28 4 5 6 7 8 9 10
1000
10
X (METERS)
20 30 40 50 60 708090100
-i 1 1 1—r
200 250
Steepness of
Slope Segment: 20°/<
20
30
40 50 60 708090.100
X(FEET)
200
300
5678
15/
Figure 3-9. Slope effect chart for irregular slopes—
56
-------
3. Read on the vertical scale the value of Ui-.
4. Enter the figure with the value of X. (the distance from the top of
the entire slope to the lower end of the jth segment), repeat Steps 1 through
3 to obtain the value of tL . .
5. Subtract U2 , froml^..
6. Repeat Steps 1 through 5 for each of the slope segments.
7. Sum n values of U2 . - U, ., divide the sum by Xe (the overall slope
length). The result is the effective LS value for the entire length of the
irregular slope.
Examples of the use of the above procedure to calculate LS factors for ir-
regular slopes are given in Appendix C of this handbook.
The percentage of the total sediment yield that comes from each of the n
segments can be obtained through a similar procedure. The relative sediment
contribution of segment j, assuming constant soil erodibility for the entire
slope, is given by:
U2.1 - UH
(U2j -
For constructed slopes or mined slopes that cut into successive soil hori-
zons, the soil erodibility K may vary considerably from upper to lower parts
of a slope. When variations in slope gradient are associated with varia-
tions in soil erodibility along an irregular slope, K and U2 - Ui must be
combined as follows to estimate the relative sediment contribution of seg-
ment j .
n
.£, K
3.2.5.4 The cover management factor (C) -
In the ULSE, the factor C represents the ratio of soil quantity eroded from
land that is cropped or treated under a specified condition to that which
57
-------
is eroded from clean-tilled fallow under identical slope and rainfall condi-
tions. C ranges in value from near zero for excellent sod or a well-
developed forest to 1.0 for continuous fallow, construction areas, or other
extensively disturbed soil.
Factor C for croplands - In order to evaluiite the cover management factor
for crops, five crop stage periods have been selected for relative uniformity
of cover and residue effects within each period. These five periods are
defined as follows:2J
Period F: Rough fallow - Turn plowing to seeding.
Period 1: Seedbed - Seeding to 1 month thereafter.
Period 2: Establishment - From 1 to 2 months after seeding. (Exception:
for fall-seeded grain, Period 2 includes the winter period and
extends to 30 April in the North and 1 April in the South, with
intermediate latitudes interpolated.)
Period 3: Growing crop - From Period 2 to crop harvest.
Period 4: Harvest, residue or stubble - From crop harvest to turn plow or
new seedbed. (When meadow is established in small grain,
Period 4 ends 2 months after grain harvest. Thereafter, it is
classified as established meadow.)
The average cover factor C for the entire year or years of crop rotation is
computed by crop stages. Input for calculation of C includes average plant-
ing and harvesting dates, productivity, disposition of crop residues, tillage,
and monthly distribution of the erosion index R. The C value for each of
these time periods is weighted according to the percentage of annual rainfall
factor occurring in that period. The summation of these RC products for the
entire year or years of crop rotation is then converted to a mean annual C.
Values of factor C for croplands are highly variable with rainfall pattern,
planting dates, type of vegetative cover, seeding method, soil tillage, dis-
position of residues, and general management level. Ranges of C value for
several types of vegetation and ground cover are listed in Table 3-3, in
order of decreasing protection against erosion (increasing C value from
near zero to 1).
The reader is advised to consult with state conservation agronomists of SCS
for appropriate C values for crops in the local area. The reader is also
referred to USDA-ARS Agriculture Handbook No. 282-/ for a listing of approxi-
mated C values for various crops at each crop stage, as well as a working
table for derivation of average C value for periods of crop rotation.
58
-------
Table 3-3. RELATIVE PROTECTION OF GROUND COVER AGAINST EROSION
(In order of increasing C factor)
Land-use groups
Permanent vegetation
Example s
Protected woodland
Prairie
Permanent pasture
Sodded orchard
Permanent meadow
Range of "C" values
0.0001-0.45
Established meadows
Alfalfa
Clover
Fescue
0.004-0.3
Small grains
Rye
Wheat
Barley
Oats
0.07-0.5
Large-seeded legumes
Row crops
Fallow
Soybeans
Cowpeas
Peanuts
Field peas
Cotton
Potatoes
Tobacco
Vegetables
Corn
Sorghum
Summer fallow
Period between plowing and
growth of crop
0.1-0.65
0.1-0.70
1.0
59
-------
Factor C for pasture, range and idle land - C values typical of permanent
pasture, range, and idle lands, with varying cover and canopy conditions,
are given in Table 3-4. These values were developed by Wischmeier.—'
Factor C for woodland - Wischmeier — also estimated factor C for some
woodland situations. Data are presented in Table 3-5.
Factor C for urban and road areas, construction and mining sites - On these
areas and sites, the factor C represents the effect of land cover or treat-
ment that may be used to protect soil from being eroded. Table 3-6 — '
lists values of the factor C for various soil covers and treatments.
3.2.5.5 The practice factor (P) -
The factor P accounts for control practices that reduce the erosion poten-
tial of runoff by their influence on drainage patterns, runoff concentration,
and runoff velocity.
For croplands, control practices refer to contour tillage, cross-slope farm-
ing, and contour strip-cropping. The practice value P is the ratio of soil
loss from a specified conservation practice to the soil loss occurring with
up- and downhill tillage, when other conditions remain constant. Table
1 O /
3-7—' shows P values for up and downhill farming, cross-slope farming with-
out strips, contour farming, cross -slope farming with strips, and contour
strip-cropping.
Terracing is also an effective practice to reduce soil erosion. The quan-
titative effect of terracing is accounted for in the slope length factor,
since the horizontal terrace interval becomes the slope length, after the
terraces are constructed.
3.2.5.6 Sediment delivery ratio
The sediment-delivery ratio, in this handbook, is defined as the fraction
of the gross erosion which is delivered to a stream. The classical method
for determining an average delivery ratio is by comparing the magnitude of
the sediment yield at a given point in a watershed (generally at a reservoir
or a stream sediment measuring station), and the total amount of erosion.
The quantities of gross erosion from sloping uplands are computed by erosion
prediction equation for surface erosion, and estimated by various procedures
for gullies, stream channels, and other sources (see Section 3.3 of this
handbook). The sediment yield at a given downstream point is obtained
through direct measurements.
60
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Table 3-4. "C" VALUES FOR PERMANENT PASTURE, RANGELAND, AND IDLE LAND
16, a/
Vegetal canopy
Type and height
of raised canopy—'
Canopy
cover£'
(%)
Type!/
Cover that contacts the surface
Percent ground cover
0 20 40 60 80 95-100
Column no.
No appreciable canopy
G
W
0.45
0.45
0.20
0.24
0.10
0.15
0.042
0.090
8_
0.013
0.043
0.003
0.011
Canopy of tall weeds 25
or short brush
(0.5 m fall height) 50
75
G
W
G
W
G
W
0.36
0.36
0.26
0.26
0.17
0.17
0.17
0.20
0.13
0.16
0.10
0.12
0.09
0.13
0.07
0.11
0.06
0.09
0.038
0.082
0.035
0.075
0.031
0.067
0.012
0.041
0.012
0.039
0.011
0.038
0.003
0.011
0.003
0.011
0.003
0.011
Appreciable brush 25
or bushes
(2 m fall height) 50
75
G
W
G
W
G
W
0.40
0.40
0.34
0.34
0.28
0.28
0.18
0.22
0.16
0.19
0.14
0.17
0.09
0.14
0.085
0.13
0.08
0.12
0.040
0.085
0.038
0.081
0.036
0.077
0.013
0.042
0.012
0.041
0.012
0.040
0.003
0.011
0.003
0.011
0.003
0.011
Trees but no appreci- 25
able low brush
(4 m fall height) 50
75
G
W
G
W
G
W
0.42
0.42
0.39
0.39
0.36
0.36
0.19
0.23
0.18
0.21
0.17
0.20
0.10
0.14
0.09
0.14
0.09
0.13
0.041
0.087
0.040
0.085
0.039
0.083
0.013
0.042
0.013
0.042
0.012
0.041
0.003
0.011
0.003
0.011
0.003
0.011
a/ All values shown assume: (1) random distribution of Mulch or vegetation, and
(2) mulch of appreciable depth where it exists.
b/ Average fall height of waterdrops from canopy to soil surface: m = meters.
c_l Portion of total-area surface that would be hidden from view by canopy in
a vertical projection (a bird's-eye view).
^/ G: Cover at surface is grass, grasslike plants, decaying compacted duff,
or litter at least 5 cm (2 in.) deep.
W: Cover at surface is mostly broadleaf herbaceous plants (as weeds) with
little lateral-root network near the surface and/or undecayed residue.
61
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Table 3-5. "C" FACTORS FOR WOODLAND-i2
16/
Tree canopy
percent of
Stand condition area^
Well stocked 100-75
Medium stocked 70-40
Poorly stocked 35-20
Forest
litter
percent of
area— '
100-90
85-75
70-40
c/
Undergrowth—
Managed^.'
Unmanaged—'
Managed
Unmanaged
Managed
Unmanaged
"C" factor
0.001
0.003-0.011
0.002-0.004
0.01-0.04
0.003-0.009
0.02-0.09-/
a_l When tree canopy is less than 207°, the area will be considered as
grassland or cropland for estimating soil loss.
b_/ Forest litter is assumed to be at least 2-in. deep over the percent
ground surface area covered.
£/ Undergrowth is defined as shrubs, weeds, grasses, vines, etc., on
the surface area not protected by forest litter. Usually found
under canopy openings.
d/ Managed - grazing and fires are controlled.
Unmanaged - stands that are overgrazed or subjected to repeated
burning.
e/ For unmanaged woodland with litter cover of less than 757o, C values
should be derived by taking 0.7 of the appropriate values in
Table 3-4. The factor of 0.7 adjusts for the much higher soil
organic matter on permanent woodland.
62
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Table 3-6. "C" FACTORS FOR CONSTRUCTION SITESll
17/
Type of cover
None (fallow)
Temporary seedings
First 60 days
After 60 days
Permanent seedings
First 60 days
After 60 days
After 1 year
Sod (laid immediately)
Rate
In metric tons
Mulch per hectare
Hay or
straw 1/2
1
1-1/2
2
Stone or gravel 14
Chemica
First
After
55
120
220
1 mulches
90 days
90 days
Woodchips 2
4
6
11
18
23
of application
In tons per acre
1/2
1
1-1/2
2
15
60
135
240
a/
£/
2
4
7
12
20
25
C
value
1.00
0.40
0.05
0.40
0.05
0.01
0.01
Maximum
slope
C value (ft)
0.34
0.20
0.10
0.05
0.80
0.20
0.10
0.05
0.50
1.00
0.80
0.30
0.20
0.10
0.06
0.05
20
30
40
50
15
80
175
200
50
50
25
50
75
100
150
200
allowable
length
(m)
6
9
12
15
5
24
53
61
15
15
8
15
23
30
46
61
a/ As recommended by manufacturer.
63
-------
Table 3-7. "P" VALUES FOR EROSION CONTROL PRACTICES-ON CROPLAND?
Slope
2.0-7
7.1-12
12.1-18
18.1-24
Up- and
down-
hill
1.0
1.0
1.0
1.0
Cross-slope
farming
without strips
0.75
0.80
0.90
0.95
Contour
farming
0.50
0.60
0.80
0.90
Cross-slope
fanning with
strips
0.37
0.45
0.60
0.67
Contour
strip-cropping
0.25
0.30
0.40
0.45
Measurements of sediment accumulations in reservoirs and sediment-load rec-
ords in streams show wide variations in sediment yields from watersheds.
Estimates show that as little as 5% and as much as 100% of the materials
eroded in some watersheds may be delivered to a downstream point. Esti-
mates of the delivery ratio for some specific watersheds, particularly in
the humid sections of the country, can be obtained from the Soil Conserva-
tion Service, USDA.
Many delivery-ratio analysis studies were aimed at finding measurable in-
fluencing factors that can be related to sediment-delivery ratio. A popular
means of developing such information is by statistical analysis using the
sediment-delivery ratio as the dependent variable and measurable watershed
factors as the independent, or controlling variables. As pointed out in
Section 3.2.1.3 of this handbook, many physical and hydrological factors of
watersheds may influence sediment-delivery ratios. Some are more pronounced
in their effect than others. Some lend themselves to quantitative expres-
sion whereas others do not. To this date, however, the science of sedimen-
tology has not progressed to the state where the relative influence of each
of the individual physical and hydrological factors has been evaluated, and
their relative influence on the delivery ratio of sediment has not been de-
termined to the degree of accuracy desired. Nevertheless, empirical rela-
tionships for delivery ratios have been proposed and are presented below.
Estimates of sediment loading can be made through the use of these relation-
ships, but such estimates should be tempered with judgment and consideration
of other influencing factors which are not included in the quantitative ex-
pressions. The user is encouraged to consult with local experts and should
use local data when available.
Sediment delivery ratio for construction sites - The MITRE Corporation re-
ported-W that the sediment-delivery ratio for construction sites can be
approximated by a function of the overland distance between the construc-
tion site and the receptor water.
64
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The format of the sediment delivery ratio proposed by MITRE for con-
struction sites has the following form:
Sd = D-°-22 (3-2)
where D = overland distance between the erosion site and the
receptor water, in ft
The above equation was empirically derived from available data. The
data base for the derivations has values of D from 0 to 800 ft. MITRE
suggests that this function should be subjected to further testing, par-
ticularly in areas of the Midwest and Central U.S. from which no data were
obtained and used for deriving the above equation.
Sediment delivery ratio for other intensely distrubed sites - For mining
sites, or for forestland areas such as logging roads, fire lanes, sedi-
ment delivery ratio relationships have not yet been established due to
lack of systematically measured data. It is suggested, however, that the
delivery ratio developed by MITRE and expressed in Eq. (3-2) be used as
the first approximation for these sites. This needs to be validated when
appropriate data become available.
Sediment delivery ratio from relatively homogeneous basins - Sediment de-
livery ratios have been evaluated in many areas of the country, particu-
larly the eastern half of the United States. The delivery ratio usually
depicts a general trend in basins that are relatively homogeneous with
respect to soils, land cover, climate, and topography. The Soil Conser-
vation Serviceiz/ has reported an analysis of data from stream and res-
ervoir sediment surveys from widely scattered areas.
This analysis shows that sediment delivery ratios vary inversely with
"drainage basin size". It also indicates the effect of soil texture of
upland soil on the sediment delivery ratio.
The delivery ratio relationships reported by SCS—' were utilized by the
MRI study group in -developing delivery ratios for sediment loading to
watercourses. The result is shown in Figure 3-10. The horizontal scale
of the figure is the reciprocal of drainage density which is defined as
the ratio of total channel-segment lengths (accumulated for all orders
within a basin) to the basin area. The reciprocal of drainage density
may be thought of as an expression of the closeness of spacing of chan-
nels, or the average distance for soil particles to travel from erosion
site to the receptor water.
65
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I/Drainage Density, Kilometers
1.0 10
i i i i i i 11
Silty Clay
Predominantly Silt
I 1 I I I I I I
I I I I I i I I I
I I I I I I I I
I I I I I I I II
0.02
1.0 10
I/Drainage Density, Miles^/Mile
Figure 3-10. Sediment delivery ratio for relatively homogeneous basins^'
100
400
a/ Source: Midwest Research Institute.
-------
The delivery ratio relationship shown in Figure 3-10 also takes into
account the effect of soil texture. For example, if soil texture of
upland soil is essentially silt or clay, the sediment delivery ratio
will be higher than when the soil texture is coarse.
The delivery ratio relationships in Figure 3-10 need to be further vali-
dated by acquisition of new data. They also need to be improved in the
future to include other factors relative to deposition mechanisms.
The following steps are to be used to obtain delivery ratio (S^) from
Figure 3-10.
1. Enter the figure on the horizontal axis with the value of the recip-
rocal of drainage density (1/DD).
2. Move vertically from the value of 1/DD to where it intersects the
curve for the appropriate soil texture.
3. Read across from the point of intersection to the vertical axis.
That number represents the delivery ratio, S^.
Values of drainage density - A great range of values of drainage
density exists in the United States, from 2 km/km2 (3 miles/miles2) for
the Appalachian Plateau Province2^/ to 500 km/km2 (800 miles/miles2) in
91 / 9 9 /
Badlands at Perth Amboy, New Jersey.—' In general, according to Strahler,££'
low drainage density is found in regions of highly resistant or highly
permeable subsoil materials, under dense vegetative cover, and where re-
lief is low. High drainage density is favored in regions of weak or im-
permeable materials, sparse vegetation, and mountainous relief.
Some typical values of drainage density for various locales in the U.S.
are given in Table 8-8. Local drainage density figures may be obtained
from agencies such as the Geological Survey and the Army Corps of En-
gineers.
Measurements of drainage density can be made from a topographic map
with a planimeter and chartometer. Care must be taken to include all
permanent stream channels to their upper ends by checking in the field
or aerial photographs in verification of topographic maps. A rapid
approximation method for determining drainage density is suggested by
Carlston and Langbein.—'
67
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Table 3-8. TYPICAL VALUES OF DRAINAGE DENSITY
Location
Appalachian Plateau
Drainage density
kin/km^ mile/mile
1.9-2.5 3.0-4.0
Reference
Smith (20)
Province
Central and eastern 5-10 8.0-16.0
United States
Dry Areas of the Rocky 31-62 50-100
Mountain Region
The Rocky Mountain Region 5-10 8.0-16
(except the above)
Coastal ranges of 12-25 20-40
southern California
Badlands in South Dakota 125-250 200-400
Badlands in New Jersey 183-510 310-820
Strahler (23)
Melton (24)
Melton (24)
Smith (20)
Melton (24)
Maxwell (25)
Smith (26)
Schumn (21)
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3.2.5.7 Summary of applicabilities of source characteristic factors -
The preceding paragraphs indicate that assessment of sediment loadings from
surface erosion requires quantitative information on soil erodibility, rain-
fall and snowmelt erosivity, topography, vegetative cover, conservation
practices, and sediment delivery ratio. Applicability of each factor var-
ies with specific location of the site and also with type of land distur-
bance. Table 3-9 gives a total summary of variations in application of
those factors.
3.2.5.8 Limitations of the loading function -
The USLE predicts soil losses from sheet and rill erosion. It does not
predict sediment from gullies, streambank erosion, landslides, road ditches,
irrigation, or from wind erosion. The USLE was developed primarily for
croplands, and has been chiefly based upon experimental plot data from the
areas east of the Rocky Mountains. The loading function therefore is best
defined for these areas of use. For croplands in the western United States
and sources outside agriculture such as silviculture, construction, and
mining, the factors have not been systematically developed, which seriously
affects the ease of using the USLE for such sources.
Specific limitations include:
R: Research is needed to determine the effective R values more accurately
in both the east and west of the continental United States.
L and S: The relationships on which the slope effect charts are based
were derived from data taken on slopes not exceeding 20% and length not
exceeding 400 ft. How far these dimensions can be exceeded before those
relationships change has not been determined.
C: More work is needed to improve definitions of cover factor, particularly
for areas outside agriculture, such as undisturbed forest, harvested or burned
forests, logging roads, mining sites, rangeland, and construction sites.
P: The reported values of the practice factor have been limited to crop-
land. Definition of practice factor values is needed for various conser-
vation practices on silviculture, mining, construction and other areas
outside agriculture.
S j: The science of sedimentology has not progressed to the state where
the sediment-delivery ratio can be predicted to the degree of accuracy de-
sired. In addition, for the benefit of pollution analysis, delivery ratios
should be developed for prediction of sediment loadings reaching the
"receptor waters" rather than "reservoirs."
69
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Table 3-9. SUMMARY OF APPLICABILITY OF CHARACTERISTIC FACTORS
Land use
Extent of land
disturbance
Zero to moderately Growing forests
disturbed Range land
Pasture land
Cropland
Orchards
Source
characteristic
factor
R
K
LS
Regions in the United States
Eastern states and Hawaii
Western states
Affected by rainfall only; use Affected by rainfall, some areas also by
Figures 3-2 or 3-3.
Erpdibility of topsoils.
Use Figure 3-8 for natural
slope steepness and slope
length (except terracing).
snowmelt (see note below).
Erodibility of topsoils.
Use Figure 3-7 for some areas in
Washington, Oregon, Idaho, and
California; the remainder use Figure
3-8 (except terracing).
For croplands and orchards, C's For croplands and orchards, C's are de-
are determined locally by termined locally by SCS. For forests,
use Table 3-5; rangeland and pasture-
land, use Table 3-4.
SCS. For forests, use Table
3-5; rangeland and pasture-
land, use Table 3-4.
For croplands, use Table 3-7;
others = 1.0.
For croplands, use Table 3-7; others
1.0.
Assume relatively homogeneous Assume relatively homogeneous land use
land use components; use
Figure 3-10.
components; use Figure 3-10.
Intensively
disturbed
Construction
sites
Mining sites
Logging roads
Fire lanes
LS
C
P
Sd
Affected by rainfall only; use Affected by rainfall, some areas also by
Figures 3-2 or 3-3.
Erodibility of topsoils and
subsoils.
Use Figure 3-9 for irregular
slopes.
Use Table 3-6.
Equals 1.0.
Use Eq. (3-2)
snowraelt (see note below).
Erodibility of topsoils and subsoils.
Use Figure 3-9 for irregular slopes.
Use Table 3-6.
Equals 1.0.
Use Eq. (3-2)
Note: See Section 3.2.5.1 for "Methods for Developing Annual R Values for the Western United States.
-------
The loading function in Eq. (3-1) and supporting data in tables and figures
were designed to predict longtime average loadings for specific conditions.
Sediment loading for a specific year may be substantially greater or smaller
than the annual averages because of differences in number, size, and timing
of erosive rainstorms, and in other weather parameters. The reader is re-
ferred to Table 11 of USDA Agriculture Handbook 282-9-/ for a listing of 50,
20, and 57= probability values of R factor at 181 key locations in the area
east of the Rocky Mountains. These may be used for further characteriza-
tion of soil-loss hazards.
Due to the uncertainties embedded in factor values, it is advisable that
sediment loading computed by Eq. (3-1) be accepted as reasonable estimates
rather than as absolute data. Table 3-10 lists the best estimate of the
range of accuracy for Eq. (3-1) and available supporting data. The range
figures pertain to annual average. For a specific year, the range may be
much larger than those given.
Table 3-10. ESTIMATED RANGE OF ACCURACY OF SEDIMENT LOADS
FROM SURFACE EROSION
Predicted loading Estimated range of accuracy
(MT/ha/year) (MT/ha/year)
0.1 0.001 ~ 1.0
1 0.1 ~ 5
10 5 ~ 15
100 50 ~ 150
1,000 500 ~ 1,500
3.2.6 Source Characteristic Factors for Predicting Maximum and Minimum
Sediment Loadings
The loading function in Eq. (3-1) can be used to predict sediment loading
other than annual averages. Variations of the loading rate are embedded
in rainfall factor R and cover factor C. The evaluation procedure is il-
lustrated in the following examples.
Example 1: Variations caused by rainfall factor alone - The rainfall
erosion index R varies within a year, as shown in percent erosion index
curves in Appendix A. For lands where cover factor is relatively constant,
such as woodland and grassland, temporal distribution of rainfall factor
R governs temporal variations in erosion.
71
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Figure 3-11 shows an example of monthly distribution of percentages of
annual R values. This distribution curve is for parts of Michigan, Missouri,
Illinois, Indiana, and Ohio based on Curve 16 in Figure A-2d. The following
steps are required for evaluating monthly variation of R values.
1. Read the percent of annual erosion index, at the predetermined time in-
terval, on the appropriate erosion index distribution curve (for this spe-
cific example, Curve 16 on Figure A-2d in Appendix A).
2. For each time interval, subtract the reading of the first date from
that of the last date.
3. Results of Step 2 are the percents of annual index that are to be ex-
pected within the particular periods. Use these data for plotting distri-
bution curve. The percent average daily is 0.274, which is obtained by
dividing 100 (percent) by 365 (days in a year).
The curve in Figure 3-11 indicates that, if other factors hold constant,
soil erosion in this area would have its maximum from 20 June to 20 July,
and minimum from late December to late January.
One estimates that, based on the R distribution in Figure 3-11, the maxi-
mum daily loading rate during a 30-consecutive-day period for woodland and
grassland in this particular area is approximately 2.5 times that of aver-
age daily loading rate for 1 year; the minimum daily rate during a 30-
consecutive-day period is approximately one-fourth of the average daily
rate.
Example 2: Variations caused by the combined effects of rainfall factor
and cover factor - For croplands, where soils are tilled and surface con-
ditions change drastically from one crop stage to another, evaluation of
erosion variation should include both the R factor and C factor.
Required steps to achieve such evaluations are:
1. Determine average dates of each crop stages.
2. Determine C factor values for each crop stage from such information
as productivity, disposition of crop residues and tillage.
3. Obtain monthly distribution of R.
4. Multiply C factor values by the R value of the corresponding period.
Variations of RC products are the temporal variations of sediment loading.
72
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0.8 r-
0.7
x
o
"D
0.6
-------
In this example, temporal variation of surface erosion rate for continuous
cornland in central Indiana was calculated. Again, the erosion index dis-
tribution Curve No. 16 on Figure A-2d was used. Assumptions were conven-
tional tillage, a yield average of 40 to 59 bu of corn per acre, and corn-
stalks left on the field after harvesting. The dates, C values, and per-
cent of erosion index for five-crop stages, and RC products are:
Percent R
Crop stage, Cover
starting- factor,
ending date C3/ Reading^/
Turn plowing, 5/1-5/19 0.55 13.8
Seeding, 5/20-6/19 0.70 19.5
Establishment, 6/20-7/19 0.58 36.0
Growing crop, 7/20-10/9 0.32 57.3
Harvest and 0.50 91.0
stubble, 10/10-4/30
Total 100
Percent
in the
period
5.7
16.5
21.3
33.7
22.8
RC
product
49.22
a/ Reference source: USDA-Agricultural Research Service Handbook No.
282,i/ Table 2.
b_/ Reading from Figure A-2d (Curve 16) for starting date.
The annual C factor is estimated at 0.49. Temporal variation of surface
erosion rate, in terms of percent of annual total, is shown in Figure 3-12.
It is seen that the maximum erosion from this continuous cornland would
occur in mid-June through mid-July, nearly identical to the period of
maximum erosion with constant soil cover (Figure 3-11). The 30-day max-
imum is approximately 3.2 times average daily, which is higher than the
previous (constant C factor) case due to the magnifying effect caused
by the overlapping of a high R period with a high C period. Figure
3-12 also shows that minimum erosion would occur during the winter sea-
son; the 30 day minimum is one-fourth of the average daily load.
3.2.7 Source Areal Data
Information and data of considerable variety are needed to assess sediment
loading by surface erosion from various sources. Pertinent source charac-
teristic data including soil erodibility, rainfall erosivity, slope length,
slope gradient, vegetative cover, conservation practices, and delivery
ratio, have been presented in the previous sections. This section presents
sources of data relevant to acreages of land use and land disturbance.
74
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1.0 r
30-Day Maximum
Cropstage
F - Turn Plowing
Cl - Seeding
C2 - Establishment
C3 - Growing Crop
C4 - Harvest and Stubble
1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 1/1
Date Month/Day
Figure 3-12. Projected variation of soil erosion on continuous corn
lands in central Indiana^;'
Source: Midwest Research Institute.
75
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The following are sources of areal data which are pertinent to assessing
sediment loadings from various nonpoint sources.
Land use -
"Conservation Needs Inventory" - Soil Conservation Service
"Census of Agriculture" - Bureau of Census
State cropland and livestock reports - State Agriculture Department
Forest survey reports - Forest Service
Range survey reports - Soil Conservation Service, Forest Service
Forest cutting and fire reports - Forest Service, State Foresters
"Watershed Conservation and Development Field Data" - Bureau of Land
Management
Housing construction -
Statistical Yearbook - U.S. Department of Housing and Urban
Development
County and City Data Book - U.S. Bureau of Census
"U.S. Census of Population and Housing" - U.S. Bureau of Census
"Housing Authorized by Building Permits and Public Contracts" - U.S.
Bureau of Census
"Construction Report" - U.S. Bureau of Census
Mining activities -
Mineral Yearbook - U.S. Bureau of Mines
Mining permits - State
Highways and roads -
U.S. Federal Highway Administration
State Highway Department
The following data sources are particularly pertinent to assessment of
surface erosion for large areas.
Data for agricultural lands — the Conservation Needs Inventory (CNI) - The
CNI is one of the major sources of data for agricultural land in the
United States. The first inventory was made in 1958 to 1960 and updated
in 1967. The objective of the inventory was to develop current, detailed
data on land use and conservation treatment, needs on rural land and to ob-
tain data on watershed project needs on both privately and publicly owned
land in the U.S. The inventory includes all acreage except urban and
built-up areas and land owned by the federal government, other than crop-
land operated under lease or permit.
76
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Inventoried lands are compiled by county in terms of land use, land capa-
bility class and subclass,* and conservation treatment needs as shown in
Table 3-11. The seven major rural land use categories are subdivided into
18 secondary land use classifications and current (1967) conservation
treatment needs. Each group is inventoried according to land capability
classes and subclasses.
It is important to note that not all land was classified or inventoried
in the CNI. For the noninventoried land (including federal noncropland,
urban buildup, and small water bodies), there has been thus far no infor-
mation concerning use of land by capability. For most regions the propor-
tion of total land in the noninventory group is not significant. However,
in the western states the proportion of this group may be very high.
Copies of state inventories may be obtained from the State Conservation
Needs Inventory Committee, and/or University Agricultural Extension Service,
Magnetic tapes of the inventory are available from the Statistical Labora-
tory, Iowa State University, Ames, Iowa.
The U.S. Soil Conservation Service in 1972 solicited soil scientists in
the United States for the soil data relevant to surface erosion, in format
compatible with the format of CNI. Data are reported by Land Resources
Area** (LRA) and by land capability class and subclass. For all LRAs east
* Land Capability Classification is one of a number of interpretive group-
ing of soil survey maps made primarily for agricultural purposes.
In this classification, the arable soils are grouped according
to their potentialities and limitations for sustained production of
the common cultivated crops that do not require specialized site con-
ditioning or site treatment. Nonarable soils (soils not suitable for
long-time sustained use for cultivated crops) are grouped according
to their potentialities and limitations for the production and per-
manent vegetation and according to their risks of soil damage if
mismanaged.
The capability classification provides three major categories:
(a) capability unit; (b) capability subclass; and (c) capability
class. The reader is advised to consult with State Conservation
Needs Inventory for detailed descriptions of classifications.
** Land Resource Areas (LRA), as delineated by the Soil Conservation
Service, U.S. Department of Agriculture, are broad, geographic areas
having similar patterns of soil (including slope and erosion), climate,
water resources, land use, and type of farming. Delineation and de-
scription of LRAs are available in USDA-SCS, Agriculture Handbook
No. 296, "Land Resource Areas of the United States," December 1965,
and USDA-ERA series on "The Look of Our Land--An Airphoto Atlas of
the Rural United States."
77
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Table 3-11.
LAND USE AND TREATMENT NEEDS CATEGORIES OF THE
CONSERVATION NEEDS INVENTORY
Primary use
classification
Cropland in tillage
rotation
Secondary use
classification
Corn and sorghum
Other row crops
Close-grown crops
Summer fallow
Rotation hay and pasture
Hayland
Conservation use only
Idle
Other cropland
Orchards, vineyards and
bush fruit
Open land formerly
cropped
Pastureland
Rangeland
Treatment classification
Treatment adequate
Treatment needed—nonirrigated
Residue and annual cover
Sod in rotation
Contouring
Strip-cropping or terracing
diversion
Permanent cover
Drainage
Treatment needed — irrigated
Cultural and management
practices
Improved system
Water management
Treatment adequate
Treatment not adequate
Treatment adequate
Treatment unfeasible
Needs change in land use
Protection only
Improvement only
Improvement and brush control
Reestablishment of vegetative
cover
Reestablishment and brush
control
Treatment adequate
Treatment unfeasible
Needs change in land use
Protection only
Improvement only
Improvement and brush control
Reestablishment of vegetative
cover
Reestablishment and brush
control
78
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Table 3-11.(Concluded)
Primary use
classification
Forestland
Secondary use
classification
Commercial
Noncommercial
Forestland grazed Commercial
Noncommercia1
Other land
On farms
Not on farms
Treatment classification
Treatment adequate
Noncommercial—stand establish-
ment and reinforcement
Commercial — stand establishment
and reinforcement
Commercial—timberstand improve-
ment
Treatment adequate
Forage improvement
Reduction or elimination of
grazing
Treatment adequate
Treatment not adequate
79
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of the continental divide, information solicited includes name of dominant
soil, dominant slope length, dominant slope percent, and K factor. These
data were reported in Data Form 1.
For LRAs west of the continental divide, where K factors had not been de-
veloped before the survey, information solicited includes dominant soil
name, dominant slope length, slope percent, and estimated soil losses
(tons/acre/year) from selected cropping systems. Data were solicited in
Form 1W.
For convenience of use, the MRI study group has combined factors in Form 1
and calculated K-LS indexes for various land capability classes and sub-
classes for LRAs in the areas east of the continental divide. Values of
the K'LS index, and questionnaire returns in Form 1W (for LRAs west of
continental divide) are presented in the Appendices D and E, respectively,
of this handbook. These data can be used together with land-use data in
the State Conservation Needs Inventory for assessing gross erosion from
agricultural lands in large areas.
Data for commercial forests - The most recent data on state and national
levels are presented in "The Outlook for Timber in the United States,"
U.S. Department of Agriculture, Forest Service, Forest Resource Report No.
20, October 1973. This is a report on the nation's timber supply and
demand situation and outlook, related primarily to the commercial timber-
lands in the U.S. that are suitable for production of timber crops. This
report provides statistical data, as of 1970, on the current area and con-
dition of the nation's forestland, inventories of standing timber- and
timber growth and removals by individual states. Information is also in-
cluded on recent trends in forestland and timber resources, trends in util-
ization of the nation's forest for timber and other purposes, and trends
in consumption of wood products. This report represents the latest in a
series of similar timber appraisals prepared by the Forest Service in the
past.
If more local detail data are needed, they likely can be provided by the
forest and range experiment stations. An important timber resources in-
ventory on a local level available from the forest and range experiment
stations is "Forest Statistics" (or "The Timber Resources"). The recent
publications present inventories of timber resources on the state and
county levels. The forest resource data and the accompanying discussions
of forest area, volume, growth, and cut are useful for planners.
Despite the availability of considerable information on the United States
timber inventory, there are important gaps in information necessary to
assess pollutant loadings from forested areas. There is far more informa-
tion available today concerning standing timber volume on forestland than
80
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there is concerning soil and topographic characteristics, the acreage of
forest harvested, method of harvest, mileage of roads built and maintained,
percent canopy and ground cover situation, and current soil and water con-
servation practices. One possible method of obtaining such information is
through personal contact with local knowledgeable persons. The following
are individuals who may be able to supply such needed data:
U.S. Forest Service
Resource management staff officers
District rangers
Forest supervisors
Regional foresters
U.S. Bureau of Land Management
State director
District manager
State and local agencies
State foresters
County foresters
Private forest industries
Data for mining and construction activities - The extent of construction
and mining activities in a given locale can be estimated directly from
sources such as building permits, construction reports, and mining permits.
Similar data also can be obtained from some other sources, such as census
data for housing units, highways, roads, utility transmission lines, etc.,
in which data are assembled periodically. Data gathered in different years
can be translated into average annual acreages of land being disturbed by
construction activities.
For example, the census in County and City Data Book, U.S. Department of
Commerce, Bureau of the Census, includes the total number of housing units
between 1967 and 1972. Also given are the number of units in single family
units and the number in multiple units. From these figures the average
annual number of new single and multiple dwelling units can be determined.
With actual data or an approximation of acreage per housing unit, one may
estimate the average annual acres of land used for new housing.
81
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Construction activities for a given site are generally of limited duration,
and so is sediment production. MRI economists estimated the average dura-
tion of construction to be:
6 months for residential buildings,
11 months for nonresidential buildings, and
18 months for nonbuilding construction.
3.3 SEDIMENT LOADINGS FROM OTHER SOURCES: GULLIES, STREAMBANKS, AND MASS
SOIL MOVEMENT
3.3.1 Overview
3.3.1.1 Gully erosion -
Gully erosion is caused by temporary concentration of runoff during and
immediately after rainfall. Sediment production from gullies is accom-
plished by scouring on the bottom or sides by running water, by slides of
materials into gullies from the side, and by erosion over the well-defined
headscarp.
Gully erosion is common to most regions in the United States. Expansion
of gully development is most vividly apparent in arid and semi-arid areas
such as southwestern U.S. where climatic changes are easily expressed in
network changes, and also in those areas where the influence of man has
been substantial or rapid, or both.
Gullies usually are found on slopes greater than 5 degrees. Gullies are
especially active during the rainy season, and are particularly well-
developed on the margins of uplands composed of highly friable sandstones.
Development of gullies is associated with improper land use and severe
climatic events. The effect of land use on gully development is connected
with modification of land cover and soil conditions, and subsequent changes
in runoff patterns. Gullies have developed following the removal of trees
on the lower part of the sides of glacial troughs, and following compaction
of ground, change in topsoil, and changes in infiltration characteristics.
The impact of land use on gully development is most striking when original
plant cover on steep slopes is removed and runoff occurs with little im-
pediment.
Climatic fluctuations also may cause gully development. Climatic fluctua-
tion may cause disappearance of vegetation cover, and lead to vivid gully-
ing activities.
82
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Sediment production from gully development has been described for some
regions in the U.s 28,29/ ^g quantity, though often large, is usually
less than that produced by surface erosion. However, economic losses from
dissection of uplands, damage to roads and drainage structures, and deposi-
tion of relatively infertile overwash on flood plains are disproportionately
large. Technical procedures for evaluation of gully erosion are available
in Soil Conservation Service, "Procedure for Determining Rates of Land Dam-
age, Land Depreciation, and Volume of Sediment Produced by Gully Erosion,"
SCS Technical Release No. 32 (1966).
The prediction of gully growth has thus far received little attention, al-
though some studies have developed empirical prediction procedures for
specific localities.
3.3.1.2 Streambank erosion -
In the streambank erosion process, energy from streamflow, ice, and floating
debris, and the force of gravity are applied to the streambank and stream-
bed. If the energy is greater than the resistance of soil particles form-
ing the channel, erosion results. Brown?./ suggests that in most forest and
range country and in areas with less than 51 cm (20 in.) of precipitation
annually, channel-type erosion (including gully, streambank, etc.) generally
produces the greater part of the sediment. Where a watershed is primarily
agricultural and has more than 51 cm (20 in.) of precipitation, a major
part of the sediment production is generally from sheet erosion. Gottschalk—'
suggests that streambank erosion is dominant in the semiarid and arid areas
of the United States and in the mountainous areas of the Central and South
Pacific Coast regions. Anderson!!/ estimated sediment yields from the North
Coast watersheds of California, and the Williamette Basin of western Oregon,
and concluded that sediment contribution from streambank erosion in that
part of the country is greater than from other sources combined.
In 1969, the Corps of Engineers, in conjunction with Soil Conservation Ser-
vice personnel, completed the "National Assessment of Stream Bank Erosion."—'
All districts in the nation provided information on the amounts of stream-
bank erosion in their areas. Stream density by land resource area was used
to determine total stream miles and bank miles. Estimates were then made
on how many of these banks erosion was negligible, moderate, and serious.
Damages were determined at the site where erosion occurred and where the
ensuing sediment was deposited. Cost of treatment was calculated for both
moderate and serious cases.
A report on the nationwide assessment was issued by the Corps in October
1969. Regional inventory reports are available from appropriate district
offices.
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3.3.1.3 Mass soil movement -
Mass soil movement is the downs lope movement of a portion of the land sur-
face under the effect of gravity. Such movements may take the form of-
landslide, mudflow, or downward creep of an entire hillside, and contribute
to sediment loadings to surface waters. In many areas this source of supply
is unimportant. However, mass soil movement may constitute the dominant
process of erosion in areas with exceptionally steep slopes, high rainfall,
or low-strength soil, such as that of mountainous areas of western North
America, as well as of southern California. In such areas, soil may remain
in place as the result of a delicate balance between forces tending to
cause downs lope movement and various forces tending to resist it. Any dis-
turbance may upset this delicate balance and result in initiation or accel-
eration of mass soil movement.
Landslide is influenced by the slope of the land, composition of soil, and
o o /
water content of the soil. Dyrness—' indicated that stony soils from
basalt and andesite were 14 to 37 times more stable than those from tuffs
and breccias, which are volcanic parent materials, and normally weather
rapidly to silts and clays. Silts and clays can retain large quantities
of water. The water adds to the soil burden and reduces its strength,
thus promoting landslides. In Oregon, landslides normally occur near peak
stream flow from winter storm runoff when the water content of soil is at
the maximum.
Man's activities may play an important role in initiation and acceleration
of mass soil movements. In a review of mass erosion research in the
western United States, Swanston—' made the following statements about the
effect of disrupting activities of man on mass soil movements:
"Road building stands out at the present time as the most damaging
activity. Soil failures relating to this activity are the result
primarily of slope loading from road fill and sidecasting, inade-
quate provision for slope drainage, and of bank cutting.
Fire, natural and man-caused, is a second major contributor to
accelerated soil-mass movement in some areas. This relates largely
to the destruction of the natural mechanical support of soils, often
abetted by surface denudation of the soil mantle, opening it to the
effects of surface erosion.
Logging affects slope stability mainly through destruction of pro-
tective surface vegetation, obstruction of main drainage channels
by logging debris, and the progressive loss of mechanical support
on the slopes as anchoring root systems decay."
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Very little work has been done to establish quantitative cause and effect
relationships between mass soil movements and causative factors, including
natural characteristics and man's activities in watersheds.
3.3.2 Methods for Quantifying Sediment Loading from Gullies, Streambanks,
and Mass Soil Movement
The cause/effect interrelationships of gully erosion, streambank erosion,
and mass soil movement have yet to be put into proper perspective. Methods
are therefore not available for any given locality and any set of existing
or assumed conditions for accurately predicting contributions of sediment
loading from these sources. The discussion and general facts presented in
the preceding paragraphs will serve as guidelines for estimation of channel
erosion and mass soil movement. These guidelines generally apply to two
options, presented below, for estimating gully and streambank erosion and
mass soil movement at the local/regional level. These options may be used
separately or in combination.
3.3.2.1 Estimation from historical local data and research results -
The local history of gully erosion, streambank erosion, and mass soil move-
ment can be obtained by local interview and from existing research results.
Research results are available in engineering surveys and basin and project
reports. Public agencies which have these results include: Department of
Army—Corps of Engineers; Department of the Interior—Bureau of Land
Management, Bureau of Mines, Fish and Wildlife Service, and National Park
Service; Department of Agriculture—Forest Service and Soil Conservation
Service; state departments of water resources; public works authorities;
and planning commissions.
3.3.2.2 Estimation from historical topographic data -
Quantification of sediment production from gullies, Streambanks, and mass
soil movement also can be made through use of aerial photographs. A large
area of the United States was photographed from the air about 35 years ago.
Many areas have been rephotographed periodically. These aerial photographs
provide valuable tools to determine the boundaries and lateral movement of
channels during various periods of time and are used extensively in water-
shed investigations whenever available. The following agencies and organi-
zations have aerial photographs of parts of the United States: Department
of the Interior—Geological Survey, Topographic Division; Department of
Agriculture—Agriculture Stabilization and Conservation Service, Soil Con-
servation Service, and Forest Service; Department of Commerce—Coast and
Geodetic Survey; Department of the Air Force; National Aeronautics and
Space Administration; various state agencies; and commercial aerial survey
and mapping firms.
85
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REFERENCES
1. Meyer, L. D., and W. H. Wischmeier, "Mathematical Simulation of the
Process of Soil Erosion by Water," paper presented at the 1968 Winter
Meeting of the American Society of Agricultural Engineers, Chicago,
Illinois, 10-13 December 1968.
2. Brown, C. B., "Effect of Land Use and Treatment on Pollution,"
Proceedings of the National Conference on Water Pollution, PHS,
Department pf Health, Education and Welfare, Washington, D.C. (1960).
3. Megahan, W. F. , "Logging, Erosion, Sedimentation—Are They Dirty Words,"
J. Forestry, 70_(7) (1972).
4. Ralston, C. W., and G. E. Hatchell, "Effects of Prescribed Burning on
Physical Properties of Soil," in Proceedings, Prescribed Burning
Symposium, pp. 68-85, 14-15 April 1971, USDA Forest Service.
5. Collier, C. R., et al., "Influence of Strip Mining on the Hydrologic
Environment of Beaver Creek Basin, Kentucky, 1955-1959," USGS Pro-
fessional Paper 427-B (1964).
6. USDA Soil Conservation Service, "Controlling Erosion on Construction
Sites," Agriculture Information Bulletin 347 (1970).
7. USDA Soil Conservation Service, National Engineering Handbook, Sec-
tion 3, "Sedimentation," Washington, D.C., April 1971.
8. USDA Agriculture Research Service, "Present and Prospective Technology
for Predicting Sediment Yield and Sources," Proceedings of the
Sediment-Yield Workshop, USDA Sedimentation Laboratory, Oxford,
Mississippi, 28-30 November 1972.
9. Wischmeier, W. H., and D. D. Smith, "Predicting Rainfall—Erosion
Losses from Cropland East of the Rocky Mountains," Agriculture
Handbook 282, U.S. Department of Agriculture, Agriculture Research
Service, May 1965.
10. Wischmeier, W. H., "Upland Erosion Control," in Environment Impact
on Rivers, p. 15-1 to 15-26, H. W. Shen (ed.), Fort Collins,
Colorado (1972).
11. U.S. Department of Agriculture, Soils Technical Note No. 3, Soil
Conservation Service, Honolulu, Hawaii, May 1974.
86
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12. Wlschraeier, W. H., and D. D. Smith, "Rainfall Energy and Its
Relationship to Soil Loss," Transaction, 39:285-291, American
Geophysical Union (1958) .
13. U.S. Department of Agriculture Conservation Agronomy Technical
Note No. 32, Soil Conservation Service, West Technical Service
Center, Portland, Oregon, September 1974.
14. Garstka, W. U., "Snow and Snow Survey," in Handbook of Applied Hydrology,
V. T. Chow (ed.), McGraw-Hill, Inc., New York, New York (1964).
15- Porter, G. R., and W. H. Wischmeier, "Evaluating Irregular Slopes for
Soil Loss Prediction," presented before the American Society of
Agricultural Engineers, Paper No. 73-227, St. Joseph, Michigan (1973).
16- Wischmeier, W. H., "Estimating the Cover and Management Factor for
Undisturbed Areas," presented at USDA Sediment Yield Workshop,
Oxford, Mississippi (1972).
17- Water Resources Administration, "Technical Guide to Erosion and
Sediment Control Design (Draft)," Maryland Department of Natural
Resources, Annapolis, Maryland, September 1973.
18- U.S. Environmental Protection Agency, "Effect of Hydrologic Modifi-
cations on Water Quality," report draft by the MITRE Corporation,
October 1974.
19. U.S. Department of Agriculture, Engineering Technical Note No. 16,
Soil Conservation Service, Des Moines, Iowa, 21 March 1973.
20. Smith, K. G., "Standards for Grading Texture of Erosional Topography,"
Amer. J. Sci.. 248:655-668 (1950).
21- Schumm, S. A., "The Evolution of Drainage Systems and Slopes in Bad-
lands at Perth Amboy, New Jersey," Geo. Soc. Amer. Bull., 6^:597-646
(1956)
22- Strahler, A. N. , "Quantitative Geomorphology of Drainage Basin and
Channel Network," in Handbook of Applied Hydrology, pp. 4-39 to
4-76, V. T. Chow (ed.), McGraw-Hill, Inc., New York, New York (1964).
o o
/-->> Strahler, A. N., "Hypsometric (Area-Altitude) Analysis of Erosional
Topography," Geo. Soc. Amer. Bull., 63:1117-1142 (1952).
87
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24. Melton, M. A., "An Analysis of the Relations Among Elements of Climate,
Surface Properties and Geomorphology," Project No. MR 389-042,
Technical Report No. 11, Columbia University, Department of Geology,
New York (1957).
25. Maxwell, J. C., "Quantitative Geomorphology of the San Dimas Experi-
mental Forest, California," Project No. NR 389-042, Technical
Report No. 19, Columbia University, Department of Geology, New
York (1960).
26. Smith, K. G. , "Erosional Processes and Landforms in Badlands National
Monument, South Dakota," Geo. Soc. Amer. Bull.. 69^975-1008 (1958).
27. Carlston, C. W., and W. B. Langbein, "Rapid Approximation of Drainage
Density: Line Intersection Method," U.S. Geological Survey, Water
Resource Division, Bulletin 11 (1960).
28. Glymph, L. M., "Relation of Sedimentation to Accelerated Erosion in
the Missouri River Basin," Soil Conservation Service, Technical
Paper No. 102 (1951).
29. Leopold, L. B., W. W. Emmett, and R. M. Myrick, "Channel and Hillslope
Process in a Semiarid Area in New Mexico," UiS. Geological Survey,
Paper No. 102 (1966).
30. Gottschalk, L. C. , "Effect of Watershed Protection Measures on Reduc-
tion of Erosion and Sediment Damages in the United States," Int.
Assoc. Sci. Hyd. Pub.. 59^426-427 (1962).
31. Anderson, H. W., "Relative Contribution of Sediment from Source Areas
and Transport Processes," in Proceedings of a Symposium on Forest
Land Uses and Stream Environment, Oregon State University, pp. 55-
63, August 1972.
32. U.S. Army Corps of Engineers, "A Study of Streambank Erosion in the
United States," submitted to Committee on Public Works, House of
Representatives, October 1969 (available from the U.S. Government
Printing Office, Washington, D.C.).
33. Dyrness, C. T., "Mass Soil Movements in the H. J. Andrews Experimental
Forest," USDA Forest Service Research Paper PNW-42, Pacific Northwest
Forest and Range Experimental Station, Portland, Oregon, 12 pages
(1967).
-------
34. Swanston, D. N., "Principal Mass Movement Processes Influenced by
Logging, Road Building, and Fire," in Proceedings of a Symposium
Forest Land Uses and Stream Environment, Oregon State University,
Corvallis, Oregon, 19-21 October 1970.
89
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SECTION 4.0
NUTRIENTS AND ORGANIC MATTER
4.1 INTRODUCTION
Nitrogen and phosphorus are the primary nutrients which are important in
agricultural and silvicultural practices. The effect of these nutrients on
receiving waters is the increased potential for algal blooms—especially in
lakes and reservoirs--thus interfering with many beneficial uses of these
waters. Of the two nutrient elements, phosphorus has received greater em-
phasis because of the available technology to control phosphorus discharges
from municipal and industrial sources. Nitrogen is also important as a
rate-limiting nutrient for algal growth in some surface waters; however,
the nitrogen pathways in plant nutrition are relatively more complex than
those of phosphorus. Technology for controlling nitrogen emissions from
point sources is not sufficiently advanced to economically justify its
adaptation to nonpoint pollutant emissions.
The magnitude of losses of these two nutrient elements from different
source activities can, in principle, be calculated by making nutrient bud-
gets of all source inputs and outputs, and specifically determining out-
puts to surface waters. Methods for estimation of quantities involved in
the several parts of a nutrient budget are not well enough developed for
use in nutrient loading functions. In addition, the quantities of nu-
trients that actually reach a stream from a given source are subject to
variation depending upon the nature of the intervening terrain. The pre-
diction of nutrient losses from various land uses can in part be accomp-
lished by loading functions which describe the changes of nutrient con-
tent in the soil in response to various external variables such as cultural
practices, fertilizers, and climatic differences, and which account for
soil losses by erosion.
Organic matter from cropland and pastureland carries oxygen-consuming ma-
terials that can degrade the quality of receiving waters by stripping its
oxygen content, and carries potentially pathogenic microorganisms from
livestock wastes and other rural runoff. A loading function for organic
90
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matter has been developed based on the organic matter content of soil
and sediment yield.
These assumptions are more nearly correct for nitrogen when erosion is
moderate to extensive, and are less correct when erosion is slight or
when surface runoff is negligible. In the latter cases dissolved forms
of nitrogen are the principle nitrogen pollutants. These are transported
either to subsurface waters or directly to surface waters in runoff.
Functions which describe either of the latter phenomena are not yet avail-
able, and the approach to estimating dissolved forms of nitrogen accord-
ingly involves a combination of local or regional experience supplemented
by measurements of soluble nitrogen forms in runoff and baseflow.
Nutrient and organic matter loading functions presented in this section
are accordingly based on the sediment loading function developed in Sec-
tion 3.0 entitled "Sediment Loading Functions." It is assumed that the
nutrients and organic matter are carried through surface runoff and that
most of these are removed with sediment.
Because the currently available data applicable to the entire U.S. may
not reflect the local conditions, it is suggested that local data when-
ever available be used in preference to the general data presented in
this section.
4.2 NITROGEN
4.2.1 Introduction
Soil nitrogen is derived from several sources which include geologic
weathering, microbial reactions, precipitation, and chemical fixation.
Addition of chemical fertilizers and organic residues to soil constitutes
man's effort to increase or supplement nitrogen forms which can be read-
ily utilized by plants. Although the cultivated soils contain a large
reservoir of total nitrogen in the plowed layer—about 2 to 4 tons/acre--
available nitrogen is usually quite small—a few pounds per acre. The
significance of this available nitrogen to water pollution is great, how-
ever. As much as 95% of total nitrogen in the soil is organically bound
and is not readily released in solutions for plant growth. The ammonium
ion in soil which is tightly bound to clay or other anionic molecules in
soil is also not readily available for plant growth. Nitrate which is
not held by soil particles can be readily transported through the soil
profile to below the root zone in the absence of an actively growing crop
and can eventually join the groundwater pool. The time of migration of
groundwater nitrogen to surface waters can extend to several decades
91
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depending upon groundwater hydrology relative to surface water hydrology,
Significant nitrogen losses to the air occur through volatilization and
denitrification processes.
4.2.2 Precipitation
Precipitation contains significant quantities of numerous substances,
including nitrogen and phosphorus.JLz^/ That precipitation which falls
on surface waters carries with it a load which becomes a part of the
total pollutant load. The direct contribution via precipitation is neg-
ligible for surface streams, and may be substantial for lakes or still-
standing waters--as much as 50% of the total nutrient input—' Contri-
butions of precipitation-borne nutrients to surface waters via overland
runoff will vary in proportion to both precipitation and runoff. The
simplest approach is/to assume that overland runoff carries with it,
without loss to the soil, the nitrogen and phosphorus load which it con-
tained when it reached the earth. Overland runoff is seldom very direct
except in high intensity/high quantity storm events or in certain types
of snowmelt, and rainfall entrained nutrients will in most runoff events
be exposed to mineral and organic matter in the soils. Phosphorus and
nitrogen should be somewhat attenuated by exposure to the soil.
That fraction of precipitation-borne phosphorus carried in precipitation
which does not discharge to streams via .overland runoff becomes a part
of the inventory of phosphorus in the soil, and becomes relatively im-
mobile in the surface layers of soil. The surface-sorbed phosphorus be-
comes a nonpoint pollutant when it is discharged to streams on eroded
sediment.
That fraction of precipitation-borne nitrogen which is not immediately
carried off in overland runoff also enters the soil compartment where it
continues its participation in the complex nitrogen cycle: some stays
in the root zone, and may be completely utilized by plant life; some
moves below the root zone, and thus becomes involved in a very ill-
defined physical-chemical-biological-hydrologic system; some of that
which stays in the root zone is a candid.ate for transport, later, to
surface streams in overland runoff.
Since only a small fraction of precipitation incident on land enters
surface waters by overland runoff, the great majority of precipitation-
borne phosphorus and nitrogen is deposited on the land and becomes a part
of its continually changing inventory of nutrients. The present discus-
sion is concerned with estimation of the fractions of the precipitation-
borne nutrients transported directly, via overland runoff, to surface
waters. An analysis of "national average" data is instructive.
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Annual average precipitation is 76 cm (30 in.)- Annual average runoff
via all processes is 25 cm (10 in.)- The fraction of runoff occurring
by the overland varies widely; for purposes of discussion 20% of total
runoff will suffice. Average annual overland runoff is thus 5 cm (2
in.), or about 7% of precipitation.
Reported deposition rates of nitrogen and phosphorus in rainfall range
from about 5 to 10 kg/ha/year (4.4 to 8.9 Ib/acre/year) for nitrogen,
and reportedly average 0.05 to 0.06 kg/ha/year (0.045 to 0.055 lb/acre/
year) for phosphorus.ii±'
Seven percent of the precipitation-borne phosphorus and nitrogen might
thus be carried directly to surface waters, if no absorption on soil is
assumed. Nonattenuated yield rates, for stream deposition, national
average basis, would accordingly be 0.35 to 0.7 kg/ha (0.31 to 0.62
lb/acre) of nitrogen, and 0.0035 to 0.004 kg/ha (0.0031 to 0.0036 lb/
acre) of phosphorus. If one assumes that phosphorus is 507=, attenuated
and nitrogen 2570 attenuated, the net yields become 0.28 to 0.53 kg/ha
(0.25 to 0.47 lb/acre) of nitrogen, and 0.0018 to 0.002 kg/ha (0.0016
to 0.0018 lb/acre) of phosphorus.
If one translates the above data into in-stream concentrations (assum-
ing no in-stream transformations), the results are 0.11 to 0.21 ppm
nitrogen, and 0.7 to 0.8 ppb of phosphorus. Comparison of these con-
centrations with the national benchmark station data summarized in
Figures 12-3 and 12-4 reveals the perhaps fortuituous comparison that
nitrogen concentrations estimated from precipitation are the same as
what appears to be an average for nationally observed concentrations
in locations relatively unaffected by man. The above estimated concen-
trations for phosphorus are lower than benchmark station concentrations
(0.7 to 0.8 ppb vs 10 to 200 ppb of total phosphorus). This compari-
son indicates that the load of precipitation-borne phosphorus is a small
fraction of the phosphorus nonpoint contribution to surface streams, but
that nitrogen contributions are a significant part of the in-stream bur-
den of available forms of nitrogen (particularly nitrate).
A comparison of nutrient contribution from precipitation with that from
croplands reveals that, on a national basis, the eroded soil from crop-
lands yields about 20 kg/ha/year (18 Ib/acre/year) of total nitrogen..3'
Assuming a 7% value for the available fraction in total nitrogen, the
load of available nitrogen from cropland becomes 1.42 kg/ha/year (1.26
Ib/acre/year). This value compares with 0.28 to 0.53 kg/ha/year (0.25
to 0.47 Ib/acre/year) of available nitrogen in precipitation. Since the
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cropland nitrogen loading function does not account for precipitation
loads, the total contribution to a stream should include both these
sources. The total load of "available" nitrogen thus is about 1.8 kg/
ha/year (1.6 Ib/acre/year), on a national average basis, from cropland.
Although available nitrogen is extremely significant in the enrichment
of stream nutrition, the role of the remainder of the total nitrogen
carried on eroded sediment is also substantial. Since streams are dy-
namic in nature, there is a continuous mineralization of soil nitrogen
by the microorganisms in the bottom sediment which is supplied with
oxygen from both stream reaeration processes and photosynthetic pro-
cesses. Thus, the delayed release of available nitrogen to the aquatic
systems can be as significant as the available nitrogen in precipi-
tation and eroded soil. For example, in-stream nitrogen burdens averaged
over the Missouri River basin translate to an average yield of about 3
lb/acre/year—' of nitrate-nitrogen, which is two to three times the de-
livered rate from nonpoint sources and precipitation.
Nitrogen loading from precipitation should be added to that from surface
erosion processes to obtain the total load. Since the load for phosphorus
from precipitation is small, the phosphorus loading function does not in-
clude the contribution from precipitation.
4.2.3 Nitrogen Loading Function
While the complex interactions in soil, air, water, and plants are rea-
sonably well understood, methods for quantifying movements within the
system are still in the research stage. Methods which are suitable for
general use oversimplify the problem, must be used with discretion, and
may be quite inadequate in certain cases. In particular, it is not
presently possible to describe leaching processes for soluble forms of
nitrogen. The nitrogen loading function is made up of two sources: (a)
erosion; and (b) precipitation. Total nitrogen loading is obtained by
adding the yields from both sources. The loading functions exclude
leaching losses, and predict the amount of total nitrogen that is re-
leased to surface waters by runoff and erosion. The nitrogen in precip-
itation is mostly in available form.
Nitrogen loading function for erosion loss is:
Y(NT) = a-Y(S) -Cq(NT)-r.T (4-1)
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where Y(NT)E = total nitrogen loading from erosion, kg/year (Ib/year)
a = dimensional constant (10 metric, 20 English)
Cg(NT) = total nitrogen concentration in soil, g/100 g
Y(S)E = sediment loading from surface erosion, MT/year (tons/
year)
rjj = nitrogen enrichment ratio
Available nitrogen can be obtained by using a fraction fxr which is the
ratio of available N to total N in sediment. Thus, the available
nitrogen in sediment is
Y(NA)E = Y(NT)E'fN (4-2)
Nitrogen loading function for precipitation is
Q(OR)
Y(N)pr = A-— - .Npr-b (4-3)
rr Q(Pr)
where Y(N)pr> = stream nitrogen load from precipitation, kg/year
(Ib/year)
A = area, ha (acres)
Q(OR) = overland flow from precipitation, cm/year (in/year)
Q(Pr) = total amount of precipitation, cm/year (in/year)
Np = nitrogen load in precipitation, kg/ha/year (lb/acre/
year)
b = attenuation factor
Almost all of Y(N)pr will be in the available form so that the total
available nitrogen from both erosion and precipitation may be obtained
by adding Eqs. (4-2) and (4-3). Thus,
Y(NA) = Y(NT)E'fN + Y(N)pr (4-4)
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4.2.4 Evaluation of Parameters in the Nitrogen Loading Function
The value of Y(S)E can be evaluated from the sediment loading function
presented in Section 3.0 "Sediment Loading Functions." The value of the
enrichment ratio rN is variable according to the soil texture and cul-
tural treatment. Vietsl/ presented the values of rN using data from
/- /
small experimental plots (see Table 4-1). Hagin and Amberger,.^' as well
as Stoltenberg and White,—' have proposed an rN value of 2.0. Massey
et al..§/ estimated the value of rjq as 2.7. Because of wide variations
in the properties of erodible soil, a single value of rN is not prob-
able; the values reported range from less than 2.0 to greater than 4.0, and
an appropriate value should be selected for a specific location from local
knowledgeable sources such as the State Agricultural Experiment Stations.
Table 4-1. NUTRIENT AND SEDIMENT LOSSES2
5/
Total loss (kg/ha)
Source Soil N P
Enrichment
ratio, r
N P
Check
Rye winter cover crop
Manure (45 MT/ha)
Rye and manure (45 MT/ha)
29,100
13,160
18,390
8,130
74.5
38.9
52.8
21.5
75.8
37.7
44.3
19.6
3.88
4.08
4.28
3.35
1.59
1.56
1.47
1.47
9/
Nutrient losses from forest soils are typically very low. Kilmer-
cited several authors toshow that nutrient losses from forestlands are_
insignificant. Clear-cutting and buring of forest areas accelerate the re-
lease of nutrients (Table 4-2). The erosion-based loading function for
nitrogen losses will obviously yield inaccurate estimates of nitrogen losses
from sources such as forests and pastureland which have good cover and from
which soil loss by erosion is negligible. For this case, it is approrpiate
to use Eq. 4-1 only with substantial reservations, and the user is advised
to use case study data which appear to best represent his area of concern.
This latter approach requires that the user define the mechanisms which de-
scribe his situation, i.e., the relative contributions from erosion, leach-
ing, and surface transport in runoff, and discharge via groundwater/subsur-
face return mechanisms.
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Table 4-2. EFFECT OF CLEAR-CUTTING AND FERTILIZATION ON NUTRIENT
OUTPUT IN DOUGLAS FIR FORESTS*/
N
Treatment (kg/ha)
P
(kg/ha)
Control 0.21 0.01
Clear-cut 0.39 0.05
Fertilized (200 Ib/acre) urea 0.28 0.03
Ammonium sulfate 0.43 0.07
a/ Source: Cole and Gessel (1965) cited by Kilmer.2'
Nitrogen losses by leaching are also negligible from actively growing
grassland. However, losses from legume grass mixtures can be high.
Lysimeter studies by Low and Armitage (page 7 of Ref. 9) showed that
clover produced about 10 times as much N loss in drainage as that in
actively growing grass; however, the loss was 100 times as much when
the clover crop died.
Runoff losses of nitrogen from grass sod plots ranged from 27» of applied
nitrogen when soil moisture was 12.5%,to 14% at 25.8% moisture.—'
Timmons et al.JLi' determined N and P losses in runoff solution and sed-
iment in Minnesota. Their results indicate that leaching losses from
a hay rotation could contribute to substantial N and P losses in solu-
tion.
The value of Cg(NT) in the plowed layer of soil is variable from location
to location and from time to time. Estimates of native soil nitrogen
in the U.S. indicate a range between 0.02 and 0.4%.—' Parker et al.
published a map in 1946 showing the nitrogen content in the top 1-ft
layer in the U.S.JJ!/ (see Figure 4-1). Data in Figure 4-1 should be viewed
in general terms; for specific sites, local sources such as SAES and SCS
Soil Surveys should be consulted.
Precipitation also contributes to the soil nitrogen. Atmospheric nitro-
gen extracted by soil microbes becomes incorporated into soil organic
matter; animal manures, crop residues, and other wastes contribute sig-
nificant amounts of nitrogen to the soil. Jenny—' expressed the nitro-
gen content of the soil in terms of temperature, T, and a humidity fac-
tor, H. Jenny's equation is:
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Highly Diverse
Insufficient Data
Figure 4-1. Percent nitrogen (N) in surface foot of soil.-
-------
CS(NT) = 0.55 e-°-08T (1 - e"0'00511) (4-5)
H, ~~ 1_ -L- _. J L .
100'
where P = precipitation, mm/year
Cg(NT) = concentration of soil nitrogen, g/100 g
T = annual average temperature, °C
RH = relative humidity, %
SVPt = saturated vapor pressure at given temperature, mm
of Hg
Equation (4-7) shows the relation between SVP*. and T.*
SVPt = lot9'2"2 - 23607(273 + T)] (4_?)
The solution of Eq. (4-5) is shown graphically in Figure 4-2. The value
of humidity factor, H, can be determined from Eqs. (4-6) and (4-7). A
nomograph solution of H is shown in Figure 4-3. For given values of
precipitation, relative humidity and temperature, the value of H can be
quickly and accurately established from Figure 4-3. For example, given
PL = 500 mm/year (19.7 in/year), RH]_ = 60%, and T]_ = 5°C (41°F), the
value of H factor can be determined as follows: using a straight-edge
ruler, align P^ and RH-, to intersect on the index line at "a" as shown
on the inset of Figure 4-3. Align "a" with T-^ on the temperature scale
to intersect the H scale. The result on the H scale is 194.
Data in Figure 4-1 may be used as a check on current data. Equations (4-6)
and (4-7) may be used to calculate nitrogen content of soil more precisely
if necessary data are available for using these equations. Again data from
State Agricultural Experiment Stations, and SCS Soil Surveys are much more
dependable than the above sources and should be consulted whenever possible.
The fraction of available nitrogen to total nitrogen in soil, fN is
variable, depending upon many factors such as soil characteristics, degree
of mineralization, and organic matter content. The most important forms
of available nitrogen are NH,+, N03~, and certain simple organic compounds
containing free amide or amino groups. Nitrate is only a minor source of
available nitrogen in soil.
Modified from Gladstone, S., Elements of Physical Chemistry, D. Van
Nostrand Company, Inc., New York, New York (1946).
99
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0.01
100
200
300 400
H. HUMIDITY FACTOR
500
600
700
Figure 4-2. Soil nitrogen vs humidity factor and temperature
100
-------
80 -
70 -
60 -
50—
40 -
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15 -
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0.9 -
0.8 -
0.7 -
0.6 -
0.5-
n A -
r2000
~r 1500 .£
~ X
- 1000 1
- 800
- 700
: 600
-500
r 400
- 300
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- 150 ~
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- 70
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The available nitrogen in soil rarely exceeds 15% of totalnitrogen. Data
from Lopez and Galvezll/ suggest that about 8% of total nitrogen in soil
is available in mineralized form for plant growth. For more precise values,
local expertise should be consulted for a given area.
The values of Q(OR) and Q(Pr) may be obtained from local data sources.
The value of Q(Pr) (annual average precipitation) is usually obtained
from the weather bureau statistics for the area. The value of overland
runoff can be roughly estimated from stream flow data. A user unfamiliar
with hydrology should consult with qualified personnel in state conser-
vation services, agricultural extension service, the Corps of Engineers,
or the Agricultural Research Service for assistance in interpretation of
stream flows. These resources will also have historical information on
overland runoff in relation to precipitation.
Values of Np are usually available from measurements made in the
local research stations. In the absence of actual data, data in Figure
4-4 may be used.
4.3 PHOSPHORUS
4.3.1 Introduction
Phosphorus occurs naturally in soil from weathering of primary phosphorus-
bearing minerals in the parent material. Additions of plant residues
and fertilizers by man enhances the phosphorus content of the surface
soil layer.
Phosphorus in soils occurs either as organic or inorganic phosphorus.
The relative proportion of the phosphorus in these two categories varies
widely. Organic phosphorus is generally high in surface soils where or-
ganic matter tends to accumulate. Inorganic forms are prevalent in sub-
soils. Soil phosphorus is readily immobilized due to its affinity to
certain minerals. In strongly acid soils the formation of iron and
aluminum phosphates, and in alkaline soils, the formation of tricalcium
phosphate reduces the availability of soil phosphorus. Once it is lost
to a stream, the nature of phosphorus existing in sediment or in solu-
tion becomes significant in the nutrition of aquatic microorganisms.
Phosphorus transport from a given site to stream can occur either by ero-
sion or by leaching. The predominant mode of transport is via soil ero-
sion. Soil solution usually contains less than 0.1 ug of phosphorus per
milliliter; the leaching losses are thus extremely low even in well-
drained soils. Exceptions are sands and peats which have little tendency
to react with phosphorus.
102
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.3 kg/ho/yr
.5 kg/hq/yr
2.0 kg/ha/yr
1.0 kg/h
o
u>
1.0 kg/ha/yr
1.5 kg/ha/yr
1.0 kg/h
kg/ho/yr
5 kg/ha/yr
Source: Personal Communication, Jay H. Cravens, Regional Forester, USDA - FS,
Eastern Region, Milwaukee, Wisconsin, August 1974.
Figure 4-4. Nitrogen (NH4-N and N03-N) in precipitation
-------
Phosphorus losses from well managed pastures and forested soils are
usually low. For example, unfertilized pastures lost about 0.03 kg/ha
of P during a 6-month period, while addition of 45 kg of P per hectare
resulted in an escape of only 0.04 kg/ha during a similiar period of
time. 2'
4.3.2 Phosphorus Loading Function
The loading function for phosphorus is based on the soil erosion mecha-
nism. The loading function is:
Y(PT) = a-Y(S)E-Cs(PT)-rp (4-8)
where Y(PT) = total phosphorus loading, kg/year (Ib/year)
a = a dimensional constant (10 metric, 20 English)
Y(S)E = sediment loading, MT/year (tons/year)
CC(PT) = total phosphorus concentration in soil, g/100 g
o
rp = phosphorus enrichment ratio
Available phosphorus may be computed from Eq. (4-9):
Y(PA) = Y(PT)-fp (4-9)
where Y(PA) = yield of available phosphorus, kg/year (Ib/year)
fp = ratio of available phosphorus to total phosphorus
4.3.3 Evaluation of Parameters in Phosphorus Loading Function
Sediment loading, Y(S)g , may be obtained from procedures outlined in
Section 3.0 "Sediment Loading Functions."
The value of CS(PT) , the total phosphorus content of the soil, is
variable. For any given location, current and local data are preferred
to generalized values given in this report. No central repository of
current nationwide data exists. Parker et al.i3./ published data on the
phosphorus content of soil in the top 30 cm (1 ft) for the 48 states, as
shown in Figure 4-5. Parker's data, although obtained 30 years ago, will
serve as a check on current data. Soil surveys periodically made by the
104
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PHOSPHORIC ACID
Percent ?2O5
0.0- 0.04
0.05- 0.09
0.10- 0.19
0.20- 0.30
Figure 4-5. Phosphorus content in the top 1 ft of
-------
Soil Conservation Service contain more recent information on soil phos-
phorus content. State agricultural extension service personnel can also
provide reasonable estimates of soil phosphorus content in a given area.
These sources should be given priority in determining the phosphorus
content of the soil.
The enrichment ratio, rp , has been the least researched parameter in
the loading function. As reported in Table 4-1, the reported rp values
average about 1.5. Massey et al..§' obtained an rp value of 3.4, and
Stoltenberg and White!/ reported a value of 2.0. Hagin and Amberger^/
have used a value of rp of 2.5 in their simulation model for nutrient
losses from agricultural sources. Massey et al..§/ have developed an
empirical equation to determine rp :
log rp = 0.319 + 0.25 (-log X) + 0.098 (-log Y) (4-10)
where X = sediment loss, tons/acre-in of runoff
Y = sediment loss, tons/acre
The determination of available phosphorus in the soil is difficult. MDst
reported data fail to distinguish between soluble phosphorus, adsorbed
or particulate phosphorus, and organic phosphorus in sediment runoff.
Total phosphorus is a somewhat meaningless parameter, since only the
soluble orthophosphate form is readily available for uptake by aquatic
organisms. Other forms of phosphorus in sediment can, however, act as
a source or sink for subsequent release in available form.
Schuman et al. have reported an empirical relation between sediment phos-
phorus (concentration in ppm, Cs(PT) ) and soluble phosphorus (concen-
tration in ppm, CQ(P) ) for Iowa soils. The relation may be stated as:
CQ(P) = a + b-Cs(PT) (4-11)
where a and b are regression coefficients. The reported values of
a and b are 0.018 and 0.047, respectively.il/ Equation (4-11) shows
that the ratio of solution phosphorus to sediment phosphorus is just
under 1:20.
Taylorl§/ suggested that about 10% of the total phosphorus in eroded
soil would be available for aquatic plant growth.
106
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4.4 ORGANIC MATTER
4.4.1 Organic Matter Loading Function
The loading function is:
YQOM)E = a-Cs(OM)-Y(S)E-rOM (4-12)
where Y(OM)E = organic loading, kg/year (Ib/year)
a = a dimensional constant (10 metric, 20 English)
CC(OM) = organic matter concentration of soil, g/100 g
O
Y(S)E = sediment loading, MT/year (tons/year)
rOM = enri-cnment ratio for organic matter in eroded soil
4.4.2 Evaluation of Parameters in the Organic Matter Loading Function
The value of Y(S)E can be obtained from procedures discussed in
Section 3.0. The value of Cg(OM) should be obtained preferably from
current or historical data for a given area, e.g., from the extension
service. For approximate values, Cg(OM) may be taken as equal to
20 x Cs(NT) , where Cs(NT) is the total nitrogen concentration in
the soil.il/
The value of TQ^ , the enrichment ratio, is more difficult to assess
due to lack of research data. Values of TQM are i-n the range of 1 to
5. The enrichment ratio for sandy soils will be high. Conversely, the
enrichment ratio will be low when the mineral fraction of the soil is
finely divided and highly erodible. The user should consult with local
soil experts and should use local data when available.
4.5 ACCURACY OF LOADING FUNCTIONS
The accuracy of predicting loads using the loading functions presented
in the preceding sections depends, to a large extent, on the availability
of reasonably accurate data for evaluating the various parameters in the
functions. For example, the nitrogen loading function is composed of
several parameters each of which is in turn a function of several other
variables. In addition, several options are available to the user to
develop the parameter values from his own sources of information which
may alter the prediction accuracy. However, if the used values reflect
the long-term average rather than a specific year, and if reasonably
large areas are used such as large watersheds O 100 S(l miles) rather
107
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than individual plots or small watersheds, the expected accuracy can be
reasonably estimated. Using the reasoning that the error in individual
parameters will tend to cumulate to a larger error, the expected ranges
of predicted values for given "true'r or estimated values of load are
presented in Table 4-3.
Table 4-3. PROBABLE RANGE OF LOADING VALUES FOR
NUTRIENTS AND ORGANIC MATTER
Estimated value Probable range
Loading function (kg/ha/year) (kg/ha/year)
Total N sediment**/ 1 0.1-10
Total N sediment 10 5-20
Total N sediment 50 30-75
Total N precipitation^' 0.3 0.1-0.6
Total P£/ 1.0 0.5-3.0
Total P 5.0 2-10
Total P 10.0 5-20
Organic matter 10.0 5-20
Organic matter 100 50-200
a/ Available N in sediment will range from 3 to 870 of total N.
b_/ Available N is equal to total N in precipitation.
£/ Available P in sediment will range from 5 to 10% of total P.
4.6 EXAMPLE OF LOADING COMPUTATION
The watershed given in Section 3.0, entitled "Sediment Loading Func-
tions," for Parke County in Indiana will be used to illustrate the metho-
dology presented in this section for computing the loads. It is required
to compute available nitrogen, available phosphorus, and organic matter
loading for the given area for the following conditions:
Average daily loading;
Maximum daily loading during a 30 consecutive day period; and
Minimum daily loading during a 30 consecutive day period.
108
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The following data, plus soil loss data, are required:
Soil nitrogen content.
Soil phosphorus content.
The preferred source of these data is local records. Jenny's equa-
tion (Eq. 4-5) and Figure 4-5 are alternate sources from which general
values may be estimated.
4.6.1 Nitrogen Loading
Using the following data, soil nitrogen content is calculated:
Average annual temperature = 10°C
Average annual precipitation = 96.5 cm
Average annual relative humidity = 70%
Using the nomograph given in Figure 4-3, the value of H factor was de-
termined to be 350. From Figure 4-2, and using H = 350 and T = 10°C,
the value of Cg(NT) , the soil nitrogen content was estimated to be
0.204% or 0.204 g/100 g. Assume that 6% of total nitrogen is available,
and rN is 2.0. Using Eqs. (4-1) and (4-2),
Y(NA)E = 20-Y(S)E-0.204-2.0-0.06
= 0.49-Y(S)E
(4-13)
The values of areal sediment yield as given in the example in Section
3.0, entitled "Sediment Loading Functions," are shown below in Table 4-4
Table 4-4. SEDIMENT YIELD IN EXAMPLE
Land use
Cropland
Pasture
Woodland
Total
Daily average
2.88
0.33
0.39
3.60
Sediment yield
Maximum 30
9.36
, 0.84
0.95
11.15
(tons/day)
days Minimum 30 days
0.72
0.09
0.09
0.90
109
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The nitrogen loadings are shown in Table 4-5 using the data in Table
4-4 and Eq. (4-13).
Table 4-5. AVAILABLE NITROGEN LOADING, Y(NA)E , IN EXAMPLE
Nitrogen loading (Ib/day)
Land use
Cropland
Pasture
Woodland
Daily average
1.41
0.16
0.19
Maximum 30 days
4.59
0.41
0.47
Minimum 30 days
0.35
0.04
0.04
Total 1.76 5.47 0.43
4.6.2 Phosphorus Loading
Assume CS(PT) = 0.255g/100g for the area, 10% of Cg(PT) is available
phosphorus, Cg(PA) ; and rp is 1.5, and using Eq. (4-8);
Y(PA)W = 20-Y(S) -0.255-1.5-0.10 (4-14)
Ci £i
= 0.765 Y(S)E
Phosphorus loadings computed from Table 4-2 and Eq. (4-8) are shown in
Table 4-6.
Table 4-6. AVAILABLE PHOSPHORUS LOADING, Y(PA)g , IN EXAMPLE
Phosphorus loading (Ib/day)
Land use Daily average
Cropland 2.20
Pasture 0.25
Woodland 0.30
Total 2.75
4.6.3 Organic Matter Loading
Maximum 30 days
7.16
0.64
0.73
8.53
Minimum 30 days
0.55
0.07
0.07
0.69
Using Eq. (4-12), data for Cg(OM) , Y(S)E , rQM are needed.
110
-------
Assume that the value of Cg(OM)/Cs(NT) equals 20 and rQM =2.5,
Y(OM)E = 20'2.5-Y(S)E-20-CS(NT)
= 1000-CS(NT).Y(S)E
Using CS(NT) = 0.2%,
Y(OM)E = 200-Y(S)E (4-15)
The values of organic loading are computed from Eq. (4-15) and presented
in Table 4-7.
Table 4-7. ORGANIC MATTER LOADINGS IN EXAMPLE
Organic matter loading (Ib/day)
Land use Daily average Maximum 30 days Minimum 30 days
Cropland 576 1,872 144
Pasture 66 168 18
Woodland 78 190 18
Total 720 2,230 180
111
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REFERENCES
1. Carroll, D., "Rainwater as a Chemical Agent of Geological Processes
A Review," GS WS Paper 1535-G, U.S. Geologic Survey (1962).
2. Loehr, R. C., "Characteristics and Comparative Magnitude of Nonpoint
Sources," JWPCF, 4£(8):1849 (August 1974).
3. Phase II Report, EPA Contract No. 68-01-2293 (Draft submitted in
November 1975).
4. McElroy, A. D., S. Y. Chiu, and A. Aleti, "Analysis of Nonpoint
Source Pollutants in the Missouri Basin Region," Office of Re-
search and Development, Environmental Protection Agency, Report
No. EPA-600/5-75-004 (March 1975).
5. Viets, F. G., Jr., "Fertilizer Use in Relation to Surface and
Groundwater Pollution," In: Fertilizer Technology and Use (2nd
ed.), p. 517, Soil Science Society of America, Madison, Wisconsin
(1971).
6. Hagin, J., and A. Amberger, "Contribution of Fertilizers and Manures
to the Nitrogen and Phosphorus Load of Waters. A Computer Simula-
tion," Technion-Israel Institute of Technology, Haifa, Israel
(1974).
7. Stoltenberg, N. L., and J. L. White, "Selective Loss of Plant Nu-
trients by Erosion," Soil Science Society of America, Proceedings,
,17:406-410 (1953).
8. Massey, H. F., M. L. Jackson, and 0. E. Hays, "Fertility Erosion on
Two Wisconsin Soils," Agron. J., 45:543-547 (1953).
9. Kilmer, V. J. , "Nutrient Losses Through Leaching and Runoff,"
Tennessee Valley Authority, Muscle Shoals, Alabama (undated manu-
script).
10. Moe, P. G., J. V. Mannering, and C. G. Johnson, "Loss of Fertilizer
Nitrogen in Surface Runoff Water," Soil Sci. , K)4.:389-394 (1967).
11. Timmons, D. R., R. F. Holt, and J. J. -Latterell, "Leaching of Crop
Residues as a Source of Nutrients in Surface Runoff Water," Water
Resources Research, 6:1367-1375 (1970).
112
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12. Jenny, H. , "A Study on the Influence of Climate Upon the Nitrogen
and Organic Matter Content of the Soil," Missouri Agr. Exp. Sta.
Res. Bui. 152 (1930).
13. Parker, C. A. et al., "Fertilizers and Lime in the United States,"
USDA Misc. Pub. No. 586 (1946).
14. Lopez, A. B., and N. L. Galvez, "The Mineralization of the Organic
Matter of Some Philippine Soils Under Submerged Conditions,"
Philippine Agr., 4-2:281-291 (1958), cited in Ref. 17-
15. Schuman, G. E., R. G. Spomer, and R. F. Piest, "Phosphorus Losses
from Four Agricultural Watersheds on Missouri Valley Loess,"
Soil Science Society of America, Proceedings, 37^(2):424 (1970).
16. Taylor, A. W., "Phosphorus and Water Pollution," J. Soil and Water
Conserv.. ,22:228-231 (1967).
17. Buckman, H. 0., and N. C. Brady, The Nature and Properties of Soil,
7th ed., The MacMillan Company, New York (1969).
113
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SECTION 5.0
PESTICIDES
5.1 INTRODUCTION
Pesticides dissipate by several mechanisms: chemical degradation (hy-
drolysis; oxidation); biochemical degradation by soil organisms and
enzymatic systems; volatilization; absorption in plant or animal tissue,
with or without decomposition; leaching into subsurface soils, possibly
into subsurface aquifers; and overland transport in surface runoff and
eroded sediment. Losses by leaching processes and by overland transport
mechanisms are relevant to contamination of water. Pesticide loading
functions must relate mechanisms for these processes to quantities de-
posited in surface waters. The total load of pesticide deposited in
surface waters equals the sum of (a) pesticide transported overland,
and (b) pesticide transported by subsurface processes (leaching, soil
moisture movement, drainage water movement, groundwater discharge to
surface). Soluble pesticides are subject to leaching into subsurface
soils and waters, solubilize in overland runoff water, and are also
transported overland as sediment-bound material. Insoluble pesticides
are transported to surface waters primarily by being carried on eroded
sediment.
Data requirements for a precise pesticide loading function are as fol-
lows:
1. Quantity of pesticide in the source, expressed as some suitable
function of the area, volume, or mass of the source, e.g., concentration
in erodible soil layer; concentration and concentration distribution in
leachable soil profile. The quantity information should be time spe-
cific, i.e., detail source quantities/concentrations as a function of
time elapsed since application, season, etc. Since most pesticides de-
grade, rates of degradation are needed to enable calculation of source
quantities as a function of time.
114
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2. Quantitative data on overland runoff, by month, season, and year.
3. Quantitative data on sediment transported from the source and deliv-
ered to surface streams.
4. Quantitative data on percolation; seepage; drainage water inventories
and movement; and groundwater inventories and movement.
5. Accurate coefficients, rate constants, etc., for desorption--
solubilization--leach transport of pesticides through soil columns, of
numerous possible soil types.
6. Information on miscellaneous modes of pesticide movement or deposition
such as by volatilization, by removal in harvested vegetative matter, and
direct deposition in surface waters.
Some of the required data is not available or is unknown, and other data
are known or available in varying accuracy and degree of coverage of
source situations.
The approach to estimation of contamination of water by pesticides will
therefore vary in response to a combination of three factors: (a) degree
of required accuracy; (b) availability of data; and (c) capabilities of
predictive functions. The greatest impediment is lack of data. Loading
functions and approaches to estimation of pesticide pollution are pre-
sented, in succeeding sections, for three source conditions. These are:
Case 1 - Water Insoluble Pesticides: Average concentrations of pesticide
in soils known. Pesticide load is calculated as a function of sediment
loads. Approach most applicable to large areas. Use limited to annual
average loads.
Case 2 - Water Insoluble Pesticides: Pesticide use history accurately
known, soil analytical data current and extensive, pesticide properties
(especially rates of disappearance) well known. Calculate load as fun-
ction of sediment loss; useful for annual average, 30-day maximum, 30-day
minimum.
Case 3 - Water Soluble and Water Insoluble Pesticides: Concentrations
in runoff waters known, runoff water flows known (stream source approach).
Calculate loads at watershed discharge points, distribute load over water-
shed land uses in proportion to known or probable pesticide use.
These approaches or options do not treat pesticides discharged to ground-
water aquifers and subsurface drainage. The latter can be treated if
115
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drainage discharge flows are known, together with concentrations of pes-
ticides in the drainage. Pesticide contamination in groundwater aquifers
is presently a research area.
These approaches do not preclude the use, in special or highly documented
situations, of research models or approaches which are being locally de-
veloped by research scientists.
5.2 PESTICIDE LOADING FUNCTIONS
5.2.1 Case 1; Insoluble Pesticides, Average Soil Concentrations Known
The loading function is:
Y(HIF)* = Y(S)E-C(HIF).rHIF=10"6 (5-1)
where Y(HIF) = pesticide yield for source, kg/day (Ib/day)
Y(S)E = sediment yield, kg/day (Ib/day)
C(HIF) = concentration o£ pesticide in soil (ppm)
rHIF = enrichment ratio
Sediment yields, YCS)^ , for the source are calculated by methods pre-
ill
sented in Section 3.0.
Pesticide concentrations in cropland soils throughout the United States
are being monitored by the Environmental Protection Agency, Office of
Pesticide Programs, in the National Pesticide Monitoring Program (NPNP).
The data base emphasizes persistent pesticides, and does not cover soils
outside croplands. It therefore is a limited source of historical data,
and should be used accordingly. Results for 35 pesticides are summarized,
for FY 1969 in Pesticides Monitoring Journal, 6(3):194-228, 1972. (This
article is reproduced in Appendix F.) The FY 1969 data cover cropland
soils in 43 states and noncropland soils in 11 states.
The NPMP FY 1969 data may be used, with considerable discretion, as C(HIF)
values in Eq. (5-1). The "range of detected residues" will serve as in-
put for calculation, with Eq. (5-1), of the range of pesticide loads which
may be expected in the area of interest. Simiarly, the "percent positive
sites" indicate whether a particular pesticide is distributed over much of
HIF denotes Herbicide, Insecticide, and Fungicide.
116
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the area or has limited distribution. The NPMP information thus tends
to be useful chiefly for estimating possible extremes in pesticide loads
from large areas and is not applicable to unmonitored areas such as
forestland.
It is imperative that the user of this function obtains up-to-date site
or area-specific data on soil concentrations. Current NPMP data should
be consulted, as should local sources of data, notably universities,
state and local health departments, and environmental agencies.
5.2.2 Case 2: Water Insoluble Pesticides, Current Area-Specific Data
Available
Case 2 covers the source with well-documented concentration data obtained
by analysis of samples taken from the source, in combination with pesti-
cide use data and knowledge of the persistence of the pesticide. If the
source is sampled frequently at well-distributed sampling sites, other
information may be unnecessary. If the sampling is less complete, in-
formation on application rates and persistency will help deduce concen-
trations. The basic loading function is the same as for Case 1, i.e.,
Eq. (5-1). The values used for C(HIF) are determined from different
sources than the sources for Case 1. Guidelines for determining C(HIF)
follows:
1. Document beginning of the season residual concentrations, if any, of
pesticides of interest.
2. Obtain data on application rates and schedules. Estimate concentra-
tion in surface soils (3 to 5 cm (1 to 2 in.)) of applied pesticide, tak-
ing into account the fraction of the pesticide which reaches the soil
surface, and the depth the pesticide is mixed into the soil.
3. Add values from Steps 1 and 2 to obtain an initial concentration.
4. From information on pesticide persistency, estimate fraction of pes-
ticide which remains after appropriate intervals of time: days for short
lived pesticides; months for pesticides with growing season persistency;
and years for long-lived pesticides.
5. If pesticide is applied more than once per season, repeat Steps 1,
2, 3, and 4 for each application and estimate concentration throughout
growing season and up to the start of the next growing season.
6. Calculate sediment loads, Y(S)g , from sources by procedures pre-
sented in Section 3.0.
117
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Calculate annual average Y(S) if pesticides are relatively persistent
and a reasonable yearly average value can be deduced. Calculate Y(HIF)
from Eq. (5-1):
Y(HIF) average = Y(S)E average-C(HIF) average-lQ-6
Calculate Y(S)E by months if pesticide concentrations vary widely through-
out the year. Calculate Y(HIF) annual average, 30-day maximum and 30-day
minimum by calculating monthly loads.
Y(HIF) monthly = Y(S)E monthly C(HIF) • 10~b
Sum for a year to obtain annual average. Select 30-day maximum and 30-
day minimum from computed monthly loads.
5.2.3 Case 3: Water Soluble and Water Insoluble Pesticides, Stream to
Source Approach
Water soluble pesticides are in part transported overland in surface
runoff and absorbed on sediments; they are also susceptible to migration
downward in the soil column, where they are not subject to overland trans-
port mechanisms. For lack of a procedure for predicting the ultimate fate
of the fraction which moves downward from the surface, it has been by-
passed in loading function development. That fraction transported over-
land may be estimated if runoff is measured and analyzed for pesticides.
Specifically, watershed hydrographs for storm events must be determined
by measurement, or calculated from predictive models,!^' and concentra-
tions of pesticides determined for water samples collected at various
stages of the hydrograph(s)." The data so obtained convert to pesticide
loads by multiplying increments of flow by the respective concentration
values:
Y(HIF) storm event = SQ^-a (5-2)
where Qj_ = increment of flow
GI = C(HIF) of the ith increment of flow
a = 10 if dimensions of Q and C are liters and ppm
a = 62 x 10~° if dimensions of Q and C are feet3 and ppm
Units of Y(HIF): kilograms (Ib)
Base flow (nonstorm event) stream data on flows and concentrations
will not suffice. Many pesticides decompose in water and may be-
come trapped in bottom sediments. Concentrations under base flow
conditions do not accurately reflect storm event loads.
118
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The storm event load can be distributed back to the land by several op-
tions; for example:
Uniformly over the watershed.
Nonuniformly to broad categories of sources, e.g., row crops.
Specifically to identified or suspect sources, in proportion to
source size.
It will be necessary to sum storm events for the season, perhaps for the
year, to obtain annual loads. The 30 day maximum loadings fall naturally
out of cumulative storm event loads.
This procedure has several limitations and disadvantages. Extensive use
will be costly, and limited use will not suffice to adequately describe
large areas. An appropriate use is as follows: with selective runoff
measurement and analysis it will be possible to develop the relatively
modest inventory of data and experience needed to estimate pesticide
loads for sensitive areas, e.g., an intensive agricultural area which
depends heavily on herbicides and insecticides, and has a relatively
stable and predictable pattern of use. Combination of accumulated in-
formation on pesticide use patterns with representative measured con-
centrations and loads of pesticides will more than adequately serve as
a predictive "loading function." Since many of the persistent pesti-
cides are being phased out, the peak loads which occur in storms which
follow pesticide application are increasingly important. This basic
approach will, if properly used, deal with this problem adequately.
5.3 GENERAL INFORMATION
5.3.1 Pesticide Solubility
The dividing line between solubility and insolubility is diffuse and is
affected by factors such as the presence of other constituents in the
solution phase, pH, soil acidity, and organic matter in soil. Solubility
denotes, for purposes of the handbook, relatively little to moderate re-
sistance to leaching, and insolubility denotes moderate to high resistance
to leaching. Limited solubility data and indices of leachability are
presented in Appendix G, Table G-2. A pesticide with a leaching index
of one or two is treated as "insoluble." An index of three or four is
treated as "soluble."
5.3.2 Pesticide Persistence
General information on persistence is presented in Appendix G. Particu-
larly relevant to load calculation are the data which, though only semi-
quantitative, permit estimation of rates of disappearance in soils. Resi-
dues, concentrations and percent losses of selected pesticides are compiled
from recent literature and presented in Table G-3.
119
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5.4 LOAD CALCULATION: EXAMPLES
Case 1 Method
Conditions: Refer to Section 3.0, entitled "Sediment Loading Function."
Dieldrin
Continuous corn
A = 73 ha
Y(S)E (30-day maximum) = 117 kg/ha/day
Y(S) (annual average) = 36 kg/ha/day
C(I) range, 0.01 to 0.58 ppm from Appendix F, Table F-3
Probable minimum load, annual average
Y(I) = 36-0.01-73 x 10-6 = 26 x 10'6 kg/day
Probable maximum load, annual average
Y(I) = 36-0.58-73 x 10-6 = 1,524 x 10'6 kg/day
Case 2 Method
Conditions: Refer to above example.
2,4-D
Application rate: 5 kg/ha
Application date: 15 June
Persistence: 4 weeks (Appendix G)
Residue zero at season start
Y(S)E = 117 kg/ha/day, for 1-month period, 15 June to 15 July
Calculations
Initial concentration in erodible soil layer (5 cm), about 5 ppm
Average concentration, estimated from persistence information
equals 2 to 3 ppm for 15 June to 15 July period
Y(H) = 117 x 2.5 x 73 x 10'6 = 0.0214 kg/day
Y(H) (30-day maximum) = 0.0214 kg/day
5.5 LIMITATIONS IN USE
As stated earlier, pesticide behavior in the environment is both complex
and variable, and the accuracy of estimation reflects these complexities.
120
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The National Pesticide Monitoring Program, which serves as the basis for
Case I, contains data which generally indicate levels of pesticides in
soils throughout the country- and the frequency at which pesticides are
observed is an indication of the intensity of the use pattern. The data
and the Case 1 method should, however, be used only to derive an estimate
of loads over very large areas, and the results should be presented with
two qualifications: (a) that peak loads for nonpersistent pesticides are
apt to be overlooked by the method; and (b) that pesticides which leach
readily into the soil (and thus may contaminate subsurface waters) will
not be accounted for. Examination of the range of values reported in the
NPMP system reveals the fact that loads calculated from that data base
may differ substantially from actual loads, especially if one wishes to
apply calculated loads to a specific small area.
The Case II method depends upon area-specific and pesticide-specific data,
and thus will calculate loads considerably closer to actual values than
Case I. Since the data requirement is fairly extensive, its use is prob-
ably restricted to a small region—several counties perhaps—in which
pesticide use is uniform and other parameters are also relatively uniform.
The Case II method will, with care in use, be somewhat sensitive to peak
loads, i.e., when it rains soon after pesticide application.
The Case III method can be accurate with care in use. As indicated in
Section 5.2, the approach is probably best used to develop data and ex-
perience at local or regional levels, so that pesticide loads can be
estimated with confidence from a base of accumulated local experience.
Estimates of accuracy expected for Cases I through III are presented in
Table 5-1.
121
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Table 5-1. ESTIMATES OF ACCURACY FOR PESTICIDES
Case 1 method
(insoluble pesticide)
Case 2 method
(insoluble pesticide)
Case 3 method
(soluble and insoluble
Annual average
(g/ha/year)
Probable
Estimated range
1-10 0.001-100
1 0.01-10
20 5-50
1 0.1-5
20 10-50
Storm event
(g/ha/day)
Probable
Estimated range
Not applicable
1 0.1-10
20 5-50
I 0.1-5
20 10-50
pesticides)
122
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References
1. Holton, H. N., and N. C. Yokes, "USDA HL-73 Revised Model of Water-
shed Hydrology," Plant Physiology Report No. 1, ARS-USDA (1973).
2. Crawford, N. H., and R. K. Linsley, "Stanford Watershed Model IV,"
Stanford University, Stanford, California, Technical Report No.
39 (1966).
3. Crawford, N. H., and A. S. Donigian, Jr., "Pesticide Transport and
Runoff Model for Agricultural Lands," Office of Research and
Development, U.S. Environmental Protection Agency; EPA-660/2-74-
013 (December 1973) .
4. Frere, M. H., C. A. Onstad, and H. N. Holtan, "ACTMO, an Agricultural
Chemical Transport Model," ARS-H-3, ARS-USDA, June 1975.
123
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Bibliography
Bailey, G. W. , and J, L0 White, "Review of Adsorption and Desorption of
Organic Pesticides by Soil Colloids with Implications Concerning Pes-
ticide Bioactivity," J. Agr, Food Chem., 12_ (1964).
Edwards, C. Ao, "Insecticide Residues in Soils," Res, Reviews, 13. (1966).
Frissel, M0 J0, "The Adsorption of Organic Compounds, Especially Herbi-
cides on Clay Minerals," Verslag, Landbouwk, 7j5 (1961)0
Getzin, L. W., "The Effect of Soils Upon the Efficiency of Systemic In-
secticides with Special Reference to Thimet," Dissertation Abstract,
1£ (1958).
Hamaker, J0 W., Mathematicl Prediction of Cumulative Levels of Pesticides
in Soils, Advances in Chemistry 60-Organic Pesticides in the Environ-
ment, American Chemical Society, Washington, D.C0
Kiigemagi, U., "Biological and Chemical Studies on the Decline of Soil
Insecticides," J. Econ0 Entomol., 5_1^ (1958).
Lichtenstein, E. P., and K0 R. Schulz, "Insecticide Residues Colorimetric
Determinations of Heptachlor in Soils and Some Crops," J. Agr. Food
Chem., 12. (1964).
Nauman, K», Einfluss von Pflanzenschutzmittel auf die Bodenmikroflora,
Hit, Biol. Bund., anst. Berlin, £7 (1959).
"Production, Distribution, Use and Environmental Impact Potential of
Selected Pesticides," Final Report by Midwest Research Institute,
Kansas City, Missouri and RvR Consultants, Shawnee Mission, Kansas,
March 1974.
UoS. Department of Agriculture, "Quantities of Pesticides Used by
Farmers in 1971," Economic Research Service, Washington, D.C., in
press (1974).
124
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SECTION 6.0
SALINITY IN IRRIGATION RETURN FLOW
6.1 INTRODUCTION
The accurate prediction of salinity emissions in irrigation return flows
requires detailed knowledge of the particular system being studied. Prac-
tice has shown that salinity in irrigation return flows varies widely in
differing regions of the country because of the specific natures of the
soils, underlying geological formations, regional topography, and irriga-
tion practices. As a result, a simple "loading function" applicable to
all irrigation cases has no validity under present state of the art. A
discussion of the data needs for irrigation return flow salinity models
pointing out this fact has been prepared by the Environmental Protection
Agency.—'
For purposes of making assessments of salinity from irrigation return
flow, three optional methods are suggested in this section. The user is
cautioned, however, that the methods are not universally applicable and
hence may yield estimates that are not accurate. The most accurate pre-
diction method remains long-term monitoring of the particular irrigation
area to quantify actual salinity outputs in irrigation return flow.
The three procedures presented here for estimating salinity in irrigation
return flow are:
Option I - Source to Stream Approach: The first option involves the es-
timation of irrigation water percolating into groundwater, which finds
its way into surface waters by subsurface return mechanisms. This ap-
proach is valid for only a few areas of the country when valid relation-
ships between applied water and return flows exist. Furthermore, this
option should not be used in cases of spray irrigation where evaporative
losses associated with the applied water are significant. This option is
most valid in those cases where the total dissolved solids in groundwater
125
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contributing to return flow are very high (ca. 10 times) compared to total
dissolved solids concentrations in applied water.
Option II - Stream to Source Approach: The second option involves a back-
estimation procedure for salinity discharges in irrigation return flow.
Salinity measurements taken at sampling points above and below irrigated
areas will establish the amount of salt discharged in the area drained
by the stream between the two points. This salt load, however, includes
that discharged from background, salt springs, and point sources, as well
as that discharged from irrigation return flow. This method requires a
good definition of salinity sources other than irrigation return flow,
particularly that of background. This method is the one which has been
most widely used by others, especially where the total salinity loads are
measured at the discharge points of drainage basins.
Option III - Loading Values for Salinity in Irrigation Return Flow: A
third method for estimating salinity loads in irrigation return flows is
the use of loading values established for given areas through reduction
of stream monitoring data. A list of such loading values for areas in
the Colorado River basin are presented. These values are applicable only
to the particular region and should not be used except where indicated.
6.2 OPTION I: SOURCE TO STREAM APPROACH
6.2.1 Load Estimation Equation and Information Needs
An equation to estimate salinity in irrigation return flow has been form-
ulated based upon data reported by Skogerboe et al.^' The equation is:
Y(TDS)IRF = a-A-C(TDS)GW-[!RR + Pr - Cu] (6-1)
where Y(TDS)IRF = salinity load in irrigation return flow, kg/day (lb/
day)
A = area under irrigation, ha (acre)
IRR = volume of water added to crop root zone annually for
irrigation, cm (in.)
Pr = annual precipitation, cm (in.)
CU = annual consumptive use of water in growing crops, cm
(in.)
126
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C(TDS)GW = average concentration of total dissolved solids in
groundwater contributing to subsurface return, ppm
a = conversion factor to obtain proper units of load. If
Y is in kg/day, a = 2.7 x 10"4; if Y is in lb/
day, a = 6.2 x ICT4
The volume of water applied to the crop root zone, IRR , can be deter-
mined by subtracting the volume of tailwater from the total water de-
livered to the irrigation site. The best information would be available
from local irrigation districts.
Annual precipitation, Pr , is available from local weather data. Aver-
age annual precipitation can be used for purposes of estimating gross
salinity loads.
The CU factor, consumptive use, can be estimated by standard formulae
such as Jensen-Haise Method or the Blaney-Criddle Method. The Jensen-
Haise Method for estimating consumptive use is described in detail in
2 /
the Skogerboe et al. report on irrigation scheduling.—' Information
needed for the Blaney-Criddle consumptive use formula can be found in
Todd's Water Encyclopedia,—' for the West.
The key data needed in the irrigation return flow loading function are
the groundwater total dissolved solids concentrations, C(TDS)GW. These
values represent that groundwater which contributes to perennial stream-
flow. The best data would be that obtained from observation wells in the
irrigation plot, information which is not always available. For large ir-
rigation areas, one should use an average groundwater TDS value obtained
from several observation wells.
The user is cautioned to avoid using the Option I method for cases in-
volving sprinkler irrigation methods. This method does not account for
evaporation losses during application. If valid information is available
concerning evaporation losses, it should be incorporated into the esti-
mation procedure. Evaporation basically will cause an increase in the
TDS of applied water which will show up as increased TDS in the ground-
water contributing to return flow.
6.2.2 Load Calculation - Irrigation Return Flow
Load calculation involves three basic steps:
1. Obtain necessary information for Eq. (6-1) from sources identified
above.
127
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2. Substitute values into Eq. (6-1).
3. Compute loads.
The Option I loading value equation (Eq. (6-1)) has been used to esti-
mate loads which can be compared directly to those reported by Skogerboe
et al.—/ Data used as inputs to the equation were those measured by
Skogerboe. Data inputs for Eq. (6-1) are tabulated in Table 6-1, to-
gether with calculated loads. These are compared with reported loads.
Table 6-1. COMPARISON OF SALINITY LOADS OBTAINED WITH OPTION I LOAD
ESTIMATION EQUATION WITH REPORTED SALINITY LOADS^/ IN THE
GRAND VALLEY, COLORADO
(Essential information: a = 6.2 x 10'^; C(TDS)GW = 6,700 ppm)
Equation
factors Plot No. 1 Plot No. 2 Plot No. 3 Plot No. 4 Plot No. 5
A (acre)
IRR (in.)
Pr (in.)
CU (in. )
Calculated
load
(Ib/day)
Reported
load
(Ib/day)
8.5
31.4
1.0
26.9
194
379
8,
23,
4.1
19.
293
344
25.7
42.1
1.2
33.5
1,046
15.0
29.1
2.7
20.7
692
1,291
521
10.7
24.7
3.3
17.1
484
545
As can be seen from the comparison, the calculated loads compare reason-
ably with reported loads in four out of the five cases. One reason for
discrepancies between the calculated and reported values may be that the
equation disregards changes in soil moisture storage during the year.
In general, the changes in soil moisture storage which occur during and
between irrigation events should add to zero over an annual period, and
hence would have little effect on annual irrigation return flow volume.
Some irrigation water applied to the crop root zone is retained as soil
moisture, and hence does not show up as either consumptive use or irriga-
tion return flow. Soil moisture storage is an information input which is
not readily accessible.
128
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The Option I loading value equation should be considered only as a first
approximation method for estimating salinity in irrigation return flow.
Its usefulness will depend primarily upon three factors: (1) the concen-
tration of total dissolved solids in shallow groundwater which is trans-
mitted to surface waters as subsurface return; (2) reliable estimates of
the volume of applied water, tailwater, and return flows; and (3) good
information pertaining to consumptive losses in the complete irrigation
system. If these data are deemed insufficient, one should estimate sa-
linity in irrigation return by other procedures.
6.3 OPTION II: STREAM TO SOURCE APPROACH
6.3.1 Loading Equation and Information Needs
A second method for estimating salinity loads from irrigation return
flow involves the stream to source approach. In this option, salinity
loads in streams are determined above and below areas of irrigation.
Differences in salinity loads represent total salt being discharged by
the area by background and point sources, as well as irrigation return
flow. Therefore, salt loadings from irrigation return flow are deter-
mined by subtracting out contributions from background and from point
sources.
The Option II loading value equation is:
Y(TDS)IRF = a-[Q(str)B-C(TDS)B - Q(str)A-C(TDS)A] - Y(TDS)BG - Y(TDS)pT (6-2)
where Y(TDS)-j-Rp = yield of salinity in irrigation return flow, kg/day
(Ib/day)
Y(TDS)BQ = salinity load contribution of background, kg/day (lb/
day)
Y(TDS)pT = salinity load contribution of point sources, kg/day
(Ib/day)
Q(str)r, = streamflow of surface water below irrigated areas,
£> ——
liters/sec (cfs)
Q(str)^ = streamflow of surface waters above irrigated areas,
liters/sec (cfs)
C(TDS)B = concentration of total dissolved solids in stream
below irrigated area, ppm
129
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C(TDS) = concentration of total dissolved solids in stream
above irrigated areas, ppm
a = conversion constant needed to obtain proper units of
load. If flow units are liters/sec, a = 0.0864
(metric system, kg/day). If flow units are cfs,
a = 5.39 (English system, Ib/day).
Flow and concentration data obtained above and below irrigated areas can
be obtained from U.S. Geological Survey records of the region, or in some
cases from local water quality monitoring data. The use of these data in
the loading value equation will indicate total salt added to surface waters
between two points.
The salt load from point sources in the area under consideration can be
determined using information supplied by persons responsible for the point
sources. Point source contributions may be estimated from data contained
in discharge permit applications available from state and local pollution
control agencies, and from regional Environmental Protection Agency offices,
The total dissolved solids from the individual point sources in the area
are summed to yield total point source contributions.
The most difficult piece of information to be obtained is quantities of
salt discharged from background. In many cases, particularly in the arid
and semiarid regions where irrigation is intensive, this estimation can
only be accomplished by knowledge of the characteristics of the particular
area.
This estimation relies upon the judicious use of information concerning
background in a particular region. The use of broad general definitions
of background such as those presented in Section 12.0 of this handbook is
not recommended for the Option II method for salinity in irrigation return
flow. An estimation of background TDS levels may be made using the U.S.
Geological Survey's Hydrologic Investigations Atlas, HA-61, Plate 1.4/
This plate contains information concerning dissolved solids concentration
for surface waters throughout the conterminous United States. It does
not differentiate between point and nonpoint contributions to salinity,
nor does it account for cumulative effects of runoff from a wide variety
of sources into stream water. The use of this map is recommended as a
first approximation of background.
The equation needed to define background total dissolved solids load can
be formulated in two ways, depending upon the units of flow. If flow is
measured as annual average runoff, the equation is:
130
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Y(TDS)BG = a-A-Q(R)-C(TDS)BG (6-3)
where Y(TDS)BG = salinity load from background, kg/day (Ib/day)
A = area under consideration, ha (acre)
Q(R) = flow, as annual average runoff, cm (in.)
C(TDS)BQ = concentration of background total dissolved solids as
determined by local information, ppm
a = conversion constant to obtain proper units of load.
If load is kg/day, a = 2.7 x 10"4; if load is Ib/day,
a = 6.2 x 10-*.
If flow is measured as actual flow in liters per sec (cfs), the equation
for estimating salinity loads in background becomes:
Y(TDS)BG = a-C(TDS)BG-[Q(str)B - Q(str)A] (6-4)
where Q(str)B and Q(str)^ are the flows below and above the irrigated
areas, respectively. If the load is kg/day, a = 0.0864; if the load is
Ib/day, a = 5.39. The concentration of total dissolved solids in back-
ground, C(TDS)BQ , is the same as defined previously.
After proper information has been obtained, it is substituted into the
correct background total dissolved solids equation (Eqs. (6-3) or (6-4)),
and background total dissolved solids load computed.
6.3.2 Option II Load Calculation
The step-by-step procedure presented below is used for Option II stream
to source load calculations.
1. Obtain needed flow and concentration information for points above and
below irrigated areas. In many cases, information obtained at the mouth
of a drainage basin containing irrigated agriculture is sufficient, thus
obviating the need for above stream data.
2. Estimate total salinity loads above and below irrigated areas using
proper flow and concentration data. The total salinity load from irri-
gated areas, including its nonirrigated land uses, is determined by sub-
tracting upstream load from downstream load, via Eq. (6-2).
131
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3. Obtain data pertaining to point source contribution and sum indi-
vidual point sources to obtain total point load.
4. Determine background total dissolved solids load using Eqs. (6-3)
or (6-4) and procedures outlined previously in this section.
5. Estimate salinity load from irrigation return flow by subtracting
values obtained in Steps 3 and 4 from the value obtained in Step 2.
Y(TDS)IRF = Step 2 - Step 3 - Step 4
= Y(total) - Y(background) - Y(point)
The Option II stream to source approach for estimating salinity loads
in irrigation return flow has been applied to several subbasins of the
Colorado River. Values generated by the Option II load estimation equa-
tion have been compared with values reported by the Environmental Pro-
tection Agency in Appendix A to their report concerning the "Mineral
Quality Problem in the Colorado River Basin." Results of the compari-
son are presented in Table 6-2.
Table 6-2. COMPARISON OF SALINITY LOADS ESTIMATED BY OPTION II
METHODS WITH THOSE REPORTED BY EPA§/
Basin
Black Forkk/
Gunnison£'
Big Sandy Creek^/
White£/
Flow at
basin
mouth
(cfs)
663
3,100
140
901
C(TDS) at
basin
mouth
(ppm)
495
558
2,190
472
C(TDS)BG
estimate
(ppm)
200
200
1,300
300
Calculated
load using
Option II
(tons/day)
527
2,990
336
217
Reported
load
(tons/day)
481
3,100
200
20
a/ U.S. Environmental Protection Agency, Regions VIII and IX, "Natural
and Man-Made Conditions Affecting Mineral Quality," Appendix A of
EPA Report, The Mineral Quality Problem in the Colorado River
Basin (1971).
b_/ Reference a, Figure 20.
£/ Reference a, Figure 34.
d/ Reference a, Figure 18.
e/ Reference a, Figure 25.
132
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From the data in Table 6-2, it is seen that Option II tends to overpre-
dict salinity in irrigation return flow. The overprediction may be due
to conservative estimates of background contributions, or to emissions
from unknown natural point sources such as salt springs. The data in
Table 6-2 clearly point out the fact that background, particularly that
in arid or semiarid areas, needs to be carefully considered. For example,
the high background level in the Big Sandy Creek area is due to water
seepage from saline lake beds in the area. Such characteristics must be
known if the Option II approach is to yield valid results.
6.4 OPTION III: LOADING VALUES FOR SALINITY LOADS IN IRRIGATION RETURN
FLOW
Perhaps the most useful method of estimating salinity loads is through
loading values determined for particular regions. Lists of such values
are presented in Tables 6-3 through 6-7 for subbasins in the Colorado
River basin, and for irrigated regions in California.
Studies in the Twin Falls area and the Colorado River basin indicate that
the range of values for salt pickup from irrigated lands is roughly 1.3
to 22 MT/ha/year (0.5 to 8 tons/acre/year).£' An average salt pickup rate
might be 5 MT/ha/year (2 tons/acre/year). On a per day basis, the range
becomes 3 to 50 kg/ha/day (3 to 44 Ib/acre/day), and the average becomes
12 kg/ha/day (11 Ib/acre/day).
6.5 ESTIMATED RANGE OF ACCURACY
The accuracy of the three optional procedures for estimating salinity
loads from irrigation return flow will be no better than the accuracy of
the input data. For this particular system, the quality of the input
data is likely to be quite variable. More often than not, the quality
of input data will be less than that desired by the user. In addition,
the estimation procedure mechanisms tend to compound errors inherent in
the input data.
With these factors taken into account, ranges of error for Options I and
II have been estimated. The Option III method--loading values — is the
most accurate method if proper input data are available. However, its
use requires loading values generated from on-site data, and such data
are most often not available.
Table 6-8 presents the estimated range of error for the Option I (Source
to Stream) procedure. The error is estimated for several ranges of areas
which emit an average annual load of either 1 or 10 MT/year.
133
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Table 6-3. SALT YIELDS FROM IRRIGATION IN GREEN RIVER SUBBASIN3./
Average salt yield
Area (tons/acre/yr) (kg/ha/day) (Ib/acre/day)
Green River above New Fork River 0.1 0.6 0.5
Big Sandy Creek 5.6 34.3 30.7
Blacks Fork in Lyman area 2.4 14.7 13.2
Hams Fork 0.3 1.8 1.6
Henry's Fork 4.9 30.1 26.9
Yampa River above Steamboat Springs 0.2 1.2 1.1
Yampa River, Steamboat Springs to Craig 0.4 2.5 2.2
Milk Creek 1.0 6.1 5.4
Williams Fork River 0.3 1.8 1.6
Little Sanke above Dixon 0.3 1.8 1.6
Little Sanke, Dixon to Baggs 0.5 3.1 2.7
Ashley Creek 4.2 25.8 23.0
Duchesne River 3.0 18.4 16.4
White River below Meeker 2.0 12.3 11.0
Price River 8.5 52.2 46.6
San Rafael River 2.9 17.8 15.9
a/ U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
Mineral Quality Problem in the Colorado River Basin (1971).
Table 6-4. SALT YIELDS FROM IRRIGATION IN UPPER COLORADO MAIN STEM SUBBASIN*./
Average salt yield
Area (tons/acre/yr) (kg/ha/day) (Ib/acre/day)
Main stem above Hot Sulphur Springs 0.3 1.8 1.6
Main stem, Hot Sulphur Springs to 0.9 5.5 4.9
Kremmling
Muddy Creek Drainage Area 2.4 14.7 13.2
Brush Creek 0.7 4.3 3.8
Roaring Fork River 3.5 21.5 19.2
Colorado River Valley, Glenwood Springs 2.3 14.1 12.6
to Silt
Colorado River, Silt to Cameo 3.5 21.5 19.2
Grand Valley 8.0 49.1 43.8
Plateau Creek 0.9 5.5 4.9
Gunnison River above Gunnison 0.3 1.8 1.6
Tomichi Creek above Parlin 0.3 1.8 1.6
Tomichi Creek, Parlin to mouth 0.3 1.8 1.6
Uncompahgre above Dallas Creek 4.5 27.6 24.7
Lower Gunnison 6.7 41.1 36.7
Naturita Creek near Norwood 2.8 17.2 15.3
a/ U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
Mineral Quality Problem in the Colorado River Basin (1971).
134
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Table 6-5. SALT YIELDS FROM IRRIGATION IN SAN JUAN RIVER SUBBASIN^-/
Average salt yield
Area
Fremont River above Torrey, Utah
Fremont River, Torrey to
Hanksville, Utah
Muddy Creek above Hanksville, Utah
San Juan above Carracas
Florida, Los Finos, Animas drainage
Lower Animas Basin
LaPlata River in Colorado
LaPlate River in New Mexico
(tons/acre/yr)
0.4
5.8
3.1
2.7
0.2
3.5
1.4
0.3
(kg/ha/day)
2.5
35.6
19.0
16.6
1.2
21.5
8.6
1.8
(Ib/acre/day)
2.2
31.8
17.0
14.8
1.1
19.2
7.7
1.6
a/ U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
Mineral Quality Problem in the Colorado River Basin (1971).
Table 6-6. SALT YIELDS FROM IRRIGATION IN LOWER COLORADO RIVER BASIN^
•a/
Average salt yield
Area
Virgin River
Colorado River Indian Reservation
Palo Verde Irrigation District
Below Imperial Dam
(Gila and Yuma projects)
(tons/acre/yr)
2.3
0.5
2.1
variable
(kg/ha/day)
14.1
3.1
12.9
_
(Ib/acre/day)
12.6
2.7
11.5
_
a/ U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
Mineral Quality Problem in the Colorado River Basin (1971).
135
-------
Table 6-7. SALT YIELDS FROM IRRIGATION FOR SELECTED
AREAS IN CALIFORNIA^/
Average salt yield
/ Y
Area
(tons /acre /year)
0.353
0.808
0.707
0.974
0.827
0.768
10.9
(kg/ha/day)
2.2
5.0
4.3
6.0
5.1
4.7
67
(Ib/acre/day)
1.9
4.4
3.9
5.3
4.5
4.2
60
North coastal
Central coastal
Sacramento
Delta-Central Sierra
San Joaquin
Tulare
Colorado Desert
a/ California Regional Framework Study Committee for Pacific Southwest
Inter-Agency Committee, Water Resources Council, "Comprehensive
Framework Study, California Region, Appendix XV, Water Quality,
Pollution, and Health Factors," June 1971.
Table 6-8. ESTIMATED RANGE OF ACCURACY FOR OPTION I (SOURCE TO STREAM)
PROCEDURE FOR ESTIMATING SALINITY FROM IRRIGATION RETURN FLOW
Area
considered
(ha)
< 100
100 - 1,000
1,000 - 10,000
> 100,000
Calculated
load
(MT/ha/year)
1
10
1
10
1
10
1
10
Probable range
of loads
(MT/ha/year)
0.7 - 1.5
8-13
0.5 - 3
6-15
0.3 - 5
4-20
0.1 - 10
2-25
136
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As can be seen from the table, the Option I procedure'is deemed most ac-
curate vhen used for small areas, and when larger loads are calculated.
This aspect of accuracy arises because the Option I function is totally
dependent upon local conditions such as total dissolved solids in ground-
water, water consumptive use variations from crop to crop, irrigation
water supplied to specific fields, and variation of deep percolation
losses* If any of these data are extrapolated to larger areas, the vari-
ations in input data become wider, and hence the procedure becomes less
accurate for large areas.
In principle, error in the Option I method can be minimized for large
areas by summing up the values obtained for small areas. However, it
is questionable whether such a summation would yield calculated values
with any higher accuracy than those obtained using the Option II method„
Estimated ranges of error for the Option II (Stream to Source) procedure
are given in Table 6-9. When Option II is used, the most accurate loads
will be calculated when large areas are considered. The ranges shown in
Table 6-9 assume that background salinity loads have been carefully con-
sidered. Since these background loads are the most uncertain component
of the procedure, the breadth of the error range is determined by this
uncertainty.
Table 6-9. ESTIMATED RANGE OF ACCURACY FOR OPTION II (STREAM TO SOURCE)
PROCEDURE FOR ESTIMATING SALINITY FROM IRRIGATION RETURN FLOW
Area
considered
(ha)
< 1,000
1,000 - 10,000
> 100,000
Calculated
load
(kg/ha/day)
1
10
1
10
1
10
Probable range
of loads
(kg/ha/day)
0.2 - 5
4-30
0.5 - 3
6-20
0.8 - 1.5
8-13
137
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The Option II method is deemed to be less accurate when small areas are
considered. The decrease in accuracy for small areas is inherent due to
uncertainty in flow measurements as well as uncertainty in background.
In general, small areas are associated with small streams draining the
area. The amount of variation for small streams is usually quite high
(and more unpredictable) than that of large streams.
No estimate of error has been given for the Option III procedure for
estimating salinity loads from irrigation return flow. The accuracy
of this option—use of salinity loading values—depends chiefly on the
trouble taken by the user to characterize his region and develop site-
specific information on his loadings. This option can be the most ac-
curate of the three discussed, provided that the values used are accu-
rate.
The availability of accurate loading values for the Option III approach
is quite limited. Accurate values can be obtained through long term
monitoring and analysis of irrigated areas, an expensive and time con-
suming operation,, However, various mathematical methods for predicting
salinity in irrigation return flow are being developed. These models
will tend to describe the complicated relationships between the water
used for irrigation and the land being irrigated which result in salin-
ity emissions. It may be that at some future time, these models will
be sufficiently validated so that their outputs can produce loading
values for use in the Option III procedure. The user of this handbook
is encouraged to keep abreast of these modeling projects so that their
output can be used to obtain accurate estimates of salinity from irri-
gation return flow.
138
-------
REFERENCES
1. Hornsby, A. G., "Prediction Modeling for Salinity Control in Irriga-
tion Return Flow," U.S. Environmental Protection Agency, Report
No, EPA-R2-73-168, March 1973.
2. Skogerboe, G. V., W. R. Walker, J. H. Taylor, and R. S. Bennett,
"Evaluation of Irrigation Scheduling for Salinity Control in Grand
Valley," Grant No. S-800278, U.S. Environmental Protection Agency,
Report No. EPA-660/7-74-052, June 1974.
3. Todd, D. K., The Water Encyclopedia, pp. 101-108, Water Information
Center, Port Washington, New York (1970).
4. Rainwater, F. H., "Stream Composition of the Conterminous United
States," U.S. Geological Survey, Hydrologic Investigations Atlas,
HA-61, Washington, D.C. (1962).
5. Skogerboe, G. V., and J. P. Law, Jr., "Research Needs for Irrigation
Return Flow Quality Control," U.S. Environmental Protection Agency,
Report No. 13030-11/71, November 1971.
139
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SECTION 7.0
ACID MINE DRAINAGE
7.1 INTRODUCTION
The emission of acid mine drainage arises from land disturbances created
by coal and metals mining activities. The mine drainage arises because
of atmospheric and hydrologic actions on pyritic materials associated
with the mined materials. The pyritic materials may be in residues left
behind at the mined-out site, or in residues produced by coal processing
or mineral beneficiation. If pyrites (or other sulfurous materials) are
not associated with a particular mined product, e.g., quarrying, sand and
gravel operations, etc., then acid mine drainage will not occur. Thus, the
presence or absence of pyritic materials is the determining factor for
nonpoint emissions of mine drainage.
Mine drainage can arise from active and inactive mines and from under-
ground and surface activities. In addition, mine drainage can arise from
processing wastes, e.g., tailings piles and gob piles. In considering
nonpoint emissions from these latter sources, processing wastes disposed
of on the land surface are considered as surface mines.
Basically, regional mine drainage problems arise because of an assemblage
of individual sources in an area. A procedure for estimating mine drain-
age loads based upon the statistical distribution of individual sources
is presented here as Option I. The procedure was developed using data
gathered by Environmental Quality Systems, Inc., in a study dealing with
estimation of mine drainage emissions in the Monongahela River Basin,!.'
and from data obtained for the Appalachian Regional Commission^/ for their
140
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report concerning mine drainage in Appalachia.—' This procedure is funda-
mentally a source to stream loading function. On the other hand, sulfate
analysis of surface waters are key indicators of nonpoint emissions of
mine drainage, since sulfate is the end product of atmospheric/hydrologic
reactions with pyrite. Thus, an Option II estimation procedure is pre-
sented which uses the stream to source approach -and is based on sulfate
concentrations in surface waters. A brief description of these two op-
tions follow.
Option I - Source to Stream Approach: A loading estimation procedure is
presented which relates the number of total sources in an area, the dis-
tribution of these sources among four categories (active underground,
active surface, inactive underground, and inactive surface), and neutral-
ization of acidic products of pyrite weathering with background alkalinity.
This approach is particularly useful for heavily mined areas of the coun-
try, such as the coal mining regions of Appalachia. In other areas where
mining is less concentrated, this statistical approach may not be adequate.
Option II - Stream to Source Approach: The second option involves compar-
ing sulfate loadings found in surface waters with sulfate loadings ex-
pected from natural background. Increases in sulfate loading as surface
waters move through an area over the background contribution can be at-
tributed to nonpoint emissions of mine drainage in the area. This second
approach should be considered when detailed information about the number
of sources is unknown, where mining density is low, or when streamflow
data are deemed more appropriate to use.
7.2 OPTION I: SOURCE TO STREAM APPROACH
7.2.1 Loading Function and Information Needs
The loading function for the Option 1 approach contains three fundamental
elements: the number of potential sources of mine drainage; the amount
of raw acidity formed from the sources; and the neutralization capacity
of the background. The second element--amount of raw drainage formed--
involves the statistical distribution term to account for the widely var-
iable source-to-source loads arising from the individual sources. The
loading function is:
Y(AMD) = N[Ka-(IAU + IAS + IID + IIS) - Kb-Q(R)-C(Alk)BG] (7-1)
where N = total number of sources which are potential emitters of
acid mine drainage
141
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Statistical Distribution Term
K = constant representing the raw acid load generated by
the "typical" site. A range of values for Ka is
presented in Table 7-1, and discussed in Section
7.2.2.
ITC = load index values for the number of Active Underground
J-b
sources, Active Surface sources, Inactive Underground
sources, and Inactive Surface sources. The load in-
dex values are presented in Table 7-2, and discussed
in Section 7.2.3.
Background Neutralization Term
Kjj = constant representing the neutralization capacity of
background alkalinity for raw acid produced at the
"typical" site. A range of values for K, is pre-
sented in Table 7-1, and discussed in Section 7.2.2.
Q(R) = flow as annual average runoff in the area, cm/year
(in/year)
C(Alk)BG = concentration of background alkalinity in the area,
ppm as CaC03- C(Alk) can be determined through
use of an isoalkalinity map (see Figure 7-1, Section
7.2.4).
7.2.2 Constants Ka and Kb in Option I Loading Function
Description of the acid mine drainage discharge from the "typical" source
was determined by subjecting a number of mine drainage data obtained in
the Monongahela River Basin!' to regression analysis. These data repre-
sented the acid load discharged at specific sites from about 7,000 poten-
tial sites. The regression analysis indicated that the distribution of
mine drainage quantities from the 7,000 sources could be well fit (index
of determination = 0.998) to a hyperbolic function dependent upon (a) the
number of sources, (b) the quantity of mine drainage from the largest
source, and (c) the cumulative amount of mine drainage emitted from all
sources. The regression equation has the form:
lim A'N = 1 (7-2)
—>- fj
.^B.+A'N
142
-------
Table 7-1. VALUES OF Ka AND Kb FOR ACID MINE
DRAINAGE OPTION I LOADING FUNCTION
Units of
load
Metric kg/day
English Ib/day
Value of
a
130
280
Range of
K.,
110-150
250-320
Value of
0.15
0.62
Range of
0.10-0.20
0.35-0.75
Table 7-2. LOAD INDEX VALUES FOR ACTIVE AND INACTIVE
SURFACE AND UNDERGROUND MINES
Fraction
of mines
in category
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Active
underground
0.00
0.33
0.50
0.60
0.67
0.71
0.75
0.78
0.80
0.82
0.83
0.85
0.86
0.87
0.88
0.88
0.89
0.89
0.90
0.90
0.91
Load
Active
surface
0.00
0.08
0.16
0.22
0.27
0.32
0.36
0.39
0.43
0.45
0.48
0.50
0.53
0.55
0.56
0.58
0.60
0.61
0.63
0.64
0.65
index
Inactive
underground
0.00
0.13
0.23
0.31
0.37
0.42
0.47
0.51
0.54
0.57
0.60
0.62
0.64
0.66
0.67
0.69
0.70
0.71
0.72
0.74
0.75
Inactive
surface
0.00
0.03
0.06
0.08
0.11
0.13
0.15
0.17
0.19
0.21
0.23
0.24
0.26
0.28
0.29
0.31
0.32
0.33
0.35
0.36
0.37
143
-------
where A = the quantity of mine drainage from the largest source
N
£B- = the cumulative amount of mine drainage from all sources
i=l1
N = the number of potential mine drainage sources in the
area
N
The ratio of £ Bj_ to N thus determines the acid load from the "typi-
i=l
cal" site. Furthermore, the equation implies that the load will be more
accurate when the number of sources considered becomes very large.
A part of the raw mine drainage generated within the mining area will
have been neutralized by background alkalinity before it is discharged
to surface waters. From the consideration of the background neutraliz-
ing capacity in the Monongahela River basin, it has been possible to
establish values of Ka and K^, for the loading function (7-1) based
upon the regression analysis represented by Eq. (7-2). These values are
presented in Table 7-1.
The value Ka represents the raw acid generated at the "typical" mine
site as determined by Eq. (7-2). The value Kj, represents the neutral-
ization of part of the raw acid by background alkalinity in the area
directly affected by the "typical" site.
7.2.3 Load Index Factors for Option I Loading Function
The Ka values presented in Table 7-1 have been established based on
data pertaining to the Monongahela River basin. In order to use them
in other regions of the country, the Ka term must be corrected to re-
flect the distribution of potential mine drainage sources. This correc-
tion is accomplished through the use of "load index factor" determined
in the following manner:
The total number of sources are separated into four components: number
of active underground (AU), active surface (AS), inactive underground
(IU) and inactive surface (IS). The fraction of each source is deter-
mined for each category by dividing the number of sources in a certain
category by the total number of sources.
After the category fractions have been determined, a load index value is
found in Table 7-2 for each category. The first column of Table 7-1 in-
dicates the fraction of mine in each category; subsequent columns contain
the load index value for each category. This procedure is exemplified in
Table 7-3, using a hypothetical situation involving 1,800 mines.
144
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Table 7-3. EXAMPLE OF DETERMINATION OF LOAD INDEX VALUES
Number of Fraction of
sources sources Load index
Active underground 180
Active surface 450
Inactive underground 630
Inactive surface 540
Total 1,800 1.00 1.48
After fractions of mines have been determined in each category, the ap-
propriate load index value is established for each category by referring
to the appropriate column in Table 7-2. After the individual load index
values have been determined, they are added together to yield a total load
index value required for the loading function. The total is the factor
(IAU + I»s + I-QJ + Ijg) in the loading function.
The load index values have been established by proportionating the total
load and total number of sources (as determined by the regression analy-
sis results of Eq. (7-2)) into contributions from active underground,
active surface, inactive underground, and inactive surface sources in the
Monongahela River basin. The bases for the proportionment were obtained
from data in the 1969 Appalachian Regional Commission report concerning
mine drainage.^/ This exercise yielded a series of four equations defin-
ing load index values for each of the four types of mine drainage sources.
The equations from which the load index values in Table 7-2 were derived
are:
I = — (7-3)
AU 0.10 + nAU U ;
0.34
where n^ , n^g , n-^ , and n^g are the fractions of mines in each of
the four categories.
145
-------
nAU + nAS + nIU + nIS =
(7'7)
The total number of sources in an area is determined by study of state
and local historical records. Basically, what is needed is the number
of active and inactive underground and surface sources. The total num-
ber of sources need not be an exact count; a reliable estimate is quite
satisfactory for use in the loading function.
Information concerning active sources can be found in the annual Minerals
Yearbook, published by the U.S. Bureau of Mines. An alternate source of
information about active sources will be state and local permit programs.
Uncontrolled waste piles associated with active mines should be counted
as active surface mines.
Information about inactive mines may be more difficult to obtain. Prob-
ably the best source of information on inactive mines will come through
analysis of historical records of the area. These records should be
available in state archives.
7.2.4 Background Alkalinity Term for Option I Loading Function
The Kb values presented in Table 7-1 have also been established from
Monongahela River basin data. These too must be corrected in order to
reflect changes in the neutralizing capacity of background. The correc-
tion factors involve alkalinity concentrations in background and average
annual runoff.
Background alkalinity concentrations are determined by locating mining
areas on the iso-alkalinity map (Figure 7-1), estimating concentration,
and using this value in the alkalinity term. If other values of back-
ground alkalinity concentrations are deemed more appropriate than those
shown on the map, then they should be used instead. In areas afflicted
with acid mine drainage emissions, one should be cautious about using
"unaffected" stream values of alkalinity. Although data may have been
generated in areas unaffected by mining activity, unknown sources of
mine drainage may be present which would lower background alkalinity es-
timates.
Average annual runoff can be estimated from standard runoff maps such as
that in the U.S. Geological Survey's National Atlas, Plates 118 and 119.
7.2.5 Procedure for Using Option I Loading Function
The procedure for putting together components of the source to stream
loading function to estimate levels is outlined below.
146
-------
Figure 7-1. Background alkalinity concentrations (ppm
-------
1. Estimate total number of potential mine drainage sources through re-
view of state and local records, permits, etc., as indicated in Section
7.2.3.
2. Establish load index values for the following categories: active
underground, active surface, inactive underground, and inactive surface,
by procedures indicated in Section 7.2.3.
3. Sum up load index values established in Step 2.
4. Determine constant Ka from Table 7-1.
5. Multiply results generated in Steps 1, 3, and 4 to obtain value of
statistical distribution term of the loading function.
6. Determine average annual runoff in area from standard runoff maps,
e.g., U.S. Geological Survey's National Atlas, Plates 118 and 119.
7. Determine background alkalinity from iso-alkalinity map (Figure 7-1)
or from other data deemed to reflect background alkalinity concentrations
more adequately.
8. Determine proper constant K^ from Table 7-1.
9. Multiply values yielded by Steps 6, 7, and 8 to obtain background
alkalinity term.
10. Subtract value obtained in Step 9 from that obtained in Step 5.
11. Multiply value obtained in Step 10 by the total number of mine drain-
age sources established in Step 1. This final step will yield the load
of acid mine drainage being emitted from the mining region under consid-
eration.
7.2.6 Examples of Option I Loading Function Utilization
The mine drainage loading function has been used to estimate loads emitted
from two basins in Appalachia--West Branch Susquehanna, and Allegheny.
These examples are presented to indicate how the mine drainage loading
function can be used.
7.2.6.1 Case I: West Branch Susquehanna
Data Source - Federal Water Pollution Control Administration, Ohio Basin
Region, U.S. Department of the Interior, "Stream Pollution by Coal Mine
148
-------
Drainage in Appalachia," Attachment A to Appendix C of the Appalachian
Regional Commission Report, Acid Mine Drainage in Appalachia, Washington,
D.C. (1969).
Step 1. Number of mine sources N: 4,400
Number of draining sources: 967
Step 2. Load index values (Table 7-2):
Active underground: 19; 2% =0.02
Active surface: 17; 2% =0.02
Inactive underground: 630; 65% =0.65
Inactive surface: 301; 31% = 0.31
Total: 967 100% =1.00
Load indexes: 1^ = 0.07
IAS = 0.02
IIir = 0.66
IIS = 0^5
Step 3. Load index summation total: 0.90
Step 4. Constant K from Table 7-1: 280
CL
Step 5. Calculation of statistical distribution term: 280 x 0.9 = 252
Step 6. Average annual runoff Q(R): 20 in.
Step 7. Background alkalinity C(Alk)gQ (from Figure 7-1): 10 ppm
Step 8. Constant K^, (from Table 7-1): 0.62
Step 9. Calculation of background alkalinity term: 0.62 x 20 x 10 = 124
Step 10. Subtract alkalinity term from statistical distribution term;
252 - 124 = 128
Step 11. Compute acid mine drainage load: 4,400 x 128 = 560,000 Ib/day
Mine drainage (calculated) = 560,000 Ib/day
Mine drainage (reported) = 500,000 Ib/day
149
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7.2.6.2 Case II: Allegheny River Basin (1966)
Data Sources - Appalachian Regional Commission Report, Acid Mine Drainage
in Appalachia (1969); Tybout, R. A., "A Cost Benefit Analysis of Mine
Drainage," paper presented before 2nd Symposium on Coal Mine Drainage
Research, Pittsburgh, Pennsylvania, 14-15 May 1968; and U.S. Bureau of
Mines, Minerals Yearbook, 1966, Washington, D.C. (1967).
Step 1. Number of mine sources N (Tybout): 6,626
Step 2. Load index values (Table 7-2):
Active underground: 228; 3% =0.03
Active surface: 310; 5% - 0.05
Inactive underground: 2,350; 36% =0.36
Inactive surface: 3,738; 56% = 0.56
Total: 6,626 100% =1.00
Load indexes: IAU = 0.10
IAS = o.os
IlU = 0.51
IIS =0.24
Step 3. Load index summation total: 0.93
Step 4. Constant Ka from Table 7-1: 280
Step 5. Calculation of statistical distribution term: 280 x 0.93 = 260
Step 6. Average annual runoff Q: 20 in.
Step 7. Background alkalinity C(Alk)BG (from Figure 7-1): 10 ppm
Step 8. Constant Kb (from Table 7-3): 0.62
Step 9. Calculation of background alkalinity term: 0.62 x 20 x 10 = 124
Step 10. Subtract alkalinity term from statistical distribution term-
260 - 124 = 136
Step 11. Compute acid mine drainage load: 6,626 x 136 = 900,000 Ib/dav
Mine drainage (calculated) = 900,000 Ib/day
Mine drainage (reported) = 866,000 Ib/day
150
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7.3 OPTION II: STREAM TO SOURCE APPROACH
7.3.1 Loading Function and Information Needs
Since acid mine drainage is basically a discharge of sulfuric acid (and
its reaction products), the presence of sulfate in stream water analyses
is often a good indicator of nonpoint emissions from mine drainage
sources. Thus, a comparison of sulfate levels detected in streams with
that expected from natural background will yield an estimate of nonpoint
emissions of mine drainage. The loading function can be expressed in
two ways, depending upon the units of flow.
Y(AMD) = a-A-Q(R)-[C(S04) - C(S04>BG - C(S04)pT] (7-8)
Y(AMD) = a-Q(str)-[C(S04) - C(S04)BG - C(S04)PT] (7-9)
where Y(AMD) = yield of acid mine drainage, kg CaC03/day (Ib CaCO-/day)
A = area containing mine drainage sources, ha (acre)
Q(R)» Q(str) = flow; Eq. (7-8) requires flow units Q(R) as annual
average runoff, in cm/year (in/year). Equation (7-9)
requires flow units Q(str) as streamflow in liters/
sec (cfs).
C(S04) = concentration of sulfate in surface waters, ppm
C(S04)gQ = concentration of sulfate in surface waters attributable
to background, ppm
C(S04)p'j = concentration of sulfate in sources attributable to
point sources, ppm
a = conversion constant for obtaining proper load
The two key elements of this loading function are in the conversion fac-
tor a and in the concentration of background sulfate C(S04)gQ. The
value of a to be used in the loading function is determined by the
units of flow. A table of a values is presented in Table 7-4. The
values take into account the conversion of sulfate concentrations (in
ppm) to their calcium carbonate equivalents (ppm as CaC03). This con-
version is necessary in order to obtain load units of kilograms
per day (Ib CaC03/day) .
151
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Table 7-4. CONVERSION FACTORS a TO BE USED FOR
OPTION II MINE DRAINAGE LOADING FUNCTION
Sulfate
concentration
units
ppm
ppm
ppm
ppm
Units of
flow
Q
cm/year
in/year
liters/sec
cfs
Units of
area
A
ha
acre
Value of
a
2.8 x 10'4
6.4 x 10'4
0.090
5.61
Units of
Y(AMD)
kg CaC03/day
Ib CaC03/day
kg CaC03/day
Ib CaC03/day
The background levels of sulfate can be estimated through the use of an
iso-sulfate background map presented in Figure 7-2. The region of in-
terest is identified on the map, and sulfate levels estimated through the
contours. If more specific data are available which are believed to de-
scribe background sulfate levels more adequately, then these data would
be preferred to the use of Figure 7-2.
Other components of the loading function are obtained through standard
sources. Sulfate concentration in streams, C(S04) , and streamflow,
Q(str) , can be obtained from U.S. Geological Survey studies and from
local water quality records. Annual average runoff can be estimated with
the U.S. Geological Survey Surface Runoff Map, Plates 118 and 119, in the
National Atlas. Sulfate contributions from point sources, C(S04)pT ,
can be estimated from data contained in permit applications for point
source discharges.
7-3-2 Procedure for Using Option II Mine Drainage Loading Function
The step-by-step procedure for using the Option II loading function is
outlined below.
1. Obtain necessary water quality data, streamflow data, and areal data
from U.S. Geological Survey records, local records, or other similar
sources.
2. From these data establish appropriate values for A , Q(R) , and
C(S04) .
3. Determine value for background sulfate, C(S04)BG , using Figure 7-2,
or from local water quality information thought to be more appropriate.
152
-------
Ln
Co
Figure 7-2. Background sulfate concentrations (ppm)
-------
4. Determine value of conversion constant a by means of Table 7-4.
5. Insert a , A , Q(R) , C(S04) and C(S04)BG values established
in the above steps into proper form of Option II loading function.
6. Compute mine drainage loads.
7.3.3 Example of Option II Loading Function for Mine Drainage
An example of how the Option II loading function can be used is summar-
ized in Table 7-5. This table contains results for the Tioga and Juniata
river basins in Appalachia obtained by the Option II loading function.
The Option II estimates are within 80 to 1107=, of the reported loads.
The loading function works out well in these cases because mine drainages
(and background) are the principal sources of sulfate in the area. If
the loading function is applied to more highly industralized areas, e.g.,
the Anthracite Region of Appalachia, it tends to overpredict the nonpoint
loads of mine drainage. In the industrial areas, point source discharges
of sulfate report as nonpoint discharges within the context of the Option
II method. Therefore, the Option II approach should be used mainly in
rural areas. If amounts of the point source contributions of sulfate are
known, however, they can be subtracted from estimates yielded by the Op-
tion II loading function. This procedure would ameliorate some of the
deficiencies of using the stream to source approach in populated or in-
dustrialized areas.
7.4 ESTIMATED RANGE OF ACCURACY
A series of estimated value ranges for several acid mine drainage loads
calculated using the Option I procedure are presented in Table 7-6. Two
ranges are presented—one for Appalachia, and one for regions other than
Appalachia. As can be seen by the ranges, the loading function is more
accurate when applied to coal mining in Appalachia than it is when used
in other parts of the country.
One major source of error in the Option I loading function lies in the
number of mine drainage sources in the area being considered with the
loading function. If not enough sources are available in an area, it
is likely that their distribution of loads will not meet that of the
"typical" mine from which the loading function was developed. This prob-
lem will most often be encountered in regions outside of Appalachia where
mining activity density (number of mines in the area being considered)
is small.
154
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Table 7-5. ESTIMATED MINE DRAINAGE EMISSIONS FROM TIOGA AND JANIATA
RIVER BASINS USING OPTION II LOADING FUNCTION
Essential information: Y(AMD) = a-Q(str)-[C(S04) - C(S04)BG - C(S04)pT]
a = 5.61
C(S04)BG = 10 ppm
Basin
Tioga
Juniata
Sulfate
Flow at concentration
confluence at confluence
(cfs) (ppm)
25
150
150
60
Total
sulfate load
Background
sulfate load
Calculated mine
drainage load
(Ib CaC03/day) (Ib CaC03/day) (Ib CaC03/day)
21,000
50,500
1,400
8,400
19,600
42,100
Reported mine
drainage load§'
(Ib CaC03/day)
24,100
37,900
_a/ Federal Water Pollution Control Administration, Ohio Basin Region, U.S. Department of the Interior,
"Stream Pollution by Coal Mine Drainage in Appalachia," Attachment A to Appendix C of the
Appalachian Regional Commission Report, Acid Mine Drainage in Appalachia, Washington, D.C. (1969).
-------
Table 7-6. ESTIMATED RANGE OF LOADS FOR OPTION I (SOURCE TO STREAM)
ACID MINE DRAINAGE LOADING FUNCTIONS
Probable range of loads
Number of Calculated (kg/day)
mine drainage load Other than
sources (kg/day) Appalachia Appalachia
> 1,000 1,000 200-5,000 a/
10,000 5,000-20,000 a/
100,000 80,000-120,000 a/
100-1,000 500 0-3,000 0-5,000
5,000 1,000-20,000 500-50,000
50,000 20,000-80,000 10,000-100,000
< 100 100 0-1,000 0-2,000
1,000 0-10,000 0-20,000
10,000 1,000-30,000 500-50,000
a_l Areal density of mining activities .outside of Appalachia is less
than 1,000 mines per total area considered.
Another source of uncertainty lies in the choice of the constants K
and Kb in the loading function. The calculated loads in Table 7-6
assume Ka = 130 and Kb = 0.54 as indicated in Table 7-1. However,
a range of values is provided for both Ka and Kb in Table 7-1 and
the user is at liberty to select values of these constants which best
represent his area. The larger ranges in areas outside Appalachia as
indicated in Table 7-6 reflect a higher degree of uncertainty in proper
choices of Ka and Kb in the loading function.
A third source of error in the Option I function is found in the estima-
tion of background neutralization capacity. The neutralization capacity
of background will vary widely throughout the area in which mine drainage
sources are located. This variation in background alkalinity, together
with the relatively large areas that need to be considered in order to
have enough sources for the statistical distribution, is probably the
biggest source of error in the loading function.
The estimates in Table 7-6 suggest that more uncertainty is to be ex-
pected with small loads than with large loads. This greater uncertainty
is due to the subtraction steps in the Option I procedure. The nearer
156
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in magnitude the statistical distribution and background alkalinity
terms, the greater the potential error in the calculated value. Indeed,
there may be instances where calculated acid mine drainage emissions
will be zero (or a negative value), when in fact acid mine drainage is
present. These occurrences are most probable in areas having high back-
ground alkalinities such as found in the Midwest.
The Option II stream to source approach for acid mine drainage depends
strictly upon measured sulfate levels in streams compared to estimated
sulfate levels in background. Estimated ranges of acid mine drainage
loads are presented in Table 7.7. An area of 1,million hectares (4,000
sq miles) has been used to differentiate between larger and small areas
in Table 7-7. The range of loads arising from smaller areas are some-
what broader on a percentage basis than are the loads from the larger
areas. The differences in breadth reflect the fewer number of sources
in the smaller area, as well as a higher degree of uncertainty in back-
ground levels.
Table 7-7. ESTIMATED RANGE OF LOADS FOR OPTION II (STREAM TO SOURCE)
ACID MINE DRAINAGE LOADING FUNCTIONS
Area containing Calculated
mining sources load Probable range of
(ha) (kg/day) loads (kg/day)
< 1,000,000 1,000 200-10,000
10,000 5,000-30,000
> 1,000,000 1,000 500-3,000
10,000 6,000-20,000
100,000 70,000-150,000
In addition to area differences, Table 7-7 also indicates a wider range
for small total loads than for large total loads. These differences are
due to uncertainties in the difference between total sulfate and back-
ground sulfate, i.e., the [C(SO,) - C(SO,) ] term. The larger loads
thus reflect a larger "net" sulfate attributable to acid mine drainage.
A larger net value is inherently more accurate than a small net value.
Thus, larger loads calculated by the Option II procedure are more ac-
curate than smaller loads calculated in the same manner.
157
-------
REFERENCES
1. Environmental Quality Systems, Inc., "Determination of Estimated
Mean Mine Water Quantity and Quality from Imperfect Data and His-
torical Records," Report to the Appalachian Regional Commission,
January 1973.
2. Appalachian Regional Commission, "Acid Mine Drainage in Appalachia,"
Washington, D.C. (1969).
158
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SECTION 8.0
HEAVY METALS AND RADIOACTIVITY
8.1 INTRODUCTION
Two options are presented for estimating nonpoint loads of heavy metals
or radioactivity. The estimation procedures for both heavy metals and
radioactivity are identical, except that input data for heavy metals re-
quire concentration reported in parts per billion, while input data for
radioactivity require units of picocuries per liter. The two options
are:
Option I - Source to Stream Approach: A summation of loads emitted by
known sources in the area under consideration. These sources represent,
in most cases,, emissions from abandoned mining sites and from their as-
sociated processing operations such as tailings piles. This approach
will be sufficient in those areas where the nonpoint sources have been
identified. Since all mines do not produce drainage to transport heavy
metals or radioactivity, the contribution of the nondraining mines to
the total load will be zero. In many other cases, drainage from many
inactive mining sites will be negligible when compared to the few major
sources. Contribution to the total load from many minor sources may be
insignificant compared to the load from the few major sources.
Option II - Stream to Source Approach: An estimation of loads obtained
by difference between total load and estimated background load. This
option should be used where specific sources of heavy metal or radioac-
tivity have not been identified or characterized. Since most heavy metals
and radioactive nuclides tend to precipitate within a short distance after
their discharge into surface waters, the possibility exists that heavy
metals or radioactive contents of stream water samples do not accurately
reflect the quantity of materials actually delivered to the stream by non-
point sources. This problem can be overcome by using water quality data
sampled at points known to be in close proximity to the nonpoint sources,
even though precise locations of nonpoint sources are unknown.
159
-------
In addition to the above methods, the special case of heavy metals associ-
ated with sediment loads is discussed in Section 8.5. The U.S. Geological
Survey has reported results of an extensive sampling and analysis program
in which heavy metal contents of surficial soils in the United States were
determined.—' This study indicates that heavy metals in sediment consti-
tute a significant nonpoint load in terms of quantity. However, the impact
of the heavy metal load on surface water quality is much less severe. The
method described in Section 8.5 "piggy-backs" the heavy metal concentration
of soils onto the sediment loading function (Eq. 3-1, Section 3.2.2) in
order to estimate sediment-borne heavy metal nonpoint loads arising from
the various sources of sediment emissions.
160
-------
8.2 OPTION I: SOURCE TO STREAM APPROACH
8.2.1 Information Requirements for Loading Value Equation
The loading value equations for estimating heavy metal or radioactivity
loads emitted by nonpoint sources using the source to stream approach
Y(HM) = a £ Qn-C(HM)n (8-1)
n
Y(RAD) = a E Q •C(RAD) (8-2)
n n n
where Y(HM) = yield of heavy metal from a given area, kg/day (Ib/day)
Y(RAD) = yield of radioactivity from a given area, picocuries/
day
C(HM)n = concentration of heavy metals emitted from ntn source,
ug/liter (ppb)
C(RAD)n = concentration of radioactivity emitted from the n1-"
source, picocuries/liter
Qn = flow transporting pollutant from the n*-n source, liters/
sec (cfs)
a = conversion factor needed to obtain proper units of load
(see Table 8-1)
Flow (Q) and concentration (C) information is obtained from data gath-
ered from recent nonpoint monitoring studies, or from permit application
records. The nonpoint sources emitting heavy metals or radioactivity are
likely to be abandoned or inactive mining sites, milling and ore beneficia-
tion sites, and associated waste rock dumps and tailings ponds. If such
data are not available, it may be necessary to use the Option II approach
rather than Option I.
8.2.2 Procedure for Using Option I Loading Value Equation
The step-by-step procedure for using Option I for heavy metals and radio-
activity loads is:
161
-------
Table 8-1. CONVERSION FACTORS a TO BE USED FOR OPTION I LOADING VALUE EQUATIONS
Constituent
Heavy metals
Radioactivity
Concentration
units
ppb
picocuries/4
Units of
flow "Q"S./
jj/sec
cfs
A/sec
cfs
Value of
"a"
8.64 x 10'5
5.39 x 10~3
8.64 x 10'4
2.45 x lO'6
Units of
"Y load"
kg /day
Ib/day
picocuries/day
picocuries/day
a_/ Flow data may be obtained from U.S. Geological Survey Records, STORET data,
U.S. Army Corps of Engineer records, or other records available at local
levels.
-------
1. Obtain data for composition and flow of drainage from individual
nonpoint sources.
2. If heavy metal concentration data are reported in units other than
parts per billion (or ug/liter), convert data to parts per billion units.
Conversion of parts per million to parts per billion is accomplished by:
ppb = 1,000 x ppm
If radioactivity units are reported in units other than picocuries per
liter, convert data to proper units.
3. If flow units in raw data are reported as gallon per minute, gallon
per day, etc., convert flows to liters per second (cfs).
4. Add concentrations of heavy metals or radioactivities to obtain total
concentrations. Heavy metals include all metallic constituents except:
sodium, potassium, calcium, magnesium, and aluminum. The metalloids
arsenic and antimony should be counted as heavy metals.
The total heavy metals may be separated into three subcategories if de-
sired:
Category A - iron 4- manganese;
Category B - arsenic + copper + lead + zinc; and
Category C - remaining heavy metals.
5. Multiply flows and concentrations for each individual site.
6. Add up all computed values obtained in Step 5.
7. Choose proper conversion constant from Table 8-1 based upon units of
flow, Q , established in Step 3.
8. Compute load by multiplying the sum obtained in Step 6 by conversion
constant identified in Step 7.
8.2.3 Example of Option I Source to Stream Approach
The Option I approach has been applied to heavy metals emissions from
abandoned mine sites in the Coeur d'Alene River Valley in Idaho. The
computations are summarized in Table 8-2.
163
-------
Table 8-2. NONPOINT HEAVY METAL EMISSIONS ESTIMATES FROM SOME INACTIVE MINES IN THE COEUR d'ALENE VALLEY, IDAHO
USING OPTION I METHODS!/
Flow
Mine and nearest town
I.
,
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Adair, Manhattan
Creek
Aldegulch, Murray
Bullion, Wallace
California Gulch,
Nine Mile Creek
Duncan Gulch
Galconda, Mullan
Monitor, Wallace
Murray, Murray
Placer Gulch,
Wallace
Silver Beaver,
Wallace
Snowstorm Peak,
Mullan
Sunset Peak,
Wallace
(gpm)
20
small
small
small
small
20
small
small
5
10
30
small
(I/sec)
1.3
~'0.3
~ 0.3
~ 0.3
~ 0.3
1.3
~ 0.3
~ 0.3
0.3
0.6
1.9
~ 0.3
Iron +
manganese
(ppb)
159
1,300
208
1,783
3,447
5,450
1,560
1,382
200
3,855
172
390
Load
iron +
manganese
(kg/day)
0.02
0.03
0.005
0.05
0.09
0.6
0.04
0.04
0.005
0.2
0.03
0.01
Load
Arsenic + copper arsenic + copper
+ lead + zinc + lead + zinc
(ppb)
15
562
15
133
109
41
11,342
109
25
64
256
212
(kg/day)
0
0
0
0
0
0
0
0
0
0
0
0
.002
.02
.0004
.003
.003
.005
.3
.003
.0006
.003
.04
.006
Load
Miscellaneous miscellaneous
heavy metals heavy metals
(ppb)
4
8
4
11
32
10
49
4
6
5
5
67
(kg/day)
0
0
0
0
0
0
0
0
0
0
0
0
.005
.002
.0001
.0003
.0008
.001
.001
.0001
.0002
.0003
.0008
.002
Total load
Heavy metals
(kg/day)
0,027
0 . C 5 2
0.0055
0.0533
0.0938
0.606
0.341
0.0431
0.0058
0.2033
0.0708
0.018
Total load for non-
point sources
1.12
0.386
0.0136
1.5196
a/ Sceva, J. E. , "Water Quality Considerations for The Met-al Mining Industry in the Pacific Northwest," Draft Report, Region X, U.S.
Environmental Protection Agency, Seattle, Washington (1973).
-------
The information in Table 8-2 points out the major weakness of the Option
I approach—it is not known whether all nonpoint sources are accounted
for. It is believed, however, that major contributors are known, and
that this sum represents a good "average" for heavy metal contributions
in the region.
8.3 OPTION II: STREAM TO SOURCE APPROACH
8.3.1 Loading Value Equations and Information Needs
Four loading value equations for estimating heavy metal or radioactivity
loads emitted by nonpoint sources using the stream to source approach
can be used. These equations are:
Case 1 Y(HM) = a'A'Q(R)•[C(HM) - C(HM)BG] (8-3)
Case 2 Y(HM) = a-Q(str)•[C(HM) - C(HM)BG] (8-4)
Case 3 Y(RAD) = a-A-Q(R)-[C(RAD) - C(RAD)Bc] (8-5)
Case 4 Y(RAD) = a-Q(str)•[C(RAD) - C(RAD)BG] (8-6)
where Y(HM) = yield of heavy metal from a given area, kg/day (Ib/day)
Y(RAD) = yield of radioactivity from a given area, picocuries/
day
C(HM) = concentration of total heavy metals emitted from non-
point sources, ppb (ug/liter)
C(HM)gQ = concentration of total heavy metals emitted from back-
ground, ppb (ug/liter)
C(RAD) = concentration of radioactivity emitted from nonpoint
sources, picocuries/liter
C(RAD')-Dn = concentration of radioactivity emitted from background
sources, picocuries/liter
A = area containing nonpoint sources, ha (acres)
165
-------
Q(R) = flow as average annual runoff, cm (in.)
Q(str) = flow as streamflow, liters/sec (cfs)
a = conversion factor needed to obtain proper units of
load (see Table 8-3)
The four cases are differentiated by the type of flow data available to
the user. Cases 1 and 3 require average annual runoff in centimeters
per year (in/year) obtainable from standard runoff maps, e.g., U.S. Geo-
logical Survey's National Atlas, Plates 118 and 119. Cases 2 and 4 can
use measured streamflow values measured in volume per time unit (i.e.,
liters/sec or cfs). The values for particular streams are available in
U.S. Geological Survey records, STORET data, and U.S. Army Corps of
Engineers streamflow records.
In areas of highly variable flow (such as mountainous regions), the use
of annual average runoff values (Cases 1 and 3) is recommended unless
precise knowledge of streamflow at the sampling site is known. If an-
nual average runoff is used, it is desirable in some cases to not con-
sider the area (A) factor if the areal extent drained above the sam-
pling point is not known accurately. If so done for Cases 1 and 3, the
units of load would become kilograms per hectare per day (Ib/acre/day).
On the other hand, if good streamflow and concentration data are avail-
able to the user, he should use the Cases 2 or 4 loading value equations
to estimate heavy metal or radioactivity emissions.
8.3.2 Estimation of Heavy Metal and Radioactivity Emissions from
Background
A key part of the Option II approach is the estimation of heavy metals
and radioactivity emissions from background. A series of maps have been
developed for estimating background concentrations of heavy metals and
radioactivity. These maps are:
Figure 8-1. Background total heavy metals (ppb)
Figure 8-2. Background iron + manganese (ppb)
Figure 8-3. Background arsenic + copper + lead + zinc (ppb)
Figure 8-4. Background miscellaneous heavy metals (ppb)
Figure 8-5. Background radioactivity (picocuries/liter)
Figure 8-6. Background alpha radioactivity (picocuries/liter)
Figure 8-7. Background beta radioactivity (picocuries/liter)
166
-------
Table 8-3. CONVERSION FACTORS "a" TO BE USED FOR OPTION II LOADING VALUE EQUATION
Case 1
Case 2
Case 3
Case 4
Concentration
Constituent units
Heavy metals ppb
Heavy metals ppb
Radioactivity picocuries/^
Radioactivity picocuries/jj
Units
Units of of
flow "Q" area "A"
cm/yr ha
in/yr ac
H ./sec
cfs
cm/yr ha
in/yr ac
a/sec
cfs
Value of
"a"
2.7 x 10-7
6.2 x 10'7
8.64 x 10"5
5,39 x 10'3
270
280
8.64 x 10'4
2.45 x 10"6
Units of
Y(HM) or Y(RAD)
kg/day or kg/ha/day£'
Ib/day or lb/ac/day£/
kg /day
Ib/day
picocuries/day or picocuries/ha/day— .
picocuries/day or picocuries/ac/day~
picocuries/day
picocuries/day
a/ Units of Y(HM) or Y(RAD) obtained if Area (A) is not used in loading value equation.
-------
00
200
200
Figure 8-1. Background total heavy metals (ppb)
-------
100
Figure 8-2. Background iron -f- manganese (ppb)
-------
50
Figure 8-3. Background aresenic + copper + lead 4- zinc (ppb)
-------
20
Figure 8-4. Background miscellaneous heavy metals
-------
Figure 8-5. Background radioactivity (picocuries/liter)
-------
2.0
Lo
Figure 8-6. Background alpha radioactivity (picocuries/liter)
-------
Figure 8-7. Background beta radioactivity (picocuries/liter)
-------
If other sources are not available, these maps should be used to esti
mate background concentrations of needed constituents.
8.3.3 Procedure for Using Option II Loading Value Equations
The step-by-step procedure for using the Option II approach for estimat-
ing heavy metal and radioactivity emissions is:
1. Obtain data for heavy metal and radioactivity concentrations from a
selected number of monitoring stations. The data should include concen-
tration and flow information.
2. Obtain data for heavy metal and radioactivity concentrations in back-
ground. These data may be local data deemed proper by the user, or from
the maps (Figures 8-1 through 8-7) presented earlier.
3. If flow data are not available with concentration data, estimate flow
as annual average runoff from standard U.S. Geological Survey Runoff Maps.
4. After proper flow and concentration data have been acquired, choose
the proper case and determine "a" value from Table 8-3.
5. Sum up heavy metal concentrations to obtain total heavy metals. The
total heavy metal may be broken into three subtotals: iron + manganese;
arsenic + copper + lead + zinc; and miscellaneous heavy metals. The
heavy metal constituents to be considered in the summing process have
been identified in Section 8.2.2, Step No. 4, of this report.
6. If flow data are limited to average annual runoff, and if good area!
data are not known, do not consider area (A) factor in loading value equa-
tion. This aspect will yield results in kilogram per hectare per day
(Ib/acre/day).
7. After data have been obtained and processed using the above steps,
insert into proper loading value equation and compute loads.
8.3.4 Example of Option II Stream to Source Approach
The Option II approach has been used to estimate heavy metal loading from
nonpoint sources in Clear Creek County, Colorado. Results of this esti-
mation are presented in Table 8-4. These computations have used the Case
1 loading value equation, i.e., flow is estimated by average annual run-
off. In addition, areal data were insufficient to estimate loads. Thus,
loads are reported as kilogram per hectare per day.
175
-------
Table 8-4. HEAVY MEIAL POLLUTANT EMISSIONS FROM SEVERAL STREAMS IN CLEAR CREEK COUNTY, COLORADO^'
Arsenic + copper +
Case 1 approach: QF = 25 cm/yr; a = 2.7 x 10~7
A factors are unknown, loads
reported in kg/ha/yr
Background: Chicago Creek above South Chicago
Creek near Idaho Springs, Colorado
Nonpoint Pollutant Emissions:
1. WF Clear Creek above Woods Creek at
Berthoud Falls, Colorado
2. Woods Creek at mouth at Berthoud
Falls, Colorado
3. WF Clear Creek near Empire, Colorado
4. Fall River near Idaho Springs, Colorado
5. Virginia Canyon Creek at mouth at Idaho
Springs, Colorado
6. Soda Creek at mouth at Idaho Springs,
Colorado
7. Clear Creek above North Clear Creek
near Hidden Valley, Colorado
8. Clear Creek above Quayle Creek at
Bakerville, Colorado
9. Clear Creek above South Clear Creek at
Georgetown, Colorado
10. South Clear Creek at mouth at Georgetown,
Colorado
11. Leavenworth Creek at mouth near Georgetown,
Colorado
12. Clear Creek above WF Clear Creek near
Empire, Colorado-
13. Ute Creek at mouth near Idaho Springs, Colorado
14. Chicago Creek above Spring Creek at Idaho
Springs, Colorado
15. Lion Creek at mouth at Empire, Colorado
16. Clear Creek above Chicago Creek at Idaho
Springs, Colorado
17. Clear Creek above STP Outfall at Idaho Springs,
Colorado
18. Clear Creek below Sawmill Gulch near Hidden
Valley, Colorado
Iron + n
Concentration
(ppb)
5,910
4,800
1,320
30
85,000
89
1,720
136
63
17
24
105
56
160
18,000
650
2,020
1,860
lanaanese
Load
(ks/ha/day)
70
0.04
0.03
0.01
£/
0.6
£/
0.01
£/
£/
£/
£/
£/
£/
0.001
0.1
0.004
0.01
0.01
lead +
Concentration
(ppb)
222
1,811
72
58
10,000
91
632
47
884
5
143
578
88
75
2,000
385
823
618
zinc
Load
(kg/ha/day)
15
0.001
0.01
c/
£/
0.07
£/
0.004
£/
0.006
£/
0.001
0.004
0.001
£/
0.01
0.002
0.005
0.004
Mia seel la neo us
heavy met£
Concentration
(ppb)
5
10
26,000b_/
6
1
1,700
1
13
0
3
0
0
1
5
0
226
7
12
9
ils
Load
(kR/ha/dav)
£/
0.2
c/
£/
0.01
£/
£/
£/
£/
£/
£/
£/
£/
£/
0.002
c_/
£/
£/
a/ Wentz, D. A., "Effect of Mine Drainage on Che Quality of Streams in Colorado,' Colorado Water Resources Circular No. 21
Colorado Water Conservation Board, Denver, Colorado (1974).
b/ All molybdenum.
c/ less than 0.001 kg/ha/day (1.0 g/ha/day).
-------
In order to obtain actual loads, drainage areas represented by each
sampling station should be known. Estimation of the areas might be
accomplished by studying U.S. Geological Survey topographic maps.
Once areas are estimated, they should be multiplied by the proper
loading rates and summed to obtain estimates for the total load
arising from the nonpoint sources.
8.4 EXPECTED ACCURACY OF METHODS
In the case of the Option I approach where individual sources are summed,
the accuracy will depend upon the variability of the emission loads from
each source. However, as more and more sources are included in the sum-
mation, the more accurate will be the estimate. This increased accuracy
will arise because of the greater number of points forming the statisti-
cal distribution of sources. Option I also assumes that the principal
sources of heavy metal and radioactivity emissions are known for the area
under consideration. Thus, the use of Option I should be restricted to
only those persons with knowledge of the area. An estimate of the ac-
curacy of Option I methods for heavy metals is presented in Table 8-5
for several calculated loads. The accuracy for radioactivity would be
expected to fall into the same percentage ranges.
Option II is inherently less accurate than Option I, since it depends
upon a good estimate of background emissions of heavy metals and radio-
activity, and involves a comparison of these components actually found
with the background estimates. Background heavy metals are particularly
difficult to estimate, since wide variations in concentrations are noted
and emissions are nonuniform throughout the country. Because specific
sources are not involved, heavy metal and radioactivity loads are con-
sidered to be diffuse and emitted uniformly from all land area considered
when Option II is used. Thus, use of the Option II method is more accu-
rate with larger areas. Accuracy ranges are narrowed further when large
loads are emitted rather than small loads, since Option II involves com-
parison of total loads with background loads. The greater the difference
between total and background levels, the less will be the error intro-
duced by the subtraction operation. An estimate of expected accuracy
for heavy metals emissions using Option II is presented in Table 8-6.
177
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Table 8-5. EXPECTED ACCURACY OF OPTION I (SOURCE TO STREAM)
METHOD FOR HEAVY METALS
Number
of
sources
10
50
100
Calculated
load
(kg/day)
0.1
1
1
5
10
1
5
10
20
Probable range
of loads
(kg/day)
0.01-1.0
0.5-5
0.7-3
2-10
5-15
0.8-2
3-8
7-15
17-25
Table 8-6. EXPECTED ACCURACY OF OPTION II (STREAM TO SOURCE)
METHOD FOR HEAVY METALS
Area Calculated Probable range
containing load of loads
sources (ha) (kg/ha/year) (kg/ha/year)
< 1,000,000 1 0.2-15
10 3-30
> 1,000,000 1 0.4-10
10 5-20
178
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8.5 HEAVY METALS ATTACHED TO SEDIMENT
8.5.1 Loading Function
The largest single nonpoint heavy metal load into surface waters will be
that which is carried by sediment. The U.S. Geological Survey has under-
taken an in-depth study— to determine the elemental composition of sur-
ficial materials in the United States. Soil samples were collected from
863 sites throughout the 48 conterminous states and analyzed for 44 differ-
ent elements. Of these 44 elements, 36 are heavy metals as defined earlier,
i.e., metallic or metalloid elements with atomic number greater than 20.
Sediment arising from various sources throughout the country will carry
these elements into surface waters. Thus, the amount of heavy metal de-
livered with the sediment is directly proportional to the sediment load.
The loading function is:
Y(HM)S = a-Cs(HM)-Y(S)E (8-7)
where Y(HM)o = yield of heavy metals in sediment, kg/day (Ib/day)
CC(HM) = concentration of heavy metals in eroded soil, ppm
O
Y(S) = sediment yield metric tons/year (tons/year)
E
a = conversion factor to obtain proper units of load. If
Y(S)E is expressed in metric tons/year, a = 2.74 x 10"°;
if Y(S)p is expressed as English tons/year, a = 5.48 x
10-6. E
The loading function assumes that there is no enrichment (or loss) of
heavy metals in the eroded soil. One would not expect to have either en-
richment or loss of heavy metals in the sediment, since they are tied up
in insoluble forms in the soils.
The loading function (Eq. (8-7)) is apt to yield very large values for
heavy metal emissions. The metals are an integral part of the soil-
sediment matrix, and most are sparingly soluble in water. The fraction
of the load which solubilizes in surface waters is usually very small,
and the impact on water quality is thus very much less than one would cal-
culate on the basis of a total load discharged to the stream.
179
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8.5.2 Information Needs
Two basic pieces of information are needed to estimate emissions of heavy
metals associated with sediment: the amount of sediment produced, Y(S)E;
and the amount of heavy metals in the eroded soils, CS(HM). The value of
Y(S) is determined using sediment loading procedures (USLE Eq. (3-1)) de-
scribed in Section 3.0. The value of Cg(HM) can be determined from the data
collected by the U.S. Geological Survey in their report concerning elemental
composition of surficial materials,— or from other data available locally.
The average concentrations and ranges of the various heavy metals in sur-
ficial materials obtained from the 863 sampling stations are presented in
Table 8-7. In addition to the arithmetic averages, the geometric means
(another form of "averaging") have also been included on a national basis,
as well as an Eastern and Western areal basis. The line separating East
from West is the 97th meridian.
In many cases, the elements were found to be in concentrations less than
the detection limits of the analytical methods employed by the USGS. The
concentrations of these elements are shown in Table 8-7 to be less than
the detection limits, and no average can be presented. The metals which
fall in this category are arsenic, cadmium, germanium, gold, hafnium, in-
dium, platinum, palladium, rhenium, tantalum, tellurium, thallium, thorium,
and uranium. Many of these elements are known, from other studies, to be
present in soils.
For those elements which were generally found to be above detection limits,
the concentration at each sampling site has been plotted and mapped in the
U.S. Geological Survey report.— Specific heavy metals in specific areas
can be estimated from these maps. Thus, the USGS report can serve as a
basic reference for U.S. data concerning heavy metals in sediment.
8.5.3 Relationship Between Heavy Metals in Soils and in Surface Waters
As can be seen in Table 8-7, the predominant heavy metal in surficial mate-
rial is iron. The metal having next greatest abundance is titanium. On
the average, these two elements constitute about 93% of all heavy metals
in soils. The remaining 77» is made up of many elements, ranked in the
following order: manganese, barium, strontium, zirconium, cerium, vanadium,
zinc, chromium, neodymium, lanthanium, yttrium, copper, lead, nickel, gal-
lium, niobium, cobalt, and scandium.
The high percentage of titanium in the soils is not reflected in surface
waters. As has been shown in Figure 8-1 and 8-2, iron and manganese con-
stitute the major portion of heavy metals in surface waters. Thus, one
concludes that the solubility mechanisms of manganese and titanium in soils
180
-------
differ substantially. From the surface water quality data, it would also
seem that the zinc, copper and lead constituents of eroded soils are rela-
tively mobile in aqueous systems.
On a total load basis, the heavy metals emitted through the sediment route
are much greater than the load detected in surface waters. A comparison
of heavy metals loads in natural background indicates that the surface wa-
ter load is approximately 1% of the load coming via the sediment route.
Most of the metals which are detected in the streams consist of iron, man-
ganese, arsenic, copper, lead and zinc, whereas those in the sediment are
primarily iron and titanium. Thus, the impact of heavy metals on the qual-
ity of surface waters is probably much smaller than the absolute loads of
sediment indicate. However, the differences in solubility and mobility
mechanisms of individual metal components are important for establishing
impacts of specific species.
8.5.4 Reliability of the Procedure
The reliability of estimating heavy metal loads through the procedure dis-
cussed above is a function of three factors: (a) the accuracy of the sedi
ment loads delivered to the stream (Table 3-10); (b) the accuracy of the
heavy metal concentration measurements in the soil; and (c) the variability
of heavy metal concentrations in the eroded soils. Of these three factors,
the one concerning variability of heavy metal content will be the most un-
certain. The estimated ranges of values for several heavy metal loads is
presented in Table 8-8.
181
-------
Table 8-7. HEAVY METAL CONCENTRATIONS IN SURFICIAL MATERIALS
IN THE UNITED STATES^-/
00
Element
Arsenic
Barium
Cadmium
Cerium
Chromium
Cobalt
Copper
Iron
Gallium
Germanium
Gold
Hafnium
Indium
Lanthanum
Lead
Manganese
Molybdenum
Neodymium
Nickel
Niobium
Palladium
Platinum
Rhenium
Arithmetic
Average
(ppm)
„„
554
--
86
53
10
25
25,000
19
--
--
--
--
41
20
560
< 3
45
20
13
--
--
--
analysis
Range
(ppm)
< 1,000
15-5,000
< 20
< 150-300
1-1,500
< 3-70
< 1-300
100-100,000
< 5-70
< 10
< 20
< 100
< 10
< 30-200
< 10-700
< 1-7,000
< 3-7
< 70-300
< 5-700
< 10-100
< 1
< 30
< 30
Conterminous
U.S.
(ppm)
„„
430
--
75
37
7
18
18,000
14
--
--
--
--
34
16
340
--
39
14
12
--
--
--
Geometric means
West of 97th
meridian
(ppm)
„
560
--
74
38
8
21
20,000
18
--
--
--
--
35
18
389
--
36
16
11
--
--
--
East of 97th
meridian
(ppm)
_.
300
--
78
36
7
14
15,000
10
--
--
--
--
33
14
285
--
44
13
13
--
--
--
-------
Table 8-7 (concluded)
Element
Scandium
Strontium
Tantalum
Tellurium
Thallium
Thorium
Titanium
Uranium
Vanadium
Ytterbium
Yttrium
Zinc
Zirconium
Arithmetic
Average
(ppm)
10
240
--
--
--
--
3,000
--
76
4
29
54
240
analysis
Range
(ppm)
< 5-50
< 5-3,000
< 200
< 2,000
< 50
< 200
300-15,000
< 500
< 7-500
< 1-50
< 10-200
< 25-2,000
< 10-2,000
Conterminous
U.S.
(ppm)
8
120
--
--
--
--
2,500
--
56
3
24
44
200
Geometric means
West of 97th
meridian
(ppm)
9
210
--'
--
--
--
2,100
--
66
3
25
51
170
East of 97th
meridian
(ppm)
7
51
--
--
--
--
3,000
--
46
3
23
36
250
Total
30,099
21,991
23,858
19,263
Note: "--" indicates all analyses showed element to be below detectable limits.
a/ Reference 1.
-------
Table 8-8. EXPECTED ACCURACY OF HEAVY METAL LOADS
DELIVERED WITH SEDIMENT
Calculated Probable range
load of loads
(kg/day) (kg/day)
0.1 0.001-1.5
1 0.05-10
10 1-30
100 30-200
184
-------
Reference
1. Shacklette, H. T., J. C. Hamilton, J. G. Boernagen, and J. M. Bowles,
"Elemental Composition of Surficial Materials in the Conterminous
United States," U.S. Geological Survey Proffessional Paper 574-D,
Washington, D.C. (1971).
185
-------
SECTION 9.0
URBAN AND RELATED SOURCES
This section describes pollutant loading functions for developed urban
areas and related sources. In the subsections that follow, the sources
and types of pollutants and factors affecting pollutant generation, as
well as loading functions and relevant data, are presented in the fol-
lowing sequence: urban runoff, Section 9.1; traffic related pollutants,
Section 9.2; and street and highway deicing salts, Section 9.3.
Discussion in this section pertains only to established areas. Loading
functions for areas under construction are presented in Section 3.0,
"Sediment Loading Functions," of this Handbook.
9.1 POLLUTANTS FROM URBAN RUNOFF
From established urban areas, stormwater may pick up various wastes
ranging from settled dust and ash to debris coming directly from man
himself. The quantities of solids from urban nonpoint sources are quite
significant in quantity. Fly ash and dust from industrial processes
such as steel mills, cement manufacturing, and certain chemical pro-
cesses are known to be profuse. Dusts from the burning of organic fuels
are a significant factor, and solids in sizable quantities also result
from off-street mud, automotive exhaust, organic debris from tree leaves
and grass trimmings, and discarded litter.
In this Handbook, the nonpoint pollutant loading function for urban
areas is formulated from pollutant loading values obtained in a recent
URS studyi' for the U.S. Environmental Protection Agency. In that study
URS reviewed a large number of published reports, extracted and statis-
tically analyzed data, and presented average solid loading values and
chemical and biological composition of solids.
In analyses of urban runoff data, URS assumed that only the runoff from
street surfaces contributed to urban nonpoint pollution. The resulting
186
-------
loading values for solid wastes are given in terms of pounds per curb-
mile per day. The user should note that these values represent contri-
butions from both street and nonstreet surfaces.
Data developed in the URS study include nationwide means, as well as a
more detailed breakdown of data into major source categories, of solids
loading rates and pollutant composition of street solids„ Table 9-1
reproduces, from the URS report, data which are divided into 13 subsets
among three major source categories including climate, land use, and
average daily traffic. These data are different from the whole set means
which are given in the last column of the table, at the 80% confidence
level. Whenever the mean of any parameter (solid loading rates or com-
position) in any subset differs significantly from the mean of the set
of all data, that number may be substituted for the mean of the set of
all data. Table 9-1 also gives the percent standard error of the mean
which indicates the degree of confidence that may be placed on the mean.
Table 9-2 presents the means and standard deviations of concentrations
of mercury and several pesticides, which resulted from a set of data
that were characterized as "very small and unreliable."
9.1.1 Loading Functions
The functions which make use of solid accumulation rates and solid com-
position and provide for quantitative assessment of pollutant loadings
in urban runoff within a specified urban area are given as follows:
Loading functions for solids
Y(S)U = L(S)u-Lst (9-1)
where Y(S)U = daily total solids loading, kg/day (Ib/day)
L(S)U = daily solids loading rates, kg/curb-km/day (Ib/curb-
mile/day)
L = street curb-length (approximately 2.0 x street
s t
length), curb-km (curb-miles).
187
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Table 9-1. SOLID LOADING RATES AND COMPOSITIONS--NATIONWIDE MEANS AND
SUBSTITUTIONS OF THE NATIONWIDE MEANS AT 80% CONFIDENCE LEVEL* I/
Iba/curb
Category Loidlntf DODj COD
CliMt* Xorthcaat 291
Scnjtheait 103,_ 29.100.
b b
Soutbvoat 30
North vest 30
J K«»ld«ntlal 14,000 82,000
3 Coraorclal 74 38,700 269,000
Llpnt Induatry
No Cartel
Concentral Ions In tnlcrotrroma per (trsn of dry solid ''?
OfO TPO NO MI4 OruN Cd Cr Cu Fe Pb V.n .Ml Sr Zn TCOL1+ FCOL1 +
3.970 2.6. 139. 17,700. 870 3S3 21 27. 250 4.415
ebb b cacbb f
3,240 1,970 137^ 1.370^ 21^ 28 7.014.
A A b b b b a
470b 241. 78. 2.320b 5?b 13g S-7",)-
A 'b'b c a " " t ' I
,b 830b 330c 1.800. 93^ l.«0h 2Bb
i 2,210 1,380 8,439 133 3.440 48 S20
CC C • ** OOQ
8.2E3
*v«nr» 0*11^ < 500
1,210
S.OOO - 13,000
> 13.000
82
3J7
18
3.8E3
All date
Sb 19,900& 140,000b l,280b 2,930c 804b 2.640e 2.950b 3.4g 211^ 104. 22.000 1.810^ 41(1 33 21 370 2.3ES 1.7ES
tbo<« jubxt Ktn* are «howi vhlch differ fron the noan of t^9 set of all dn» «t the 80-porccnt confidence level (Student t s 1.39, Octrees of Freedon j 10). Tot»l
au^t>«r of permitted »ub«t i tutlon* « 103. Porcunt Standard Error of Ihe llcaa Subscripting Code: ««0-9. ti«10*- 19, e w 20 - 29, d • 30 - 39, « » 40 - 49 f • 50 « £2
Collfon couati ar< axpreiiod in computer notation, I.e., ES • 10 .
-------
Table 9-2. MEAN CONCENTRATIONS OF MERCURY AND CHLORINATED
HYDROCARBONS IN STREET DIRT FROM NINE U.S.
Mean
Standard
deviation
Concentrations in raicrograms per
Methoxy-
Hg Endrin Dieldrin PCB chlor
83 0.2 28 770 500
111 - 28 770 1,050
kilogram of dry solid
Methyl
DDT Lindane parathion ODD
76 2.9 2 82
118 7.1 - 78
-------
Loading functions for other pollutants
Y(i)u = a-Y(S)u-C(i)u (9-2)
where Y(i)u = daily total loading of pollutant i , kg/day (Ib/day);
MPN(x 10~6) per day for total coliform and fecal
coliform
a = conversion factor
= ID'6 (metric and English)
Y(S) = daily total loading of solids, kg/day (Ib/day), cal-
culated in Eq. (9-1)
C(i) = concentration of pollutant i in solids, ug/g; MPN/g
for total coliform and fecal coliform
Equations (9-1) and (9-2), along with solid loading values and composi-
tions in Tables 9-1 to 9-2, provide the means to assess daily average
pollutant loadings from urban areas.
It is important to note that pollutant loadings so calculated are street
surface loadings rather than loadings at outfalls to the receiving waters.
The transport of storm runoff in sewers and removal of pollutants in some
treatment systems would reduce pollutant loads to some extent. Such ef-
fects are not included in loading factors suggested in Tables 9-1 and 9-2.
Furthermore, the methodology presented above does not reflect the effect
of housekeeping practices in the urban area. Good housekeeping practices
such as cleaning of street solids by sweeping, and the use of catchment
basins to remove solids and organic matter, will reduce pollutant loads
from streets to receiving waters.2/
9.1.2 Procedure for Loading Calculations
Data in Tables 9-1 and 9-2 represent two options as well as two levels of
accuracy for a user to assess pollutant loadings from a given urban area.
Application of the "subset" data may result in higher accuracy, but re-
quire more data and more computation effort, than if "nationwide means"
are used.
Option I - In this option the user will use nationwide means presented
in Tables 9-1 and 9-2. Proceed as follows:
1. Determine solid loading rate and solid composition from tables.
190
-------
2. Determine street length (include that of primary and secondary streets
but not driveways, alleys, or parking lots).
3. Calculate daily solid loading using Eq. (9-1).
4. Calculate daily loading of other pollutants using Eq. (9-2).
Option II - In this option the user will make use of data presented for
source categories in Table 9-1. Steps needed for loading calculations
are:
1. Characterize the study urban area. When applicable, the entire area
should be divided into individual homogeneous sections with unique char-
acteristics. Each individual section is then defined as a subarea (e.g.,
residential area).
2. Determine street length in each subarea.
3. Enter the Table 9-1 at the line labeled "All Data."
4. Select a category of climate, land use, or average daily traffic,
which best applies to an area and move upward to the line of data to the
right of the category heading.
5. Substitute those values available in the row selected for the cor-
responding values in the row labeled "All Data." In choosing the sub-
stitute loading factors, the following priority sequence of source cate-
gories is suggested: (a) climate; (b) land use; (c) average daily traf-
fic. The climatic zones of the U.S. delineated by the URS are shown in
Figure 9-1. Caution: it is not permissible to use more than one row of
substitutions at a time, i.e., to use a BOD value for land use and COD
for climate in order to form a new row of loading rate and composition
data. It is both proper and useful, however, to repeat the above process
to obtain several new rows of data to present a range of composition
and loading rates. Use data from Table 9-2, if desired.
6. Repeat Steps 4 and 5 for all subareas.
7. Use Eq. (9-1) to calculate total solid loading in a subarea.
8. Use solid loading (Step 7), Eq. (9-2) and selected composition data
to calculate total loading of other pollutants in a subarea.
9. Sum up loadings of subareas to obtain the loading of the entire study
area.
191
-------
•4. [SOUTHEAST]
> 1
<—. i
Figure 9-1. Climate zone for the cities from which data are available
and used in the URS studyi/
192
-------
The calculation procedure delineated for Option I and Option II above is
illustrated in Section 9.1.4.
Option III - In this option, the user will make use of site specific
data.
The recent URS study has assembled all presently available data on the
rates of accumulation of solids and on the concentrations of various
pollutant constituents in those solids that collect on street surfaces.
These data are probably adequate for most urban planning operations.
The user, however, may alternatively replace these loading factors by
site specific data to obtain better prediction.
If site specific data are lacking, users are encouraged to conduct samp-
ling and analytical programs of their own. The data from site specific
tests, if handled properly, may be used in analyzing the area's runoff
problems instead of using values given in this Handbook. This would be
desirable in most instances, especially in areas or under specific con-
ditions that were not documented in the URS study.
Recommended procedures for conducting site specific tests are given in
Appendix B of the URS Report.I/
With the lack of site specific data, the user may wish to examine the
available published data for source and reliability. The user is re-
ferred to Appendix A of the URS Report!/ for description of available
data sources, as well as procedures for processing these data.
9.1.3 Street Length and Land Use Data for Urban Areas
Data on street length - Street length data are available from local
public works departments or street departments. They can also be ob-
tained by measurement on aerial photographs.
Survey statistics for the U.S. indicate that street surfaces occupy on
the average about one-sixth of the urban area.-' The American Public
Works Association!/ recently developed a.regression between curb length
of urban area versus population density. Data from many cities across
the country were used. The resulting regression equation is:
193
-------
CL=413.11- (352.66)(0.839)pD (9-3)
where CL = curb length density, ft/acre
PD = population density, number/acre
The correlation coefficient for the equation is 0.72. The regression
curve is shown in Figure 9-2.
Curb length can be estimated if street surface acreage is known. Table
9-3 presents equivalent curb length per unit area of street surface, sug-
gested by URS.i/ However, if actual values are known, it is best to use
known values.
Land use data - The following references provide survey data and analy-
sis results relative to land uses in major urban areas of the U.S.:
Bartholomew, H. , Land Use in American Cities, Harvard University Press,
Cambridge, Massachusetts (1955).
Niedercorn, J. H., and E. F. R. Hearle, "Recent Land-Use Trends in
Forty-Eight Large American Cities," The RAND Corporation, Santa Monica,
California, Memorandum RM-3664-FF, June 1963.
Manuel, A. D., R. H. Gustafson, and R. B. Welch, "Three Land Research
Studies," National Commission on Urban Problems, Research Report No. 12,
Washington, D.C. (1968).
The American Public Works Association^-/ estimated land consumption rates
for various land uses in American cities, shown in Table 9-4. These
rates can be used to estimate acreages in different land uses if the
number of population is known.
9.1.4 Example
The study area is a 250-acre urban watershed in Atlanta, Georgia. The
area is mainly residential and has 17 curb-miles of primary and second-
ary streets. Predict the average daily loadings of BOD and lead in run-
off from the entire area.
Option I - Use nationwide means of solid loading rate and compositions
given in Table 9-1.
194
-------
LU
LU
LU
LU
Q
GROSS POPULATION DENSITY, POP/HECTARE
600
550
500
450
400
350
300
250
200
) 20 40 60 80 100 120 140 160 180 200 220 240
i i i i i i i i i i i i
-"
• * _ •
" /^ •' *
•*•/•*
-•/ .
/
-J
797
750
700
650
600
550 £
\—
500 LU
X
450 §
\—
LU
400 ^
350 £
Z
i | j
300 Q
X
250 o
o
Z
J 200
CQ
V
U 150 W
,oc •*
50
0
:
i i i i i i i i i
300 Q
X
250 o
Z
i i [
200 -"
CO
150 U
100
50
n
0 10 20 30 40 50 60 70 80
GROSS POPULATION DENSITY, POP/ACRE
90 100
Figure 9-2.
Correlation between population density and
curb length density.—
195
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Table 9-3. EQUIVALENT CURB-LENGTH PER UNIT AREA
OF STREET SURFACE, ARRANGED BY LAND USE
TYPES±/
Equivalent curb-km
per hectare of
street surface
Equivalent curb-miles
per acre of
street surface
Open land
General residential
General commercial
Light industrial
Heavy industrial
All land use types
2.11
2.15
1.63
1.71
1.59
1.83
0.53
0.54
0.41
0.43
0.40
0.46
Table 9-4. GENERAL LAND CONSUMPTION RATES
FOR VARIOUS LAND USES^t/
Land use
Residential
Commercial
Industrial
Park
Land
< 100,000
Population
0.1049
0.0101
0.0177
0.0146
consumption (acres/capita)
> 100,000
Population
0.0714
0.0084
0.0083
0.0093
> 250,000
Population
0.0585
0.0073
0.0077
0.0078
196
-------
Calculate solid loading -
L(S)U = 156 Ib/curb-mile/day
Y(S)U = 156-17 = 2,652 Ib/day
Calculate BOD and Pb loadings -
C(BOD)U = 19,90(MO-6 Ib/lb solid
C(Pb)u = 1,810-10-6 Ib/lb solid
Y(BOD)U = 2,652-19,900-10-6 =52.8 Ib/day
Y(Pb)u = 2,652-l,810-10-6 = 4.8 Ib/day
Option II - Use substitutions at 80% confidence level.
Atlanta is in the southeast. Move upward in Table 9-1 to southeast
climate category. A loading substitution is available. Make all avail-
able substitutions into the row labeled "All Data." The new row has,
among others:
Solid loading rate, L(S)U = 103 Ib/curb-mile/day
BOD concentration, C(BOD)U = 19,900-KT6 Ib/lb solid
Pb concentration, C(Pb)u = 1,370-10'6 Ib/lb solid
Calculate solid loading -
Y(S)U = 103-17 = 1,751 Ib/day
Calculate BOD and Pb loadings -
Y(BOD)U = 1,751-19,900-10'6 = 34.8 Ib/day
Y(Pb)u = 1,751-1,370-10"6 = 2.40 Ib/day
Pollutant loadings calculated in Option II are lower than those in Op-
tion I and probably better represent the real situation in Atlanta,
Georgia.
197
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9.1.5 Techniques for Assessing Urban Runoff Pollution Characteristics
The material presented in the preceding sections provides states and local
water quality planners with methodologies and data for predicting urban
nonpoint pollutant loadings. It is not intended to serve as a basis for
characterization of runoff flowing from an urbanized area. Rather, it
is intended to give a first-cut assessment of nonpoint urban pollutant
loading without extensive data generation.
The water pollution characteristics of urban runoff are related to both
the quantity and quality of runoff. There are numerous analytical methods
which have been developed for assessing the quality and quantity of run-
off following a rainfall or snowmelt incidence, as a function of time.
Variations of runoff characteristics with respect to time are especially
important if storage, treatment, or other methods of disposal are under
consideration; identification of temporal variations will enable one to
identify and treat the most polluted portion of the runoff.
The presently available analytical methods to assess the water pollution
characteristics of urban runoff consist of several levels of sophistica-
tion. The most accurate and definitive methods are the most difficult
to utilize. Simplistic methods are available to allow the user to ob-
tain approximate estimates.
The user is referred to the literature listed below for methods to assess
the pollution characteristics of urban runoff. The analytical tools
presented in these references range from simple desk calculations to sophis-
ticated computer techniques.
Amy, G., R. Pitt, R. Singh, W. L. Bradford, and M. B. LaGraff, Water
Quality Management Planning for Urban Runoff, U.S. Environmental Pro-
tection Agency, Washington, D.C. (EPA-440/9-75-004) (NTIS PB 241 689/
AS) December 1974.
Brater, E. F., and J. D. Sherrill, Rainfall-Runoff Relations on Urban and
Rural Areas, a study by the University of Michigan for the U.S. Environ-
mental Protection Agency, Cincinnati, Ohio (EPA-670/2-75-046) May 1975.
DiGiano, F. A., and P0 A. Mangarella (Ed.), Applications of Stormwater
Management Models, Short Course Proceedings prepared by the University
of Massachusetts, for the U.S. Environmental Protection Agency, Cincinnati,
Ohio (EPA-670/2-75-065) June 1975.
Metcalf and Eddy, Inc., University of Florida, and Water Resources Engineers,
Inc., Stormwater Management Model, U.S. Environmental Protection Agency
(Report 11024 DOC 07/71), 4 Volumes, October 1971.
198
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U.S. Corps of Engineers, Urban Runoff: Storage, Treatment and Overflow
Model "STORM," U.S. Army, Davis, California, Hydrologic Engineering
Center Computer Program 723-58-L2520, May 1974.
9.2 POLLUTANTS FROM MOTOR VEHICULAR TRAFFIC ON ROADWAYS
Motor vehicular traffic contributes a substantial portion of pollutant
material accumulated on the surface of roadways. Significant levels of
toxic heavy metals, asbestos, and slowly biodegradable petroleum products
and rubber are deposited directly from motor vehicles. Contributed by
traffic are also large quantities of particulate materials and nutrients.
All of these constitute a significant source of water pollution.
In a recent study conducted by Biospherics, Inc.,— for the U.S. Environ-
mental Protection Agency, the deposition rates of traffic related mater-
ials were measured. The sampling activities of that study were conducted
at different locations of urban roadways in Washington, D.C., with a
principal objective of determining the specific contributions of motor
vehicular traffic to materials deposited on roadways. During the investi-
gation, efforts were made to isolate pollutant contributions through other
mechanisms unrelated to motor vehicular traffic, such as land use, street
litter, air pollutant fallout, etc.
Traffic-dependent rates of deposition of roadway surface contaminants
determined by Biospherics, Inc., are given in Table 9-5. These deposition
rates (Kg/axle-km, or Ib/axle-mile) are highly correlated with total traf-
fic at sampling sites and therefore considered to be traffic dependent.
This is not to imply that these materials are directly emitted by motor
vehicles. To the contrary, some of the traffic related materials may have
origins other than with the motor vehicle itself.
Information developed by Biospherics can be used to estimate, for a speci-
fic section of a roadway, the traffic related pollutant loads using the
function below:
Y(i)tr = y(i)tr'LH-TD-AX (9-4)
where Y(i)tr = loading of pollutant i , kg/day (Ib/day)
y(i)tr = deposition rate of pollutant i , kg/axle-km (lb/
axle-km).
LH = length of highway section, km (mile)
TD = traffic density, vehicles/day
AX = average number of axles per vehicle
199
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Table 9-5. DEPOSITION RATES OF TRAFFIC-RELATED MATERIALS^
5/
t-o
O
O
Deposition
(kg/axle-km)
rate Significance of
(Ib/axle-mile) correlation (7<>)
(unless otherwise stated)
Dry weight
Vo lume
Volatile solids
BOD
COD
Grease
Total phosphate - P
Nitrate - N
Nitrite - N
Kjeldahl - N
Chloride
Petroleum
n- Paraffins
Asbestos
Rubber
Lead
Chromium
Copper
Nickel
Zinc
Magnetic fraction
6.67 x 1CT4
1.77 x 10~4
(quarts /axle -km)
3.39 x 10~5
1.52 x 1C)-6
3.58 x 1CT5
4.26 x 1CT6
4.03 x 10'7
5.29 x 10'8
6.33 x 10~9
1.04 x 10-7
6.16 x 10
2.39 x 10~6
1.68 x 10"6
1.08 x 105
(f ibers/axle-km)
3.47 x 10'6
7.81 x 10"6
5.18 x 10"8
7.95 x 10~8
1.23 x 15~7
9.8 x 10'7
3.53 x 10~5
2.38 x 10~3
6.33 x 10~4
(quarts/axle-mile)
1.21 x 10~4
5.43 x 10-6
1.28 x KT4
1.52 x ID'5
1.44 x 10~6
1.89 x 10-7
2.26 x 10-8
3.72 x 10~7
2.20 x 10~6
8.52 x 10"6
5.99 x 10"6
3.86 x 105
(fibers/axle-mile)
1.24 x 10~5
2.79 x 10"5
1.85 x 10"7
2.84 x 10"
4.40 x 10~7
3.50 x 10~6
1.26 x 10-4
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 2
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 0.1
< 1
< 1
< 0.1
< 0.1
< 1
-------
The following comments are made regarding the data (Table 9-5) developed
by Biospherics, Inc.
1. These data are deposition rates of traffic related materials on road-
way surfaces. They do not represent the discharge of pollutant into the
surface waterways. Correlation has not yet established between the loads
emitting to the streams and the dry weather accumulation on road sur-
face.
2. These deposition rates, however, may represent, on a high side, the
emission of pollutants from traffic related sources. It appears that the
loads flushed to receiving water by storm events depend on the surface
deposition and an attenuation factor which is influenced by the climatic
characteristics of the specific location, particularly the return fre-
quency of rainfall and runoff events sufficient to flush the surface.
3o It has been reported!/ that a total rainfall of 0.5 in. will remove
90% of road surface particulates. The storms of following duration and
intensities are considered to produce such a result:
• 0.1 in/hr for 300 min (5 hr)
• 0.33 in/hr for 90 min (1-1/2 hr)
• 0.5 in/hr for 60 min (1 hr)
• 1.0 in/hr for 30 min (1/2 hr)
It has also been reported that total rainfalls of 0.27, 0.15, 0.08 and
0.02 in. will remove 70, 50, 30, and 10% respectively, of road surface
particulates. The return frequency of storms in various regions of the
United States has been developed in a study conducted by the American
Public Works Association for EPA.-'
4. A very limited amount of work on highway runoff has been reported
and the loading functions or values which include effects from all pol-
lutant sources on highways are still not available. With the absence of
available data, the deposition rates established by Biospherics may be
used as a first approximation, with the following understandings:
a. Pollutants originating from highways, in addition to traffic related
pollutants, may also come from sources such as atmospheric fallout, litter,
spill, and runoff from adjacent areas. Influences from these and other
sources are not included in the given deposition rates.
201
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b. These deposition rates were measured on urban roadways. If directly
applied to highway situations, they may result in a higher prediction
than that actually occurred, due to the following reasons: (a) a higher
travel speed on highways than on the urban roadways, and (b) a lower
frequency of stop-and-go on highways.
9.2.1 Sources of Roadway Traffic Data
Data on mileage of urban and rural roadways and annual vehicle-miles of
travel are generally available at state highway departments. These data
are presented in reports, such as New Mexico's "Traffic Survey, 1973,"
and Oregon's "Traffic Volume Tables for 1973." The states also prepare
traffic flow maps showing travel on major routes.
9.2.2 Example
A 100-km section of a highway has a traffic density of 40,000 cars/day.
Assuming two axles per vehicle, the following calculations are made to
estimate loadings of BOD and total phosphate from Eq. (9-4).
y(BOD)tr = 1.52 x 10"6 kg/axle-km
Y(BOD)tr = 1.52 x 10"6 x 100 x 40,000 x 2 = 12.2 kg/day
Y(PT)tr = 4.03 x 10'7 kg/axle-km
Y(PT)fcr = 4.03 x lO'7 x 100 x 40,000 x 2 = 3.2 kg/day
9.3 STREET AND HIGHWAY DEICING SALTS
A set of loading functions for deicing salts has been developed which
describes (a) average daily loading in a year, (b) average daily load-
ing in the winter season, and (c) maximum daily loading in a 30-
consecutive day period.
The 30-day minimum is negligible, since practically all of the deicing
salt enters surface waters or moves to subsurface or groundwater during
the winter and early spring months.
9.3.1 Loading Functions
Loading function for annual daily average - The deicing salt loading
function for daily average in a year for an area under consideration is
developed from (a) quantity of salt applied per year and (b) proportion
of salt reaching surface water.
202
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Y(DI)average daily (annual) - a-b-DI/365 (9-5)
where Y(DI)average daily
(annual) = quantity of salt loading to water course,
average over 1 year, kg/day (Ib/day)
a = conversion factor,
= 1,000 (MT, kg)
= 2,000 (tons, Ib)
b = attenuation factor, dimensionless
DI = amount of deicer applied in the area,
MT/year (tons/year)
For urban streets, the attenuation factor "b" is 1.0, with the assump-
tion that applied salt is completely flushed into the storm sewer system
and into the receiving waters.
For rural areas, the attenuation factor "b" has been found to be in
the range of 0.5 to 0.9, due to losses to subsurface and groundwater.
A value of 0.7 is recommended. If local values for deicing salt losses
are available, however, they should be used in the loading function in
preference to 0.7.
Loading function for daily average in winter season - This function is
the same as Eq. (9-5) except that the denominator (365 days) is substi-
tuted by the number of days in the winter season.
Loading function for 30-day maximum - This loading function was developed
by evaluating snowfall frequency in an area and salt loading per snow-
fall day. For the latter, the function has the form:
*
-------
SD3Q
where DI30 = the tonnage of salt applied during the 30 day maximum
period
The loading function which describes the maximum daily loading in a 30
consecutive day period, therefore, is:
Y30-day maximum = ^'b-DI30)/30 (9-8)
In the northern latitudes, especially in rural areas, the largest frac-
tion of the snowfall and applied salt remains on the ground until the
spring thaw, and hence, the 30 day maximum load is shifted to the spring
months. The user should rely on local experience to determine the 30
day maximum period.
9.3.2 Sources of Required Data
Number of snowdays (SD) - The number of snowdays in the winter season
can be estimated with climiatic maps in The National Atlas of the United
States, U.S. Department of the Interior, Geological Survey (1970), or
Climatic Atlas of the United States, U.S. Department of Commerce, June
1968, or other equally suitable sources.
Amount of deicing salt applied - Data may be obtained from the follow-
ing agencies:
Street departments;
Public work departments;
Highway departments; and
Tollway authorities.
Nationwide data on deicing salt application are periodically collected
by and available from the Salt Institute, Alexandria, Virginia.
Appendix H of this Handbook presents available statistics relative to
deicing salts application on highways. Information includes tonnage
of salt (sodium and calcium chlorides) applied, application rates per
snowday per unit length of single-lane roads, mileage of highways and
tollways treated, and mean annual snowdays. Application figures were
determined by survey in the late 1960's.
204
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REFERENCES
1. Amy, G., R. Pitt, R. Singh, W. L. Bradford, and M. B. LaGraff,
"Water Quality Management Planning for Urban Runoff," a study
by the URS Research Company for the U.S. Environmental Protection
Agency, Washington, D.C. (EPA 440/9-75-004) (NTIS PB 241 689/AS),
December 1974.
2. Starter, J. D., and G. B. Boyd, "Water Pollution Aspects of Street
Surface Contaminants," a study by the URS Research Company for
the U.S. Environmental Protection Agency, Washington, D.C. (EPA-
R2-72-081), November 1972.
3. Manuel, A. D., R. H. Gustafson, and R. B. Welch, "Three Land Re-
search Studies," National Commission on Urban Problems, Report
No. 12, Washington, D.C. (1968).
4. American Public Works Association, "Nationwide Characterization,
Impacts and Critical Evaluation of Stormwater Discharges, Non-
sewered Urban Runoff and Combined Sewered Overflows," Monthly
Progress Report to the U.So Environmental Protection Agency,
August 1974.
5. Biospherics, Inc., "Effect of Urban Roadway Use on Runoff Pollution
Loading Factors," for the U.S. Environmental Protection Agency,
Final Report (draft), August 1974.
6. American Public Works Association, "Nationwide Characterization, Im-
pacts and Critical Evaluation of Stormwater Discharges, Nonsewered
Urban Runoff and Combined Sewer Outflows, (Final Report Draft),"
for the U.S. Environmental Protection Agency, Washington, D.C.,
August 1975.
205
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SECTION 10.0
LIVESTOCK IN CONFINEMENT
10.1 INTRODUCTION
The loading function for livestock in confinement is applicable only to
feedlots that operate without adequate runoff control facilities. The
feedlots which come under either the federal NPDES permit program, or
state and local regulations which require runoff control are excluded from
the scope of the handbook.
State and local regulations concerning feedlot runoff control requirements
vary. Sometimes these requirements may exceed those of NPDES or encompass
smaller lots than the lower limits of NPDES. Thus, the loading function
includes those feedlots not covered under NPDES, less those which ade-
quately manage/control waste and pollutant runoff in response to local
regulatory requirements or for other causes. Some of the added exclusions
are: completely closed confinement hog and poultry lots sized below NPDES
limits; and dairy lots below the NPDES limit, which control both milking
operation wastes and loafing/feeding area wastes. Turkey and laying hen
operations operated with the confined range management system will be in-
cluded (unless runoff is controlled). The principal livestock operations
covered in this handbook are thus the smaller beef, dairy, and hog opera-
tions, and poultry operations which involve confined range.
The present (1975) requirements for NPDES permits for animal confinement
facilities were published in the Federal Register dated 3 May 1973. Ac-
cording to these requirements, the following categories of animal feedlot
facilities are included under the NPDES permit program:
Slaughter steers and heifers, 1,000 head or more;
Dairy cattle, 700 head or more;
Swine over 55 Ib, 2,500 or more;
Sheep, 10,000 or more;
Turkeys, 55,000 or more;
Laying hens and broilers--continuous flow watering, 100,00 or more;
206
-------
Laying hens and broilers--liquid manure handling system, 30,000 or more;
Ducks, 5,000 or more; and
Combination of animals within a facility, 1,000 animal units.
The following multipliers are used to calculate the number of animal units
in lots with more than one type of animal.
Slaughter steers and heifers - 1.0;
Dairy cattle - 1.4;
Swine - 0.4; and
Sheep - 0.1.
The above enumerated size limits are under review, and the handbook user
should ascertain what current limits are specified before proceeding with
load calculations.
Most livestock operations eventually dispose residual wastes on land--
cropland, pasture, etc. The land-disposed wastes are nonpoint sources of
pollution, which are covered in Section 4.0 entitled "Nutrients and Organic
Matter." The loading functions presented in Section 4.0 are satisfactory
for wastes disposed on land by practices which minimize or eliminate runoff
incidents with land-spread manure. The data base for mismanaged land
spreading is not adequate for development and use of a loading function;
local judgment and estimates will be required.
, "V,.
On-site feedlot wastes are quite variable--by region, by season, by type
of animal, and by lot management practices. Particularly variable is the
on-site inventory of wastes. Beef cattle operations typically will develop
a permanent net inventory of wastes over a few centimeters of the lot sur-
face. Open poultry and hog lots will have a considerably smaller average
inventory of on-site wastes on a per unit area basis. These variabilities
lead naturally to wide variations in pollutant loads and concentrations
carried off the lots in runoff. Thus, it has been concluded that average
or typical numbers should not be presented for the convenience of the hand-
book user. Rather, a range of values will be presented, and the burden of
determining the proper position within the range is shifted to the user.
10.2 LOADING FUNCTION FOR LIVESTOCK OPERATIONS
The loading function is based upon the premise that the size and area of
individual and cumulative feedlots can be determined and located within
the area under assessment, and that the following factors can be adequately
established: (a) quantities of runoff, Q, from lots, as a function of
appropriate units of time; (b) concentrations, C, of pollutants in the
runoff; and (c) the fraction, FL,, of runoff-contained pollutant delivered
to streams. The loading function based upon these premises is:
207
-------
Y(i)FL = a.Q(R).C(i)FL.FLd.A (10-1)
where Y(i) = loading rate of pollutant i from a livestock facility,
FL kg/day (Ib/day)
Q(R) = direct runoff, cm/day (in/day)
C(i)FL = concentration of pollutant i in runoff, mg/liter
FLd = delivery ratio
A = area of livestock facility, ha (acres)
a = a dimensional constant (0.1 metric, 0.23 English)
10.3 FEEDLOT RUNOFF EVALUATION
10.3.1 Factors in Runoff Estimation
Runoff volume is dependent upon many factors. The most important variables
are: (a) amount and intensity of precipitation; (b) soil moisture condi-
tion; (c) topography including slope and surface cover; and (d) soil charac-
teristics. Stocking rates (area per animal), which are determined in part
by local precipitation patterns such as humid and arid condition, may affect
the degree of compaction of the surface and thus the runoff volume.
Precipitation - Precipitation varies in duration and intensity for a given
location, and average precipitation values may lead to errors in calcula-
tion of runoff volumes. Feedlot surfaces can absorb and store a definite
water volume in any specific period of time, and runoff may not occur
until the volume of rainfall exceeds the absorptive and storage capacity
of the surface. Similarly, rainfall intensity has a significant effect
on the rate of runoff and may affect the runoff volume.
Snow may accumulate on feedlots in cold climates and may not result in run-
off until thaw conditions set in. Significant runoff may result from snow-
melt in middle and northern latitudes of the U.S. The volume of snowmelt
may be computed from records of total snowfall. The water equivalent of
snow in precipitation varies significantly, but it is generally assumed
that 10 in. (25 cm) of snowfall contains 1 in. (2.5 cm) of water.I/
Soil moisture - The amount of runoff is affected by the degree of satura-
tion of soil with water. A dry soil-manure mixture has greater capacity
to absorb precipitation and to retain moisture than a wet mixture. Ante-
cedent precipitation is thus an important factor in determining the soil
208
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moisture. When one rain follows closely after another of equal intensity
and duration, a greater volume of runoff may result from the second rain.
Topography - The slope and surface cover, i.e., whether concrete lot or
dirt lot, may affect the runoff volume. For feedlot situations, the effect
of slope on runoff volume was not shown to be significant. The effect of
paving the lot surface, however, was reported to be significant. Manure
handling practices, including frequency of cleaning the surface, may have
an effect on the amount of runoff. Even on unsurfaced feed lots, the sur-
face soil-manure mixture is subject to compaction and tends to provide a
sufficient and effective barrier to seepage. This is especially true in
a continuously operating feedlot.
Soil characteristics - A coarse, sandy soil has a greater infiltration
capacity than a clay soil. The infiltration capacity for bare soils is
in the range of 0.5 to 1.0 in/hr (1.25 to 2.5 cm/hr) for sandy soils,
0.1 to 0.50 in/hr (0.25 to 1.25 cm/hr) for intermediate soils, and 0.01 to
0.10 in/hr (0.025 to 0.25 cm/hr) for clay and clay loam type soils. These
rates are for bare soils which are not excessively trampled or excessively
compacted. The movement of animals within the lot will often create a
soil-manure mixture at the surface, which will reduce the natural infiltra-
tion capacity of the soil and increase the runoff volume.
10.3.2 Precipitation Data Analysis
The single most important characteristic of precipitation is its variability.
Estimation of runoff will be greatly influenced by the quality of precipita-
tion data. In general, the longer the record, the better is the estimate
of probable precipitation for a given location. A 1-year precipitation rec-
ord is not a good indicator of the probable occurrence of precipitation in
the future, but there is no simple way to determine a priori what length
of records will give a reliable estimate of average precipitation in a given
location.
Depending upon the time and resources available, the local planner should
determine the length of records that must be included in the analysis. A
wide variety of precipitation data is available. Local climatological data
are issued on a monthly basis by the U.S. Department of Commerce—National
Oceanic and Atmospheric Administration. Table 10-1 shows the locations by
state for which weather records are issued. There are three categories of
publications which will help to determine the amount of daily precipitation
for a given location:
209
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Table 10-1. STATIONS FOR WHICH LOCAL CLIMATOLOGICAL DATA ARE ISSUED,
AS OF 1 JANUARY 1974
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
•be
abc
•be
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ALABAMA
Birmingham
Huntsvil le
Mobile
Montgomery
ALASKA
Anchorage
Annette
Barrow
Barter Is land
Bethel
Bettles
Big Delta
Cold Bay
Gulfcana
Homer
Juneau
King Salmon
Kodiok
Kotzebue
McCrath
Nome
St. Paul Island
Summit
Talkeetna
Unalakleet
YakUtat
ARIZONA
Flagstaff
Phoenix
Tucson
Wins low
Yuma
ARKANSAS
Fort Smith
Little Rock
CALIFORNIA
Bakers fie Id
Bishop
Blue Canyon
Eureka
Fresno
Long Beach
Los Angeles Airport
Los Ange les
Civic Center
Mt. Shasta
Oakland
Red Bluff
Sacramento
Sand berg
San Diego
San Francisco
Airport
City
Santa Maria
Stockton
COLORADO
Alamos a
Colorado Springs
Denver
Grand Junction
Pueblo
CONNECTICUT
Bridgeport
Hartford
DELAWARE
Wilmington
DISTRICT OF COLUMBIA
Washington-National AP
Washington-Dulles lnt'1 AP
ac
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
FLORIDA
Apalachicola
Daytona Beach
Fort Myers
Jacksonville
Key West
Lakeland
Miami
Orlando
Tal la has see
Tan) pa
West Palm Beach
GEORGIA
Atlanta
Augus ta
Columbus
Macon
Rome
Savannah
HAWAII
Hilo
Honolulu
Kahului
Li hue
IDAHO
Boise
Lew is ton
Pocatello
ILLINOIS
Cairo
Chicago
Midway Airport
O'Kare Airport
Moline
Rockford
Springfield
INDIANA
Evansvil le
Fort Wayne
Ind iana polis
South Bend
IOWA
Bur lington
Des Moines
Dubuque
Sioux City
Waterloo
KANSAS
Concordia
Dodge City
Good land
Topeka
KENTUCKY
Lexington
Louisville
LOUISIANA
Alexandria
Baton Rouge
Lake Charles
New Orleans
Shreveport
MAINE
Caribou
Portland
MARYLAND
Baltimore
abc
ac
abc
abc
abc
abc
•be
abc
abc
ac
abc
abc
abc
abc
abc
•be
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
ac
abc
a
abc
ac
abc
ac
abc
abc
abc
MASSACHUSETTS
Boston
Blue Hill Obs.
Worcester
MICHIGAN
Alpena
Detroit
City Airport
Flint
Grand Rapids
Houghton Lake
Lansing
Marquette
Sault Ste, Marie
MINNESOTA
Duluth
Internat ional Palls
Minneapolis -St. Paul
Rochester
St. Cloud
MISSISSIPPI
Jackson
Meridian
MISSOURI
Columbia
Kansas City
St. Joseph
St. Louis
Springfield
MONTANA
Bil lings
Glasgow
Great Falls
Havre
Helena
Kalispell
Miles City
Missoula
NEBRASKA
Grand Island
Lincoln
Norfolk
North Platte
Omaha
Scottsbluf f
Va le nt ine
NEVADA
Elko
Ely
Las Vegas
Reno
NEW HAMPSHIRE
Concord
Mt. Washington
NEW JERSEY
Atlantic City
'Airport
State Marina
Newark
Trenton
NEW MEXICO
Albuquerque
Clayton
Boa we 11
NEW YORK
Albany
Binghamton
•be
abc
abc
abc
abc
abc
abc
abc
abc
abc
•be
a be
abc
abc
abc
•be
ac
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
ac
abc
abc
a
abc
abc
NEW YORK (Contd. )
Buffalo
New York
Central Park
J.F. Kennedy Int'l AP
LaGuardia Field
Rochester
Syracuse
NORTH CAROLINA
Asheville
Cape Hatteras
Charlotte
Greensboro
Raleigh
Wilmington
NORTH DAKOTA
Bismarck
Fargo
WilHston
OHIO
Akron-Canton
Cincinnati
Abbe Obs.
Airport
Cleveland
Columbus
Dayton
Mansfield
Toledo
Youngs town
OKLAHOMA
Oklahoma City
Tulsa
OREGON
Astoria
Burns
Meacham
Medford
Pendleton
Portland
Salem
Sexton Summit
PACIFIC ISLANE6
Guam
Johnston
Koror
Kwajalein
Majuro
Pago Pago
Ponape
Truk (Moen)
Wake
Yap
PENNSYLVANIA
Allentown
Erie
Harr isburg
Philadelphia
Pittsburgh
Airport
City
Scranton
Williams port
RHODE ISLAND
Block Island
Providence
SOUTH CAROLINA
Char les ton
Airport
City
Columbia
Greenville-
Spartanburg
• be
•be
abc
• be
abc
• be
abc
• be
abc
•c
abc
abc
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
abc
abc
abc
• be
abc
abc
abc
abc
abc
ab
abc
abc
abc
•C
abc
abc
ac
abc
abc
abc
abc
abc
abc
ac
abc
abc
abc
abc
abc
abc
abc
• be
SOUTH DAKOTA
Aberdeen
Huron
Rapid City
Sioux Falls
TENNESSEE
Bristol
Chattanooga
Knoxvi 1 le
Memphis
Nashville
Oak Ridge
TEXAS
Abilene
Amarillo
Austin
Brownsville
Corpus Christi
Dallas
Del Rio
El Paso
Ga Iveston
Houston
Lubbock
Midland
Port Arthur
San Angel o
San Antonio
Victoria
Waco
Wichita Falls
UTAH
Mil ford
Salt Lake City
Wend over
VERMONT
Burlington
VIRGINIA
Lynch burg
Norfolk
Richmond
Roanoke
Wallops Island
WASHINGTON
Olynpia
Quillayute Airport
Seattle-Tacoma AP
Seattle Urban Site
Spokane
Stampede Pass
Walla Walla
Yakima
WEST INDIES
San Juan, P. R.
WEST VIRGINIA
Beckley
Charleston
Elkins
Huntington
Parkersburg
WISCONSIN
Green Bay
La Crosse
Madison
Milwaukee
WYOMING
Casper
Cheyenne
Lander
Sheridan
Monthly suB&ary issued.
b. Monthly
•ry includes *valtable 3-hourly observation*.
Published U 5 or mor* available per day.
lary issued.
210
-------
1. Hourly precipitation data at various stations in each state are re-
ported by month. Daily summaries are also included. These data are ex-
tensive, and can be used to determine precipitation amounts for a given
location quite accurately. Table 10-2 shows typical results for Missouri
in January 1974.
2. Local climatological data for a given station are summarized by month.
Data are given on a daily basis, and at 3-hr intervals. An example of the
type and extent of data in this category is shown in Table 10-3 during the
month of January for the weather station located at International Airport
in Kansas City, Missouri.
3. Climatological data for each state are reported monthly. These data
include both the official observatory data and data from other private and
public climatological records. These data are presented on a daily basis.
Typical precipitation data for parts of Missouri during January 1974 are
presented in Table 10-4.
10.3.3 Estimation of Runoff from Feedlots
The quantity of pollutants discharged from a feed lot depends largely upon
the runoff volume and the pollutant concentration in the runoff. Limited
data on cattle feedlot runoff characteristics in terms of various pollutant
concentrations are presented later in Tables 10-10 and 10-11.
The overall method consists of estimation of probable storm events for the
period of interest, by analysis of historic data, calculation of runoff
from individual storm events, and summation of runoff from all storm events.
The period of interest may be a year, or some fraction of a year--usually
30 days.
Methods for estimating runoff volumes from feedlots may be divided into
two categories:
1. Soil Conservation Service (SCS) Method; and
2. Empirical Regression Method.
Both the methods predict runoff volume from a given precipitation event.
The SCS method utilizes the concept of soil cover and hydrologic (infil-
tration) capacity of soil in calculating runoff. The regression method,
as the name implies, is based on the linear regression of observed rainfall-
runoff relationships for any given location. Because of the variability
of the observed runoff patterns, and also because the regression coeffi-
cients may not be established adequately for a given location, the regres-
sion method is considered to be less reliable in predicting runoff volumes.
211
-------
Table 10-2. HOURLY PRECIPITATION
HOl'RM \MOl'NTS
MISSOURI
STATION
HDPNEHSvIlLE
JEFFERSON CITY |_ U
JEFFERSON BARRACKS ZSW
JEWETT 7 E
*•
KANSAS CITY SHDPE PARK
KEARNEY
KIRXSVI LLE RADIO KIR*
L4KEM3E
=
1
2
3
10
14
3
I
\
31
4
15
25
A M HourKndme
1 2 3
•1 c, 6
7 8 9
10 11 12
P M Hour Ending
1 2 3
456
i 8 9
10 11 12
MONTHLY MAXIMUM AMOUNTS
HOURS
MINUTES
1
15
2
30
3
45
6
60
12
120
24
180
- ACCTMl'I.ATION
(AppKheadme a. appropriate)
AMOUNT
DATE/TIME OF
ENT ING
. J -^ .2
. i
AMOUNT
DftTE/TlME DF
ENDING
AMOUNT
DATE/TIME OF
AMOUNT
DATE/TIME OF
ENDING
AMOUNT
ClflTE/TlKE OF
ENDING
AMOUNT
DATE/TIME DF
ENDING
AMOUNT
OATE/TTME OF
ENDING
• '
AMOUNT
DATE/TIME OF
F.NDING
AMOUNT
DATE/TIME OF
END ING
AMOUNT
DATE/TIME OF
END JNG
AMOUNT
DATE/TIME OF
ENDING
AMOUNT
ENDING
AMOUNT
DATF/TIME OF
PNDING
•I -2 .2
. 1
.43
22/10:OOP
•'
.2
4/3 : COA+
-
-
-
14/4IOOP
.34
3/9IOOP
. D
3/11IOOP
3/,0:4=P
.3
3/8: OOP
.3
3/eiooP
: :
-
.64
22/10IOOP
4/3 lOOA
"
.43
14/4IOOP
.66
3/10IOOP
1.1
3/1 ] IOOP
• 9
3/111 OOP
3/9IOOP
.4
3/flilSP
: : :
•
-
.66
22/12IOOP
4/4 OOA+
.2
4/3 1 15A+
-
-
14/4IOOP
.01
.79
3/UIOOP
i . .1
. 1
;
TOTAL
.6
.1
.1
3
.13
.45
1.12
.01
1.2
.1
.6
-
.t
.1
.1
,3
212
-------
' V
ISBI
Table 10-3
LOCAL CLLMATOLOGICAL DATA
U.S. DEPARTMENT OF COMMERCE
NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION
ENVIRONMENTAL DATA, SERVICE
Kansas City, Missouri
Nat Weather Service Met Obsy
International Airport
January 1974
Longitude
Elevation 'ground'
10U fl
Standard Ume used CENTRAL weAN «03947
1>
o
1
1
J
3
4
5
6
7
8
10
11
1?
11
14
15
16
17
18
19
20
21
2?
21
?4
25
26
27
28
29
30
Jl
Temperature
E
E
X
S
E
E
i
c.
n
S i <
2 | 3 4
0
10
15
17
18
17
17
13
12
6
S
27
39
42
43
43
40
3«
39
37
35
41
48
53
44
36
47
56
60*
44
94fl
Avg
30.6
-13
0
3
-1
1
1
-3
2
3
-9
-13«
7
24
29
33
30
33
31
33
33
30
25
28
30
34
29
29
35
39
26
527
Avg
17.0
-7*
5
9
8
10
9
7
8
8
-2
-3
17
32
36
38
37
37
33
36
35
33
33
38
42
39
33
38
46
50*
35
Avg.
23.6
i
t-
0
D
5
Weather types
* on dates of
1 •• Dc,srec davs i ^"urrence
E
S
;
2
-35
-23
-; j 4 Ice pellets
t - M no ' 5 Hall
" D. £ S
o > n ' "p
6 Glaze
j; £ £ ,0 8 Smoke. Haze
•^ "° * u ° Fllowlna snow
6
-16
- 5
-19 0
-2
0-2
-18 3
-19
-2
1
-19
-19
-29
-30
-]
0
5
9
11
in
10
:
6
fi
7
5
5
0
14
1
1
4
9
17
1
\
5
Dep
-4.0
Number of days
Maximum Temp [ Minjmurr
:»90'tl .c 32' -=.32'
0 ! 13 1 24
6
- 4
2
4
-11
-13
12
22
25
30
35
32
?8
?0
29
26
?3
25
?7
31
25
77
32
34
?o
Avg_
15
Temp.
< Oc
7
7A
72
60
56
57
55
56
58
57
57
67
68
48
33
29
27
28
26
32
29
30
32
32
27
23
26
32
27
19
15
30
Total
Dep
119
Total
3153
Dep
67
7B
0
0
_ 0
0
0
0
8
U
8
1 8
1
ol
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Total
0
to date
Total
0
Dep
0
46 R
1 8
1
1
8
1 8
8
1
2 8
2
2 6
2 8
1
i e
i
i
4 6
1 468
Smiw
ice
pcllcis
or
ce on
at
06AM
In
a_
4
4
4
4
4
3
3
3
7
7
7
7
7
6
0
3
T
T
T
T
T
T
0
0
0
0
T
0
0
0
Number of days
Precipitation
=- .01 inch 11
Snow, ice pellels
> 1 0 inch l_
Thunderstorms o
"Heavy log X 4
Precipitation
1 Snow.
Water | ice
T'"" i ^i'n""
lent ! ln
In ,
10
11
.04 .3
. 07
0
0
0
T
0
.12
.03
0
0
0
0
0
0
0
T
.02
.12
T
.12
0
0
0
.30
.02
.01
0
0
0_
Total
-0.20
.7
0
0
0
T
0
,9
.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
T
T
0
0
0
Tolal
4jJ_
AVR Wind
st.]tmn .
pres-
sure '
-a
In £ J
Elev T= c c E \l
1025 -r. ^TJ P*
m.s.l
£J ij, £ O
K'-S' a g-
12 ] 13
1-1
29.40 :35 2.5
£9.32
29.36
29.20
29.08
28.92
29.00
28.92
26.98
29.23
29.40
29.16
28.99
28.93
28.63
28.82
26.87
26.87
28.66
26.87
28.92
29.09
29.14
29.02
28.60
26.87
28.60
28.80
26.67
2_6_JU94
Fo
28.99
07
33
07
06
35
14
02
02
32
12
1 /
21
21
21
19
36
04
26
03
34
24
?]
19
19
33
2 1
21
?0
uoJ
5.0
7.5
2.2
5.1
4.6
2.6
6.4
10.9
7.6
3.2
6.9
9.4
10.4
11.7
6.3
7.6
5.5
4.0
3.9
9.7
5.0
4.6
6.3
6.8
5.3
2.8
10.3
14.5
7.6
the
„ a
Sunshine
Fastest ,
mile [
r 1
Z a. j g a J
< E |w E
lj_
4.6
6.5
7.6
4.0
5.3
8.2
6.6
12.4
11.7
7.6
7.3
6.8
10.1
10.5
12.1
6.9
8.9
6.5
4.8
6.6
10.5
6.2
5.2
6.6
7.6
6.8
5.0
10.4
14.'5
LUji
m o n
8. ?
Greatest in 24 hours and dates
Precipitation 1 Snow, ice pellets
.30 26 2.6[ 9-10
1C
D
17
9 E
13
10
10
12
13
10
20
13
11
13
15
16
17
19
1 1
18
15
11
17
17
10
7
?1
19
17
16
16
?3
,?2
h .
?3
Dale
E
NW
ME
N
NW
E
NW
N
NH
E
S
S
S
SH
S
N
N
H
NE
NW
SW
SH
SH
S
NW
SH
S
SH
NW
SH
_3J)_
c
fi 70' at A1a?kan stations
+ Also on an earlier date, or < ates.
X Heavy ((.£ ^ % ^ ^
ments and orders to National Climatic -^fn^l.^k ••*.
Center, Federal Building, Asheville,
North Carolina 28801. *
;
00^ 4
D3l 5
0
£>
QJ "p
29.001 22 20
2B.99; 21 19
u
u.
3 o
Q
14
14
Wind direction* are ih<-e frr-m »hich thi- « n
-------
Table 10-4. DAILY PRECIPITATION
NORTHWEST PRAIRIE 01
AMTY
BITKANY
BROOKHELO
BRUNSWICK
CARROLLTON
CMULICDTKE
CHILLICDTHE RADIO KCHI
COLDMA
CONCEPTION
CONCOROIA
EOGERTON
FAIRFAX
FAYETTE
FOUNTAIN GROVE WL
6ALLATIN
GRANT CITY
HAMILTON 2 V
•KANSAS CITY INT USD A** R
KING CITY
LEXINGTON
LUCERNE
MARSHALL
MARYVULE 2 E
MERCER 6 NW
MILAN
NEW FRANKLIN 1 M
ODESSA
OR.EGDK
POLO
PRINCETON 6 Sw
SALISBURY
SdCKARO T W
SWEIT SPRINGS
TARKIO
TRENTON
UNIONVULE
WAVERLY
* • *
AUXVASSE
BOWLING GREEN 2 NE
CENTRALIA
COLUMBIA WSO AP R
ELSBE4RY 1 S
FREEDOM
FULTON
tERALD
HANNIBAL WATERWORKS
HERMANN
KAHOKA
LA BELLE
LOUISIANA
MACDN
MADISON
KARTmSBURG
MEMPHIS
WEXICQ
MOBERLY RADIO KW1X
MONROE CITY
NEW FLORENCE
OWENSVILLE
PACIFIC
PALKYRA
PARIS
SAINT CHARLES
ST LOUIS GATEWAY ARCH
SAJhT LOUIS WSO AP R
SAVERTON L AND 0 22 //
SHE IS I NA
SHEIIYVILLE
STEFFENVILLE
SULLIVAN 3 SE
TROY
UNION
VALLEY PARK
VANDAL1A
WEBSTIR GROVES
WELDON SPRNG WLPLFE »K
* • *
WEIT cFNT«Ai
PLAINS 03
APPLETQN C 1TV
600*Y1LLI WATERWORKS
BUTtEl
CALIFORNIA
CAMCENTON
C*'l INGFR MILLS
CLI'TDN C MY
U1XA* SPRING!
CLINTON 1 NW
COL! CAMP 9 SE
EICON
ELDDR1QO SPRINGS
H*M 1 JOHvlLLE
jEMilinN CITY i U
LAKfSlDE
N(v*Di SIWACE PLANT
OiCIOli
•a
H
1.56
1.90
2.06
1.83
1 .49
1.84
1.821
1.55
2.26,
1.69
1.83
1.57
1.33
L.05
1.84
> r.9e
1.31
1.80
1.61
3.12
3.79
1.26
2.54
1.53
2.V7
1.31
1.56
1.97
4.02
1.10
3.53
2.99
1.76
3!&l
i. i i
1.931
3.7*.
4.27
2.70
2.8T
3.01
1.52
4.30
1.B9
2.80
3.47
*.7l
2."i6
3.51
2.73
2.24
2.33
I. 6*
4.90
4.42
3.6C
2.61
2.9*
1.21
1.07
1.2C
ill!
1,57
2.21
2.2!
1.9)
2.51
1.5«
1 2 3
.10
T .12
.01 .09
.02
.20
.06
T .10
.02 T
.19
.04
.12
.05
.04 .07
.02 .03
.06
T
T .05
.10
.04 .03
.20
.02
.06
T
.20
.05
T .06
T
T
.05
T
T
T
.02 .05
T
T
T
.04
T T
.03 .03
.02
.08
.05
...
T
.01 T
.02
.02
T
4 5 6
T
.06
T
.01
T
.10
.04
T
T
.07
T
.08
T T
.05
.10
T
.03
T
.02
.03
T
.07
T
T
.24
T
T
T
T
.04
T
T
T
T
m
T
T
789
.02 .23
,02 .29
T .32
.21 .09
T
.22 .10
. 12
.48
.16
.27
.01 .23
•18 .06
T .36
• 20
.12 .20
.390 ,40
T .19
T
.010 .15
T .30
.25
.18
.13 .05
.22
.20
.20
.11 T
.1* .11
.20 T
.20 .11
.21 T
T .IB
.06 .21
T .45
. 16
•15 .40
.08 -
T .13
.10 .30 .05
T .31
T .13
.20 ,11
T .13
T ,23
.23
.02 .03
.01 .IB
• 12
•21 .06
T .11 T
.05
.14
.16
.21 .51
.23
T
.19 .62
T .23
. 1 5
.02 .21
T .08
.13
.12
.15
.20 .70
.07
.U
•10 .19
T
.05 ,25
.21
.12
.02 .16
.60 .25
.10 -
• 10
. 10
'11 .50
10 U 12
.31 T
.21 .06
.29 .08
.37
.28 .05
.28 T
.22
.19 .05
.47 .02
.72
.04
.38
.25 .03
.21 T
.09
.08
.42 .02
.200 ,70
.06 ,02
.10 .10
.19 .OB
.47
.61
.03 .33
.21 T
• 12
,40 .09
.15 T
.61 T
.22
.1! .08
.11 .07
.65
.68
.56
.07 T T
.36 .01
.20
.27 .30 T
.55 .03 T
.14 T
.25 .04
.31 ,05
.41 .02
.40
.23 ,09
.44 ,05
.4!
.50
.50 T
T ,85 T
.6? .02 T
.29 .08
.60 T
.70
.08 .01
.30 .01
.23 ,04
.22 .07
.72
.51 T
.81 ,01
.58 T
-43 .03
.55 T
.45 .07
1.08 T
.46
,69
.16
.51 T
.59 T
.49 T
1 .00
.12 T
,50
.74 T
.90
Day of Month
13 14 15
T
T
T
T
1.53
T
T
T
T
1.40
T
,6B
T
16 17 18
.02
.01 .02
T
T
T
T
T
T T
.04
.03
.07
.02
T .08
T T
T ,04
T
T T
.63
.32
T .45
.19
.02
. 12
.20
T
T
1.10
.42
.15
.27
T
.45
T
.06 .01
.29
T
T
.02
.17
.05
.0*
.0!
T
.0]
T
19 20 21
.22
T .30 .OJ
.15 .07
.40 .20
.15 .08 T
.21
.19 ,04 .02
T .50 .02
T .42 T
.06 .20 T
.17 .16
.27 .a
.35 .05
.18 T
.02 .12 T
.25
.01 ,17 ,0*
.35 .15
T .44 .06
T .50
T .30 T
.22 .41 T
.26
.35 .05
T .16 .10.
T .24
.43 .12
.03 .31 T
.48 .06 T
T .52
.04 .21
T .33 .02
.32
.70 1.30
.30 .26
.12 .14
.13 1.36 .58.
.07 .63
.18 .12 .*
.30 1,00 . 5
.16 .10 • 4
T ,35 . 0
.97
.30 «07
.64 .40 >48
.68 .IB
.03 ,42 .01
.46 .03
.14 .25 ,08
.30 .35 .01
.25 -
.03 .93 T
1.45 .45
.18 .8; ,83
.47 .24 .13
.65 .05 .15
.41 .49 1.89
.07 .1,5 .34
.27 .78
.09 .22 .29
.05 .44 -07
T ,53 .07
.20 .94
.50 1,00 .90
.11 .70 1.05
.36 ,7ft .27
.66 .12 .32
•10 .60 >23
.IB ,01
T ,60 T
.05 ,25 T
•20 ,06
.33 .16 T
.32 T
.21 .15
.02 .23 T
.43 ,18
.91 .08
.20
,2T .0!
.42
22 23 24
.15
.20 .07
.07 .02
.05 T
.12
.03
.13 T
. IB
.05 T
T
.23
.05
.14
.12
.22
.21 T
.28
.06
.25
.25
.57
.35
.07
:J! T
.15
.20
.42
.04
.12 .12
.14 .19
.74
.65
.16 .37
Il4 .33
.04 ,16
.02 .60
.74
.11 .10
.17 .28
.70
.36 . 15
.25 .10
.25
.41
.38
T ,20
• 24. .24
.11 .05
.02 .53
T
.45
. 15 T
.14 .Q9
.07 .13
.20 .55
.01 .44
T .38
.42 .07
.58
.43 .10
.6S T
.56
.39
.06
.31
.24 .09
.53
.20
.41
.66
.04 . ,56
.17 .07
.21 T
25 26 27
.11 .40
.52
.93
.12
.60 T
.72
.69 .06
.70
.20
.63
.4!
.*8
.49 .25
.14
.95
.10 .02
.56
.45
.63 .10
.19
.50
T .80
.99 .12
.56
.06
.56
.54
\ll T
.71
.05
.63
.76
1.09
1.16
1-25
.86
.65 .04
„ '•«
1.1°
.„ :I5
.89
1.34
1.81
1.23 .03
1 .OB
.05 .87
' "•:{!
.90 .12
T 1.00
• 42
1.02
.06 .6; .33
... :;;
.12
1.35
T .76
.85
.73
.08 .63
.52
.46
.66 .18
.60
.14
.92
.16
.97 .02
,67
1.07
.76
.12 .76
1.12
.55 T
.42
1.05 .01
1.06
.41
.71 -02
JANUARY
28 29 30
.07
,16
.02
.08
.26
.35
T
.01
.17
.27
T
.49
.26
.03
.22
.34
.26
.30
.25
,36
.18 .17
.35
.15 .23
.51
.18
.05 ,42
T
,04
.17
.41
.30
.04
. 10
.39 T
.26
.15 .15
.23 .15
.23 T
.05 .40
.2! .24 T
.50
T .09
.22 .05
.07 .03
.25
.25 .18
.09 ,*7
.33 .27
.26
.18
.41 .05
.IB T
.25
.56
.47 ,o!
.71
.55 .01
.61
.JO
.51
.40 T
!TO
mi
31
214
-------
However, because of the simple format, the regression method may be easier
to use on a routine basis, especially when adequate experimental data exist.
10.3.3.1 Soil conservation service (SCS) method -
The Soil Conservation Service of the U.S. Department of Agriculture has
developed a method of estimating direct runoff from small agricultural
plots due to single storm events. jj-y The rainfall-runoff relationship given
by SCS is:
Q = 2 (10-2)
(Pr + 0.8S)
where Q = direct runoff, cm (in.)
Pr = storm rainfall, cm (in.)
S = potential infiltration, cm (in.)
S is defined in terms of runoff curve number (CN)
CN = I>000 (10-3)
S + 10
or
s =
CN
where S = potential infiltration, in.
CN is related to hydrologic soil-cover complex, i.e., a combination of
specific soil and specific cover. The soils in the U.S. are classified
into four groups according to their hydrologic properties.?./ Group A soils
have low runoff potential and high infiltration rates. Group D soils have
high runoff potential. Groups B and C have intermediate potentials.
The index of watershed wetness is the Antecedent Moisture Condition (AMC).
Three levels of AMC are used:
AMC - I: Lowest runoff potential. The watershed soils are dry enough for
satisfactory plowing or cultivation to take place.
215
-------
AMC - II: The average condition.
AMC - III: Highest runoff potential. The watershed is practically satu-
rated from antecedent rains.
The AMC for feedlots can be estimated from 5-day antecedent rainfall by the
use of Table 10-5, which given the rainfall limits for "dormant season."
No upper limit is intended for AMC - III (see Figure 4-9, Ref. 2).
Table 10-5. SEASONAL RAINFALL LIMITS FOR VARIOUS
ANTECEDENT MOISTURE CONDITIONS
Total 5-day
antecedent rainfall
AMC group
I
II
III
O f I
Using feedlot runoff data from various authors—*—' and Eqs. (10-2) and (10-3),
the amount of runoff from a given rainfall amount is computed for the
three antecedent moisture conditions, as shown in Table 10-6.
Table 10-6. RUNOFF (IN INCHES) FROM FEEDLOT SURFACES FOR
VARIOUS ANTECEDENT MOISTURE CONDITIONS
Precipitation (in.)
(in.)
< 0.5
0.5-1.1
> LI
(cm)
< 1.3
1.3-2.
> 2.8
Runoff condition 0.5 1.0 1.5 2.0 2.5 3.0
AMC III
Surfaced 0.318 0.792 1.282 1.776 2.272 2.770
Unsurfaced 0.258 0.707 1.185 1.673 2.166 2.660
AMC I and II
Surfaced 0.138 0.505 0.938 1.398 1.871 2.352
Unsurfaced 0.071 0.360 0.741 1.165 1.612 2.072
216
-------
The following procedure is suggested to calculate runoff from feedlots
using the SCS method:
a. Determine feedlot area, which includes feeding pens, sick pens, feed
mixing and handling and equipment storage areas, alleys, and other open
areas associated with feedlot management. In the absence of actual data,
assume total area as 115% of feeding pen area.
b. Select the time period for which storm data are required. While no
simple procedure is available to determine the representative storm periods
for a given location, storm data during the past 3 years may be used to
approximate the most recent trends in precipitation. Lesser time intervals
may be used if the records show no substantial deviation from expected
norms in precipitation patterns. Select precipitation data on a daily basis.
Express all precipitation in terms of equivalent water by using the ratio of
one volume of water to 10 volumes of snowfall.
c. Determine storm rainfall (P) from Step (b) above. For each location,
the nearest weather station data may be used unless special rain gages were
installed for the location.
d. Determine the amount of runoff for a given storm using data in Table
10-6. If local data permit an accurate determination of CN values, Eqs.
(10-2) and (10-3) may be applicable for a given location.
e. Determine runoff (inches or centimeters) by adding runoffs for each of
the storms over a given period of time. It is useful to determine monthly
values in order to obtain maximum and minimum 30-day runoff volumes. By
adding the monthly runoff volumes, annual runoff may be obtained.
f. Calculate total runoff volume by multiplying runoff depth in Step (e)
with area of feedlot determined in Step (a). The result may be expressed
in volume units (acre-inch to ft^ or hectare-centimeter to m^).
10.3.3.2 Example -
An example computation of runoff for a hypothetical feedlot located near
the climatological station at International Airport, Kansas City, Missouri,
is shown below.
Assume that the feedlot comprises 220 acres (88 ha). Calculate total run-
off volume from the feedlot using 1974 climatological data for the location.
The daily precipitation data for the station are presented in Table 10-7.
These data may be obtained from any one of the three categories of
217
-------
Table 10-7. DAILY PRECIPITATION DATA (INCHES) FOR KANSAS CITY, MISSOURI - 1974
ho
I—'
oo
Date
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
31
Total
J
0.04
0.07
0
0
0
T
0
0.12
0.20
0.03
0
0
0
0
0
0
0
T
0.02
0.12
T
0.12
0
0
0
0.30
0.02
0.01
0
0
0
1.05
F
0
T
T
0
0.02
T
T
T
T
0
0
0
0
0
0
0
0
0.29
0
0
0.78
T
0.03
0
0
0
0
0
-
-
-
1.12
M
0
0
0.25
T
0
0
0
0.30
0
0.49
0.03
0.01
0
0
0.03
0
0
0
T
0.01
0
T
0.05
0
0
0
0
0
0.01
0
0
1.18
A
T£/
0
0.24
0.04
0
0
T
0
0
T
0.08
0
0.03
0
T
T
0
T
0
0.66
0
0
0
0
0
0
T
0.28
1.60
0.01
-
2.94
M
0
0
0
0
0
0
0.06
0
0.13
0.52
0.14
0
1.02
0.22
0
0.77
3.13
3.21
0
0
0.05
0.09
0.01
0
0.29
0
T
T
T
0.43
0
10.07
_J_
0
0
0.06
0.03
0
0.96
0.01
0.72
0.02
0
0.32
0
0
0
T
0
0
0
0.04
0
0
0
0
0
0
0
0
0
0
0
-
2.16
J
0
0
1.12
T
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
T
0
0
0.01
0
0
0
0
0
0
1.13
A
T
0
0
0
0
0.62
0.03
0
0.13
0
0
0
0
0
0.01
0.88
0.25
0
0.03
0
0.12
0.88
0
0
0
0
0.12
0.56
0
0.08
1.27
4.98
S
0.71
0.29
0
0
0
0.02
0
0
0
0
0
0.07
0
0
0.04
0
0
0
0
0
0
0
0
0
0
0
0
T
T
0
0
1.13
0
0
0
0
T
0.44
6.50
0
0
0
0
0.46
0.04
1.26
0
0
0
0
0
0
0
0
0
0.10
T
0.04
T
0
0.33
0
3.48
0.57
7.22
N
0
0.15
1.07
0.04
0
0
0
0
0.15
0.15
0
0
T
T
0
0
0
0
0
0
0
0
T
0
0
0
0
T
0.03
0.03
_
1.62
D
T
0
0
0
0
0.35
0.02
0
0
0
0.40
0
0
0.16
T
T
T
T
T
T
0
0
T
0.11
0
0.02
T
0
0.01
0.27
0.18
1.52
Annual precipitation: 36.12 in.
a/ T - trace.
-------
climatological reports discussed earlier (i.e., hourly precipitation data -
Missouri; local climatological data - Kansas City, Missouri; and climato-
logical data - Missouri).
Using Eqs. (10-2) and (10-3), and substituting a CN value of 91, Eq. (10-2)
becomes :
Q = (Pr - 0.1978)2 (10-5)
(Pr + 0.7912)
Table 10-8 was prepared using data in Table 10-7 and Eq. (10-5) for each
daily event.
The results in Table 10-8 show that rainfall of less than 0.4 in. produces
no runoff. Only 37 calculations were involved for the 1974 data. On an
annual basis, the results in Tables 10-7 and 10-8 show that 36.12 in. pre-
cipitation resulted in a runoff of 14.58 in. On a 220-acre feedlot, the
annual runoff volume thus amounts to 267.30 acre-ft (32.60 ha-m). This is
equivalent to an annual runoff volume of 87 million gallons or 0.326 million
cubic meters .
10.3.3.3 Empirical regression method -
The general empirical relation between rainfall and runoff developed in
literature for feedlots may be expressed as follows:
Q = L-Pr - B (10-6)
where Q = runoff, cm (in.)
Pr = precipitation, cm (in.)
L = regression coefficient (slope)
B = regression constant, cm (in.) (intercept)
The regression constant B may be regarded as that amount of precipitation
that is stored on the feedlot surface and hence not available as runoff.
The coefficient L may be similarly regarded as a fraction of the net avail-
able precipitation (i.e., total precipitation minus total storage and other
seepage losses) that results in surface runoff. Under dry conditions, the
value of B may be higher than that under wet conditions. The value of L
may be higher for surfaced lots than for unsurfaced lots.
219
-------
Table 10-8. ESTIMATED RUNOFF (INCHES) FOR KANSAS CITY, MISSOURI - 1974
Date
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
31
Total
0.0017
0.0096
0.0666
0.0079
0.2157
0.1482
0.0025
1.2806
0.0792
0.0175
0.2097
2.7872
2.8665
0.0079
0.4450
0.3317
0.1804
0.0134
0.1263
0.2785
0.0712
0.2785
0.0971
0.5577
0.1748
0.0969
0.0476
0.2568
0.0549
0.5501
0.0156
2.5222
0.3159
0.4087
0.0203
0.0343
0.0049
0.2236 0.0762 1.4330 5.9680 0.5255 0.4450 1.4093 0.2717 3.7631 0.4087 0.0595
Annual runoff: 14.5836 In.
-------
The reported values of L and B are based on the least-squares fit of
experimental data to Eq. (10-6) under different climatic and geographic
conditions. Consequently, significant variations may be found in these
data.
Kreis et al.—' have determined the values of L and B for a commercial
feedlot in central Texas having an annual precipitation of 37 in. to be 0.5
and 0.124, respectively. Wells et al.—' showed similar values for south-
western cattle feedlots. They obtained L and B of 0.746 and 0.192 for
surfaced feedlots and 0.345 and 0.309 for unsurfaced lots.
Loehr—' reviewed literature for feedlot runoff and evaluated the regression
coefficients L and B in Eq. (10-6) for various conditions. Loehr's
results are shown in Table 10-9-
Table 10-9. RUNOFF AND RAINFALL RELATIONSHIPS ON BEEF
CATTLE FEEDLOTS?-/
Minimum rainfall
to produce runoff
_B (cm) (in.) Conditions
0.945 0.34 1.0 0.4- Surfaced lot
0.882 0.37 1.0 0.4 Unsurfaced lot
0.53 0.14 1.0-1.3 0.4-0.5 3 to 9% slopes
0.93 0.41 1.2 0.45 1968 runoff
0.45 0.05 1.3 0.5 1969 runoff
0.49 0.06 1.3 0.5 1970 runoff
0.50 0.12 0.5-6.8 0.2-0.32 1969 to 1970
The following procedure is suggested to determine the runoff volume using
the Regression Method:
a. Determine feedlot area and precipitation for a given site using the pro-
cedure described in SCS method, Steps (a), (b), and (c).
b. Determine the regression coefficients for the site conditions in the
area from local experimental data or other reported values applicable to
the area. Otherwise, assume the following ranges of values:
221
-------
Site condition
Surfaced lot
Surfaced lot
Unsurfaced lot
Unsurfaced lot
Moisture condition
Wet
Dry
Wet
Dry
0.5-0.95
0.5-0.95
0.5-0.95
0.5-0.95
(in. )
0.0-0.2
0.2-0.4
0.0-0.3
0.3-0.5
(cm)
0.0-0.5
0.5-1.0
0.0-0.8
0.8-1.0
c. Calculate runoff (centimeters or inches) using Eq. (10-6) and data in
Steps (a) and (b) above.
d. Calculate monthly or annual runoff using the procedure described in SCS
method, Step (e).
e. Calculate total volume of runoff by multiplying runoff depth (Step (d))
with feedlot area (Step (a)).
10.4 POLLUTANT CONCENTRATION IN FEEDLOT RUNOFF
Some of the reported data on feedlot runoff characteristics are presented
in tabular form in Tables 10-10 and 10-11. As indicated earlier, the range
in concentrations is wide. The handbook user has two alternatives.
1. He may use the range of values given in the tables as guidelines for
selecting concentrations for livestock operations in his area. If this al-
ternative is selected, he should use values at both the lower and upper
range of the data which appear to represent his area, and estimate a prob-
able range of loads rather than an assumed average load.
2. He may use data obtained on a current basis in his area. If this al-
ternative is selected, he should be careful to determine and specify the
local range of values. This second alternative is preferred over alterna-
tive (1).
Essentially no data exist for concentrations of pollutants in runoff from
hog lots, poultry ranges, and dairy and sheep lots. In lieu of actual
local data (alternative (2) above), the beef cattle runoff data can be used
as guideline data for other livestock. Pollutant concentrations in runoff
are relatively insensitive to the quantity of waste exposed to the runoff,
particularly if the lot surface has been in use for extended periods. It
is not proper to attempt to factor down the concentrations in proportion to
relative rates (hogs versus beef cattle, e.g.) of animal waste deposition
on feedlot surfaces. If actual data are not available, pollutants in run-
off from lots other than beef cattle feedlots should be assumed to lie
within the ranges reported for beef cattle.
222
-------
Table 10-10. BEEF CATTLE FEEDLOT RUNOFF CHARACTERISTICS
Nebraska
Snowmelt runoff
Rainfall runoff
M Texas
f^0 Dirt lots
Concrete lots
Texas
Dirt lot
Kansas
Nonsurfaced lot
Concrete lot
Suspended
Total solids COD
solids (mg/,2) (mg/i)
3.0-19.83/ - 14,100-78,000
0. 024-1. 74§/ - 1,300-8,200
2,964-28,000
5,000-48,000
3, 100-28, 900k/ 745-17,702 1,440-16,320
1,500-10,500 1,900-8,900
1,400-12,000 2,760-19,400
BOD5 Org-N
(mg/l) (mg/l)
1,600-7,900
370-600
1,150-3,210 6-434
2,400-10,000 35-797
1,075-3,450 31-495
216-1,010-''
314-2, 200S./
NH3-N
( mg / i )
270-2,028
26-82
2-100
33-774
4-173
1-65
1-140
Total
N03-N N
(mg/l) (mg/l)
0-80 1,429-5,765
0-17 65-555
0-163
0-1,270
0-2.3 35-668
0.1-6 50-540
0.1-11 94-1,000
Alka-
Total Unity
P (mg/i Ref.
(mg/1) as CaCOQ £H no.
7-750 - 6.7-7.6 10
14-47 - 6.7-9.4 10
70-1,600 7.1-7.95 8
86-2,600 5.6-7.3 8
21-223 - 7
11
11
a/ Percent.
b/ Ks/i.
c/ Calculated using a COD/BOD ratio.
-------
Table 10-11. RUNOFF CHARACTERISTICS FROM CATTLE FEEDLOTS IN KANSAS*!!/
Concrete Nonpaved
Ammonia-N
Winter 1.3-7.0 mg/£ 1.0-3.8 mg/jj
Spring- fall 20-77 rag/£ 13-45 tng/jj
Summer 50-139 mg/4 26-62 rag/£
NH3-N: Kjeldahl-N, %
Winter 0.01-0.05 0.02-0.6
Spring-fall 0.3-0.4 0.06-0.2
Summer 0.1-0.4 0.1-0.3
Nitrite-N
October-November 1.0-5.0 mg/jfc 1.0-2.3
July-August 1.0-6.0 mg/jj 1.0-7.0
Suspended solids
July-August
Moist - 1 in/hr 6,000 rag/j& 5,000
Dry - 0.4 in/hr 3,000 mg/£ 1,500
Dry - 2.5 in/hr 1,400 mg/£ 2,000 rag/4
Wet - 2.5 in/hr 3,000 mg/£ 3,000 mg/4
Wet - 0.3 in/hr 12,000 mg/4 10,500 mg/£
October -November
Wet - 1 in/hr 2,000 mg/£ 1,800 mg/4
Wet - 0.5 in/hr 2,500
Bacterial densities (in
millions of organisms per
100 ml), 7070 limits
July-November
Total coliform 33-348 22-348
Fecal coliform 35-240 8-79
Fecal streptococci 13-240 8-79
Kansas data shown here are typical for Midwestern states. These values
tend to increase in the West and decrease in the East.
224
-------
Measurements of pollutant concentrations indicate trends helpful in select-
ing data for doad calculation. These trends are: (a) runoff from winter
thawing conditions produce greater concentrations of pollutants than that
produced by rainfall under warmer conditions, and (b) runoff from concrete
(surfaced) feedlots contain higher concentrations of COD, BOD, and nitrogen
than that from unsurfaced feedlots. BOD concentrations in runoff from sur-
faced lots are approximately twice those from unsurfaced feedlots.
10.5 POLLUTANT DELIVERY RATIO, FL,,
a
The proportion of on-site-generated pollutants in feedlot runoff delivered
to streams has not been documented. Delivery ratios have therefore been
developed by the study group in consultation with EPA personnel. Literature
information on sediment delivery and the system developed and presented in
Section 3.0 are the basis for development of values for FLj. The following
additional considerations were involved:
1. The majority of the pollutant load is carried away in the first part of
the runoff hydrograph.
2. Feedlot solids are fine textured and tend not to settle out of overland
runoff.
3. Observation has shown that buffer strips have limited value for
permanent retention of runoff-contained sediment.
The delivery ratio is therefore expected to be higher than delivery ratios
for sediment from similarly located cropland. Recommended delivery ratios
are:
Case I - Feedlot near (within 0.2 km, 0.1 mile) a permanent unobstructed
waterway: FL, ^0.9.
Case II - Feedlot located more than 0.2 km (0.1 mile) from stream or un-
obstructed waterway: FL^ = 0.7 to 0.9.
10.6 FEEDLOT AREA, A
The A factor in Eq. (10-1) is determined in effect by multiplying feedlot
populations by stocking rates and proportioning areas among specific lots.
In practice, A is determined by approximation from data from various
sources such as: "Cattle on Feed,"!^/ state departments of agriculture
and state environmental or health agencies, design manuals, and Special
Census of Agriculture Reports.JA/ Some statistics on beef cattle feedlots
are shown in Table 10-12.
225
-------
Table 10-12. NUMBER OF CATTLE FEEDLOT AND FED CATTLE MARKETED--
IN SMALL LOTS, BY STATES (1974)14»a/
State
Arizona
California
Colorado
Idaho
Illinois
Indiana
Iowa
Kansas
Michigan
Minnesota
Missouri
Montana
Nebraska
New Mexico
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Dakota
Texas
Washington
Wisconsin
23 States
Under 1,000 head feedlot
capacity
Cattle marketed
(1,000 head)
Lots (No.)
6
28
425
502
14,445
10,477
31,835
5,660
1,667
10,970
11,979
211
14,510
7
880
8,175
358
305
5,997
9,123
1,001
165
7,084
135,810
1
13
131
11
755
336
2,710
400
177
795
348
26
1,330
1
53
328
36
22
114
407
85
33
149
8,261
Total
Lots (No.)
47
167
613
574
14,500
10,500
32,000
5,800
1,700
11,020
13,000
276
14,970
48
900
8,200
400
331
6,000
9,200
1,200
186
7,100
all feedlots
Cattle marketed
(1,000 head)
895
2,002
1,892
344
850
361
3,097
2,240
242
864
400
187
3,355
355
84
386
566
126
123
585
3,899
301
180
137,732
23,334
a/ Number of feedlots under 1,000 head capacity is number of lots
operating at end of year.
226
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The feedlot area should include area devoted to feed handling and mixing,
sick pens, alleys and equipment storage. Beef cattle lots are typically
15% larger than the feeding pen area.
The following procedure illustrates how A may be calculated. Data for
Nebraska reported in "Cattle on Feed,"!!/ have been used to estimate aver-
age areas and total area of small feedlots.
Data reported, for feedlots with < 1,000 head:
Number of lots
Cattle marketed annually
Data assumed:
Stocking rate
Turnover rate
Calculated data:
Average area/lot
Total pen area
Total production area
(Pen area x 1.15)
14,510
1,330,000
23 m2/animal
250 ft2/animal
2/year
0.1 ha (0.26 acre)
1,525 ha (3,800 acres)
1,754 ha (4,370 acres)
10.7 METHODS FOR DEVELOPING FEEDLOT STATISTICS
Several options are available to the user to evaluate the parameters in
loading function for feedlots. The following discussion is intended to
facilitate the selection and use of appropriate data and data sources,
especially when the direct use of field data is not possible.
The data on the total number of livestock by county and state are published
in Census of Agriculture statistics, by the U.S. Department of Agriculture.
The census data also show the number of livestock "on feed." State sum-
maries of livestock on feed, by capacity of feedlots, are published by the
Agricultural Statistics divisions of the State and U.S. Departments of
Agriculture. A large fraction of the published state data is related to
beef cattle feedlots. State data sources contain livestock data on a
county basis. The locations of feedlots within a given region can only be
obtained by reference to state/local statistics. If the data specify the
total number of animals marketed, a turnover rate should be used to com-
pute yearly livestock numbers. If the data specify animal on feed or on
hand as of a certain date, then the turnover rate is not considered.
227
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The distribution data will also help to assess probable distances to given
surface waters, and hence, the amounts of pollutants delivered.
The areas of feedlots may be obtained either from actual inventories of
feedlot data for a region, or estimated by using statistical projections of
sanpled sites for which data exist. An indirect method of estimating feed-
lot area involves a knowledge of animal type, total number of animals, and
stocking rates (area per animal). The stocking rate differs for different
livestock types and usually falls within the following ranges:
Beef cattle
Dairy cattle
Swine, breeding
Swine, growing-finishing
Sheep
Turkeys, range
- 100 to 400 ft2 (9 to 36 m2)
80 to 400 ft2 (7 to 36 m2)
- 100 to 250 ft2 (9 to 23 m2)
- 200 to 1,500 ft2 (18 to 135 m2)
15 to 100 ft2 (1 to 9 m2)
- 100 to 200 ft2 (9 to 18 m2)
Feedlot surface conditions, climatic conditions, and other factors determine
the actual stocking rate within the above range.
The required data on feedlot numbers, areas, and locations can thus be de-
veloped from several sources of data as indicated by the following cases:
Case 1. Little or No Local Data Available
Beef Cattle
Given Data -
Number of small lots (< 1,000 head), by state, Census of
Agriculture statistics.
Total number of cattle, by county, Census of Agriculture
statistics.
Turnover rate of 2/year.
Estimated Data -
• Average lot size (small lots) equals number of cattle per
turnover divided by number of lots.
• Average lot area equals average size times stocking rate se-
lected from range given above (100 to 400 ft2/animal, 10 to
40 m2/animal).
228
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Number of ]ots by county, i.e., number of small lots per
county equals number of small lots in state times total
cattle in county divided by total cattle in state.
Delivery ratio: in absence of information on distance to
watercourses, use 0.9.
Hogs
Given Data -
Hog population, by county, Census of Agriculture statistics.
• Sixty percent of hogs in small lots (< 2,500 head per lot).
Fifty percent of all hogs are raised under roof.
. Stocking rate, in range of 200 to 1,500 ft2/animal (20 to 140 m2/
animal).
Delivery ratio: 0.9.
Turnover rate: 2/year.
Estimation -
Total lot area, in county equals 0.15 times total county
population times stocking rate. Convert to hectares or acres.
Turkeys
Given Data -
Total population, by county, Census of Agriculture statistics.
Assumptions -
Eighty percent of turkeys on range.
• Stocking rates, in the range of 100 to 200 ft2/bird (10 to 20
m2/bird)
Delivery ratio: 0.9.
Turnover rate: 2/year.
Estimations -
• Lot area equals 0.4 times county population times stocking
rate. Convert to hectares or acres.
229
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Sheep
Given Data -
Total population, by county, Census of Agriculture statistics.
Assumptions -
Eighty-five percent in open lots.
Stocking rates range from 15 to 100 ft^/animal (1.5 to 10 m^/
animal).
Delivery ratio: 0.9.
Turnover rate: 2/year
Estimation -
Lot area, in county equals 0.4 times county population times
stocking rate. Convert to hectares or acres.
Case 2. Local, Actual Data Available
In an idealized case, perhaps for a small watershed, data on feedlot sizes,
locations, and areas will either be a matter of record, or can be readily
obtained by questionnaire or other means. Feedlots covered by NPDES permits
should be subtracted from the total, and other lots with runoff control also
deleted. The remainder will be counted as nonpoint sources.
Given Data -
Number of small lots and livestock population per lot, local
data.
Area of each lot, local data.
Location of each lot from the nearest water course, actual data
of record.
Assumptions -
Delivery ratios:
0.9 (less than 0.1 mile or 0.2 km from stream).
0.7 (greater than 0.1 mile or 0.2 km from stream).
230
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Case 3. Combination of Local Data and Area-Wide Data
Determination of area, location, and livestock population of small feedlots
at the local level (county/state) involves a search of various data sources--
including an evaluation of unpublished data of record. State departments of
agriculture—agricultural statistics divisions, animal husbandry divisions
of state agricultural extension services, agricultural economics departments
of land grant universities, state environmental protection agencies, state
public health departments, county tax assessors' offices, and state revenue
departments are some of the sources of local data. Because of the variations
in jurisdiction in different state governments, the local planner responsible
for making the assessment of nonpoint source pollution from livestock in con-
finement should ascertain the availability of data from appropriate sources
within the state.
The county based livestock population data are published by the U.S. Depart-
ment of Agriculture—Census of Agriculture. However, areal data for small
lots are not available directly from the census data. An example calcula-
tion of the area, delivery ratio, and livestock population in small beef
cattle feedlots from a mix of local and area-wide data is shown below:
Given Data -
Number of livestock, county, Census of Agriculture statistics.
Number of small lots (< 1,000 head) by county, from state
agricultural extension division.
Number of cattle in small lots, by county, from state agri-
cultural extension division.
Stocking rates--local data from land grant university, agri-
cultural economics department.
Turnover rate equals 2/year.
Assumptions -
• Delivery ratio — for lots less than 0.1 mile (0.2 km) from
stream: 0.9; for lots more than 0.1 mile (0.2 km): 0.7.
• None of the small lots reported runoff control.
Estimation -
Area of small lots equal stocking rate time either (1) number of
cattle marketed from small lots divided by 2, or (2) number of
cattle on feed as of January 1.
231
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10.8 ACCURACY OF PREDICTION
The major uncertainties in the loading function are pollutant concentrations
and delivery ratios. If reliable data on pollutant concentration, feedlot
areas by source, and precipitation-runoff are obtained for the local condi-
tions, the accuracy of prediction can be reasonably good. The pollutant
delivery ratio tends to be quite high for existing feedlots located near
streams. For others, the determination of FL, from local data accurately
will improve the prediction accuracy. Using average, long-term conditions,
the range of accuracies expected are presented in Table 10-13.
Table 10-13. ESTIMATED RANGE OF ACCURACY FOR PREDICTING
POLLUTANT LOADS FROM FEEDLOTS
Estimated value Probable range
Pollutant (kg/ha/year) (kg/ha/year)
BOD5 10,000 2,000-50,000
N-total 600 100- 3,000
N-available 50 10- 200
P-total 250 50- 1,000
Suspended solids 10,000 5,000-25,000
10.9 PROCEDURE FOR COMPUTING POLLUTANT LOADING
The following step-by-step procedure is suggested to compute the potential
pollutant loading from feedlots, based on the discussion of loading function
presented in this section. It is assumed that the regional boundary is es-
tablished for assessing the loadings.
1. Determine the number of feedlots.
2. Determine the number and kind of livestock in each feedlot.
3. Determine the area A of individual and total feedlots using either
actual data or procedures outlined earlier in this section.
4. Obtain precipitation data Pr for the time interval required, i.e.,
storm event, 30-day period, year, from local weather stations, or from the
National Climatic Center, U.S. Weather Bureau.
5. Compute runoff volume Q from options presented above.
232
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6. Determine the range of pollutant concentrations in feed lot runoff either
from local records or from Tables 10-10 and 10-11.
7. Determine the value of the delivery ratio FL^ from a knowledge of feed-
lot location in relation to the stream or from drainage density in the basin.
8. Determine load of each pollutant by using Eq. (10-1), Items 3, 5, 6, and
7 above.
9. Convert results to annual average, daily value, expressed as a range of
loads consistent with ranges of input data on pollutant concentrations.
10.10 EXAMPLE
An open, unsurfaced feedlot in eastern Kansas has an area of about 5 acres
and carries on an average 900 head of cattle at any given time. The feedlot
is located 1/4 mile from a small creek which eventually discharges into the
Kansas River. Assuming that the BOD^ concentration of feedlot runoff ranges
from 5,000 to 10,000 mg/liter and a monthly precipitation of 6 in., calculate
the daily load delivered to the creek during the 30-day period.
In the absence of precipitation event data, interpolation of precipitation
and runoff data presented in Table 10-7 and Table 10-8 for the months of
August and October show an average runoff of 2.5 in.
The delivery ratio is estimated to be 0.8 for a silt-clay soil and a drain-
age density of 4 miles/sq mile.
Thus the BODr loading for a concentration of 5,000 mg/liter is, from Eq.
(10-10),
Y(BOD) = 0.23 x 5,000 x 2.5 x 0.8 x 5
FL 30
= 380 Ib/day
For BOD of 10,000 mg/liter, the loading is Y(BOD)pL = 760 Ib/day.
233
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REFERENCES
1. Chow, V. T., Handbook of Applied Hydrology. McGraw-Hill Book Co., New
York (1964).
2. National Engineering Handbook: Section 4 - Hydrology, Soil Conservation
Service, U.S. Department of Agriculture (1972).
3. Bergsrud, F. G., Masters Thesis, Kansas State University, Manhattan,
Kansas (1968).
4. Miner, J. R., "Water Pollution Potential of Cattle Feedlot Runoff,"
Ph.D. Thesis, Kansas State University, Manhattan, Kansas (1967).
5. Shuyler, L. R., D. M. Farmer, R. D. Kreis, and M. E. Hula, "Environment
Protecting Concepts of Beef Cattle Feedlot Wastes Management,"
Environmental Protection Agency, Corvallis, Oregon (1973).
6. Koelliker, J. K., H. L. Manges, and R. I. Lipper, "Performance of Feed-
lot Runoff Control Facilities in Kansas," Paper presented at the 1974
Annual Meeting of American Society of Agricultural Engineers, Oklahoma
State University, Stillwater, Oklahoma, 23-26 June 1974.
7. Kreis, R. D., M. R. Scalf, and J. F. McNabb, "Characteristics of Rainfall
Runoff from a Beef Cattle Feedlot," U.S. Environmental Protection
Agency, Report No. EPA-R2-72-061 (1972).
8. Wells, D. M., R. C. Albin, C. W. Grub, E. A. Coleman, and G. F. Meenaghan,
"Characteristics of Wastes from Southwestern Cattle Feedlots," EPA
13040 DEM 01/71, U.S. Environmental Protection Agency (1971).
9. Loehr, R. C., Agricultural Waste Management, Academic Press (1974).
10. Gilbertson, C. B., T. M. McCalla, J. R. Ellis, 0. E. Cross, and W. R.
Woods, "The Effect of Animal Density and Surface Slope on Character-
istics of Runoff, Solid Wastes and Nitrate Movement on Unpaved Beef
Feedlots," University of Nebraska Technical Bulletin SB 508, June 1970.
11. Miner, J. R., L. R. Fina, J. W. Funk, R. I. Lipper, and G. H. Larson,
"Stormwater Runoff from Cattle Feedlots," Proceedings National Sym-
posium on Animal Waste Management, East Lansing, Michigan (1966).
234
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12. Smith, S. M., and J. R. Miner, "Stream Pollution from Feedlot Runoff,"
Proceedings 14th Sanitary Engineering Conference, University of
Kansas, Lawrence, Kansas (1964).
13. Crop Reporting Board, "Cattle on Feed," SRS-USDA, January 1975.
14. U.S. Department of Commerce, "Census of Agriculture 1969 Dairy Cattle,"
Volume V, Part 8, Special Reports (1973).
235
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SECTION 11.0
TERRESTRIAL DISPOSAL
11.1 INTRODUCTION
Solid wastes and slurries disposed on landfill sites have a significant
potential to pollute local ground-water aquifers, and thus to pollute
nearby surface streams. Water that infiltrates landfill cover soil may
produce leachate, in quantity dependent on precipitation, antecedent
moisture condition of the landfill soil, solid waste composition, and
groundwater hydrology. The absorptive capacity of the landfill, its
areal extent, and the amount of recharge water available for infiltra-
tion are the key parameters that determine the total volume of leachate.
Open dumps can be expected to produce more leachate than sanitary land-
fills.
Leachates contain significant concentrations of BOD, COD, iron, chlo-
rides, and nitrates. Where toxic wastes have been discharged, the
leachates also contain heavy metals and toxic substances. The charac-
ter of leachate, thus, is highly sensitive to the type of waste in the
land disposal site, the age of the site, and the temperature and mois-
ture content of the fill.
Once a leachate is produced, it may react with soil constituents at
rates depending upon the reactivity of the substances in the leachate.
The concentration and the total quantity of a given pollutant in the
leachate may be attenuated by physical-chemical processes and biologi-
cal processes. The attenuation may proceed both in saturated and un-
saturated zones of the soil, as shown in Figure 11-1.
The degree of attenuation cannot be predicted with reasonable accuracy.
Soil, especially in the unsaturated zone, is probably most important in
this attenuation. The leachate is also in effect attenuated by dilution
in groundwaters, and groundwater movement through underground aquifers
results in reactions (chemical reaction, physical absorption-desorption,
236
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WELL
ro
UJ
UNSATURATED ZONE
u
Figure 11-1. Leachate flow through path through zones where
attenuation may be effected.—'
-------
and biological reactions) which degrade the pollutant, equilibrate it with
geological strata, and possibly transform certain constituents to insoluble
minerals.
11.2 LOADING FUNCTION FOR LANDFILLS
The actual loading rate for a given pollutant cannot be made without knowl-
edge of soil properties, hydrology and landfill characteristics. It is not
possible, therefore, to predict the extent of pollutant load that is actu-
ally transmitted to a stream with presently available data. Approximate
at-site leachate emission rates can be determined from the knowledge of
percolation rates and pollutant concentrations expected from landfill sites.
If a site is located close to a surface water course, the at-site emission
rate may be close to the stream loading rate. If the site is distant from
surface waters, the emissions may be markedly attenuated.
The loading function for a given pollutant is thus given by:
Y(i)LF = a.CQ(i)LF-p-LFd.A (11-1)
where Y(i) = average loading rate of pollutant i, kg/year (Ib/year)
LF
p = percolation rate, cm/year (in/year)
CQ(i)TF = average concentration of pollutant i, in leachate at
site, mg/liter
a = a dimensional factor, 0.1 metric (0.23 English)
LF, = leachate delivery ratio for landfill
A = area of landfill, ha (acres)
The delivery ratio, LF varies in theory from 0 (no delivered pollutant)
to 1.0 (100% delivery). Values of LF are a matter of local judgment.
Pollutant concentrations, CQ(I) , vary with the site characteristics and
vary widely even within a given region. A range of reported values is
presented in Table 11-1.!' Pollutant concentration is influenced by the
amount of leachate produced. Leachate volume is in turn influenced by
several factors including surface cover, subsurface lining characteristics
of the landfill, and climatic conditions. Percolation rate may, in some
cases, be higher or lower than leachate flow rate. Published average
annual percolation rates for the United States are shown in Figure 11-2.-'
238
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Table 11-1. CHEMICAL CHARACTERISTICS OF LEACHATEsl/
Constituent Concentration, mg/1
BOD5 80 - 33,100
COD 150 - 71,000
Organic nitrogen 50 - 200
Nitrate (as N) 0.2 - 1,300
Ammonia (as N) 0 1,000
Sulfate 28 - 3,770
Chloride 4.7 - 2,467
Iron (total Fe) 0 - 2,820
Hardness 0 - 22,800
Copper 0 - 9,9
Zinc 0 - 370
Manganese 0.1 - 125
Lead 0.1 - 2
Cadmium 0.03 - 17
239
-------
-P-
O
Centimeters
50 and over
30
20
7.5
0
CZ]
Data Un-
available
Figure 11-2. Average annual percolation^/
-------
The percolation map indicates potential leachate quantities throughout
the country. Most severe leachate problems are expected east of the
Mississippi and in the Pacific Northwest. For conditions east of the
Mississippi, where an average of 30 cm (12 in.) of percolation and 80,000
ha (200,000 acres) of landfill surface were assumed, the net annual amount
of leachate produced has been estimated to be 246 million meters3 (65
billion gallons), or 3,000 m3/ha (325,000 gal/acre) of landfill surface.I/
This amount would be reduced by 507o or more with proper cover and vegetation
on the site. The percolation map (Figure 11-2) should serve principally
as a guideline for local analysis. For example, percolation in areas which
experience highly seasonal precipitation will not conform well to data on
the map, and its use would give results in error. Landfill sites should
therefore be analyzed on a local or an areal basis, and percolation data
developed should take into account engineering practice in the area as well
as climatological and hydrological data specific to the area. In this re-
gard, it must be emphasized that old sites as well as current sites are to
be included in the analysis.
11.3 PROCEDURE FOR COMPUTING LANDFILL POLLUTANT LOADINGS
In order to compute pollutant loadings from landfill leachates in a region,
the following data are needed:
Landfill characteristics including number, size, location, age, and
surface area.
Percolation and leachate data.
Pollutant concentration data.
Leachate delivery ratio.
The availability of specific data will dictate the degree of accuracy one
can achieve in computing the loading rates. Thus, several options are
open to determine the pollutant loadings in a given region.
11.3.1 Landfill Characteristics Including Number, Size, Location, Age,
and Surface Area
o /
The 1968 National Survey— published extensive statistics on community
solid waste practices by region. Waste Age_L/ made a telephone survey of
solid waste disposal practices by region. These reports, while estimat-
ing the number and area of sites, are too broad for use in a local situa-
tion. Local and state health departments should be consulted for specific
site information.
241
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11.3.2 Percolation and Leachate Data
Case I - When the landfill site is not engineered as a sanitary landfill,
i.e., the surface and bottom are not adequately lined with impervious
material, the leachate flow rate can reasonably be assumed to be equal to
percolation rates typical of the area. Rates indicated in Figure 11-2
may be adequate, but locally specific data are to be preferred. When
groundwater recharge occurs during wet conditions or when the groundwater
table is shallow, upwelling may occur; calculation of leachate rates will
be very difficult in such cases and will be the province of the local
engineer. Monitoring stations will be needed to obtain accurate infor-
mation.
Case II - When the site is engineered to reduce leachate and/or percola-
tion, such as in lined or compacted landfills, considerably smaller
amounts of leachate will leave the site. Data on local design condi-
tions and monitored parameters should be obtained to determine the
actual rates of leachate production. Sites with similar physical and
climatological characteristics and waste constituents should provide
reasonably accurate data.
11.3.3 Pollutant Concentration Data
As shown in Table 11-1, the concentrations of pollutants vary greatly.
For example, the reported BOD5 concentration ranges are 2,000 mg/liter
to 30,000 mg/liter. The factors affecting leachate composition are
complex. There is no simple way to predict the pollutant concentra-
tion for given site conditions. Monitored or field data should be
obtained for specific situations. In general, leachate from a com-
pleted fill where no more waste is being disposed of can be expected
to decrease with time.
11.3.4 Leachate Delivery Ratio
Most published studies describe the on-site pollution potential, and not
the actual load delivered to a stream. The delivery ratio is thus a re-
search area.
The approach to selection of a delivery ratio should be on a site-by-site
basis, with the delivery ratio developed after consideration of the fol-
lowing factors:
1. Proximity of landfill to surface waters.
2. Proximity to subsurface aquifers.
242
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3. Subsurface water quantities, flows, and direction of flow.
4. Quantity of leachate in proportion to aquifer inventories and flows.
5. The attenuating characteristics of soils for the pollutant of concern.
6. The age of the site.
Confidence in the delivery ratio (as well as leachate quantities and pollu-
tant concentrations) can be markedly increased by analysis of groundwaters
and soils at strategically located sampling spots.
Selection of a delivery ratio is thus the province of local specialists in
hydrology, water and soil chemistry, and landfill design. The delivery
ratio should seldom be more than 0.5 save in exceptionally poorly designed
and managed dumps. Conversely, a delivery ratio near zero should be accepted
only after rigorous examination of site characteristics.
11.4 Accuracy of Predicted Loads
The accuracy of prediction depends upon the accuracy of parameters used in
the loading function. For local situations where small areas are involved,
the area of landfill can be easily and accurately determined from local
data sources. Determination of percolation rates for the area can also be
obtained from experimental data and other reported results for similar soil
characteristics and precipitation rates. The percolation rate also is de-
pendent upon the engineering design of the landfill site, its age, and
groundwater characteristics. Long-term average rates are generally more
precise than short-term, yearly averages. The delivery ratio is usually
obtained with less certainty. The delivery ratio can usually be estimated
to be near zero for small leachate rates. At high rates the uncertainty
in the delivery ratio becomes greater. Concentrations of pollutants in
the leachate are extremely variable and are subject to greater fluctuation
than other parameters. Consequently, greater error is introduced in the
prediction even if other parameters are accurately estimated. The expected
range of pollutant loads is shown in Table 11-2. The ranges were estimated
on the assumption that some actual site data are available, and that actual
characteristics have been evaluated in estimating load; i.e., the range of
values presented in Table 11-1 has not been used in the estimations.
243
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Table 11-2. ESTIMATED RANGE OF PREDICTED'LOADS FOR
VARIOUS POLLUTANTS IN LEACHATES IN LANDFILLS
Estimated value Range of predicted values
Pollutant (kg/ha/year) (kg/ha/year)
BOD5 10,000 1,000-100,000
COD 20,000 2,000-200,000
Nitrogen - Total 500 50-5,000
11.5 EXAMPLE
A well engineered sanitary landfill operating during the past 5 years is
located in eastern Kansas vhere the annual precipitation is 36 in/year.
The site has a total area of 35 acres and is located about 1 mile from a
major river. The rate of percolation is estimated, from local data on
rainfall plus landfill surface characteristics, to be 1.5 in/year through
the fill material, which is primarily composed of municipal refuse.
Leachate from a test well located on-site was analyzed during high flow
period, with the following results:
BOD5 = 8,000 mg/liter
COD = 12,000 mg/liter
pH = 6.3
Alkalinity as CaC03 = 3,620 mg/liter
Chloride as Cl = 284 mg/liter
NH4-N = 84 mg/liter
Assuming that the leachate directly enters the river, calculate the
pollutant loadings for BOD5, chlorides, and nitrogen.
Site data and engineering features of the landfill were used by local
engineers to arrive at a delivery ratio in the range of 0.05 to 0.2.
Assuming a LFd value of 0.1 in Eq. (11-1), the computed loading rates
are shown in Table 11-3.
244
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Table 11-3. POLLUTANT LOADING EATES IN EXAMPLE
Annual load Daily load
Pollutant (Ib/year) (Ib/day)
9,660 26.5
Chloride 343 0.94
Nitrogen (Nlty-N) 101 0.28
245
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REFERENCES
1. Unpublished document, Leachate Meeting - Office of Solid Waste
Management Programs, Environmental Protection Agency, Washington,
B.C., August 1974.
2. Nelson, L. B., and R. E. Uhland, "Factors that Influence Loss of
Fall Applied Fertilizers and Their Probable Importance in Dif-
ferent Section of the United States," Soil Science Society of
America, Proceedings, 19(4) (1955).
**~0Ks,
3. Muhich, A. J., A. J. Klee, and P. W. Britton, "1968 National Survey
of Community Solid Waste Practices," Preliminary Data Analysis,
USPHS, Cincinnati (1968).
4. "Exclusive Waste Age Survey of the Nation's Disposal Sites," Waste
Age, 6(1):17-24, January 1975.
246
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SECTION 12.0
BACKGROUND POLLUTANT LOAD ESTIMATION PROCEDURES
12.1 INTRODUCTION
Nonpoint pollution loads can arise from land which has not been disturbed
by man's activities. Such loads, referred to as "background" loads,
represent natural nonpoint emissions, and have a -significant effect upon
surface water quality. In general, a clear-cut distinction between
loads arising from background sources and loads arising from man's land
use practices is virtually impossible to achieve, either philosophically
or technically. Therefore, one should approach the problem of background
pollutant loads somewhat warily, but also firmly. Any estimation of back-
ground pollutant loads will have an unavoidable element of arbitrariness.
This section will present estimation procedure options for background
pollutant emissions.
Two different approaches are discussed--a stream-to-source approach (Sec-
tion 12.2) and a source to stream approach (Section 12.3), together with a
discussion at the expected accuracy of each method.
12.2 STREAM TO SOURCE METHODS
12.2.1 Options Available
Four options for estimating nonpoint pollution loads emitted from natural
background have been developed using the stream to source approach. These
options, together with their constraints, are:
Option I - A method of estimating general background loads over large
areas. The method utilizes annual average runoff in the area considered
and iso-pollutant concentration maps developed for this purpose. The
method yields estimates of .pollutant loads on an average annual basis,
reported on a per day basis. Use of Option I methods should be restricted
to areas of minor water basin size (80,000 miles2) or larger.
247
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Option II - A second method for estimating general background levels over
a large area. The method utilizes the iso-pollutant maps as in Option I,
but streamflow carrying the pollutant is used rather than average annual
runoff. This method can be used to estimate maximum and minimum pollu-
tant loads within a year by utilizing maximum and minimum flows during
the year, and represents the stream to source approach. The size restric-
tions are the same as those for Option I.
Option III - A method for estimating general background levels on a local
or small watershed scale. Average annual runoff for the watershed is used.
Background 'pollutant concentrations deemed appropriate from local water
quality data are used instead of iso-pollutant maps. This method should
be used when considering local nonpoint problems, and will yield estimates
of annual average background loads reported on a per day basis. This op-
tion uses the same approach as the first one except that provisions are
made for the user to use his judgment to define natural background concen-
trations .
Option IV - A method, applicable to localized areas and to small water-
sheds, for estimating background loads, based on local data and experience.
The method utilizes streamflow data for pollutant transport as in Option
II, and local information deemed appropriate concerning background pol-
lutant concentrations as in Option III. This method permits ready estima-
tion of 30 day maximum and minimum loads by considering flow volumes at
various times of the year. If a detailed description of natural background
over a large area is desired, the area can be subdivided into local units,
Option IV applied to each of the units, and the loads computed for each sub-
unit summed over the whole area.
12.2.2 Information Needs for Background Loading Value Equations
The following information is needed for use in the background loading
value equations presented below.
Area (A) from which background pollutants are being emitted.
Flow (Q) of water in which background pollutants are transported.
Concentration (C) of pollutants arising in the area and transported by
flow.
Conversion factor (a) needed to yield proper dimensional units of pol-
lutant loads.
248
-------
Methods for obtaining this information for each option, together with
descriptions of the loading value equations, are presented below.
12.2.3 Loading Value Equations and Definition of Conversion Factors
Options I and III - Flow as average annual runoff, in centimeters per
year (in/year). Average annual runoff can be obtained from standard run-
off maps available from the U.S. Geological Survey (National Atlas, Plates
118 to 119), Water Information Center's Water Atlas of the United States
(Plate 21), or from local records if available.
Y(i)BG = a-A-Q(R)-C(i)BG (12-1)
where Y(i)gQ = yield of background constituent i in kilograms per day
(lb/day)" except where noted in Table 12-1
a = dimensional constant; see Table 12-1
A = area under consideration, in hectares (acre)
Q(R) = flow as average annual runoff, in centimeter per year
(in/year)
C(i) = estimated concentration of background constituent i
(see Section 12.4, Figures 12-1 through 12-19)
Options II and IV - Flow as streamflow, in liter per second (cfs). Stream-
flow data may be obtained from U.S. Geological Survey records, STORET data,
U.S. Army Corps of Engineers records, or other records available at the
local level. Annual average streamflow should be used when considering
background nonpoint emissions on the annual basis. When estimating back-
ground emissions at specific times, e.g., 30 day maximum or minimum, the
proper streamflows at those times should be used in the computations.
If load units of mass per unit area per day are desired, the area A
term is not used.
249
-------
K)
Ln
O
Table 12-1. CONVERSION FACTORS "a" TO BE USED FOR OPTIONS I AND III LOADING VALUE EQUATION:
FLOW AS DIRECT RUNOFF, Q(R)
Constituent
All except those
below
Heavy metals
Radioactivity
Colifonns
Concentration
units
ppm
ppb
picocuries/A
MPN/100 m£
Units of
flow "Q(R)M
cm/year
in/year
cm/year
in/year
cm/year
in /year
cm/year
in/year
Units of
area "A"
ha
acre
ha
acre
ha
acre
ha
acre
Value of
"a"
2.7 x 10'4
6.2 x ID'4
2.7 x 1(T7
6.2 x ID'7
270
280
2,700
2,800
Units of
load
kg /day
Ib/day
kg/day
Ib/day
picocuries/day
picocuries/day
MPN/day
MPN/day
-------
Y(i)BG " a-Q(str)-C(i)BG (12-2)
where Y(i)BG = yield of background constituent i , in kilograms per
day (Ib/day) except where noted in Table 12-2
a = dimensional constant; see Table 12-2
Q(str) = flow as streamflow, in liter per second (cfs)
C(i)BG = concentration of background constituent i (see Section
12.4, Figures 12-2 through 12-20
12.2.4 Estimation of Background Pollutant Concentrations
Options I and II: Background Maps for Estimating Concentrations C(i)
for Loading Value Equations - A series of maps have been developed indi-
cating general levels of background pollutant concentrations throughout
the United States. These maps are based upon data collected at surface
water quality stations comprising the National Hydrologic Bench-Mark Network
established by the U.S. Geological Survey. These iso-pollutant maps are
presented in Section 12.4 along with a descrption of the National Hydrologic
Bench-Mark Network. The background pollutants considered are presented in
Table 12-3.
Options III and IV: Use of Local Information for Estimation of Background
Pollutant Concentrations - If local information is believed to reflect a
better definition of background than the maps presented in Section 12.4,
it should be used in the loading value Eqs. (12-1) and (12-2).
12.2.5 Procedure for Using Loading Value Eqs. (12-1) or (12-2)
1. Identify pollutant.
2. Determine area to be considered.
3. Choose units of volume flow to use in equations, i.e., liters per
second (cfs) or centimeters per year (in/year).
4. Choose method of estimating pollutant concentration, i.e., back-
ground maps or local information.
5. Decisions at Steps 3 or 4 establish option for estimation. When
option is established, use Tables 12-1 or 12-2 as appropriate to identify
correct value of "a", the conversion factor to obtain proper units of
load.
251
-------
ro
Ul
Table 12-2. CONVERSION FACTORS "a" TO BE USED FOR OPTIONS II AND IV LOADING VALUE EQUATION:
FLOW AS STREAMFLOW, Q(str)
Constituent
All except those
belov
Heavy metals
Radioactivity
Coliforms
Concentration
units
ppm
ppb
picocuries/4
MPN/100 ml
Units of
flow "Q(str)"
I/sec
cfs
A /sec
cfs
4/sec
cfs
£/sec
cfs
Value of
"a"
0.0864
5.39
8.64 x 10"5
5.39 x 10"3
8.64 x 10~4
2.45 x 10'6
8.64 x 10~5
2.45 x 10'7
Units of
load
kg/day
Ib/day
kg/ day
Ib/day
picocuries/day
picocuries/day
MPN/day
MPN/day
-------
Table 12-3. LISTING OF BACKGROUND ISOPOLLUTANT MAPS
Constituent
Suspended sediment
Nitrate
Total phosphorus
80154
00630
00650
STORET
code
Figure No.
(see Section 12.4)
12-2
12-3
12-4
BOD
Total coliform
00310
31501
12-5
12-6
Conductivity
PH
Total dissolved solids
Alkalinity
Hardness
Chloride
Sulfate
Total heavy metals
Iron and manganese
Arsenic, copper, lead,
and zinc
Miscellaneous heavy
metals
Total radioactivity
Alpha radioactivity
Beta radioactivity
00095
00400
00515
00410
00900
00940
00945
E Heavy metal parameters
01045 + 01055
01002 + 01042 + 01551 +
01092
£ Remaining heavy metals
01515 + 01516 + 03515 +
03516
01515 + 01516
03515 + 03516
12-7
12-8
12-9
12-10
12-11
12-12
12-13
12-14
12-15
12-16
12-17
12-18
12-19
12-20
253
-------
6. Steps 2 through 6 will yield all necessary inputs for loading values
Eqs. (12-1) and (12-2).
7. Compute background loads, Y(i) , for pollutant identified in Step 1.
12.2.6 Examples of Using Loading Value Equations
The following are examples of loading value estimations using Option I.
Options II, III, and IV are used in the identical manner, except for
input data units.
1. Case I: Background phosphate emisssions from 4,040 ha (10,000 acres)
of wheat in western North Dakota
a = 2.7 x 1(T4 (6.2 x 10'4).
Area = 4,040 ha (10,000 acres).
Phosphate concentration (Figure 12-4) = 0.15 ppm.
Average annual runoff = 1.3 cm (0.5 in.).
Load (metric): 0.15 x 1.3 x 0.00027 x 4,040 ha = 0.21 kg/day = 210 g/day.
Load (English): 0.15 x 0.5 x 0.00062 x 10,000 acres = 0.46 Ib/day.
2. Case II; Background heavy metals from Spokane River Basin above
Roosevelt Lake (Water Resources Council subbasin 1603)
a = 2.7 x 10"7 (6.2 x 10"7).
Area = 6,404 sq mile = 1,670,000 ha (4,100,000 acres).
Heavy metal concentration (Figure 12-14) = 300 ppb.
Average annual runoff = 25 cm (10 in.).
Load (metric): 300 x 25 x 2.7 x 10'7 x 1,670,000 ha = 3.4 x 103 kg/day =
3.4 MT/day.
Load (English): 300 x 10 x 6.2 x 10~7 x 4,100,000 = 7,600 Ib/day =
3.8 tons/day.
254
-------
3. Case III: Background radioactivity emissions from the Cheyenne
River Basin (Water Resources Council subbasin 1072);
a = 270 (280).
Area = 20,497 sq miles = 5,300,000 ha (13,000,000 acres).
Radioactivity level (Figure 12-18) = 20 picocuries/liter.
Average annual runoff = 2.5 cm (1.0 in.).
Load (metric): 20 x 2.5 x 270 x 5,300,000 = 7.2 x 1010 picocuries/day =
7.2 x 10^ microcuries/day = 0.072 curies/day.
Load (English): 20 x 1.0 x 280 x 13,000,000 = 7.2 x 1010 picocuries/day =
7.2 x 10^ microcuries/day = 0.072 curies/day.
12.2.7 Estimated Ranges of Accuracy for Stream to Source Options for
Background Pollutant Loads
Typical background pollutant loads have been estimated on an annual basis
for the background pollutants identified in Table 12-3. As has been
stated earlier, the accuracy of these loads is dependent upon the size of
the area being considered. Thus, the probable range of values will vary
depending upon the size of the area to which the estimation methods are
applied.
Table 12-4 through 12-6 present typical loads together with probable
ranges of values for the background pollutant loads. Table 12-4 repre-
sents small areas (less than 10,000 ha) of county or watershed size.
Table 12-5 is concerned with the 10,000 to 10,000,000 ha size, represent-
ing an areal range between county and minor river basin. Table 12-6
deals with areas greater than 10,000,000 ha representing minor river
basins and larger areas.
As can be seen in the probable ranges of Table 12-4, the use of iso-
pollutant maps for the small areas leads to a fairly broad range of ac-
curacy. Local data, where available, will lead to much more accurate
loads for areas of county level or smaller. The user is encouraged to
use local data for small areas, and consider the iso-pollutant maps as
a back-up or reference method.
255
-------
Table 12-4.
EXPECTED ACCURACY OF BACKGROUND POLLUTANT LOADS CALCULATED USING STREAM TO SOURCE METHODS
(Area Considered - 100,000 ha (400 sq miles) or Less)
ro
L/l
Pollutant
Sediment
Nitrate
Phosphorus
BOD
Total coliform
TDS
Alkalinity
Hardness
Chloride
Sulfate
Total heavy metals
Fe + Mn
As + Cu + Pb + Zn
Miscellaneous heavy metals
Total radioactivity
Alpha radioactivity
Beta radioactivity
Calculated
load
(kg/ha/year)
Probable range of loads
using iso-pollutant maps:
Options I and II'
(kg/ha/year)
Probable range of loads using
local data: Options III and IV
(kg/ha/year)
250
0.5
0.1
2
1 x 1010-
500
200
250
10
250
1
0.5
0.1
0.05
25b/
5k/
10^'
' 100
0.1
0.01
0.5
106
100
50
100
1
100
0.1
0.1
0.01
0.001
10
1
3
- 1,000
- 3
- 1
- 1015~
- 2,000
- 500
- 1,000
- 50
- 1,000
- 5
- 3
- 0.8
- 0.5
- 40k/
- 20k/
- 3c£'
200 -
0.3 -
0.07 -
1.5 -
108 -
400 -
150 -
200 -
8 -
200 -
0.5 -
0.3 -
0.05 -
0.03 -
20 -
3 -
7 -
400
1
0.3
5
600
300
300
12
300
2
1.0
0.2
0.2
15—
a/ Load units of MPN/ha/year.
b/ Load units of microcuries/ha/year.
-------
Table 12-5. EXPECTED ACCURACY OF BACKGROUND POLLUTANT LOADS CALCULATED USING STREAM TO SOURCE METHODS
(Area Considered - 10,000 to 10,000,000 ha (400 to 40,000 sq miles)
Probable range of loads
Calculated using iso-pollutant maps: Probable range of loads using
load Options I and III local data: Options III and IV
Pollutant (kg/ha/year) (kg/ha/year) (kg/ha/year)
Sediment
Nitrate
Phosphorus
BOD
Total coliform
TDS
Alkalinity
Hardness
Chloride
Sulfate
Total heavy metals
Fe + Mn
As + Cu + Pb + Zn
Miscellaneous heavy metals
Total radioactivity
Alpha radioactivity
Beta radioactivity
250
0.5
0.1
2
1 x IQlO^
500
200
250
10
250
1
0.5
0.1
0.05
25t/
5^/
lot/
150 -
0.2 -
0.02 -
1 -
107 -
200 -
100 -
150 -
3 -
150 -
0.5 -
0.2 -
0.02 -
0.005 -
18 -
2 -
5 -
1,000
2
0.5
10 _/
1 1 a'
10i:S
1,000
500
500
20
500
3
2
0.5
0.3
35-^
10k/
25^
180 -
0.2 -
0.04 -
1 -
107 -
300 -
100 -
150 -
5 -
150 -
0.3 -
0.2 -
0.03 -
0.01 -
18 -
2 -
5 -
800
2
0.5
8 .
io13£
700
500
500
15
500
3
2
0.3
0.5.
35
lot/
25-
aj Load units of MPN/ha/year.
b/ Load units of microcuries/ha/year.
-------
Table 12-6.
EXPECTED ACCURACY OF BACKGROUND POLLUTANT LOADS CALCULATED USING STREAM TO SOURCE METHODS
(Area Considered - 10,000,000 ha (40,000 sq miles) or Greater)
Ul
OO
Pollutant
Sediment
Nitrate
Phosphorus
BOD
Total coliform
TDS
Alkalinity
Hardness
Chloride
Sulfate
Total heavy metals
Fe + Mn
As + Cu + Pb + Zn
Miscellaneous heavy metals
Total radioactivity
Alpha radioactivity
Beta radioactivity
Calculated
load
(kg/ha/year)
Probable range of loads
using iso-pollutant maps:
Options I and II
(kg/ha/year)
Probable range of loads using
local data: Options III and IV
(kg/ha/year)
250
0.5
0.1
2
i ,, -mlO-'
J. A JLV ^^
500
200
250
10
250
1
0.5
0.1
0.05
25^
5b/
iok/
200
0.3
0.05
1
108
400
150
200
8
200
0.8
0.3
0.05
0.01
20
3
8
- 500
- 1.0
- 0.3
- 5
- 1012£/
- 800
- 300
- 400
- 15
- 300
- 2
- 1.5
- 0.3
- 0.2
- 8—'
-15*'
100
0.1
0.02
0.5
106
100
100
100
3
100
0.1
0.1
0.01
0.001
10
1
3
- 1,000
- 3
- 1.0
- 12
- io15-
- 1,000
- 1,000
- 1,000
- 20
- 1,000
- 5
- 3
- 1.0
- 0.5
20—
-SO*'
&J Load units of MPN/ha/year.
b/ Load units of microcuries/ha/year.
-------
Background pollutant loads and their probable ranges for areas varying
in size from a county (10,000 ha) to a minor river basin (10,000,000 ha)
are shown in Table 12-5. In these intermediate sized areas, the differ-
ence in accuracy between the use of the iso-pollutant maps and local data
is judged to be relatively small. Thus, either method should result in
satisfactory estimation of background loads. It is recommended, however,
that the user should lean towards the local data if he is considering the
low range of the areal spread indicated in Table 12-5.
For larger areas, the use of iso-pollutant maps will yield satisfactory
results. The uncertainty represented by Table 12-6 is no greater than
the differences between contours on the iso-pollutant maps. On the other
hand, if local data are extrapolated to large areas, a significant amount
of error can be introduced into the calculations. In principle, back-
ground pollutant loadings for large areas could be obtained by summing
many smaller areas for which background loadings have been obtained us-
ing local data. It is questionable, however, whether this summing pro-
cedure would be any more accurate than the use of Option I and II methods
using the iso-pollutant maps for the large areas.
12.3 SOURCE TO STREAM OPTION
12.3.1 Description of Source to Stream Option
The source to stream approach for estimating pollutant loads from back-
ground involves using the Universal Soil Loss Equation and its associ-
ated delivery ratio factor to estimate soil losses from land having nat-
ural cover. These "natural" areas include grassland, rangeland, desert,
forest, or woodlands, and areas transitional between forest-grassland
etc. A table of cover C factors are presented to facilitate the iden-
tification of the proper cover factor to be used. These C values are
presented in Table 12-7 and 12-8. Background sediment loads should be
estimated using the methods outlined in Section 3.0, together with the
appropriate C factor. Regional vegetative cover patterns needed to
identify specific C values in Table 12-7 and 12-8 can be established
using descriptors of natural vegetation such as that presented in the
U.S. Geological Survey's National Atlas, Plates 90 and 91 (potential
natural vegetation).
This method can also be used for estimating pollutant loads transmitted
by sediment attachment, e.g., nitrogen and phosphorus. In this case,
one would substitute the Y(S) value for background into the appropri-
ate loading functions described in Section 4.0. Heavy metals in the
sediment can be estimated using procedures in Section 8.5.
259
-------
Table 12-7. "C" VALUES FOR PERMANENT PASTURE,
RANGELAND, AND IDLE LAM)!/
Vegetal canopy Canopy
Cover that
Type and height cover£/
of
No
raised canopy^/ (%)
column no. :
appreciable
2
canopy
Type!/
3
G
W
0
4
0.45
0.45
contacts
the surface
Percent ground cover—'
20
5
0.20
0.24
40
6
0.
0.
10
15
60
7
0.
0.
042
090
80
8
0.013
0.043
95-100
9
0.003
0.011
Canopy of tall weeds
or short brush
(0.5 m fall height)
Appreciable brush
or bushes
(2 m fall height)
Trees but no appreci-
able low brush
(4 m fall height)
25
50
75
25
50
75
25
50
75
G
W
G
W
G
W
G
W
G
W
G
W
G
W
G
W
G
W
0.36 0.17 0.09 0.038 0.012 0.003
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.36
.26
.26
.17
.17
.40
.40
.34
.34
.28
.28
.42
.42
.39
.39
.36
.36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.20
.13
.16
.10
.12
.18
.22
.16
.19
:14
.17
.19
.23
.18
.21
.17
.20
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
13
07
11
06
09
09
14
085
13
08
12
10
14
09
14
09
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.082
.035
.075
.031
.067
.040
.085
.038
.081
.036
.077
.041
.087
.040
.085
.039
.083
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.041
.012
.039
.011
.038
.013
.042
.012
.041
.012
.040
.013
.042
.013
.042
.012
.041
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
a/ All values shown assume: (1) random distribution of mulch or vegetation,
and (2) mulch of appreciable depth where it exists.
b_/ Average fall height of waterdrops from canopy to soil surface: m = meters,
c_/ Portion of total-area surface that would be hidden from view by canopy in
a vertical projection (a bird's-eye view).
jl/ G: Cover at surface is grass, grasslike plants, decaying compacted duff,
or litter at least 5 cm (2 in.) deep.
W: Cover at surface is mostly broadleaf herbaceous plants (as weeds)
with little lateral-root network near the surface, and/or undecayed
residue.
260
-------
Table 12-8.
"C"
FACTORS FOR WOODLAND-^
I/
Stand condition
Well stocked
Medium stocked
Poorly stocked
Tree canopy
percent of
100-75
70-40
35-20
Forest
litter
percent of
areai/
100-90
85-75
70-40
Undergrowth—'
Managed—'
UnmanagedS/
Managed
Unmanaged
Managed
Unmanaged
"C" factor
0.001
0.003-0.011
0.002-0.004
0.01-0.04
0.003-0.009
0.02-0.09£/
aj When tree canopy is less than 207o, the area will be considered as grass-
land, or cropland for estimating soil loss. See Table 13-1.
b/ Forest litter is assumed to be at least 2 in. deep over the percent
ground surface area covered.
c/ Undergrowth is defined as shrubs, weeds, grasses, vines, etc., on the
surface area not protected by forest litter. Usually found under
canopy openings.
d_/ Managed - grazing and fires are controlled.
Unmanaged - stands that are overgrazed or subjected to repeated burning.
_e/ For unmanaged woodland with litter cover of less than 75%, C values
should be derived by taking 0.7 of the appropriate values in Table 13-1,
The factor of 0.7 adjusts for the much higher soil organic matter on
permanent woodland.
261
-------
12.3.2 Estimated Ranges of Accuracy for the Stream to Source (USLE-
Sediment) Option for Background Pollutant Loads
Background pollutant loads for several sediment related pollutants are
presented in Table 12-9, together with their probable ranges. The prob-
able ranges can be translated as a percentage range based on the calcu-
lated load, e.g., the range for a calculated total phosphorus load of
1.5 kg/ha/year would be 0.3 to 15 kg/ha/year.
Table 12-9. EXPECTED ACCURACY OF BACKGROUND POLLUTANT LOADS CALCULATED
USING THE SOURCE TO STREAM (USLE-SEDIMENT) OPTION
Calculated Probable range
load of loads
Pollutant (kg/ha/year) (kg/ha/year)
Sediment 500 100 - 1,000
Total nitrogen 3 0.3-10
Total phosphorus 0.5 0.1-5
Organic matter 50 5 - 200
BOD 5 0.5 - 20
Heavy metals 5 0.5-20
12.4 ISO-POLLUTANT MAPS FOR ESTIMATING BACKGROUND POLLUTANT LOADS
The U.S. Geological Survey established the National Hydrologic Bench-
mark Network!/ in order to obtain water quality data for "natural back-
ground ."
This network consists of 27 surface water stations in 37 dates chosen
using the following criteria:
1. No man-made storage, regulation, or diversion currently exists or
is probable for many years.
2. Groundwater within the basin will not be affected by pumping from
wells.
3. Conditions are favorable for accurate measurement of streamflow,
chemical and physical quality of water, groundwater conditions, and
the various characteristics of weather, principally precipitation.
262
-------
4. The probability is small of special natural changes due to such
things as major activities of beavers, overgrazing or overbrowsing by
game animals, or extensive fires.
The approximate locations of the 57 benchmark stations are mapped in
Figure 12-1, and defined more specifically in Table 12-10.
A series of iso-pollutant maps have been developed based upon average
concentration ranges obtained at the stations comprising the National
Hydrologic Benchmark Network. The maps (Figures 12-2 through 12-20)
are for the pollutants identified in Table 12-3.
263
-------
Table 12-10. LOCATION OF HYDROLOGIC BENCHMARK STATIONS^
2/
Station location
Blackwater River near Bradley, Alabama
Sipsey Fork near Grayson, Alabama
Wet Bottom Creek near Childs, Arizona
Cossatot River near Vandervoort, Arkansas
North Sylamore Creek near Fifty Six, Arkansas
Elder Creek near Branscomb, California
Merced River near Yosemite, California
Wildrose Creek near Wildrose Station, California
Halfmoon Creek near Malta, Colorado
Vallecito Creek near Bayfield, Colorado
Sopchoppy River near Sopchoppy, Florida
Falling Creek near Juliette, Georgia
Tallulah River near Clayton, Georgia
Honolii Stream near Papaikou, Hawaii
Hayden Creek below North Fork, near Hayden Lake, Idaho
Wickahoney Creek near Bruneau, Idaho
South Hogan Creek near Dillsboro, Indiana
Elk Creek near Decatur City, Iowa
Big Creek at Pollock, Louisiana
Wild River at Gilead, Maine
Washington Creek at Windigo, Isle Royale, Michigan
Kawishiwi River near Ely, Minnisota
North Fork Whitewater River near Elba, Minnisota
Cypress Creek near Janice, Mississippi
Beauvais Creek near St. Xavier, Montana
Swiftcurrent Creek at Many Glacier, Montana
Dismal River near Thedford, Nebraska
South Twin River near Round Mountain, Nevada
Steptoe Creek near Ely, Nevada
McDonalds Branch in Lebanon St. Forest, New Jersey
Mogollon Creek near Cliff, New Mexico
Rio Mora near Tererro, New Mexico
Esopus Creek at Shandaken, New York
Cataloochee Creek near Cataloochee, North Carolina
Bear Den Creek near Mandaree, North Dakota
Beaver Creek near Finley, North Dakota
Upper Twin Creek at McGaw, Ohio
Station
No.
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
31
32
33
34
35
36
37
uses
code
02369800
02450250
09508300
07340300
07060710
11475560
11264500
10250600
07083000
09352900
02327100
02212600
02178400
16717000
12416000
13169500
03276700
06897950
07373000
01054200
04001000
05124480
05376000
02479155
06288200
05014500
06775900
10249300
10244950
01466500
09430600
08377900
01362198
03460000
06332515
05064900
03237280
264
-------
Table 12-10 (Concluded)
Station
No.
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
uses
code
07311200
07335700
11492200
13331500
01545600
02135300
02197300
06409000
06478540
03604000
03497300
08431700
08103900
10172200
02038850
12447390
12039300
04063700
13018300
06623800
Station location
Blue Beaver Creek near Cache, Oklahoma
Kiamichi River near Big Cedar, Oklahoma
Crater Lake near Crater Lake, Oregon
Minam River at Minam, Oregon
Young Womans Creek near Renovo, Pennsylvania
Scape Ore Swamp near Bishipville, South Carolina
Upper Three Runs near New Ellenton, South Carolina
Castle Creek near Hill City; South Dakota
Little Vermillion River near Salem, South Dakota
Buffalo River near Flat Woods, Tennessee
Little River above Townsend, Tennessee
Limpia Creek above Fort Davis, Texas
South Fork Rocky Creek near Briggs, Texas
Red Butte Creek near Salt Lake City, Utah
Holiday Creek near Andersonville, Virginia
Andrews Creek near Mazama, Washington
N.F. Quinault River near Amanda Park, Washington
Popple River near Fence, Wisconsin
Cache Creek near Jackson, Wyoming
Encampment River near Encampment, Wyoming
265
-------
• 14 ( Hawaii >1
Figure 12-1. The National Hydrologic Benchmark Network
-------
Figure 12-2. Background suspended sediment (ppm)
-------
o-.
CD
\
0.05
Figure 12-3. Background nitrate concentrations (ppm as N)
-------
Figure 12-4. Background total phosphorus concentrations (ppia as P)
-------
Figure 12-5. Background BOD concentrations (ppm)
-------
500
Figure 12-6. Background total coliform count (MPN/100 ml)
-------
100
Figure 12-7. Background conductivity (micromhos)
-------
Figure 12-8. Background pH (standard units)
-------
^
100
o o o o
"0 O o o
Figure 12-9. Background total dissolved solids (ppm)
-------
Figure 12-10. Background alkalinity (ppm
-------
Figure 12-11. Background hardness (ppm as CaCO )
-------
Figure 12-12. Background chloride concentrations (Ppm)
-------
ho
~-j
CO
Figure 12-13. Background sulfate concentrations (ppm)
-------
200
200
Figure 12-14. Background total heavy metal concentrations (ppb)
-------
00
o
100
Iron + Manganese (ppb)
i
r
Figure 12-15. Background iron + manganese (ppb)
200
-------
ro
CO
2Q
50
Figure 12-16. Background arsenic + copper + lead + zinc (ppb)
-------
oo
NO
20
Figure 12-17. Background miscellaneous heavy metals (ppb)
-------
00
Figure 12-18. Background total radioactivity (picocuries/liter)
-------
.2.6
00
1.0
2.0
Figure 12-19. Background alpha radioactivity (picocuries/liter)
-------
CO
Ln
Figure 12-20. Background beta radioactivity (picocuries/liter)
-------
REFERENCES
1. Wischmeier, W. H., "Estimating the Cover and Management Factor for
Undisturbed Areas," presented at USDA Sediment Yield Workshop,
Oxford, Mississippi (1972).
2. Biesecker, J. E., and D. K. Leifste, "Water Quality of Hydrologic
Bench-Marks--An Indicator of Water Quality," U.S. Geological Survey
Circular 460-E, Washington, D.C. (1973).
286
-------
GLOSSARY
Active surface mine - A site at which coal (or other mineral associated
with pyrite) is being actively mined representing a potential source
of acid mine drainage.
Active underground mine - A coal mine (or metal mine associated with py-
rite) in active operation. A potential site for generation of acid
mine drainage.
Antecedent moisture condition (AMC) - The degree of wetness of a water-
shed at the beginning of a storm.
Average daily traffic (ADT) - An average value for the daily vehicular
traffic on a specific roadway.
Background - A description of pollutant levels arising from natural
sources, and not because of man's utilization of the land.
Base flow - Stream discharge derived from groundwater sources. Sometimes
considered to include flows from regulated lakes or reservoirs.
Fluctuates much less than storm runoff.
Biochemical oxygen demand (BOD) - The amount of oxygen required by bac-
teria to stabilize decomposable organic matter under aerobic condi-
tions. Usually the test is limited to 5 days, when it is termed
5-day BOD or BOD5-
Canopy - The cover of leaves and branches formed by the tops or crowns
of plants.
Chemical oxygen demand (COD) - Total quantity of oxygen required for
oxidation of organic (carbonaceous) matter to carbon dioxide and
water using a strong oxidizing agent (dichromate) under acid
conditions.
Commercial forest - The forest which is both available and suitable for
growing continuous crops of raw logs or other industrial timber
products, and is judged capable of growing at least 20 ft^ of timber
per acre per year.
Conservation Needs Inventory - An inventory, based upon sampling for
field surveys, of soil, slope, erosion, land use, and other factors.
Needed conservation practices are also recorded. A given percent
of an area, generally a county, is sampled. The data are expanded
to the entire area.
287
-------
Consumptive use factor - A factor which measures the amount of water
transpired and evaporated during irrigation.
Contour farming - Conducting field operations, such as plowing, planting,
cultivating, and harvesting, on the contour.
Contour stripcropping - Layout of crops in comparatively narrow strips
in which the farming operations are performed approximately on the
contour. Usually strips of grass, close-growing crops, or fallow
are alternated with those in cultivated crops.
Cover crop - A close-growing crop grown primarily for the purpose of
protecting and improving soil between periods of regular crop pro-
duction or between trees and vines in orchards and vineyards.
Cover factor "C" - A factor based on a maximum value of 1.0 that reflects
the effectiveness of vegetative land cover in controlling erosion.
The factor is used in the Universal Soil Loss Equation.
Cover, ground - Any vegetation producing a protecting mat on or just
above the soil surface. In forestry, low-growing shrubs, vines,
and herbaceous plants under the trees.
Creep - Slow mass movement of soil and soil material down relatively
steep slopes primarily under the influence of gravity, but facili-
tated by saturation with water, strong wind, and by alternate freez-
ing and thawing.
Cross-slope fanning - Conducting field operations, such as plowing, plant-
ing, cultivating, and harvesting across the general slope of the
field.
Curb length - The distance of single street curb, or the length of one
side of a street or other thoroughfare. Distinguished from street-
length which normally represents two or more curb length.
Curie - A unit of radioactivity equivalent to 3.7 x lO1^ disintegrations
per second.
Direct runoff - The water that enters the stream channels during a storm
or soon after. It may consist of rainfall on the stream surface,
surface runoff, and seepage of infiltrated water.
Diversion terrace - Diversions, which differ from terraces in that they
consist of individually designed channels across a hillside.
-------
Drainage area - The area draining into a stream at a given point.
Drainage density - Ratio of the total length of all drainage channels in
a drainage basin to the area of that basin.
Enrichment ratio - The ratio of concentration of a substance in eroded
sediment to that in the soil.
Erosion, rill - An erosion process in which numerous small channels only
several inches deep are formed; occurs mainly on recently culti-
vated soils.
Erosion, sheet - The removal of a fairly uniform layer of soil from the
land surface by runoff water.
Field stripcropping - A system of stripcropping in which crops are grown
in parallel strips laid out across the general slope but which do
not follow the contour. Strips of grass or close-growing crops are
alternated with strips of cultivated crops.
Gob pile - Waste material generated during the processing of coal. A
potential source of acid mine drainage.
Heavy metal - A metallic (or metallaoid) element of atomic number greater
than 20.
Humidity factor - A functional term relating relative humidity, precipi-
tation, saturated vapor pressure, and temperature.
Inactive surface mine - An abandoned or unreclaimed surface mining site
at which acid mine drainage may be generated.
Inactive underground mine - An abandoned or inactive underground mine in
which acid mine drainage may be generated.
Infiltration - The flow of a liquid into a substance through pores or
other openings, connoting flow into a soil.
Irrigation return flow - The return to surface waters of water used to
irrigate agricultural land. It consists of tailwater, deep percola-
tion, by-pass water, and canal seepage.
Load index, I - A dimensionless number between 0 and 1.0 reflecting
the probability of acid mine drainage from one of four types of
sources: active underground, active surface, inactive underground,
or inactive surface.
289
-------
Most probable number (MPN) - A statistical indication of the number of
bacteria present in a given volume (usually 100 ml).
Nitrification - The biological oxidation of ammonium salts to nitrites
and the further oxidation of nitrites to nitrates.
Nitrogen, available - Usually ammonium, nitrite, and nitrate ions, and
certain simple amines are available for plant growth. A small
fraction of organic or total nitrogen in the soil is available at
any time.
Nutrient, available - That portion of any element or compound in the soil
that can be readily absorbed and assimilated by growing plants.
Organic matter (soil) - The organic fraction of the soil that includes
plant and animal residues at various stages of decomposition, cells
and tissues of soil organisms, and substances synthesized by the
soil population.
Organic nitrogen - "Original" form of nitrogenous nutrients. Gradually
converted to ammonia nitrogen and to nitrites and nitrates, if
aerobic conditions prevail.
Percolation - The downward movement (or flow), of water through the
pores of any substance (such as soil).
Phosphorus, available - Inorganic phosphorus which is readily available
for plant growth. Only a small fraction of total phosphorus in the
soil is available at any time.
Practice factor "P" - A factor based on a maximum value of 1.0 that re-
flects the effectiveness of supporting conservation practices in
controlling erosion. The factor is used in the Universal Soil Loss
Equation.
Pyritic material - Materials containing pyrite (FeS2). A generic term
including other disulfides which can oxidize to form acid mine
drainage such as arsenopyrite (AsFeS2) or chalcopyrite (CuFeS2)«
Radioactivity, alpha - The spontaneous emission of alpha particles
(helium nuclei) by a radioactive substance.
Radioactivity, beta - The spontaneous emission of beta particles (elec-
trons) by a radioactive substance.
290
-------
Rainfall factor "R" - A numerical expression of rainfall used in the
Universal Soil Loss Equation.
Relief - The difference in elevation between the high and low points of
a land surface.
Relief ratio - The ratio between the relief of watershed and the maximum
length of watershed.
Rill - A small, intermittent watercourse with steep sides, usually only
a few inches deep and, hence, no obstacle to tillage operations.
Root zone - The part of the soil that is penetrated or can be penetrated
by plant roots.
Runoff - That portion of the precipitation on a drainage area that is
discharged from the area in stream channels. Types include surface
runoff, groundwater runoff, or seepage.
Runoff, urban - The flow of waters in urban areas from precipitation or
thaw incidents from gutters into street inlets or from other con-
nections into storm or combined-sewer system.
Sediment yield - The quantity of sediment, measured in dry weight or
by volume, transported through a stream cross-section in a given
time.
Sediment delivery ratio - The fraction of the soil eroded from upland
sources that actually reaches a point of measurement or estima-
tion.
Slope length factor "L" - A factor used in the Universal Soil Loss Equa-
tion to reflect relative effect of slope length on soil erosion.
Slope length is defined as the average distance, in feet, from the
point of origin of overland flow to whichever of the following
limiting conditions occurs first: (a) the point where slope de-
creases to the extent that deposition begins or (b) the point where
runoff enters well-defined channels.
Slope steepness factor "S" - A factor used in the Universal Soil Loss
Equation to represent relative effect of slope gradient on soil
erosion. Slope gradient is defined as the degree of deviation of
a surface from the horizontal, in percent.
291
-------
Soil erodibility factor "K" - A factor used in the Universal Soil Loss
Equation to reflect relative basic erodibility differences of soils.
Soil texture - The relative proportions of the three broad particle size
classifications: sand, silt, and clay, in a soil mass.
Tailings pile - Residues generated during the beneficiation of metal
ores. If material is pyritic, it is a potential source of acid
mine drainage.
Terrace - An embankment or combination of an embankment and channel con-
structed across a slope to control erosion by diverting or storing
surface runoff instead of permitting it to flow uninterrupted down
the slope.
Topographic factor "LS" - A dimensionless factor used in the Universal
Soil Loss Equation to represent the combined effects of slope length
and steepness.
Total dissolved solids - The dissolved salt loading in surface and sub-
surface waters. Equivalent to salinity.
292
-------
SYMBOLS
A
AMD
AS; AU
AX
BG
C
source
C(Alk)BG
CL
CU
D
DD
DI
DI30
P
FL
FLd
H
Source area, ha
Acid mine drainage
Active surface or underground mine
Average number of axles per vehicle
Background source
Cover management factor
Concentration of pollutant i in sediment
Concentration, C of pollutant i in source
Concentration of background alkalinity, mg/liter
Curb-length density, m/ha
Annual consumptive use of water, cm/year
Overland distance between erosion site and receptor
water, ft
Drainage density, km~l
Amount of deicer applied in the area, kg/year
30-Day maximum, DI
Annual average erosion rate, MT/ha/year
Ratio of NA:NT in eroded sediment
Ratio of PA:PT in eroded sediment
Small feedlot source
Feedlot delivery ratio
Humidity factor
293
-------
Herbicide, Insecticide, Fungicide; any pesticide
HM Heavy metals
I Load index for acid mine drainage
Irrigation return flow
Irrigated water added annually to crop root zone,
cm/year
IS; IU Inactive surface or underground mine
K Soil erodibility factor
L Slope length factor
Lgt Street length, km
L(S) Daily street solids loading rate, kg/curb-km/day
LF; LFd Landfill, landfill delivery ratio
LH Length of highway section, km
LS Topographic factor
X (Lambda) Slope length, m
NA Available (or mineralized) nitrogen
Npr Nitrogen yield rate per unit area from precipitation,
kg/ha/year
NT Sum of nitrogen of all chemical forms
OM Organic matter
OR Overland runoff
P Percolation rate, cm/year
P Conservation practice factor
Pr Annual average precipitation, cm/year, storm precipitation, cm
294
-------
PA Available phosphorus
PD Population density, number/ha
PT Total phosphorus; also point source
Q^; Q Runoff due to a storm event, cm
Q(FL) Feedlot runoff, cm/year
Q(LF) Landfill leachate flow rate, cm/year
Q(OR) Overland runoff, cm/year
Q(P) Total precipitation flow rate, cm/year
Q(Perc) Percolation flow rate, cm/year
Q(R) Direct runoff, cm/year
Q(Str) Stream flow rate, liters/sec
Q(t) Runoff over a period of time, t
R Rainfall erosivity factor
Rr Rainfall erosivity factor due to rainfall
RS Rainfall erosivity factor due to snowmelt
RAD Radioactivity
RH Relative humidity, 7o
TJJ Enrichment ratio for nitrogen (ratio of concentration
of nitrogen in sediment to that in soil)
TQ^J Enrichment ratio for organic matter (ratio of concen-
tration of organic matter in sediment to that in soil)
r Enrichment ratio for phosphorus (ratio of concentration
of phosphorus in sediment to that in soil)
S Slope gradient factor; also sediment
295
-------
SD
SD30
SVPt
T
TD
IDS
U
Y(AMD)
Y(DI)
Y(HIF)
Y(HM)
Y(i)FL
Y(i)
tr
Y(N)pr
Y(NA)
Y(NT)E
Y(OM)E
Sediment delivery ratio (ratio of the amount of sedi-
ment delivered to a stream to the amount of on-site
erosion)
Number of snow days
Thirty-day maximum SD
Saturated vapor pressure at given temperature, mm Hg
Annual average temperature, °C
Traffic density; number of vehicles/day
Total dissolved solids
Composite topographic factor for irregular slopes
Traffic related pollutant i , kg/axle-km/day
Acid mine drainage loading, kg/year
Deicing salt loading, kg/year
Total pesticide loading, kg/year
Heavy metal loading, kg/year
Loading of pollutant i from small feedlots, kg/year
Loading of pollutant i from landfills, kg/year
Loading of pollutant i from traffic sources, kg/year
Loading of pollutant i from urban areas, kg/year
Nitrogen loading from precipitation runoff, kg/year
Available nitrogen loading, kg/year
Total nitrogen loading from erosion, kg/year
Organic matter loading, kg/year
296
-------
Y(PA)
Y(PT)
Y(RAD)
Y(S)E
Y(TDS)BG
Y(TDS)IRF
Y(TDS)PT
Available phosphorus loading, kg/year
Total phosphorus loading, kg/year
Loading of radioactive substances, microcuries/year
Sediment loading from surface erosion, MT/year
Loading of street solids from urban areas, kg/year
Salinity (TDS) load from background, kg/year
Salinity (TDS) load in irrigation return flow, kg/year
Salinity (TDS) load from point sources, kg/year
Yield of pollutant i from background, kg/year
297
-------
APPENDIX A
MONTHLY DISTRIBUTION OF RAINFALL EROSIVITY FACTOR R
Distribution Curves for the Eastern United States
Figure A-l - Key Map for Selection of Distribution
Curve
Figure A-2a through A-2i - Distribution Curves
Distribution Curves for Hawaii (Figures A-3a
through A-3c)
Methods for Developing R Distribution Curves for
the Western United States
298
-------
Figure A-l. Key map for selection of applicable erosion-index distribution curve*
* Wischmeier, W. H., and D. D. Smith, "Predicting Rainfall—Erosion Losses from Cropland East of the
Rocky Mountains," Agricultural Handbook 282, U.S. Department of Agriculture, Agriculture Research
Service, May 1965.
-------
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Hoolchuo and Kualapuu Ar«
-------
HAWAII. Eo«t€rn Port-vicinity of Hilo
North Hilo. South Hilo.Puno Dim.
HAWAII, Northern Part of liland
Hamakua DUtrict
HAWAII. Sooth.m Port oF l,lond
Ko'u Diitrict
I/I 2/1 V V V «/' VI B/l V
Oar.
1/1
2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 12/31 1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 12/31
D<»« Do».
Figure A-3b
-------
100
90
60
I 70
c
o
I 60
UJ
1 50
"
20
10
0
100
90
80
™
c
o
g 60
J
50
30
20
10
MAUI. Lohainr and puulcoli Areas
MAUI, Wailuku and Waikopu Areas
l/l 2/1 3/1 V 5/1
V 8/1 9/1 1°/' "/' 12/1 12/31
Dare
100
90
80
I™
c
to
UJ
1 50
_, ! , r
MAUI, Neck and West Peninsula of Island
Wailuku District
1 1 T
1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 12/31
Date
Figure A-3c
-------
METHODS FOR DEVELOPING R DISTRIBUTION CURVES FOR THE WESTERN UNITED STATES^
I/
RS is significant in portions of this area. Divide the annual Rj. for
the location by the average annual precipitation to obtain a factor.
Multiply each month's precipitation by this factor to obtain monthly Rr
values. Add the prorated monthly Rg values to RT for the months when
snowmelt occurs, to obtain the monthly R values. Compute the monthly
accumulative percent. The following example is for Hylton, in Elko County,
Nevada. The 2-6 rainfall for this area is 0.9 in.
mined from the Type II curve on Figure 3-4, is 18.
average is 12.72 in. Factor is 18 i 12.72 = 1.42.
The annual Rr deter-
Annual precipitation
Monthly precipitation (water depth) for December through March is 4.92 in.
Rs = 4.92 x 1.5 = 7.38. This is prorated, based on local judgment to
January 10% or 0.7
February 20% or 1.5
March 50% or 3.7
April 20% or 1.5
Precipitation
(inches water
Month
(1)
January
February
March
April
May
June
July
August
September
October
November
December
depth)
(2)
1.18
1.14
1.29
1.49
1.48
0.91
0.63
0.52
0.63
1.17
0.97
1.31
R
R*
111
Cumulative
I 7.
Ill
1.68
1.62
1.83
2.12
2.10
1.29
0.89
0.74
0.89
1.66
1.38
1.86
0.7
1.5
3.7
1.5
-
-
-
-
-
-
-
-
2.38
3.12
5.53
3.62
2.10
1.29
0.89
0.74
0.89
1.66
1.38
1.86
2.38
5.50
11.03
14.65
16.75
18.04
18.93
19.67
20.56
22.22
23.60
25.46
0.093
21.6
43.3
57.5
65.8
70.9
74.4
77.3
80.8
87.3
92.7
100
* Columns (3) + (4).
\f Conservation Agronomy Technical Note No. 32, U.S. Department of Agri-
culture, Soil Conservation Service, West Technical Service Center,
Portland, Oregon, September 1974.
312
-------
Values in cumulative percent column (7) are the points used in plotting
the monthly R distribution curve.
For A-2, A-3, and A-4 Areas Shown in Figure 3-4
R0 is not significant in most parts of these areas. Use the monthly rain-
s
fall distribution as the R distribution. Simply accumulate monthly pre-
cipitation amounts and divide each by the annual precipitation. The re-
sults obtained for each month will be the points for plotting the monthly
R distribution curve.
For B-l and C Areas Shown in Figure 3-4
Rs in most parts of these areas is significant.
1. "Multipliers" are used to time average monthly precipitation amounts.
Sum the results of multiplications to obtain the "factored annual precipi-
tation." Divide the annual Rr for the location by the "factored annual
precipitation" to obtain a factor which will be used to convert monthly
precipitation amounts to the monthly R values (see the previous section
for A-l area). Values of multipliers are:
Month(s) Multipliers
January, February, March 0.1
April 1.0
May 4.0
June, July, August 7.0
September, October 2.0
November, December 0.1
2. Add the prorated R values to the months when the snowmelt occurs to
obtain the monthly R values. Compute the monthly accumulative percents
which are points used in plotting the monthly R distribution curve. The
following example is for a hypothetical area which has an annual rainfall
factor Rr of 25, and a R factor of 7.5 (4.94 x 1.5 rounded to 7.5).
The 4.94 in. is total precipitation for December, January, February,
and March. R factor is prorated to:
January 0% or 0 in.
February 33.3% or 2.5 in.
March 33.37. or 2.5 in.
April 33.3% or 2.5 in.
313
-------
Factored
Month
(1)
January
February
March
April
May
June
July
August
September
October
November
December
Total
Month
(1)
January
February
March
April
May
June
July
August
September
October
November
December
Total
Precipi-
tation
(in.)
(2)
1.33
1.14
1.35
1.48
1.43
1.00
0.80
0.78
0.85
1.14
0.92
1.12
13.34
Monthly
Rs
(6)
-_
2.5
2.5
2.5
—
--
--
--
--
--
—
^—
7.5
Multiplier
(3)
0.
0.
0.1
1.0
4.0
2.0
0.1
0.1
Monthly R
=R
R
(7)
pptn. (Col 2
x Col. 3)
(4)
0.13
0.11
0.13
1.48
5.72
7.00
5.60
5.46
1.70
2.28
0.09
0.11
29.81
Monthly
Rr*
(5)
0.11
0.09
0.11
1.24
4.80
5.78
4.69
4.58
1.43
1.91
0.08
0.09
25.0
Cumulative
R
(8)
0.1
2.7
5.4
9.1
13.9
19.8
24.5
29.0
30.5
32.4
32.4
32.5
%
(9)
_ _
8
17
28
43
61
75
89
94
99
100
100
32.5
In this example, the calculated factor value is 0.84 (25 -t- 29.81).
Monthly Rr is obtained by multiplying each "factored monthly
pptn." with 0.84.
314
-------
For B-2 Area Shown in Figure 3-3
In this area, no R0 values are needed. Follow the same procedure and
t>
use the same set of multipliers as the preceding section for areas B-l
and C, except that steps for obtaining monthly Rg values are not used.
The cumulative R and cumulative percent are computed from monthly Rj.
(column 5 in the preceding example).
315
-------
APPENDIX B
METHODS FOR PREDICTING SOIL ERODIBILITY INDEX K
Nomograph for predicting K values of surface
soils using chemical and physical parameters.
Nomograph for predicting K values of high clay
subsoils using chemical mineralogical and
physical parameters.
316
-------
NOMOGRAPH FOR PREDICTING K VALUES OF SURFACE SOIL
In 1971 Wischmeier et al.—' presented a soil erodibility nomograph
derived from statistical analysis of 55 soil types. Five soil param-
eters are included in the nomograph to predict erodibility: percent
silt plus very fine sand; percent sand greater than 0.10 millimeter;
organic matter content; soil structure; and permeability. Values of the
parameters may be obtained from routine laboratory determinations and
standard soil profile descriptions.
The nomograph is reproduced here as Figure B-l.
21
Description of Factors-
Grain size distribution
Grain size distribution has a major influence on a soil's erodibility:
the greater the silt content, the greater the soil's erodibility; the
smaller the sand content, the greater the soil's erodibility.
Particles in the very fine sand classification behave more like silt
than sand. Therefore, the percentage of very fine sand should be
subtracted from the total percentage of sand and added to the per-
centage of silt.
I/ Wischmeier, W. H., C. B. Johnson, and B. U. Cross, "A Soil Erodi-
bility Nomograph for Farmland and Construction Sites," J. Soil
and Water Conservation, 26_:189-193 (1971).
2/ "Technical Guide to Erosion and Sediment Control Design (Draft),"
Water Resources Administration, Maryland Department of Natural
Resources, Annapolis, Maryland, September 1973.
317
-------
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Figure B-l. Soil credibility nomograph^'
a/ Wischmeier, W. H., C. B. Johnson, and B. U. Cross, "A Soil Erodibility Nomograph for Farmland and
Construction Sites, J. Soil and Water Conservation. 26:189-193 (1971).
-------
Organic matter
The percentage of organic matter was determined, in work by Wischmeier,
et al., by the Walkley-Black method.!' The organic matter content is
approximately 1.72 times the percent carbon. Soil erodibility decreases
as organic matter content increases.
Soil structure
The soil structure is descriptive of the overall arrangement of the soil
solids. The four parameter values and their descriptions are as follows:
Parameter
Value Descriptions
Granular - All rounded aggregates may be placed in this
category. These rounded complexes usually lie loosely
and are readily shaken apart. When wetted, the voids are
not closed readily by swelling.
1 Very fine granular - less than 1 mm.
2 Fine granular - 1 to 2 mm.
3 Medium granular - 2 to 5 mm.
3 Coarse granular - 5 to 10 mm.
4 Blocky - Aggregates have been reduced to blocks, irregularly
six-faced, and with their three dimensions more or less
equal. In size, the fragments range from a fraction of an
inch to 3 or 4 in. in thickness.
4 Platy - Aggregates are arranged in relatively thin plates
or lenses.
4 Prismatic - Aggregates or pillars are vertically oriented,
with tops plane, level, and clean cut. They commonly occur
in subsoils of arid and semi-arid regions.
4 Columnar - Aggregates or pillars are vertically oriented,
with rounded tops. They commonly occur when the soil pro-
file is changing and the horizons are degrading.
_!/ Walkley, A., and I. A. Black, "An Examination of the Degtjareff Method
for Determining Soil Organic Matter," Soil Sci. , 37_, pp. 29-38 (1934)
319
-------
Massive - Soil units are very large, irregular, feature-
less as far as characteristic aggregates are concerned.
Soil permeability
Soil permeability is that property of the soil that enables the soil
to transmit water. Since different soil horizons vary in permeability,
the relative permeability classes refer to the soil profile as a whole.
The relative permeability classes are as follows:
Class Permeability rates in in/hour
1 Rapid over 6.0
2 Moderately rapid 2.0 to 6.0
3 Moderate 0.6 to 2.0
4 Moderately slow 0.2 to 0.6
5 Slow 0.06 to 0.2
6 Very slow less than 0.06
Reading the Nomograph
Entry values for all of the nomograph curves, except permeability class,
are for the upper 6 or 7 in. of soil. For soils in cuts, the entry
values are for the upper 6 or 7 in. of the newly exposed layer. In
reading the nomograph, interpolate linearly between adjacent curves
when the entry data do not coincide with the plotted curves of percent
sand or percent organic matter. The percent of coarse fragments may be
significant and is not included in the nomograph. Therefore, reduce
the value of K read from the nomograph by 10% for soils with stratified
subsoils that include layers of small stones or gravel without a seriously
impeding layer above them.
Enter the left scale of the nomograph with the appropriate percent silt
plus very fine sand, move horizontally to intersect the correct percent-
sand curve (interpolating to the nearest percent), vertically to the
correct organic matter curve, and then horizontally to the right scale
for first approximation of soil erodibility.
320
-------
For soils having a fine granular structure and moderate permeability,
the value of K can be obtained directly from this scale. However, if
the soil is other than of fine granular structure, or permeability is
other than moderate, it is necessary to proceed to the second part of
the nomograph, horizontally to intersect the correct structure curve,
vertically downward to the permeability curve, and horizontally to the
soil credibility index scale.
NOMOGRAPH FOR PREDICTING K VALUES OF HIGH CLAY SUBSOILS
Subsoils are commonly heavier in texture than the surface soils. In
addition, subsoils likely have aggregating agents that are very much
different from those found in surface soils and the degree of aggre-
gation is known to have a profound influence on erodibility.
From an EPA study-' conducted at Purdue University, a multiple linear
regression equation and nomograph were developed which can be used to
estimate the erodibility factor, K, of many high clay soils. Multiple
regression analysis revealed that amorphous iron, aluminum and silicon
hydrous oxides serve as soil stabilizers in subsoils (whereas, organic
matter is the major stabilizer in surface soils). The nomograph was
developed from the multiple linear regression equation relating the
erodibility factor to the soil texture factor, M, the amount of CDB
(citrate-dithionite-bicarbonate) extractable iron and aluminum oxides,
and the amount of CDB extractable silica oxide.
The equation used to derive the nomograph was:
K red = 0.32114 + 2.0167 x 10"4 M - 0.14440 (7. Fe203 + 7. A1203)
- 0.83686 (7. Si02)
where Kprecj = Predicted K value of subsoil
M = Soil texture factor, defined by percent new silt (percent
new silt + percent new sand). "New" silt has 2 to 100 jam
mean diameter. "New" sand has 100 to 2,000 urn mean diameter,
7o Fe90o = Percent CBD extractable iron oxide of soil.
£- J
7o A1203 = Percent CDB extractable aluminum oxide of soil.
7o SiOo = Percent CDB extractable silica oxide of soil.
If Roth, C. B., D. W. Nelson, and M. J. M. Romkens, "Prediction of
Subsoil Erodibility Using Chemical, Mineralogical, and Physical
Parameters," for the U.S. Environmental Protection Agency (EPA-660/
2-74-043), Washington, D.C., June 1974.
321
-------
The nomograph for estimating the credibility factor, K, of high clay
subsoils is reproduced in Figure B-2.
322
-------
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Tons/Acre/tnglish R Unit
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Facfor M Metric Tons/Hectare/Metric R Unit
Soil Erodibility Factor, K
Figure B-2. Nomograph for estimating the credibility factor K of high clay subsoils^/
a/ Roth, C. B., D. W. Nelson, and M. J. M. Romkens, "Prediction of Subsoil Erodibility Using Chemical,
Mineralogical, and Physical Parameters," for the U.S. Environmental Protection Agency (EPA-660/
2-74-043), Washington, D.C., June 1974.
-------
APPENDIX C
TOPOGRAPHIC FACTOR LS FOR IPJIEOTLAR SLOPES
324
-------
This appendix presents examples of calculating LS values for irregular
slopes, one of convex slope and the other concave slope.
EXAMPLE I--CONVEX SLOPE
The slope is shown below with values of slope length and slope percent
indicated on each of the three segments.
Segment I
Enter the slope effect chart in Figure 4-8 at 85' (X^) on the horizontal
scale, move upward to the curve for 270 slope, and read Uo T = 17.
^ 11
The upper end of Segment I is at zero length, therefore XQ = 0 and
U
2,1
= 17
Segment II
X2 = 85' + 60' = 145'
Xn =85'
Enter the slope effect chart with lengths of 145' and 85', use the curve
for 5%. Obtain U0 _ = 91 and U, T =41. Thus
f-, 11 •*•»*•*-
u - u - 9i - 4i - so.
Segment III
\, = 85' + 60' + 65' = 210'
X2
85' + 60' = 145'
325
-------
Enter the slope chart with lengths of 210' and 145', use 8% curve,
obtain U2 II]: = 310 and U^m = 170.
= 310 - 170 = 140
The computation is summarized below. The effective topographic factor
LM is estimated at 0.99 for the entire slope.
Seg-
ment,
j
(1)
I
Segment
Length,
ft
(2)
85
Segment
Slope,
%
(3)
2
h *J-1 U2,J
(4) (5) (6)
85 0 17
U1,J
(7)
0
U2,j-
ui:3
(8)
17
Segment
LS,
Col(8)-r(2)
(9)
0.20
cent
of
Total
Yield*
(10)
8
II
60
Entire
Slope 210
145 85 91 41
50
210 145 310 170 140
207
0.83
2.15
0.99
24
68
100
* Assume constant soil erodibility for the entire slope, computed by
dividing Col. (8) by 207, i.e., [S(U2 j - l^ .)].
EXAMPLE 2--CONCAVE SLOPE
A concave slope consists of three segments with values of slope length
(feet) and slope gradient (70) shown in the graph below:
Segment I
XL = 65'
1^ = 0
326
-------
Use curve for 8% in Figure 4-8. Obtain
U2,I - 52
Segment II
X2 = 65' + 60' = 125'
X! = 65'
Use 57, curve, obtain
U2,n - 68
u - 27
Segment III
X3 = 65' + 60' + 85' = 210'
X2 = 65' + 60' = 125'
Use 270 curve, obtain
= 62
= 32
Computations are summarized in the following table. The effective LS
value for the entire slope is estimated at 0.59.
Seg- Segment Segment
ment Length, Slope
j ft % X, X.
_____ _
(1) (2)
i
*
Per-
Segment cent
LS of
U
Col. (6)- Col.(8)-f Total
2 * Ui • Col. (7) Col. (2) Yield
J J y J
(10)
II
65
60
III 85
Entire
Slope 210
(3)
8
5
2
(4)
65
125
210
(5)
0
65
125
(6)
52
68
62
(7)
0
27
32
(8)
52
41
30
123
0
0
0
0
(9)
.84
.68
.35
.59
42
33
25
100
327
-------
APPENDIX D
K • LS INDEXES FOR LAND RESOURCE AREAS EAST OF
THE CONTINENTAL DIVIDE*
* Calculated from results of 1972 SCS questionnaire survey.
328
-------
K-LS INDEX -- metric tons per hectare per unit of erosion Index. R
LAND
i:i.ASS and
SUBCLASS
I
lie
Us
IIu
He
Hie
Ills
IIIv
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VI Iw
Vile
VHIe
VIIIs
VlIIu
VIIIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
32 33 42 46 50/51
0.14 (0.06)
0.27 (0.12) 0.38 (0.17) 0.41 (0.19) 0.22 (0.10)
0.14 (0.06) 0.14 (0.06) 0.31 (0.14)
0.14 (0.06) 0.19 (0.08)
0.31 (0.14) 0.18 (0.08)
0.49 (0.22) 0.45 (0.20) 0.94 (0.42)
0.09 (0.04) 0.29 (0.13) 0.22 (0.10) 0.27 (0.12)
0.31 (0.14) 0.13 (0.06) 0.26 (0.12) 0.43 (0.19)
0.25 (0.11) 0.18 (0.08)
0.40 (0.18) 0.94 (0.42) 0.11 (0.05) 2.2 (0.99)
0.18 (0.08) 0.23 (0.10)
0.25 (0.11) 0.13 (0.06) 0.18 (0.08)
0.58 (0.26) 0.27 (0.12)
0.04 (0.02) 0.22 (0.10) 0.19 (0.09)
1.19 (0.53) 4.9 (2.2) 3.6 (1.6) 4.5 (2.0)
0.07 (0.03) 5.2 (2.3) 0.18 (0.08) 2.8 (1.2) 4.3 (1.9)
0.25 (0.11) 0.25 (0.11)
0.43 (0.19) 0.49 (0.22) 0.29 (0.13) 0.54 (0.24)
4.3 (1.9) 2.9 (1.3) 0.16 (0.07) 0.63 (0.28)
7.8 (3.5) 1.0 (0.45) 9.3 (2.4)
0.29 (0.13)
0.74 (0.33)
0.76 (0.34) 15.0 (6.7) 8.1 (3.6)
9.5 (4.2) 22.0 (10.0)
52
0.13 (0.06)
0.74 (0.33)
0.25 (0.11)
0.25 (0.11)
0.22 (0.10)
0.33 (0.15)
0.25 (0.11)
1.0 (0.45)
0.22 (0.10)
3.4 (1.5)
0.22 (0.10)
0.25 (0.11)
9.3 (4.1)
0.19 (0.09)
6.5 (2.9)
329
-------
K-LS INDEX — metric tons per hectare per unit of erosion Index, R
LAND
CLASS ,ind
SUBCLASS
I
lie
Us
llw
lie
llle
Ills
Illw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VIIIc
VlIIs
VIIIw
VIIIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
53 54 55 56 57 58
0.19 (0.08) 0.18 (0.08) 0.22 (0.10) 0.13 (0.06)
0.58 (0.26) 0.65 (0.29) 0.22 (0.10) 1.2 (0.10) 0.43 (0.19) 0.25 (0.11)
0.58 (0.26) 0.49 (0.22) 0.38 (0.17) 0.43 (0.19) 0.13 (0.06) 0.13 (0.06)
0.29 (0.13)
0.36 (0.16) 0.43 (0.19) 0.22 (0.10) 0.25 (0.11) 0.13 (0.06) 0.43 (0.19)
1.1 (0.48) 1.1 (0.48) 1.1 (0.48) 0.18 (0.08) 1.2 (0.54) 0.76 (0.34)
0.22 (0.10) 1.1 (0.49) 0.25 (0.11) 0.25 (0.11) 0.11 (0.05) 0.13 (0.06)
0.49 (0.22)
0.11 (0.05)
2.1 (0.93) 0.90 (0.40) 2.1 (0.93) 0.11 (0.05) 3.2 (1.4) 0.99 (0.44)
0.29 (0.13) 0.67 (0.30) 0.38 (0.17) 0.22 (0.10) 0.76 (0.34) 0.34 (0.15)
0.25 (0.11)
1.6 (0.70) 2.4 (1.07) 1.6 (0.70) 0.27 (0.12) 5.4 (2.4) 2.3 (1.0)
1.1 (0.49) 0.13 (0.06) 0.09 (0.04) 0.76 (0.34) 0.22 (*0.10)
1-4 (0.62) 0>25 (0.n)
0.43 (0.19)
4.2 (1.89) 0.34 (0.15) 0.34 (0.15) 6.9 (3.1) 7.1 (3.2)
0.58 (0.26) 1.1 (0.51) 1.0 (0.45) 0.22 (0.10) 3.1 (1.4) 0.29 (0.13)
8.6 (3.8)
6.5 (2.9)
330
-------
K'LS INDKX -- metric tons per hectare per unit of erosion index, R
LAND
CLASS and
SUBCLASS
I
lie
Us
llw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
Vila
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of eroalon Index, R)
LAND RESOURCE AREA
59 60 61 62 63 64
0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.13 (0.06)
0.34 (0.15) 0.13 (0.06) 0.43 (0.19) 0.49 (0.22) 0.22 (0.10)
0.16 (0.07) 0.16 (0.07) 0.25 (0.11) 0.09 (0.04) 0.11 (0.05)
0.13 (0.06) 0.25 (0.11) 0.29 (0.13) 0.14 (0.06) 0.11 (0.05)
1.1 (0.51) 0.72 (0.32) 0.43 (0.19) 0.85 (0.38) 0.99 (0.44) 0.43 (0.19)
0.22 (0.10) 0.43 (0.19) 0.45 (0.20) 0.22 (0.10) 0.13 (0.06)
0.13 (0.06)
0.13 (0.06) 0.29 (0.13) 0.25 (0.11) 0.43 (0.19)
0.99 (0.44) 0.99 (0.44) 0.99 (0.44) 1.6 (0.70) 1.3 (0.59) 1.1 (0.48)
0.85 (0.38) 0.45 (0.20) 0.45 (0.20) 0.20 (0.09) 0.22 (0.10)
0.27 (0.12)
0.13 (0.06)
3.7 (1.7) 4.4 (1.96) 4.6 (2.1) 3.7 (1.6) 3.0 (1.3) 1.2 (0.54)
0.16 (0.07) 0.99 (0.44) 2.8 (1.3) 3.3 (1.5) 2.2 (0.96)
0.13 (0.06)
17.0 (7.7) 110. (49.0) 6.6 (2.9) 6.9 (3.1) 8.8 (3.9) 10.0 (4.5)
8.2 (3.6) 8.8 (3.9) 8.8 (3.9) 16.0 (7.0)
12.0 (5.5)
6.5 (2.9) 13.0 (6.0)
331
-------
K'LS INDEX -- metric tons per hectare per unit of erosion Index. R
(tons per acre per unit of erosion Indax. R)
LAND.
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
TVs
IVw
IVc
Ve
V3
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
LAND RESOURCE AREA
65 66 67
0.22 (0.10)
0.09 (0.04) 0.43 (0.19) 0.49 (0.22)
0.13 (0.06)
0.22 (0.10)
0.13 (0.06) 0.22 (0.10) 0.22 (0.10)
0.18 (0.08) 0.27 (0.12) 0.25 (0.11)
0.18 (0.08)
0.16 (0.07)
0.25 (0.11)
0.11 (0.05) 0.81 (0.36) 0.07 (0.03)
0.20 (0.09) 0.34 (0.15)
0.11 (0.05)
0.11 (0.05)
0.11 (0.05)
0.92 (0.41) 1.6 (0.72) 2.2 (0.96)
0.09 (0.04) 0.34 (0.15) 0.11 (0.05)
0.07 (0.03)
3.6 (1.6) 3.1 (1.4) 2.3 (1.0)
3.6 (1.6) 0.60 (0.27) 0.43 (0.19)
0.07 (0.03)
9.4 (4.2)
70 71
0.16 (0.07) 0.13 (0.06)
0.16 (0.07) 0.29 (0.13)
0.18 (0.08) 0.20 (0.09)
0.13 (0.06)
0.31 (0.14) 1.3 (0.56)
0.34 (0.15) 0.2 (0.09)
0.31 (0.14)
0.65 (0.29) 1.4 (0.64)
0.18 (0.08)
0.16 (0.07)
0.31 (0.14)
0.27 (0.12)
0.65 (0.29) 7.6 (3.4)
0.22 (0.10) 0.09 (0.04)
0.18 (0.08)
0.58 (0.26)
12. (5.3) 13. (5.8)
1.5 (0.67) 2.4 (1.1)
0.13 (0.06)
72
0.22 (0.10)
0.36 (0.16)
0.29 (0.13)
0.22 (0.10)
0.35 (0.16)
0.29 (0.13)
0.25 (0.11)
0.58 (0.26)
1.7 (0.77)
2.2 (1.0)
3.7 (1.7)
332
-------
K-I.S INDEX -- metric tons per hectare ppr unit of erosion Index, R
1 ANI>
Cl.ASS jnd
SIIJC1.ASS
1
lU-
II-.
Uw
II..
Illc
ITIs
IIlw
Itlc
IVc
TVs
IVu
IVc
Vc
Vs
Vw
Vc
Vie
Vis
VIw
VTc
VIlo
Vlls
Vllu
Vile
Vllle
VHl.-.
VIUw
VIllc
(ton1; per acre per unit of erosion Index, R)
LAND RESOURCE AREA
73 Ik 75 (Nebr.) 75 (Kan3.) 76 77
0.16 (0.07) 0.07 (0.03)
0.41 (0.19) 0.49 (0.2:) 0.34 (0.15) 0.43 (0.19) 0.43 (0.19) 0.07 (0.03)
0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.09 (0.04)
0.25 (0.11) 0.25 (0.11) 0.16 (0.07) 0.25 (0.11)
0.78 (0.35) 0.58 (0.26) 0.49 (0.22) 0.49 (0.22) 0.43 (0.19)
0.20 (0.09)
0.43 (0.19)
0.78 (0.35) 1.7 (0.74) 1.1 (0.47) 0.56 (0.25) 0.13 (0.06)
0.22 (0.10) 0.22 (0.10) 0.34 (0.15) 0.22 (0.10)
0.43 (0.19)
0.04 (0.02)
1.7 (0.77) 1.5 (0.67) 3.3 (1.5) 4.2 (1.9) 1.7 (0.77) 0.18 (0.08)
3.3 (1.5) 0.45 (0.20)
0.07 (0.03)
0.76 (0.34)
13.0 (5.8) 2.2 (1.0) 0.04 (0.02)
4.2 (1.9) 13.0 (5.8) 0.78 (0.35) 0.78 (0.35) 0.56 (0.25)
0.04 (0.02)
4.9 (2.2)
333
-------
K-l.S INDEX -- metric tons per hectare per unit of erosion tndex. R
LAND
CLASS .ind
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VI I Is
VIIIw
vine
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
78 79 80 81 82 83
0.18 (0.08) 0.18 (0.08) O.OA (0.02)
0.43 (0.19) 0.18 (0.08) 0.49 (0.22) 0.29 (0.13) 0.31 (0.14) 0.22 (0.10)
0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.07 (0.03) 0.07 (0.03)
0.09 (0.04) 0.22 (0.10) 0.07 (0.03)
0.22 (0.10) 0.25 (0.11) 0.07 (0.03) 0.04 (0.02) 0.07 (0.03)
0.65 (0.29) 0.20 (0.09) 0.49 (0.22) 0.36 (0.16) 0.43 (0.19) 0.76 (0.34)
0.29 (0.13) 0.07 (0.03) 0.09 (0.04)
0.07 (0.03) 0.09 (0.04)
0.31 (0.14) 0.11 (0.05)
0.45 (0.20) 0.67 (0.30) 0.43 (0.19) 0.31 (0.14) 0.25 (0.11)
0.22 (0.10) 0.22 (0.10) 0.38 (0.17) 0.07 (0.03) 0.07 (0.03)
0.31 (0.14) 0.25 (0.11) 0.09 (0.04)
0.07 (0.03) 0.07 (0.03)
0.22 (0.10)
0.22 (0.10) 0.07 (0.03) 0.07 (0.03)
1.7 (0.74) 0.72 (0.32) 1.7 (0.74) 0.36 (0.16) 0.43 (0.19)
!-7 (0.74) 0.72 (0.32) 0.65 (0.29) 0.90 (0.14)
0.04 (0.02) 0.04 (0.02)
0.49 (0.22)
0.83 (0.37) 1.9 (0.87) 0.83 (0.37) 0.34 (0.15)
l'° (°-A5> 0.11 (0.05) 2.9 (1.3) 1.3 (0.68) 0.81 (0.36)
0.54 (0.24)
1.6 (0.72)
334
-------
K-LS INDEX -- metric tons per hectare per unit of erosion Index, R
LAND
CLASS and
SUBCLASS
I
He
IIs
ITw
He
Hie
Ills
lllw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
Vlw
Vic
Vile
VIIs
Vllw
VIIc
Vine
VI1I8
VIIlw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
84 85 86 87 88 90
0.22 (0.10) 0.16 (0.07)
0.27 (0.12) 0.43 (0.19) 0.22 (0.10) 0.38 (0.17) 0.49 (0.22) 0.67 (0.30)
0.29 (0.13) 0.19 (0.09) 0.25 (0.11)
0.49 (0.22)
0.25 (0.11)
0.38 (0.17) 0.58 (0.26) 0.65 (0.29) 0.67 (0.30) 1.4 (0.63) 1.4 (0.63)
0.22 (0.10) 0.11 (0.05)
0.29 (0.13)
3.7 (1.7)
0.65 (0.29) 1.0 (0.45) 1.2 (0.54) 0.67 (0.30) 0.31 (0.14) 2.6 (1.2)
0.29 (0.13) 0.45 (0.20"> 0.07 (0.03)
0.13 (0.06)
0.22 (0.10)
1.3 (0.58) 1.0 (0.45) 2.2 (0.96) 1.6 (0.73) 5.0 (2.2) 4.6 (2.1)
0.65 (0.29) 0.85 (0.38) 0.8 (0.34) 1.3 (0.59)
0.83 (0.37) 8.0 (3.6) 4.0 (1.8)
2.0 (0.89) 4.1 (1.8) 3.0 (1.4) 1.6 (0.70)
0.29 (0.13)
335
-------
K-LS INDEX -- metric tons per hectare per unit of erosion Index. R
LAND
CLASS and
SUBCLASS
I
lie
IIs
IIw
lie
IIlc
Ills
lllw
IIlc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIls
VI Iw
VIIc
Vlllt
VIlIs
VlIIw
VIlIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
91 92 (Mich.) 92 (Wise.) 93 94 95
0.16 (0.07) 0.13 (0.06) 0.13 (0.06)
0.67 (0.30) 0.58 (0.26) 0.67 (0.30) 0.58 (0.26) 0.31 (0.14) 0.67 (0.30)
0.13 (0.06) 0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.20 (0.09) 0.16 (0.07)
0.25 (0.11) 0.38 (0.17) 0.16 (0.07) 0.29 (0.13) 0.16 (0.07)
1.4 (0.63) 1.5 (0.65) 1.3 (0.60) 1.2 (0.54) 0.87 (0.39) 1.4 (0.63)
0.27 (0.12) 0.31 (0.14) 0.11 (0.05) 0.27 (0.12) 0.43 (0.19)
0.16 (0.07) 0.20 (0.09) 0.09 (0.04)
0.85 (0.38) 3.1 (1.4) 3.6 (1.6) 3.2 (1.4) 2.0 (0.90) 3.4 (1.5)
0.11 (0.05) 0.38 (0.17) 0.27 (0.12) 0.07 (0.13) 0.27 (0.12) 0.27 (0.12)
0.07 (0.03) 0.27 (0.12) 0.13 (0.06) 0.07 (0.03) 0.07 (0.03)
°-49 (0.22) 0.85 (0.38)
0." (0.10) 0.22 (0.10)
2.2 (0.98) 4.6 (2.1) 7.1 (3.2) 6.0 (2.7) 2.9 (1.3) 2.6 (1.2)
0.65 (0.29) 0.20 (0.09) 0.54 (0.24) 0.65 (0.29) 0.27 (0.12) 2.5 (1.1)
*-° (1-8) 7-7 0.4) 9.6 (4.3) 6.0 (2.7) 5.0 (2.2) 5.4 (2.4)
l-6 (0.70) 1.4 (0.63) 1.7 (0.77) 2.2 (0.99) 0.27 (0.12)
336
-------
K-LS INDEX — metric tons per hectare per unit of erosion Index. R
LAND
CLASS and
SUBCLASS
I
lie
IIS
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIu
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
96 97 98 99 100 101
0.16 (0.07) 0.16 (0.07)
0.45 (0.20) 0.43 (0.19) 0.47 (0.21) 0.49 (0.22) 0.31 (0.14) 0.58 (0.26)
0.13 (0.06) 0.13 (0.06) 0.16 (0.07) 0.20 (0.09) 0.16 (0.07) 1.1 (0.49)
0.11 (0.05) 0.16 (0.07) 0.22 (0.10)
1.3 (0.59) 0.92 (0.41) 1.3 (0.56) 1.5 (0.69) 1.2 (0.52) 2.1 (0.93)
0.31 (0.14) 0.38 (0.17) 0.31 (0.14) 0.11 (0.05) 0.31 (0.14) 0.20 (0.09)
0.18 (0.08)
1.4 (0.62) 0.65 (0.29) 2.7 (1.2) 3.1 (1.4) 1.6 (0.73)
0.27 (0.12) 0.27 (0.12) 2.5 (1.1) 0.27 (0.12) 0.20 (0.09) 0.65 (0.29)
0.07 (0.03) 0.07 (0.03) 0.34 (0.15)
0.13 (0.06)
2.6 (1.2) 1.7 (0.77) 2.2 (0.96) 2.5 (1.1) 6.2 (2.8) 6.9 (3.1)
0.65 (0.29) 0.58 (0.26) 0.25 (0.11) 0.27 (0.12) 1.6 (0.7) 0.25 (0.11)
4.3 (1.9) 4.1 (1.8) 3.3 (1.5) 4.7 (2.1) 9.3 (4.1) 10.1 (4.5)
1.9 (0.83) 0.45 (0.2) 0.99 (0.44) 0.65 (0.29) 8.8 (3.9) 0.65 (0.29
337
-------
K-LS IrtDEX -- metric tons per hectare per unit of eroalon index. R
LAND
CLASS and
SUBCLASS
I
lie
IIS
IIw
He
Hie
Ills
I IIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VI Iw
VIIc
VHIe
VIIU
VIIIw
VIIIc
(cons per acre per unit of erosion index, R)
LAND RESOURCE AREA
102 103 104 105 106 107
0.27 (0.12) 0.22 (0.10) 0.29 (0.13) 0.25 (0.11) 0.16 (0.07)
0.65 (0.29) 0.43 (0.19) 0.49 (0.22) 0.67 (0.30) 0.43 (0.19) 0.36 (0.16)
0.18 (0.08) 0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.20 (0.09) 0.22 (0.10)
1.1 (0.51) 1.* (0.54) 0.94 (0.42) 1.6 (0.70) 1.7 (0.74) 1.3 (0.58)
0.45 (0.20) 0.11 (0.05) 0.27 (0.12) 0.11 (0.05) 0.11 (0.05)
0.20 (0.09) 0.22 (0.10)
1.7 (0.77) 3.7 (1.7) 2.9 (1.3) 3.7 (1.7) 1.9 (0.84) 3.6 (1.6)
0.38 (0.17) 0.27 (0.12) 0.2 (0.09) 0.27 (0.12)
0.04 (0.02)
0.09 (0.04)
3-7 (!•') 5.1 (2.3) 3.5 (1.6) 3.7 (1.7) 7.8 (3.5) 4.8 (2.2)
0.54 (0.24) 0.76 (0.34) 0.72 (0.32) 0.54 (0.24) 7.8 (3.5) 0.96 (0.43)
.
4-2 C1-9) 9-9 <4-«) 5.1 (2.3) 5.2 (2.3) 12.9 (5.8) 15.0 (6.9)
2.2 (0.96) 0.76 (0.34) 2.9 (1.3) 17.0 (7.8) 12.9 (5.8) 0.96 (0.43)
338
-------
K-LS INDEX -- metric tona per hectare per unit of erosion Index. R
LAND
CLASS and
SUBCLASS
I
He
us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IV 8
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Via
VIu
Vic
Vile
VIIs
VIIu
VIIc
Vllle
VIIIs
VIIIw
VIIIc
(tone per acre per unit of erosion Index, R)
LAND RESOURCE AREA
108 109 110 111 112 113
0.09 (0.04)
0.43 (0.19) 0.43 (0.19) 0.43 (0.19) 0.43 (0.19) 0.36 (0.16) 0.25 (0.11)
0.09 (0.04) 0.29 (0.13)
0.09 (0.04)
1.2 (0.54) 1.4 (0.63) 0.87 (0.39) 1.2 (0.52) 0.92 (0.41) 0.92 (0.41)
0.22 (0.10) 0.04 (0.02)
1.7 (0.77) 1.4 (0.63) 4.1 (1.8) 3.1 (1.4) 0.92 (0.41) 1.5 (0.67)
0.16 (0.07) 0.27 (0.12) 0.34 (0.15)
6.0 (2.7) 2.6 (1.2) 6.9 (3.1) 4.5 (2.0) 2.0 (0.89) 5.8 (2.6)
0.83 (0.37) 0.34 (0.15) 0.49 (0.22) 2.6 (1.2)
1.7 (0.77) 6.0 (2.7) 7.9 (3.5) 13.0 (5.8) 11.7 (5.2)
0.72 (0.32) 6.6 (2.9) 0.72 (0.32) 0.20 (0.09) 0.78 (0.35)
339
-------
LAND
CLASS and
SUBCLASS
I
He
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVu
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
K-LS INDEX -- metric tons per hectare per unit of erosion Index, R
(cons per acre per unit of erosion index, R)
(Ohio)
114 (111.)
LAND RESOURCE AR£A
115
116
117
0.43 (0.19) 0.58 (0.26) 0.36 (0.16)
0.34 (0.15) 0.22 (0.10)
0.43 (0.19)
3-1
6.0 (2.7)
3.1 (1.4)
6.1 (2.7)
2.5 (1.1)
2.6 (1.2)
1.8 (0.79)
18.0 (7.8)
21.0 (9.5)
3.1 (1.4)
4.6 (2.1)
10.0 (4.6)
6.6 (2.9)
118
0.15 (0.07) 0.16 (0.07)
0.22 (0.10) 0.27 (0.12)
1.4 (0.63) 1.1 (0.47) 1.2 (0.54) 0.43 (0.19) 0.76 (0.34) 0.92 (0.41)
0.20 (0.09) 0.20 (0.09)
2.2 (0.96) 0.72 (0.32) 0.49 (0.22) 1.7 (0.76)
0.83 (0.37) 0.72 (0.32) 0.54 (0.24)
4.3 (1.9) 5.3 (2.4) 3.0 (1.3)
1.8 (0.79) 1.1 (0.48) 1.1 (0.48)
7.6 (3.4) 14.1 (6.3) 9.3 (4.1)
2.7 (1.2) 14.1 (6.3) 9.3 (4.1)
340
-------
K-LS INDEX -- metric tons per hectare per unit of erosion Index, R
IAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VIHe
VIIIs
VHIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
125 126 127 128 (Va.) 128 (Ga.) 129
0.07 (0.03)
0.45 (0.20) 0.72 (0.32) 0.69 (0.31) 0,65 (0.29) 0.29 (0.13) 0.38 (0.17)
0.18 (0.08) 0.18 (0.08)
0.16 (0.07) 0.09 (0.04)
1.2 (0.53) 2.4 (1.1) 2.1 (0.95) 5.0 (2.2) 1.6 (0.70) 0.63 (0.28)
0.31 (0.14) 0.49 (0.22)
0.87 (0.39)
2.9 (1.3) 5.0 (2.2) 5.0 (2.2) 5.2 (2.3) 4.9 (2.2) 1.4 (0.62)
0.45 (0.20) 0.99 (0.44) 0.20 (0.09) 3.2 (1.4) 0.22 (0.10)
0.87 (0.39)
0.11 (0.05)
6.1 (2.7) 10.0 (4.5) 8.2 (3.6) 11.5 (5.2) 7.7 (3.4) 2.6 (1.2)
6.0 (2.7) 2.1 (0.93) 1.2 (0.54) 5.7 (2.5)
1.9 (0.86)
8.9 (4.0) 18.2 (8.1) 10.8 (4.8) 18.2 (8.1) 4.9 (2.2) 2.6 (1.2)
10.5 (4.7) 18.2 (8.1) 13.0 (6.0) 10.8 (4.8) 5.8 (2.6) 4.8 (2.1)
341
-------
K'LS INDEX -- metric tons per hectare per unit of erosion Index. R
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
Vila
VIIw
VIIc
Vllle
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
130 131 132 133 (N.C.) 133 (Ala.) 133 (La.)
0.18 (0.08) 0.25 (0.11) 0.18 (0.08) 0.13 (0.06)
0.76 (0.34) 0.25 (0.11) 0.38 (0.17) 0.49 (0.22) 0.25 (0.11) 0.22 (0.10)
0.22 (0.10) 0.11 (0.05) 0.18 (0.08)
0.18 (0.08)
1.0 (0.45) 0.22 (0.10) 0.96 (0.43) 0.76 (0.34) 0.56 (0.25) 0.34 (0.15)
0.27 (0.12) 0.20 (0.09) 0.27 (0.12)
0.13 (0.06)
3-1 (1.4) 1.8 (0.82) 1.4 (0.62) 1.1 (0.48) 0.49 (0.22)
0.27 (0.12) 0.43 (0.19)
6-5 <2-9> 4-1 (1-9) 1.7 (0.78) 3.09 (1.4)
0.81 (0.36) 0<65 (0.29)
6.5 (2.9) 8.0 (3.6) 4.9 (2.2)
9.9 (4.4) 7.0 (3.1) 2.2 (0.99)
0.49 (0.22)
342
-------
K-LS INDEX -- metric tona per hectare per unit of erosion index, ft
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIu
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
VI8
VIw
Vic
Vile
VIIs
VI Iw
VIIc
VHIe
Villa
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
135 136 (N.C.) 136 (Ga.) 137 138 139
0.09 (0.04) 0.13 (0.06)
0.29 (0.13) 0.49 (0.22) 0.65 (0.29) 0.49 (0.22) 0.22 (0.10) 0.58 (0.26)
0.16 (0.07) 0.43 (0.19) 0.40 (0.18) 0.09 (0.04) 0.16 (0.07)
0.16 (0.07) 0.09 (0.04)
0.43 (0.19) 1.14 (0.51) 0.65 (0.29) 0.92 (0.41) 0.25 (0.11) 1.1 (0.48)
0.25 (0.11) 0.07 (0.03) 0.11 (0.05) 0.27 (0.12) 0.20 (0.09)
0.22 (0.10)
0.22 (0.10) 1.9 (0.83) 1.3 (0.58) 1.7 (0.78) 0.45 (0.20) 1.9 (0.83)
0.83 (0.37) 0.16 (0.07) 0.20 (0.09)
1.2 (0.54) 4.1 (1.8) 2.4 (1.1) 3.4 (1.5) 5.1 (2.3)
1.2 (0.53) 1.0 (0.46) 0.83 (0.37)
5.0 (2.2) 6.3 (2.8) 4.9 (2.2) 7.0 (3.1)
4.8 (2.2) 0.4 (0.18) 1.4 (0.61) 9.8 (4.4)
343
-------
K-LS INDEX -- metric tons per hectare per unit of erosion Index. R
LAND
CLASS and
SUBCLASS
I
lie
IIS
Ilw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIu
Vine
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
140 141 142 143 144 145
0.40 (0.18) 0.43 (0.19) 0.76 (0.34) 0.40 (0.18) 0.31 (0.14) 0.58 (0.26)
0.13 (0.06) 0.16 (0.07) 0.16 (0.07) 0.38 (0.17) 0.22 (0.10) 0.20 (0.09)
0.13 (0,06) 0.16 (0.07) 0.22 (0.10) 0.38 (0.17) 0.38 (0.17) 0.31 (0.14)
1.7 (0.74) 1.1 (0.49) 2.4 (1.1) 0.85 (0.38) 1.2 (0.52) 1.3 (0.58)
0.20 (0.09) 0.20 (0.09) 0.27 (0.12) 0.27 (0.12) 0.22 (0.10) 0.54 (0.24)
0.54 (0.24) 0.40 (0.18) 0.45 (0.20) 0.11 (0.05) 0.13 (0.06) 0.13 (0.06)
2.9 (1.3) 2.4 (1.1) 5.6 (2.5) 2.2 (1.0) 4.2 (1.9) 0.43 (0.19)
1.4 (0.37) 0.83 (0.37) 0.83 (0.37) 0.72 (0.32) 0.83 (0.37) 0.38 (0.17)
0.22 (0.10) 0.25 (0.11) 0.25 (0.11) 0.45 (0.20)
0.13 (0.06) 0.18 (0.08)
4-3 (1.9) 5.2 (2.3) 6.0 (2.7) 3.4 (1.5) 2.6 (1.2) 1.8 (0.82)
1.9 (0.84) 0.56 (0.25) 0.81 (0.36) 0.85 (0.38) 0.83 (0.37) 0.92 (0.41)
1.6 (0.70)
6.4 (2.8) 5.4 (2.4) 5.4 (2.4) 11.0 (4.7)
6-4 <2-8) 0-18 (0.08) 3.8 (1.7) 0.38 (0.17) 3.2 (1.4) 5.8 (2.6)
5.8 (2.6)
344
-------
LAND
CLASS and
SUBCLASS
I
He
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VI Iw
VIIc
VIHe
VIIIs
VIIIu
VIIIc
K-LS INDEX -- metric tons per hectare per unit of erosion index. R
(tons per acre per unit of erosion index, R)
146
0.56 (0.25)
0.38 (0.17)
0.38 (0.17)
0.25 (0.11)
0.85 (0.38)
0.27 (0.12)
0.38 (0.17)
2.2 (1.0)
0.72 (0.32)
0.25 (0.11)
2.9 (1.3)
2.2 (1.0)
2.8 (1.2)
LAND RESOURCE AREA
148
1.9 (0.86)
0.76 (0.34)
3.9 (1.7)
0.38 (0.17)
11.0 (5.0)
6.0 (2.7)
16.0 (7.2)
11.0 (5.1)
0.81 (0.36) 0.65 (0.29)
0.22 (0.10)
1.6 (0.70)
1.4 (0.61)
0.83 (0.37)
2.7 (1.2)
2.8 (1.3)
8.9 (4.0)
6.4 (2.9)
149
1.4 (0.62)
0.22 (0.10)
0.20 (0.09)
4.5 (2.0)
6.0 (2.7)
0.34 (0.15)
150
151
0.27 (0.12)
0.58 (0.26)
0.27 (0.12)
0.34 (0.15)
1.1 (0.48) 0.38 (0.17) 0.38 (0.17)
0.27 (0.12) 0.16 (0.07) 0.07 (0.03)
1.1 (0.48)
0.16 (0.07)
7.1 (3.2)
345
-------
K'LS IKDEX — metric tons per hectare per unit of ero.iion Index. R
(tons per acre per unit of erosion index, R)
LAND
CLASS and
SUBCLASS
I
He
Us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VIHe
VHIs
VI IIw
VIIIc
LAND RESOURCE AREA
152
2.2 (0.98)
0.07 (0.03)
0.04 (0.02)
153 (S.C.)
0.13 (0.06)
0.43 (0.19)
0.31 (0.14)
153 (N.C.)
0.18 (0.08)
0.43 (0.19)
0.83 (0.37)
0.18 (0.08)
154
0.04 (0.02)
0.09 (0.04)
0.13 (0.06)
0.45 (0.20)
0.09 (0.04)
155
2.5 (1.1) 1.3 (0.60) 1.5 (0.67) 0.34 (0.15)
0.20 (0.09) 0.27 (0.12) 0.31 (0.14) 0.34 (0.15) 0.27 (0.12)
1.5 (0.67)
0.20 (0.09) 0.31 (0.14) 0.38 (0.17) 0.31 (0.14) 0.13 (0.06)
2.0 (0.89) 1.7 (0.75) 0.07 (0.03)
346
-------
APPENDIX E
ESTIMATED SOIL LOSSES FROM SELECTED CROPPING SYSTEMS IN
AREAS WEST OF THE CONTINENTAL DIVIDE
From 1972 SCS Survey
347
-------
STATE
LRA
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.CapabU- :2. Dominant Soil
ity Class:
and :
Subclass .
* 'Cloquate sil, 0-3%
He :Knappa sil, 2-5%
Us :oi&qua sil, 0-3%
II* JSestCle muck
He :Kr.appa sil, 0-27,
Ille roiympic sicl, 0-8%
Ills :Queets sil, 0-5%
IIIw rCinger cl, 0-4%
I lie :NA
IVe -.Boistfort cl, 3-25%
IVs :Cam sil, 0-3%
IVw :0costa sicl, diked
IVc :NA
Ve : NA
V» rSalzar sic
Vs :Hoquiara sil, 0-3%
Vc :NA
Vie :Copalis cl, 0-15%
VIw : Yaquina Is
Vis : Solleks channery sicl, 30-50%
Vic : NA
Vile :Lytell gr cl, 50-70%
VIIs : Dimal v channery sicl, 50-90%
Vllw : Alluvial land
VIIc : NA
VI lie : Dune land, 0-15% I/
VIIIs : Beach land & coastal beach 2^
VTIIw : Riverwash 3_/
VI lie i NA
S.Dora
Slope
Length
(it)
800
200
800
NA
400
800
900
1000
600
800
NA
NA
800
1000
300
800
800
700
100
100
200
100
4.DOm
or
/o
Slope
6i) "
2
4
2
0
1
4
2
1
7
2
0
0
2
7
1
35
55
70
1
12
2
4
5.T Fac-
tor
Tons
5
5
5
5
5
4
5
5
5
5
5
5
5
5
2
3
1
2
5
0
0
6. Estimate Soil Losses for Selected Cropping Systems
Pasture
per acre
0
0.3
0
0
0.1
0.2
NA
0
NA
0.5
o
0
0.5
1
0.5
NA
NA
NA
NA
NA
NA
NA
Grass
Clover
Hay
er year
0
0.3
0
NA
0.1
0.2
NA
0
NA
0.5
0
0
0.5
1
0.5
NA
NA
NA
NA
NA
NA
NA
Forest
0
0 .1
0
NA
0
0.1
0
0.1
0
0
0
0
0.1
0
0.2
0.4
0.6
0.1
NA
NA
NA
Row
Crop
2
•~t
L
i
NA
NA
»« i
ftA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
I/ Tons of soil loss is IT-. 2/ Tons of soil loss is O.IT. 3/ Tons of soil- loss is 50 T.
oo
-------
STATE OREGON
2
LRA
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
1
lie
Us
IIw
lie
IlJc
Ills
Illw
Illc
£ IVe
0 IVs
3 Vvv
IVc
Vc
Vw
Vs
Vc
Vie
VJw
Vis
Vic
Vile
VIIs
VI Iw
VIIc
VHIe
VIIls
VIIJw
VIIIc
2. Dominant Soil
thehalis sicl
Salkum sicl, 2-6%
Salem grsil
Woodburn sil, 0-3%
None
Jory sicl, 7-12%
Multnoraah 1, 0.-3%
Wapato sicl
None
Nekia sicl, 20-30%
None
Dayton sil
NONE
NOME
NONE
NONE
NONE
McCully cl, 30-50%
Panther sicl, 4-20%
Klickitat stl, 3-30X
None
Kinney kl, 50-70%
Klickitat vstl, 50-75%
NONE
NONE
NONE
Riverwash I/
Tidal marsh (fresh water)
NONE
3. Dora
Slope
Length
OT)
500
400
600
400
300
300
NA
500
NA
1200
300
1200
1200
1200
!!A
NA
4 .Dom
%
Slope
(%)
1
4
1
1
10
2
0
25
0
40
12
15
60
65
0
0
5.T Fac-
tor
Tons
5
5
3
5
5
2
5
2
5
5
3
3
3
3
6. Estimate Soil Losses for Selected Cropping Systems
— j-, n . . 1 I 1 1 - _ i_ f^ — _1_ — — J T1 rC*?^. — ~?Z ' — " -J ' fir*_A
~KOW \. L U [J
Cover
Crop
per acre
1
4
1
i
10
20
— parley ,
Rye
Grass
Seed
er year
1
6
0
10
0
15
— rmtrm;:
Clover
1
3
•i
1
6
2
0
10
15
— err
-------
STATE WASHINGTON
LRA 3
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
j
He
Us
IIw
He
Hie
Ills
I IIw
IHc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VIIw
VIIc
Villa
VIIIs
VIIIw
VIIIc
2. Dominant Soil :
NA
Nekia c, 3-9%
HA
Semiahraoo muck, 0-2%
NA
Cinebar grsil, 8-15%
Puyallup fsl, 0-3%
Bellingham sicl, 0-3%
NA
Olympic sicl, 8-15%
Olympic stcl, 3-15%
Schooley sil, 0-3%
NA
NA
NA
NA
NA
Wilkeson 1, 6-15%
Minniece sicl, 2-5%
Klaus grsl, 8-15%
NA
Rough mountainous land, 25-6(
Cathcart cl, 3-15%
NA
NA
NA
Lava flow
Riverwash I/
Snow and ice fields
3 . Dom :
Slope
Length
(ft)
500
800
800
350
800
1200
1200
300
1000
300
800
% 100C
1000
1200
100
1000
4.Doia :
%
Slope
(%)
5
1
12
2
1
10
10
2
10
2
10
35
8
8
A
60
5.T Fac-
tor
Tons
2
5
5
5
5
A
4
5
5
5
3
3
3
0
0
NA
6. Estimate Soil Losses for Selected Cropping Systems
Forest
per acre
0.1
0
0
0
0
0.1
0.1
0
0.1
0
0
0.5
0.1
NA
NA
NA
Hay and-
Pasture
er year
.5
NA
NA
.5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA.*
NA
Row
Crops
4.0
NA
NA
2.0
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
I/ Tons of soil loss is 50 T.
LO
Ul
O
-------
STATE CALIFORNIA
LRA 4
Form IV. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- : 2. Dominant Soil
ity Class :
and :
Subclass .
I =Fcrndale sil, 0-27.
He : Tinmons 1, 2-5%
Us :Corralitos si, 0-2%
IIw : Rhonerville si, slightly wet
He :NA
Hie : Josephine 1, 9-15%
Ills :Arcata fsl, 0-2%
lilw : Russ sil, slightly wet
II Ic : &V
IVe :Hugo 1, 15-30%
IVs :KA
IVw : Coguille sicl
IVc : NA
Ve :NA
Vw : NA
Vs :KA
Vc :NA
Vie :Hugo 1, 30-507.
Vlw :Wet Alluvial Land
VTs :NA
Vic : NA
yiJ« :Hugo 1, 50-757.
VUs :NA
VIlw :NA
Vile :NA.
VI He :Badland I/
VlIIs :Mayman 1, 50-757. 2f
VI IIw :Riverwash 3/
Vine :NA
3 .Dom
Slope
Length
(ft)
1200
1200
600
450
400
600
1200
350
NA
400
NA
350
500
300
1200
4.Dom
Slope
(7»)
1
3
1
1
10
1
1
20
0
35
0
55
75
60
2
5.T fac-
tor
Tons
5
5
5
3
3
5
5
3
5
3
5
3
1
1
5
6. Estimate Soil Losses for Selected Cropping Systems
Irrigated
Pasture
per acre
a J
.25
.5
.5
.25
.25
.5
er year
Timber
not
Grazed
.1
.5
.75
1.5
2.0
Timber
Grazed
.3
1.5
2.0
3.0
3.5
J7 Tons of soil loss is 75T«
2/ Tons of soil loss is 2T.
3_/ Tons of soil loss is 50T-
-------
STATE CALIFORNIA
LRA 5
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping'Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
IIIo
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VI I w
VIIc
Vllle
VIIIs
VIIIw
VIIIc
2. Dominant Soil
NA
Ferndale sil, 2-9%
Arbuckle grl, 0-2%
NA
NA
Ettcrsburg grl, 2-9%
Columbia kfsl, 0-2%
NA
NA
Josephine 1, 15-30%
Neuns stl, 15-30%
Red Bluff grl, wet, 0-5%
NA
NA
Chunrny cl
NA
NA
Hugo 1, 30-507.
NA
Neuns stl, 30-507.
NA
Hugo stl, 50-75%
Kinkel rol, 30-50%
NA
NA
NA
Maymen 1, 30-757. I/
Riverwash 2/
NA
3.Dom
Slope
Length
(ft)
750
1200
500
600
350
400
1200
NA.
350
300
200
350
150
1200
4. Dora
tr
l»
Slope
(%)
5
1
8
1
25
25
3
0
35
40
51
35
40
2
5.T Fac-
tor
Tons
5
4
4
5
3
2
2
4
3
3
3
3
1
5
6. Estimate Soil Losses for Selected Cropping Systems
Irrigated
Pasture
per acre
.75
.25
1.25
.25
er year
Woodland
not
grazed
.25
.5
.5
.75
.5
1.0
1.0
1.0
Woodland
Grazed
.50
1.5
1.5
1.75
2.0
2.25
2.5
2.0
Co
Ln
KJ
J./ Tons of soil loss is 2T
2/ Tons of soil loss is SOT
-------
STATE Washington
LRA 6
Form 1W. Dominant Soil L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
Jlle
Ills
IIIw
IIlc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
Vlw
Vis
Vic
Vile
VIIs
VI I w
VIIc
Vllle
VIIIs
VlIIw
VIIIc
2. Dominant Soil
NA
Chemawa shotty 1, 2-5% (I)
Wenas 1, 0-2% (I)
NA
Maupin 1 (I)
KcGowan 1, 3-8% (I)
Guler 1, 3-5% (I)
Colville sil, 0-3% (I)
NA
McGowan 1, 15-30%
Yakima grsl, 0-2%
Chepaka sil, 0-3%
NA
NA
Chinchallo sil
NA
NA
Underwood stl, 0-15%
Bonneville cobbly si, 3-8%
Conconully vstfsl, 0-15%
NA
Rough Mountainous Land, 30-65
Pend Oreille stl, 0-8%
NA
NA
NA
Rock land, 30-100% I/
Rivervash 2]
Snow and Ice Fields
3.Doni
Slope
Length
atv
500
200
50
1000
500
1000
800
400
500
500
300
600
1000
400
800
100
1000
4.Dom
%
Slope
<%>
2
1
2
6
4
2
20
2
2
0
12
5
10
45
e
60
4
60
5.T Fac-
tor
Tons
5
5
2
5
c
Cj
5
3
5
5
3
2
5
3
5
0
0
6. Estimate Soil Losses for Selected Cropping Systems
Forest
per acre
0.1
0.1
0
0.2
0
0
0.1
0.1
0.5
0.1
Hay
and
Pasture
er year
.1
.1
.2
.3
.2
.1
0.1
Range
Good
.1
.1
.2
Range
Poor
i
1
1.5
CO
Ln
LO
I/ Tons of soil loss is 2T
2J Tons of soil loss is 70T
-------
STATE Washington
LRA 6
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
He
Us
IIw
lie
IIIo
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
Vlw
Vis
Vic
Vile
VIIs
VI I w
Vile
VIIIc
VIIIs
VIIIw
VIIIc
2. Dominant Soil
NA
Chemawa shbtty 1, 2-5% (I)
Wenas 1, 0-2% (I)
NA
Maupin 1 (I)
McGowan 1, 3-8% (I)
Guler 1, 3-5% (I)
Colville sil, 0-3* (I)
NA
McGowan 1, 15-30%
Yakima grsl, 0-2%
Chepaka sil, 0-3%
NA
NA
Chinchallo sil
NA
NA
Underwood stl, 0-15%
Bonneville cobbly si, 3-8%
Conconully vstfsl, 0-15%
NA
Rough Mountainous Land, 30-65
Pend Oreille stl, 0-8%
NA
NA
NA
Rock land, 30-100% I/
Riverwash 2/
Snow and Ice Fields
3. Dora
Slope
Length
(ft)
500
200
50
1000
500
1000
800
400
500
500
300
600
1000
400
800
100
1000
4.Dom
Slope
(%)
2
1
2
6
4
2
20
2
2
0
12
5
10
45
5
60
4
60
5.T Fac-
tor
Tons
5
5
2
5
5
5
5
3
5
5
3
2
5
3
5
0
0
G. Estimate Soil Losses for Selected Cropping Systems
Forest
per acre p
0.1
0.1
0
0.2
0
0
0.1
0.1
0.5
0.1
Hay
and
Pasturo
er year
.1
.1
.2
.3
.2
.1
0.1
Range
Good
.1
.1
.2
Range
Poor
1
1
1.5
I/ Tons of soil loss is 2T
2/ Tons of soil loss is 70T
-------
STATS WASHINGTON
LRA 7
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EtfT-2
l.Capabil- :2. Dominant Soil
ity Class:
and :
Subclass :
I 'Esquatzel sil, 0-27. (I)
He : warden sil, 2-57. (I)
Ils : Naches 1, 0-27. (I)
IIw iTeppenish sil, 0-27. (I)
He : NA.
Hie rRitzville sil, 5-307.
Ills :Ashue 1, 0-2% (I)
Illw :pasco sil, 0-2% (I)
flic rRitzville sil, 0-57.
S IVe :Shano sil, 5-30%
01 IVs : White Sway sil, 2-57. (I)
IVw : Fiander sil (I)
IVe : Warden sil, 0-57.
Ve :NA.
Vs :NA
Vc :NA
Vie : Warden sil, sev. eroded, 15-3
vi w :NA
Vis :cie Elum ksil, 0-37.
Vic :NA.
Vile :quincy Is, 0-307.
vHs : Lickskillet vstsil, 30-657.
Vllw :HA
Vile :NA.
VIIIo :Dune Land I/
VHIs :Rock Outcrop
VIIIw rRiverwash Zf
Vl T I c : NA
3. Dora
Slope
Length
at)' '
1200
1200
1200
1200
800
1200
1200
1200
800
500
1200
1200
37. 300
400
300
300
200
300
100
4.Dom
Slope
(7°)
1
4
1
.5
12
1
.5
3
12
4
.5
4
20
2
10
45
5
60
3
5.T Fac-
tor
Tons
5
5
4
4
5
2
4
5
5
2
2
5
5
4
5
2
5
0
0
6". Estimate Soil Losses for Selected Cropping S
Irrigated
Row
Crops
per acre
O
6
6
2
6
2
6
f\
Irrigated
Close-
Grown
Crops
er year
1
3
]
.5
"-
1
.5
3
.5
Orchard
Permanent
Cover
1
1
1
.5
1
.5
i
Wheat :
lyr.
fallow
5
2
4
1.5
2
Rangeland '
Good
Cover
1
1
1
1
.5
1
jystems
flange land
Poor
Cover
3
2
3
2
4
1
3
*j
I/ Tons of soil loss Is IT. "
2/ Tons of soil loss is SOT.
-------
STATS WASHINGTON
LRA 7
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
US DA - SCS
Attachment to EVT-2
l.Capabil- -2. Dominant Soil :3.Dora
ity Class: :Slope
and : : Length
Subclass : ;
(ft)
I 'Esquatzel sil, 0-27. (I) = 1200
He : Warden sil, 2-57. (I)= 1200
Us Caches 1, 0-27. (I): 1200
Hw :Toppenish sil, 0-27. (I): 1200
lie : NA :
Hie :Ritzville sil, 5-307. : 800
Ills :Ashue 1, 0-2% (I): 1200
IIlw jpasco sil, 0-27. (I): 1200
Illc :Ritzville sil, 0-57. : 1200
IVe :Shano sil, 5-307. : 800
IVs :White Sway sil, 2-57. (I): 500
IVw -.Fiander sil (I): 1200
IVc : Warden sil, 0-57. : 1200
Ve :liA :
"Vw :NA :
Vs :NA :
vc :NA :
Vie : warden sil, sev. eroded, 15-307. 300
VIw :NA :
Vis :cie Elum ksil, 0-37. • 400
Vie : NA :
Vile. ;0uincyls, 0-307. : 300
VHS .Lickskillet vstsil, 30-657. : 300
Vllw :NA :
VIIc :NA :
VI He :Dune Land _!/ : 200
\IIIs :Rock Outcrop : 300
VIIIw : Riverwash _2/ : 10°
VT TTi-. • NA I
4 .Dom
of
/e
Slope
<7o)
1
4
1
.5
12
1
.5
3
12
4
.5
4
20
2
10
45
5
60
3
5.T Fac-
tor
Tons
5
5
4
4
5
2
4
5
5
2
2
5
5
4
5
2
5
0
0
6. Estimate Soil Losses for Selected Cropping Systems
Irrigated
Row
Crops
3er acre
3
6
6
2
6
2
6
2
Irrigated
Close-
Grown
Crops
er year
1
3
1
.5
1
.5
3
.5
Orchard
'ermanenC
Cover
1
1
1
.5
1
.5
Wheat I
lyr.
fallow
5
2
4
1.5
2
langeland X
Good
Cover
1
1
1
1
.5
1
^angeland
Poor
Cover
3
2
3
2
4
1
3
3
I/ Tons of soil loss is IT.
2/ Tons of soil loss is SOT.
-------
STATE
LRA
Oregon
Form 1W. Dominant Soil, L, S, and I Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
US DA - SCS
Attachment to EVT-2
l.'Capabil-""-2.Dominant Soil
:3.Don :4.Dom :5.T i'ac-:6. Estimate Soil Losses for Selected Cropping Systems
LO
Ul
ity Class
and
Subclass
I
He
I2s
IIw
lie
llle
Ills
IIlw
JIIc
IVe
IVs
IVw
IVc
Vc
Vw
Vs
Vc
Vie
VI w
Vis
Vic
Vile
VIIs
VIIw
Vile
vine
VIIls
VJIlw
VI13c
Onyx sil (I)
Walla Walla sil, 3-8% (I)
Scooteney 1, 0-2% (I)
None
Walla Walla sil, 0-87.
Walla Walla sil, 8-30%
Condon sil, 1-7%
None
Ritzville sil, 0-8%
Shano sil, 5-30%
Scooteney kl, 0-5% (I)
None
None
None
None
None
None
Walla Walla sil, 35-652
None
Stanfield sil, 0-6%
None
CTjincy fs, 0-10%
Lickskillet vstl, 7-40%
None
None
Dune land JL/
Riverwash 2j
None
None
Slope
Length
(ft)
600
600
400
600
400
800
500
500
300
500
400
300
200
300
NA
C*
iO
Slope
(%)
1
5
1
5
12
3
5
10
3
50
^
M
25
15
1
tor
Tons
c
5
2
C
r
2
5
5
2
5
4
5
1
5
\Tieat ,
fallow
per acre
i^
10
5
4
10
50
Row cro;
Grain,
Hay
or year
2
5
2
Hay
Pasture
1
2
1
2_
Range -
Good
0.3
0.5
0.3
0.3
0.3
0.5
0
0 3/
0.5
NA3/
Range- J
Poor \
•
•
•
•
•
1 :
1.5 :
1 :
:
i .
J. •
1
J
•
•
•
:
•
•
1.5 :
•
o 1
•
0 3/ |
1.5 :
•
•
•
•
NA3/ :
•
*
*
•
•
•
I/ Tons of soil loss is
5T ~2/ Tons of soil loss is 70T 3_/ This does not show soil loss due to wind
erosion which may be severe.
-------
STATE WASHINGTON
LRA 9
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1 . Capabil-
ity Class
and
Subclass
I
Ho
Us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VI I w
VIIc
VHIe
VII I 3
VlUw
VIHc
2. Dominant Soil
NA
Athena sil, 3-7%
NA
Caldwell sil
Mondovi sil
Palouse sil, 7-25%
Kaschmit sic, 0-7%
Latah sil
NA
Athena sil, 25-40%
NA
NA
NA
NA
Seraiahmoo muck
NA
NA
Athena sil, 40-55%
Emdent sil
Hesseltine stsil, 0-20%
NA
Waha sil, 45-60C
Gwin rosil, 30-60%
NA
NA
Gwin stl, 50-80% I/
Rock outcrop
Riverwash 2J
NA
3 .Dora :
Slope
Length
(ft)
400
800
800
300
400
800
300
100(
200
IOOQ
700
400
400
100
300
100
4.Dom
%
Slope
(%)
5
1
1
12
6
2
30
0.5
47
1
8
50
45
60
60
3
5.T fac-
tor
Tons
5
5
5
5
4
5
5
5
5
5
2
3
1
1
0
0
G. Estimate Soil Losses for Selected Cropping Systems
WHEAT
Fallow
per acre
4
1
1
14
4
1
25
NA
65
NA
NA
NA
NA
WHEAT,
Peas or
Lentils
er year
3
1
1
6
NA
1
11
NA
18
NA
NA
NA
NA
WHEAT
Contin.
2
1
1
2
NA
1
6
NA
12
NA
NA
NA
NA
WHEAT 4Yr
ALFALFA
Grass 4Y*
1
0.5
0.5
1.5
NA
0.5
4
NA
8
NA
NA
NA
NA
RA
. Cover
GOOD
.5
0
0
1
1
0
1
0
2
0
0.5
1
1
GELAND
Cover
POOR
2
1
1
2
2
1
3
0
4
0.5
1.5
2
2
U)
Ln
00
I/ Tons of soil loss is IT
2/ Tons of soil loss is 60T
-------
STATE OREflftft
LRA in
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
US DA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
VTe
VI w
Vis
Vic
Vile
VIIs
VIIw
VI Ic
Vile
VIIs
VIIIw
Vlllc
2. Dominant Soil
Powder sil (I)
Madras si, 3-7% (I)
Deschutes si, 6-30%(I)
Wingville sil (I)
Newell 1, 0-2% (I)
Tub grcl, 1-12%
Deskamp Is, 0-3% (I)
Baldock sil (I)
None
Ladd sil, 7-12% (I)
Halfway c
Camascreek 1
None
None
Caraascreek 1, very wet
None
None
Simas ksicl, 10-35%
None
Gem stcl, 7-20%
None
Nagle sil, 35-65%
Simas vstcl, 35-70Z
Kone
None
Badlands I/
Rockland 2/
None
None
3 . Dora : 4 . Don :
Slope : %
Length : Slope
(ft) (%)
800 : 1
500: 5
400: 2
100Q 1
500 : 1
800: 7
400 : 2
120Q 1
;
500 : 10
800 : 1
300; 1
J
•
NA 1 0
•
*
.
120Q 25
.
200 \ 10
•
1200 50
1200 60
.
1200 50
AGO ! 15
•
*
*
5.T Fac-
tor
Tons
5
2
2
5
5
2
2
5
5
5
5
5
2
2
3
2
1
6. Estimate Soil Losses for Selected Cropping Systems
Row Cro
Hay , an
Grain
per acre
2
4
2
2
3
2
2
8
5 Hay,
d Pastu
er year
1
1
^
1
1
1
i
3
0.1
Wheat
re Fallo
8
Range
v GOOD
0.3
0
0
0.5
0.3
0.5
0.5
Range
POOR
1
0
0
1.5
1
1.5
1.5
U)
t_n
I/ Tons of soil loss is 5DT
2J Tons of soil loss is IT
-------
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
US DA - SCS
Attachment to EVT-2
OJ
ON
O
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
Hie
Ills
JIIw
IIIc
IVe
IVs
IVw
IVc
Va
Vw
Vs
Vc
Vie
VI w
Vis
Vic
Vile
Vlls
VI I w
VIIc
VIIIo
VIIIs
VII2w
VIIIc
2. Dominant Soil :3.Dom
:Slcpe
:Length
(ft)
Power sil, 0-2% (I) = 1500
Portneuf sil, 2-4% (I): 900
Turbyfill si, 0-2% (I): 600
Draper 1, 0-1% (I) : NA
Portneuf sil, 0-2% (I) : 1000
Minidoka sil, 4-8% (I) : 200
Bannock 1, 0-2% (I) : 1000
Moulton fsl, 0-1% (I) : Nft
Neeley sil, 0-4% (I) : 300
Pancheri sil, 8-12% (I): 200
Wardboro grsl, 0-2% (I): 200
Bramwell sil, mod sal, 0-2% (I) 200
Pancheri sil, 0-2% : 400
NA :
LaJara si, 0-2% : NPT
NA :
NA :
Minidoka sil, 0-30% : 100
Baldock sil st sal-alk,0-l% : tlfr
Trevino rosil, 4-8% : 100
Matheson si, 0-2% : 200
Quincy s, 0-30% : 100
Trevino vstsil, 0-30% : 50
Wardboro undif ferentiatedO-lZ Wft
Turbyfill fsl, 0-2% : 300
Gullied Land, undiff. _!/ : 20
Rockland, undiff. 2/ : 50
March, undiff. 3/ : [\|)V
None •
4 . Dom
%
Slope
te>
i
3
1
0
1
6
1
0
2
10
1
1
1
0
13
0
6
1
5
8
0
1
30
15
0
5.T Fac-
tor
Tons
5
5
5
5
5
2
2
3
5
5
1
5
5
3
2
5
1
4
5
1
1
5
1
1
1
6. Estimate Soil Losses for Selected Cropping Systems
rrigated
Row
Crops
per acre
2
7
4
0
2
•j
2
0
3
15
Irrigate
Row Crop
GrainS. Ha
or year
1
6
3
0
1
6
1
0
2
11
0
0
Irrig.
Hay &
Pasture
0
1
0
0
0
1
0
0
1
3
0
0
Range-
land ;
Rood
cover
NA
NA
0
0
2
0
0
0
0 (wind
0
0
0
langeland
Poor
cover
NA
NA
1
0
10
0
3
1
0(wind
3
0
1
I/ Tons of soil loss is SOT
21 Tons of soil loss is 5T
3/ Tons of soil loss is OT
-------
STATE Idaho
LRA 12
Form IW. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-/2
1. Capabil-
ity Class
and
Subclass
I
He
Us
IIw
He
Ilia
Ills
11 Iw
iric
IVe
IVs
IVw
IVc
Ve
V*
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VI Iw
VIIc
Vllle
VIIIs
VIIIw
VIIIc
2. Dominant Soil
NA
NA
NA
NA
NA
Berniceton 1, 2-4% (I)
Bemiceton l.raod deep,0-2%(I
Fcxcrs^k 1, 0-2% (I)
Berniceton 1, 0-2% (I)
Pattee si, nod deep,8-12%(I)
Bart 1, 0-2% (I)
Furniss si|.cl,0-2% (I)
NA
NA
Fury si^cl (I)
NA
NA
Bartonflat grl, 8-12%
Borah grsl, flooded, 0-2%
Bart 1, shallow 0-8%
NA
Typic Argixerolls, stony
Xerollic Calciorthoids, cobb
NA
NA
NA
Rockland I/
NA
NA
3. Dora
Slope
Length
600
1000
200
1000
200
600
200
100
100
200
100
y 100
100
4.Dom
%
Slope
<*>
3
1
1
1
10
1
1
0
10
1
4
AO
2
60
5.T Fac-
tor
Tons
4
3
3
4
3
3
4
5
1
^
2
2
1
1
6. Estimate Soil Losses 1'or Selected Cropping Systems
rrigated
Inall grai
iay&.Pastur
per acre
2
1
1
3
1
Irrigate
iHay &
?. Pasture
er year
1
0
0
0
1
0
0
0
Range-
land
good
cfwp r
1
0
0
2
0
Range lanr
Poor
cover
5
2
3
30
2
\
OJ
ON
I/ Tons of soil loss is 20T.
-------
STATE IDAHO
LRA 13
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
lie
IIw
lie
Ille
Ills
IIIw
JIIc
IVe
IVs
IVw
IVc
ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VI lw
VIIc
Vllle
VIIIs
VI IIw
VIlIc
2. Dominant Soil
NA
NA
NA
NA
Blackfoot sil, drained, 0-27,(
Bancroft sil, 8rl27,
Driggs grl, 0-27. (I)
Zufelt sil, 'drained, 2-47. (I)
Tetonia sil, 4-87.
Ririe sil, 4-127.
Bennock 1, 0-47.
Bear Lake sicl, 0-2%
Alex cl, deep 0-47.
NA
Furnis cl
NA
NA
Sessions sil, 0-30%
NA 4-87.
Eaglecona 1 - Rock outcrops
Enochville sil, drained, 0-27.
Highams stsil, 30-60%
Swanner vstl, 30-60%
NA
NA
NA
Rockland I/
Marsh 2/
NA -
3 .Dora
Slope
Length
(ft)
) 600
300
1000
300
400
200
500
200
200
*
-------
STATE CALIFORNIA
LRA
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment Co EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVc
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
VI 3
Vic
Vile
VII3
VI I w
VIIc
Villa
VIIIs
VIIIw
VIIIc
2. Dominant Soil
Sorrento cl, 0-2% (I)
Pajaro 1, 2-9% (I)
Clear Lake, c, drained (.1)
Pacheco Sicl (.!)
NA
Antioch cl, 2-5% (1)
Metz els, 0-9% (I)
Sunnyvale sicl Cl)
NA
Diablo c, 15-30% (I)
Oceano s, 0-9% (I)
Pescadero c (I)
NA
NA
NA
NA
NA
Tierra si, 15-30%
NA
Baywood s, 15-30%
NA
Chamise grl, 30-50%
Antioch Is, 30-50%
NA
NA
Gullied land !_/
Maymen 1, 50-85%
Alviso sic
NA
3.Dom
Slope
Length
at)
1200
1000
NA
NA
—
750
1200
NA
—
650
900
NA
—
—
—
—
—
600
—
600
—
400
450
—
—
150
350
NA
—
4. Deo : 5.T Fac-
% : tor
Slope :
<%) Tons
1 :5
2 :5
0 :4
0 :5
— . —
3 J2
1 -5
0 :3
— . —
20 :3
7 :5
0 :5
• ___
•
— w * — r -.
•
™~™ • — —
20 :2
*
•>~_ • » —
20 :3
* — •.
35 :2
35 :3
__ • ~*™
;
60 :1
55 :1
0 :3
— : —
6. Estimate Soil Losses for Selected Cropping Systems
Img
Row
Crops
per acre
0.5
—
0.5
0.25
—
0.5
0.5
0.25
--
--
1.0
0.25
—
—
—
—
—
—
—
—
—
—
—
— —
— —
—
—
— —
—
Irrig
Field
Crops
cr year
0.5
0.5
0.5
0.25 -
--
0.5
0.5
0.25
—
0.75
1.0
0.25
—
—
—
—
—
—
—
—
™™
—
—
— —
—
—
— —
— —
—
irny*
Orchard
0.5
0.5
0.25
—
NA
0.5
0.5
— —
1.0
1.0
—
—
— —
— —
—
— —
—
—
—
— —
— —
—
— —
—
irny
Pasture
and
Kay
0.5
•0.5
0.5
0.5
—
0.75
0.5
0.25
0.75
1.25
0.5
—
—
—
—
—
—
— —
— —
— —
—
~—
— *~
— —
—
— —
—•~
—
it trig
Grain
—
0.5
0.5
0.5
—
0.75
0'.5
0.5
~~~
1.0
—
0.5
—
—
— —
— —
— _
__
— —
— —
~"~
—
~" —
—
Kinge
Average
—
—
—
—
—
0.75
0.5
—
1.5
2.0
0.75
"~~~
— —
~*~
*"•"
——
2.0
^^
3.5
^"~
2.0
3f\
.0
^—
—
!_/ Tons of soil loss is 75T 27 Tons of soil loss is 2T
"* Only shallow rooted or water tolerant trees, eg. Poor (?)
3/ Tons of soil loss is 0 T
-------
STATE California
LRA
15
(All Dryland)
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
IIs
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IV3
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VI I w
VIIc
vine
VIIIs
VIIIw
VIIIc
2. Dominant Soil
NA
NA
NA
NA
NA
Linne sicl, 9-15%
Cajon fs, 0-2%
Clear Lake c
Agueda 1, 0-2%
Linne sicl, 15-30%
Maxwell c
Pescadero c
Docas sil, 0-2%
NA
NA
NA
NA
Laughlin 1, 30-50X
NA
Arnold Is, 15-30%
Panhill 1, 0-2%
Vallecitos rol, 50-75%
Montara rosicl, 30-50%
Alluvial Land
NA
Badlands I/
Maymen 1, 50-85% 2/
Alviso sic 3/
NA
3. Dora
Slope
Length
(ft)
NA
800
1200
NA
1000
700
NA
NA
1200
600
500
1000
500
500
800
250
350
NA
4 .Don
Slope
NA
12
1
0
1
20
0
0
AO
20
1
50
AO
2
75
70
0
5.T Fac-
tor
Tons
NA
3
5
A
5
3
A
A
5
2
A
5
2
1
5
1
1
2
6. Estimate Soil Losses for Selected Cropping Systems
Dryland
Grain
per acre
NA
0.75
0.75
0.25
0.5
1.0
0.25
0.25
0.5
Dryland
Pastures.
er year
NA
1.25
1.0
0.25
0.5
1.75
0.5
0.5
0.75
Range
Good
NA
0.75
0.75
0.25
0.5
1.0
0.25
0.25
0.5
1.75
2.0
0.25
1.75
1.75
0.5
Range
Poor
NA
1.5
1.5
0.5
0.5
2.25
0.5
0.5
0.75
3.25
3.75
0.5
3.50
3.25
0.75
y Tons of soil loss is 75T 2/ Tons of soil loss is 2T 3/ Tons of soil loss is OT
-------
STATE California
LRA
16
Form 1W. dominant So±^, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
IIs
IIw
He
IJIe
Ills
IIlw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
Vlls
VIIw
Vile
Vllle
VlIIs
VlIIw
VlIIc
2. Dominant Soil
NA
NA
NA
Columbia 1 (I)
NA
NA
NA
Ryde cl (I)
NA
Piper fsl (I)
NA
Sacramento c (I)
NA
NA
NA
NA
NA
NA
Tamba mucky c (I)
NA
NA
NA
NA
NA
NA
NA
NA.
Tidal friarSh
NA
3 .Dom :
Slope
Length
(ft)
0
NA
NA
NA
NA
NA
4 . Dorr. :
%
Slope
<*>
0
0
0
0
0
0
5.T Fac-
tor
Tons
5
5
5
5
5
5
6. Estima
Irrigated
Row
Crops
per acre
0.5
0.5
1.5
0.5
0.75
e Soil Losses for Selected Cropping Systems
Irrigate*:
Pasture
& Hay
er year
0.25
0.25
0.5
0.25
0.5
1 Irrigat
Field
Crops
0.5
0.5
0.75
0.5
' 0.75
2d
OJ
c^
Ol
-------
STATE California
LRA
17
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
Ille
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIIs
VIIw
VIIc
VI lie
VIIIs
VI IIw
VIIIc
2.Doninant Soil
Panoche 1, 0-2% (I)
Snelling fsl, 2-9% (I)
Chino sil alkali, 0-2% (I)
Columbia 1, wet (I)
NA
Diablo c, 9-15% (I)
Fresno 1 (I)
Stockton c (I)
NA
Redding grl, 2-5% (I)
San Joaquin si, (I)
Rossi cl (I)
NA
NA
NA
NA
NA
Kettleman 1, 15-30%
Or land fsl, wet
Auburn rol, 30-50%
San Emigdio fsl, 0-2%
Altamont c, 50-75%
Araador 1, 9-30%
Alluvial Land
NA
NA
Tailings I/
Riverwash 2l
NA
3.Dom
Slope
Length
1200
950
NA
NA
850
1200
1200
900
NA
NA
550
900
425
1200
400
600
1200
15
1200
4.Dom
%
Slope
(.%)
1
3
0
0
12
0
0
4
0
0
25
0
35
1
55
10
2
30
2
5.T Fac-
tor
Tons
5
5
4
5
3
3
4
2
2
5
2
5
1
5
2
1
5
5
5
6. Estimate Soil Losses for Selected Cropping Systems^
Irrigated
Row Crops
per acre
0.5
0.5
0.5
0.5
0.5
0.5
0.5
Irrigate;
Field
Crops
er year
0.5
0.75
0.5
0.5
0.75
0.5
0.5
0.5
0.5
Irrig.
'astures
& Hay
0.5
0.5
0.5
0.5
1.0
0.5
0.5
0.75
0.5
0.5
0.75
Range
Good
1.5
0.75
1.75
1.0
1.75
1.0
1.5
Range
Poor
3.0
2.0
3.0
2.5
3.5
2.25
2.5
CO
OS
ON
I/ Tons of soil loss is IT
2/ Tons of soil loss is SOT
-------
STATE
LRA
19
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
ity Class:
and :
Subclass •
3
JIc
Us
llw
Uc
IIJc
Ills
IIIc
IVc-
IVs
IVc
Va
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VI Is
VI Iw
Vile
VHIe
VIIls
VIIIw
VIIIc
2. Dominant Soil
Hanford fsl, 0-2% (I)
Hanford fsl, 2-9% (I)
Garretson gl, 0-2% (I)
Camarillo 1 (I)
NA
Placentia fsl, 5-9% (I)
Tujunga s, 0-9% (I)
Willows c (I)
NA
Fallbrook fsl, 9-15% (I)
Tujunga gs , 0-9% (I)
Domino sil, saline-alkaline(I
NA
NA
NA
NA
NA
Linno sicl, 30-50%
: Tujunga Is, channeled, 0-15%
. Cajalco rocky vfsl, 9-30%
NA
. Vista cosl, 50-75% (I)
: Cieneba rocky si, 30-75% (I)
: Dello s, poorly drained, 0-2%
: NA
: Badlands I/
: Gaviota v ro fsl, 50-95% 2f
: Riverwash 3/
: NA
3.1>om
Slope
Length
<*t)
1200
800
950
NA
750
950
NA
800
1200
NA
700
500
500
600
550
600
250
325
4 . Dom
el
,0
Slope
(%)
1
5
1
0
7
2
0
12
2
0
35
5
12
50
40
1
75
60
5.T Fac-
tor
Tons
5
5
5
5
2
5
3
2
5
2
2
5
2
2
1
5
1
1
6. Estimate Soil Losses for Selected Cropping Systems
Irrig.
Row
Crops
per acre
0.25
Q.5
0.25
0.25
0.75
0.5
0.25
0.5
NA
Irrig
Field
Crops
er year
0.25
0.5
0.25
0.25
0.75
0.25
0.5
0.5
Citrus
0.25
0.25
0.25
0.25
0.5
0.5
0.75
0.5
1.75
1.25
1.75
2.0
Avocados
0.25
0.25
0.25
0.25
0.5
0.5
0.75
0.5
1.5
1.25
1.75
2.0
trrig. I
"asture & ;
Hay
0.25
0.5
0.25
0.25
0.75
0.5
0.25
0.5
0.25
lLange
Average
1.75
1.5
1.5
0.75
loss
I/ Tons of soil loss is 75 T. 2/ Tons of soil loss is 2 T. 3/ Tons of soil/ is 50 T.
-------
STATE California
LRA 20
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
He
Us
IIw
He
IlJe
Ills
II Iw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
V3w
Vis
Vic
Vile
VIIs
VI I w
VIIc
VI lie
VIIIs
VIIJw
VI He
2. Dominant Soil
NA
NA
NA
NA
NA
Bull Trail si, 5-9%
Chino fsl
Foster fsl, slightly wet
Reiff fsl, 0-2%
Calpine si, 2-9%
Mottsville Icos, 0-9%
Foster fsl, wet
Greenfield 1, 0-2%
NA
NA
NA
NA
Holland fsl, 30-50%
Bishop 1
La Posta Icos, 2-9%
NA
Bancas stl, 50-75%
Cieneba hosl, 50-75%
Cortina kls, wet, 0-9%
NA
Badland I/
Gaviota rsl, 30-85% 2j
Riverwash 31
NA
S.Dom
Slope
Length
(ft)
750
NA
NA
1200
900
1200
NA
1200
700
NA
700
500
350
600
250
350
J.200
4. Dora
%
Slope
<7o)
6
0
0
1
o
3
0
j
30
0
7
50
50
2
75
70
2
5.T Fac-
tor
Tons
4
5
c
c
5
5
c
5
o
5
3
2
1
5
1
1
5
C. Estimate Soil Losses for Selected Cropping Systems
Dryland
Grain
per acre
0.5
0.25
0.25
0.25
0.5
0.25
0.25
0.25
Dryland
Pasture
& Hay
er year
0.75
0.25
0.25
0.25
0.75
0.5
0.25
0.25
Range
Good
0.75
0.25
0.25
0.25
0.75
0.5
0.25
0.25
1.0
0.25
0.5
2.25
2.5
0.5
Range
Poor
1.25
0.5
6.5
0.5
1.5
1.0
0.5
0.5
2.0
0.5
1.25
3.75
4.5
1.0
OJ
ON
OO
I/ Tons of soil loss is 75T 2/ Tons of soil loss is 2T 3/ Tons of soil loss is SOT
-------
STATE CALIFORNIA
LRA 21
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
-L • Ud^CllJ _LJ_~ » JL« f V/1-W.L 11 ai.) 1* tj VJJ. J.
ity Class:
and :
Subclass ;
I :NA
He :Bidwell 1, 2-5% (I)
Us :Standish fsl (I)
IIw :Stillwater cl (I)
He :Bicivjell 1, 0-2% (I)
Hie : Fredrickson 1, 2-5% (I)
Ills :Modoc si, 0-2% (I)
IIlw : Gazella 1 (I)
IIIc : Greenhorn 1 (I)
IVa : Kuck sicl, 9-15% (I)
IVs :Bieber 1, 0-2% (I)
IVw : Beckwourth Icos (I)
IVc : Simpson 1, 0-2% (I)
Ve : NA
Vv/ : Greenhorn si, wet (I)
Vs :NA
Vc :NA
Vie : Windy 1, 15-30%
VIw : Pasquetti mucky c
Vis : Bieber stl, 5-9%
VI c * ^A-
Vi ic : Duzel 1, 30-50%
Vjis ': Madeline kl, 15-30%
VI Iw : Alluvial land
VIIc : NA
VIHe : NA
VIII s : Rockland I/
VI IIw : Riverwash Z/ 1200
VII I c : NA
O « i-AJJll . •* . h/vm
Slope : %
Length :Slope
(ft) (%)
.
1200; 3
1200! 1
NA : 0
1200: 1
900; 3
800; 1
1200 i 1
900. 1
7501 10
1000 1 1
1000 ! 1
1200! 1
.
1000! i
.
600 ! 20
NA ! 0
800 j 5
500 ! 35
550! 25
600 ! 2
:
20oi 65
1200:
:
•j , i JT a t_-
tor
Tons
5
5
5
5
5
A
5
5
5
3
A
5
A
A
3
2
3
3
5
1
5
o. ^o v_ J. ll)a t *; o u J ± Jjuaaea xvji ot: a. v-\~ u»iu ^iwpp^Jij, uy^i_i=iiio
Pas ture
Irrig.
per acre
0.5
0.5
0.25
0.5
0.5
0.75
0.5
0.5
1.0
0.25
0.75
0.5
0.25
0.5
Range
Good
er year
0.25
0.5
0.75
2.0
1.5
1.0
Range
Poor
0.5
0.75
1.5
3.5
2.5
2.25
hay Trr.
0.25
0.25
0.25
0.25
0.25
0.5
0.25
0.25
0.5
0.25
0.25
0.25
0.25
0.25
Woodlana
0.5
It Tons of soil loss is 2T.
21 Tons of -soil loss is SOT.
-------
STATE CALIFORNIA
I.RA
22
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- :2. Dominant Soil
ity Class :
and :
Subclass ;
I :NA
He :Aikcn 1, 2-9% (I)
Us -.Tournquist grl, 0-27. (I)
IIw :Qak Glen si, wet (I)
lie :NA
I lie :Cohasset 1, 9-157. (I)
Ills :Montague c (I)
Illw :James Canyon si (I)
IIIc rMassack 1 (I)
IVe :Aiken 1, 15-307. (I)
IVs :Bieber si, 0-27. (I)
IVw :0phir grl, 0-27. (I)
JVc :NA
Ve :NA.
Vw : Welch 1 (I)
Vs :NA
Vc :NA
Vie : Shaver cosl, 30-507.
VI w rFoster 1, wet-climatic variant
Vis :Windy stsl, 15-30%
Vic :NA
Vile :Auberry rol, 50-75%
VII s : Josephine rol, 50-757.
VI 3 w lAlluvial land, sandy
VIIc :Bishop 1, drained
VI lie Badlands JL/
VIIIs :Iron Mountain vstl, 30-757. 2/
VIIlw tRiverwash 3/ ~
VIIIc :NA
3 .Dom
Slope
Length
(ft)
900
1000
1200
900
1200
1200
1000
750
1200
1200
1000
550
1000
700
500
400
1000
1200
1250
500
1200
4 .Dom
cf
JO
Slope
<%>
5
1
1
10
1
1
1
20
1
1
1
45
1
20
50
60
5
1
75
60
1
5.T Fac-
tor
Tons
3
3
5
2
5
5
5
3
2
4
5
2
5
2
2
2
5
5
1
1
5
6. Estimate Soil Losses for Selected Cropping Systems
; Irrigatec
: Pasture
Orchard : &
: Hay
per acre per year
,5
.25
.25
,5
.25
.25
.75
.5
.25
1.0
.25
.25
.25
.5
.5
.25
.5
.75
.25
I
Woodland
.5
1.0
1.25
2.5
3.0
I/ Tons of soil loss is 75T 3/ Tons of soil loss is SOT
2/ Tons of soil loss is 2T
-------
STATE OREGON
LRA
23
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2.
l.Capabil- '• 2. Dominant Soil
ity Class:
and :
Subclass ;
•
I 'Jett sil (I)
Ho JBidwcll 1, 2-4% (I)
Us :Virtue sil, 0-27. (I)
IIw :Wingville sil (I)
lie :Harriman 1, 0-2% (I)
II le '.Fordney Is, 0-2% (I)
Ills :Umapine sil, 0-37, (I)
IIIw :Baldock sil, 0-2% (I)
II Ic :Mone
IVe rBonnick Is, 0-27., (I)
IVs :Hager 1, alkali var, 1-15%(I)
IVw :Hovey sicl (I)
IVc :Fort Rock 1 (I)
Ve :None
Vw : Crump muck
Vs :None
Vc : None
Vie : Lookout stl, 2-457.
VIw : Boulder Lake c
Vis :Horning Ifs, 5-257.
Vic :Nevador fsl, 0-2%
Vile :Lyonman si, 30-50%
Vila :Hart vksil, 0-15%
VI I w :None
VI Ic -.None
Vllle ;Badlands 21
VIIIs :Rock Outcrop
VI IIw -.Playa
VI lie :
3 .Dora
Slope
Length
(ft")
800
1000
600
800
500
300
600
1200
400
600
NA
1200
NA
800
NA
400
1200
800
800
1200
400
4. Dora
a
IK
Slope
M '
1
3
2
1
1
1
1
1
1
3
0
1
0
25
0
12
1
40
6
50
200
0
5.T Fac-
tor
Tons
5
2
2
5
4
4
5
C
2
r\
3
2
5
2
3
5
2
2
1
6. Estimate Soil Losses for Selected Cropping Systems
Row Crop,
Grain
Hay
per acre
O
3
2
0
o
2 I/
5
5
2 I/
0
2
Hay,
Pasture
er year
1
1
1
^
1
1 I/
2
2
i y
i
0
i
Range
Good
0
• 3
0
.3 I/
0
.5
.3
Range
Poor
0
1.5
0
1 I/
.3
1.5
1
OJ
JY This does not show soil loss due to wind erosion which may be severe.
2/ Tons of soil loss is 501.
-------
STATE IDAHO
25
LRA
?orm 1W. Dominant Soil, L, S, and T Factos
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- : 2.. Dominant Soil :
ity Class:
and ;
Subclass •
I : NA
lie :NA
Us : MA
llw : Fluvaqtsent. Haploxeroll sal.Q
lie : Paniogue 1, deep, 0-27, (I)
lllo :Aridic Calcic Argixerolls
Ills : Pocker sil, 0-2% (I)
lllw : Welch 1 (I)
Illc : Fluventic Haploxerolls (I)
IVe : Justesen 1, 4-127.
IVs -.Earaes grl, 2-8%
IVv/ : Camascreek 1, 0-27,
IVc :NA
Vc : NA
Vw : Aquic Cryoborolls
Vs :NA
Vc : NA
V3c :Toeja gl, 15-307.
Vlw : Camascreek 1, wet, 0-27.
Vis : Typic Argixerolls, rocky
V2c rXerollic Camborthid
Vile :Argic Pachic Cryoborolls, st
VXIs :Lithic Xerollic Haplargids vs
VIlw :Himboldt sicl, str. saline
VIIc ;Rad sil, 2-87.
Vllle :None
VIIIs : Rockland \J
VI llw :Playas ^/
VIIIc :None
S.DORI :
Slop*
Length
(ft)
) 400
900
300
600
800
400
300
300
300
100
400
100
100
300
100
: 200
KfV
500
50
HA-
4 .Donn
%
Slope
(%*) '
1
1
9
1
1
1
7
4
1
1
20
3
12
1
45
8
0
4
15
0
S.T Fac-
tor
Tons
4
4
4
2
3
4
4
3
5
5
3
5
2
3
2
1
3
3
1
5
6. Estima-te Soil Losses for Selected Cropping Systems
Grain-
fallow
per acre-
6
5
3
Range -
land,
Good
cover
er year
0
0
0
1
0
0
0
2
0
1
1
2
1
0
0
Range-
land,
Poor
cove,r
0
4
0
3
1
0
0
15
0
3
2
20
2
0
3
rrigatei
Hay
&
Grain
0
1
0
0
I/Tons of soil loss Is 5T. 2/ Tons of soil loss is OT.
-------
STATE
LIU
UTAH
28
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-.2
l.Capabil- :2. Dominant Soil r3.Dom
ity Class r rSlope
and : : Length
Subclass ; .
(ft)
I Parleys 1, 0-3% (I): 1200
He rGenola 1, 1-2% (I): 1200
Us rEscalante fsl, 1-2% (I)r 1200
IIw rGreenson 1, 0-3% (I): 1200
He rGenola 1, 0-1% (I): 1200
Hie rKearns sil, 3-6% r 1000
Ills :Taylorsville sicl, 1-3% (I)r 1000
IIIw rLogan sicl (I)r 1200
IIIc rTirapanogos si, 1-3% : 1200
IVe rThiokol sil, 1-6% ; 1000
IVs rBingham 1, 1-6% ; 600
IVw rAirport sil (I); 1000
IVc rHansel sil, 0-1% : 1200
Ve rNA :
Vw :Rosh« Springs sil (I); 600
Vs :NA
Vc :NA :
Vie rMiddle sil, 10-30% ; 400
Vlw :Chipman sicl (I): 1000
Vis :Abela gl, 10-20% ; 400
Vic :NA :
Vile ;Penoyer sil, 1-3% ; 1000
VIls :Decca 1, 1-3% : 1000
VIIw rLeland fsl : 1000
Vile -.Penoyer sil, 0-1% : 1200
VHIe :NA :
VIIIs :Duneland I/ (Poor Range) • 50
VIIIw :Saltair sicl(Poor Range) 21 : 1000
VIIIc :NA :
4.Dom
Slope
(%)
2
2
2
3
1
6
2
2
2
6
6
2
1
2
20
2
10
3
3
1
1
20
5.T Fac-
tor
Tons
4
5
3
2
5
3
2
2
A
3
1
2
3
2
2
2
2
5
1
2
5
5
1
6. Estimate Soil Losses for Selected Cropping Systems
WHEAT : ROTATED :
FALLOW rCROPLANDrPASTURE
(Dry Crop)IRRIGATE»
per acre per year
10.3
4.1
11 .-9
5.2
3.6
1.7
2.3
1.7
1.7
1.5
1.3
9
0.3
0.1
GOOD
RANGE
1
1.2
1.2
1.2
1.5
1.0
3.0
3.0
1.0
3.0
POOR
RANGE
1.5
1.8
1.8
1.8
2.0
1.5
4.6
4.5
1.0
4.6
UJ
^J
LO
I/ Tons soil loss 5T
2/ Tons soil loss 1.0
-------
STATE CALIFORNIA
LKA 30
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- :2. Dominant Soil
ity Class =
and :
Subclass ;
I : Rosamond sicl (I)
lie :Kesperia fsl, 2-5% (I)
Us : Mohave si (I)
IIw : Calico fsl (I)
He : NA
IIIc : Sunrise si, 2-5% (I)
Ills ; Rosamond sicl, alkali (I)
IIIw ;NA
IIIc :NA
IVe :Cajon fs, 2-9% (I)
IVs :Brazito s (I)
IV* : Imperial sic (I)
IVc : NA
Vc : NA
Vw :NA
Vs : NA
Vc :NA
VI o :NA
VIw :NA
Vis :NA
Vic :NA
Vile 2/:Calvista si, 9-15%
Viis :Bittersprings grl, 2-9%
VIIw :Soboga Is, wet
VIIc : Rosamond sicl
Vllle : Badlands IJ
VlIIs :Daggett stls, 0-9% 2_/
VIIIw :Playa 3/
VIIIc :NA
3. Dora
Slope
Length
(ft)
NA
1200
1200
NA
800
NA
1000
1200
NA
500
1000
1000
NA
250
1200
NA
4. Dem
or
JO
Slope
(fc)
0
2
1
0
5
0
2
1
0
10
7
2
0
75
1
0
5.T fac-
tor
Tons
5
5
3
5
1
5
5
5
2
1
5
5
5
6. Estimate Soil Losses for Selected Cropping Systems
Alfalfa
per acre p
0.25
0.25
0.25
0.25
0.5
0.25
0.75
0.5
0.25
Irrig.
Row
Crop
er year
0.25
0.25
0.25
0.25
0.75
0.5
0.25
Irrig.
Small
Grain
0.25
0.25
0.25
0.25
0.5
0.25
0.75
0.5
0.25
Range
2.5
2.5
3.0
1.0
u>
~-J
-p-
I/ Tons of so.il loss is 75 T. 2/ Tons of soil loss is 0.5 T.
3/ Tons of soil loss is 0 T.
-------
STATE CALIFORNIA
LRA 31
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to L'.rosion for
Selected Cropping Systems
USDA - SCS
Attachment to EV7-2
l.Capabil- :2. Dominant Soil
ity Class :
and :
Subclass ;
I :Indio sil (I)
He :NA
Us : Oilman fsl (I)
IIw : Glenbar sicl, wet (I)
lie :NA
Hie :NA
Ills :Rositas fs (I)
IIIw : Meloland fsl (I)
IIIc :m
IVe : NA
IVs :Carrizo grs (I)
IVw : Imperial sic (I)
IVe : NA
Ve :NA
Vw :KA
Vs :NA
Vc :NA
Vie :KA
VIw :NA
Vis :NA
Vic :NA.
Vile :NA
VIIs :NA.
VIIw ;NA
VIIc :KA
VIHe :Badland 2/
VIIIs : Rock ^,and 3/
VI IIw rRiverwash 4/
VIIIc : Indio sil 5/
3. Don
Slope
Length
(ft)
NA
1200
1200
1200
NA
1200
NA.
250
300
1200
NA.
4. Don
Slope
(%)
0
1
1
1
0
1
0
75
70
2
0
5.T Fac-
tor
Tons
5
5
5
5
5
5
5
1
1
5
5
6. Estimate Soil Losses for Selected Cropping Systems
Row
Crops
per acre
.25
.25
.25
.5
.25
.5
.25
Alfalfa
er year
.25
.25
.25
.5
.25
.5
.25
Irr.
Field
Crops
.25
.25
.25
.5
.25
.5
.25
Orchard
.25
.25
.25
.5
.25
.5
\J Unless Irrigated, all soils are class VIII 2J Tons of soil loss is 75T 3/ Tons of eoil loss is 2T
4/ Tons of soil loss Is SOT 5_/ Tons of soil loss is IT
-------
STATE
LRA
WYOMING
34
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-,2
l.Capabil- :2. Dominant Soil
ity Class:
and :
Subclass :
1 :Ravola 1, Ext. season (I)
Us :Moffat, 0-3% ^
iiw :NA
lie :Ravola 1 (I)
Hie :Alcova si, 0-3% (I)
Ilia :Crowheart 1, 0-3% (I)
IIIw ;Canninger 1, 0-3% (I)
IIIc :Ryan Park Ifs, 0-3% (I)
IVe :High Park fsl, 3-6%
IVs ;Fluvents, saline (I)
IVw :Canninger 1, 0-3%
IVc ;Havre 1, 0-3%
Vo :NA
Vw ;Typic Fluvaquents
Vs ;NA
Vc :NA
Vie ;Ryan Park Ifs, 3-6%
Viw ;Aquic Ustifluvents
Vis ; Space City Ifs, 6-10%
Vic : Ryan Park Ifs, 0-3%
Viie JBlazon 1, 3-10%
Viis :Shinbara 1, 6-30%
VIIw :TyPic Torrifluvents, flooded
VIIc ;TyPic Torrifluvents
Yllle .Gullied land if
VIIIs ; Rack outcrop
VIIIw : Harsh land
VIIIc :NA
3 .Dom
Slope
Length
(ft)
1200
1200
1200
1200
1200
100
1200
1200
400
100
1000
400
1200
1200
50
1200
200
200
300
1200
40
200
NA
—
4.Doni
Slope
(7«)
1
2
1
2
2
2
2
4
1
2
2
1
2
8
2
8
20
2
1
30
60
0
—
5.T Fac-
tor
Tons
5
3
5
5
3
5
5
5
-
5
5
5
5
5
5
2
1
1
5
—
-
—
—
6. Estimate Soil Losses for Selected Cropping Systenvs
Small
Grain and
Hay or
Pasture
per acre p
.5
2.0
.5
3
3
3
2
2
er year
lay or
Pasture Q
with occi
sional
Establisb
•4
2.0
.4
2.5
2.5
2.5
1.5
1.5
PctragaenS
ay or
Pasture
.3
1.0
.3
2
2
2
.1
Rang a
Good
1
1
1
0
2
1
3
1
2
1
1
1
Range
Eoor
5
3
3
1
10
3
12
3
10
5
3
3
I
LO
—I
I/ Tons of soil loss ls-25 T.
-------
STATE
LRA
WYOMING
34
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- : 2. Dominant Soil
ity Class:
find :
Subclass :
1 :Ravola 1, Ext. season (I)
IIe :NA
Us :Koffat, 0-3Z U)
iiw :KA
lie jRavola 1 (I)
Hie :Alcova si, 0-3Z (I)
Ills :Crowheart 1, 0-3Z (I)
IIIw :Canninger 1, 0-3Z (I)
IIIc :Ryan Park Ifs, 0-3Z (I)
IVe :High Park fsl, 3-6Z
IVs :Fluvents, saline (I)
IVw 'Canninger 1, 0-3Z
IVc : Havre 1, 0-3%
Ve :NA
Vv : Typic Fluvaquents
Vs :«A
Ve :NA
Vie ;Ryan Park Ifs, 3-6Z
VIw :Aquic Ustifluvents
Vis 'Space City Ifs, 6-10Z
Vic ; Ryan Park Ifs, 0-3%
Vile ; Blazon 1, 3-10Z
VHs .Shinbara 1, 6-30Z
VIIw : Typic Torrifluvents, flooded
Viic .Typic Torrifluvents
Villa .Gullied land I/
Tills :Rock outcrop
VIIIw ; Marsh land
VIIIc :NA
3. Don
Slope
Length
(ft)
1200
1200
1200
1200
1200
100
1200
1200
400
100
1000
400
1200
1200
50
1200
200
200
300
1200
40
200
HA
4,Dom
Slope
(%)
1
2
1
2
2
2
2
4
1
2
2
1
4
2
8
2
8
20
2
1
30
60
0
5.T Fac-
tor
Tons
5
^
3
5
5
3
5
5
5
-
5
5
5
5
5
5
2
1
1
5
—
-
—
••
6. Estimate Soil Losses for Selected Cropping Systems
Small
Grain and
Hay or
Pasture
per acre
.5
2.0
.5
3
O
3
2
2
er year
Hay or
Pasture
with occ
sional
Establis
•4
2.0
.4
2.5
2.5
2.5
1.5
1.5
Piiragaen1
ay or
Pasture
f
.3
1.0
.3
2
2
2
1
1
LRansa-
Good
1
1
1
1
0
2
1
3
1
2
1
1
Range
Boor
5
3
3
1
10
3
12
3
-10
5
3
1 3
,
\
t $
Co
I/ Tons of soil loss ±8^25 T.
-------
STATE ARIZONA
LRA 35
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
He
Hie
III3
IIIw
IIIc
IVe
IVs
IVw
IVc
Vc
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
Vlls
VI I w
VIIc
Vine
VIIIs
VIIIw
VIIIc
2. Dominant Soil :3.Dom :4.Dom :5.T Fac-:S. Estimate Soil Losses for Selected Cropping Sys-t-eins
:Slope : % : tor :irrig
:Length : Slope : ^Row
: : : :Crop
(ft) (%) Tons per acre
Clovis sandy loam, 0-1% (I):NA ;0 :3 :0.25
Tours cl, 1-3% (I): 800 :2 :4 :0.4
Jocity scl, sal-alk, 0-1% (I):NA :0 :3 :0.2
Jocity scl, 0-1%, flooded (1):'NA :0 :3 :0.5
Ravada cl, 0-1% (I):1200 :1 :5 :0.2
Jocicy scl, 3-5% (I):600 .4 ,2 .0.8
Navajo c, 0-1% (D:NA .0 .4 .0.4
Navajo c, 0-1%, flooded (I): NA .0 .4 .0.1
Aridic Argiboroll, flra (I). 1200 .2 .3 .NA
Clovis si, 5-10% (I): 600 :7 :2 -.1.2
Navajo c, 0-1%, sal-alk (I): NA :0 :4 :0.6
Perron 1 (I): 300 :2 :2 :NA
Aridic Argiustoll, flrame : 300 :2 ;3 :NA
NA : : : :
NA ; s ! !
NA : : : s
NA : : : :
Moencopi Is, 8-15% : 600 : 10 :0.5 :
Jocity scl, 0-1%, flooded : NA :0 :3 :
Moencopi Is, 0-8% : 400 -6 S0.5 :
Clovis si, 0-1% : NA : 0 :3 :
Moencopi vfls ; 1200 : 40 :0.5 :
Koencopi v rocky Is : 1200 ; 20 .0.5 .
». i ....
NA ... .
Typic Torrifluvent, flmrae : 1200 : 1 :5.0 .
Gullied land I/ : 1200 : 1 -.1 :
Rock Outcrop "" : 1200 : 50 : NA :
NA : : : :
NA : : : :
Irrig
Close
Grown
Crop
er year
O.J.2
0 .2
0.3
0.3
0.1
0.4
0.2
0.3
NA
0.5
0.3
0.2
NA
Range a
POOR ^
2.0
3.0
2C
. J
3.5
4 S
*+ . j
2.0
2.0
2.0
NA
-------
STATE NEW MEXICO
LRA
Form 1W. Dominant Soil, L, S, and T Factors
" and Estimated Tons Soil Lost to Erosion for
3b (New Mexico & Arizona Plateaus & Mesas) Selected Cropping Systems
USDA - SC5
Attachrnent to EVT-2
l.Capabil- : 2. Dominant Soil
ity Class:
and :
Subclass :
I :Penistaja fsl, 0-1% (I)
lie :Fruitland si (I)
Us :San Mateo 1, sli saline (I)
IIw :NA.
lie :NA.
II le :E1 Rancho scl, 3-9%, (I)
Ills :San Maceo 1, nod saline (I)
IIIw :NA
I He :NA.
IVe :NA.
IVs :pureco c (I)
IV-.v :prewitt 1
IVc :NA
Ve :NA.
Vw :NA.
«r_ • »T A
vs :NA
vc :NA
Vie rciovis 1, 5-9%
VIw :werlow 1
Vis :Rudd stl, 3-9%
Vic :Clovis 1, 0-5%
Vile :Prieta grl, 9-25%
VI I s tPersayo sicl, 9-25%
VI I w :NA
VIIc :NA
Vllle badlands I/
VII Is dliverwash 2f
VI IIw :NA
VIIIc :NA.
3. Don
Slope
Length
(ft)
1200
1200
1200
1000
1200
1200
1200
1200
250
750
1200
500
1000
750
1200
4. Dem
at
1°
Slope
«>
1
1
1
4
T
1
0
6
1
6
1
20
15
45
1
5.T fac-
tor
Tons
c
5
5
5
5
5
5
5
5
1
5
1
2
5
5
6. Estimate Soil Losses for Selected Cropping Systems
Range land
Good
Coyer
per acre
.06
3.64
.28
4.59
* 7
24.48
30.96
Range -
land
Poor
Cover
er year
.11
7.23
.42
6.12
1.12
32.64
86.69
Woodland
Corn
Silage
Barley
.44
.44
.73
2.54
.73
1.12
Cont.
Snail
Grain
.04
Hay
Alfalfa
Small
Grain
.11
.11
.18
.63
.18
.28
II Tons of soil loss 10CT
2/ Tons of soil loss 751
-------
STATE NEW MEXICO
37 (San Juan River Vallty,
Wesas, and Plateaus)
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
LO
00
O
1. Capabil-
ity Class
and
Subclass
I
He
ITs
llw
lie
IIlc
Ills
IT Iw
iric
IVe
IVs
IV*
IVc
Ve.
Vw
Vs
*Vc
Vie
Vis
T/T «
Vic
VI Tf>
V J. At?
VI3s
VI I w
VI I c
VHIe
VI Its
VIIIw
VIIIc
2.. Dominant Soil
Doak 1, 0-1% (I)
Fruitland si (I)
Billings sicl, 0-1% (1)
NA
NA
Turley 1. 3-5% (I)
Fruitland 1, saline (I)
NA
NA
Sheppard Is, 2-5% (I)
Sheppard Is, 0-2% (I)
Werlow 1 (I)
NA
NA
NA
NA
NA
Negeesi si, 0-2%
Werlour 1
Navajo cl, saline
San Hateo 1, 0-3%
Farb stl, 5-25%
Deaver c, 0-3%
NA
Doak 1, 1-3%
Badlands I/
Rock land 2/
NA
NA
3 .Dora
Slope
Length
(ft)
1000
1200
1200
1200
1200
500
1200
NA
1200
NA
1200
1200
1000
1200
1200
750
750
4.Dt»m
Slope
(W
1
2
1
4
1
3
1
0
2
0
1
1
20
2
2
30
30
5.T Fac-
tor
Tons
5
5
5
5
5
5
5
5
3
5
5
5
1
5
5
5
1
G. Estimate Soil Losses for Selected Cropping S^s-t-ems
RANGELAND
Cover
GOOD
per acre
0.98
1.22
1.51
3.97
0.84
1.19
0.34
0.17
1.22
0.25
1.72
2.85
82.56
3.53
2.01
NA
NA
Cover
POOR
or year
1.26
1.57
1.94
5.10
.1.12
2.08
0,60
0.30
1.57
0.38
1.94
3.52
92.88
3.97
2.58
NA
NA
Corn Si
Barley
0.73
0.91
1.12
2.95
0.73
1.55
0.44
0.60
—
__
—
""•"'
—
—
:
I/ Tons of soil loss is 100T
2/ Tons of soil loss is 2T
-------
STATE ARIZONA
LRA 39
Form r.Y. Dominant Soil, L, S, and T Factors
and Esti-ated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to
1. Capabil-
ity Class
and
Subclass
I
lie
Us
IIw
lie
Hie
nib-
IIl^v
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
Vlw
Vis
Vic
Vile
VIIs
VHw
Vile
Vllle
VIIIs
VIIIw
VIIIc
2. Dominant Soil :3.Dom :4.Do;a :5.T Fac-
: Slope : % : tor
:Length :Slopo :
(ft) (7») Tons
Wineg si, 0-1% (I) :NA .0 :1
Wineg si, 1-3% (I) :800 .2 .1
Lynx 1, 0-1%, sal-alk (I) :NA JO .3
NA '•• '.
Sponseller gl, 0-2% (I) '.500 '.I !l
Poley grl, 103% (I) .*800 \2 '.2
Springerville c, 0-1% (I) '. NA '.0 '.3
Lynx 1, wet var, 0-1% (I) >A "0 '.3
Clover Springs scl, 0-1% (I)>A '.Q '.3
* • . •
Sponseller gl, 5-10% : 300 :8 :1
Thunderbird grcl, 0-5% (I) j 600 :2 ;2
NA : : :
|J A « » •
i^rv * • •
NA : : :
NA : : :
NA : % :
NA ; ; :
Showlow grcl, 8-15% : 1200 :10 -2
NA : : :
Thunderbird cl, 0-8% : 1000 :4 :1
Gordo si, 2-25% : 1200 .15 :3
Bandera grl, 2-30% .1200 : 20 .0.5
Gaddes grsl, 3-35% .1200 : 20 :0.5
NA ; : :
NA • : :
NA ...
Rock Outcrop .1200 .30 .NA
NA : : :
NA . : :
6. Estimate Soil Losses for Selected Cropping Systems
Irrig
Row
Crop
per acre
0.4
0.8
0.7
0.6
0.3
0.6
0.7
0.6
1.2
0.8
Irrig
Close
Grown
Crop
er year
O.I
0.2
0.2
0.2
0.1
0.1
0.2
0.1
0.6
0.2
Range
POOR
2.5
3.5
NA
4.0
2.5
NA
Kange
COOD
JO. 20
0.25
NA
0.25
0.20
NA
Forest
NA
NA
0.35
NA
NA
NA
1-0
00
-------
STATE
LRA 40
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-.2
1. Capabil-
ity Class
and
Subclass
I
IIc-
IIs
IIw
He
Hie
Ills
III-*
IIIc
IVe
IVs
IV*
IVc
Ve
Vw
Vs
Vc
Vie
Vlw
Vis
Vic
Vile
VIIs
VIIw
VIIc
Vllle
mis
VIIIw
VIIIc
2.Dofflinant Soil
Cilnan 1, 0-1% (I)
Ancho si, 1-3% (I)
Glenbar cl, 0-1%, sal-alk (I)
Estrella 1, 0-2%, flooded
NA
Vint Is, 3-5% (I)
Cadsden c, 0-1% (I)
NA
NA
NA
Cashion c, sal-alk, 0-1% (I)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Gachado vgrl, 5-10%
Growler grl, 0-5%
Brios gls, flooded, 0-2%
Mohall cl, 0-1%
NA
Rock Outcrop
NA
NA
3 .Dom
Slope
Length
(ft)
NA
800
NA
800
600
NA
NA
1200
1200
500
1200
1200
4 .Dora
c*
I"
Slope
(%)
0
2
0
1
4
0
0
8
4
1
1
30
5.T Fac-
tor
Tons
5
5
5
3
5
5
2
0.5
4
3
NA
6. Estimate Sell Losses for Selected Cropping SystewS
irrig
Row
Crop
per acre
0.6
0.8
0.7
0.6
2,2
0.4
0.7
1 L L 1 Y,
Close
Grown
Crop
or year
0.3
0.4
0.35
0.3
0.8
0.06
0.35
lUUJjbi J
POOR i
4.0
3.0
2.0
2.5
NA
vu;g;~
300D
0.35
0.25
0.15
0.20
NA
Forest
CO
NJ
-------
STATE ARIZONA
LRA
41
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
1. Capabil-
ity Class
and
Subclass
I
He
Us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
JVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VI13
VIIw
VIIc
Vllle
VIIIs
VIIIw
VIIIc
2. Dominant Soil
Elfrida sicl, 0-2% (I)
Comoro si, 2-5% (I)
Anthony si, 0-2% (I)
NA
NA
Tubac scl, 0-5% (I)
Continental scl, 0-2% (I)
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Caralampi si, 10-60%
Cogswell cl, 0-2%
NA
Elfrida sicl, 0-2%
Cellar vgrs£^. 3-60%
House Mountain stl, 2-50%
NA
NA
NA
Rock outcrop
NA
NA
3.Dom
Slope
Length
(ft)
NA
800
600
1200
8
4.Dom
%
Slope
(%)
0
3
1
2
1
t
:
1000
1200
•1200
1200
1200
1200
40
1
NA
30
30
30
5.T Fac-
tor
Tons
3
4
5
1
2
0.5
3
3
0.5
0.5
NA
§. EstimateTSoil Losges^for greeted Cropping Systems
•i t r i g i
Row
Crop
per acre
U • j
1.2
0.6
0.5
0.4
- - ~ — o '
Close
Grown
Crop
er year
0.12
0.4
0.3
0.2
0.1
POOR
4.5
2.5
3.5
3.0
4.0
NA
GOOD
0.25
0.20
0.15
0.20
0.15
NA
OJ
00
LO
-------
STATE NEW MEXICO
LRA 42 (South Desertic Basins,
Plains, and Mountains)
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2.
1. Capabil-
ity Class
and
Subclass
I
lie
IIS
Ilw
lie
Hie
III3
III^
IIIc
IVs
iVw
IVc
Va
Vw
Vs
Vc
Vie
VIw
Vis
VJc
TT
Vllw
Vile
Vllle
VIIIs
VHIw
VI He
2. Dominant Soil
Cila fsl, 0-1% (I)
Agua 1, 0-1% (I)
NA
NA
Vinton fsl, 1-3% (I)
NA
NA
NA
NA
Arizo grsl, 0-1% (I)
NA
NA
NA
NA
NA
NA
Grabs fsl, 0-3%
NA
NA
Pina sil, 0-1%
NA
NA
Fife grl, flooded, 0-5X
NA
Badlands I/
Rock land 2j
NA
NA
Slope
Length
(ft)
1200
1200
NA
1200
1200
1200
1200
750
750
750
•l.Doa
Slope
(.%)
1
1
0
2
1
1
1
4
30
20
5.T Fac-
tor
Tons
5
3
5
5
5
5
5
5
1
6. Estinate Soil Losses for Selected Cropping SysKmS
RANGifLAHp
Cover
GOOD
per acre
—
—
0.34
0.86
5.64
—
Cover
POOR
er year
•" —
—
0.60
1.51
7.25
—
Woodlanc
(NA)
—
—
—
— —
— —
Cotton
Barley
\lfalfa(?
0.43
0.70
0.87
0.43
—
— ^
— —
Cotton
Barley
r)
0.97
1.60
1.99
0.97
*
— —
LO
CO
I/ Tons of soil loss is 100T
2/ Tons of soil loss is 2T
-------
STATE Idaho
LRA 43
Form 1W. Dominant Soil,,L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
oo
1. Capabil-
ity Class
and
Subclass
I
lie
Us
Ilw
lie
111*
Ills
Hi*
Illc
IVs
IVs
IVw
IVc
Ve
•Vw
Vs
Vc
Vie
VIw
Vis
Vic
Vile
VIls
VIlw
VI Tc
Vine
VIIIs
VI Ilw
vine
2. Dominant Soil
NA
Nez Perce sil, 4-8%
Depew sic, 0-2%
Latah sil, 0-4%
Carlinton sil, 0-4%
Kooskia sil, 4-8%
Mires 1, 2-7%
NA
Santa sil, 0-4%
Santa sil, 0-30%
Bonner — grsil, 4-8%
Roseberry 1, 0-2%
Alex 1, 0-2%
NA
Lam 1
NA
NA
Jughandle 1, 0-30%
Slocum 1, cold, 0-4%
Klicker rol, 0-30%
NA
Jughandle 1, 30-60%
Shoeffler stsil, 30-60%
NA
NA
Gwin stl, 50-80% If
Rockland 2/
Alluvial Land 3/
None
3.Dom
Slope
Length
(ft)
300
500
200
600
400
700
500
200
300
200
200
100
100
500
100
100
100
50
100
100
4 .Dom
0*
10
Slope
(%)
6
1
2
2
6
4
2
12
6
1
1
1
15
2
15
45
45
60
45
1
5.T Fac-
tor
Tons
3
5
4
4
4
3
4
4
2
5
1
1
2
2
2
2
2
1
1
1
C. Estimate Soil Losses for Selected Cropping Sys-femS
Wheat
Peas
per acre
3
2
2
7
Grain
Hay
cr year
2
1
1
1
5
2
3
11
0
0
0
Range
Good
Cover
0
0
0
0
0
0
0
0
0
Range
Poor
Cover
2
1
0
1
2
0
1
0
0
Woodland
1
1
3
1
2
1
2
2
I/ Tons of soil loss is 10T 2/ Tons of soil loss is 5T 3/ Tons of soil loss is 2T
-------
STATE COLORADO
LRA
45
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to E\T-2
1 . Capabil-
ity Class
and
Subclass
I
He
Us
IIw
lie
Hie
Ills
IIIw
IIlc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
VTe
Vlw
Vis
Vic
Vile
VII3
VIIw
VIIc
Vllle
Villa
VIIIw
VIIIc
2. Dominant Soil
NA
NA
NA
NA
NA
NA
NA
NA
NA
Shrine cl, 3-9% (I)
NA
NA
NA
NA
Rosane loam, 1-5% (I)
NA
Hosier si, 1-87. (I)
Tomichi si, 5-25%
Almont sil, 5-10% (I)
NA
NA
Bross, grsl, 9-45%
Meredith, vstl, 8-50%
Vasquez stsl, 5-20%
NA
Shale outcrop I/
Rock outcrop
NA
NA
3.Dom
Slope
Length
(ft)
500
—
—
—
—
1000
—
800
1200
500
—
—
1000
800
300
—
50
50
—
—
4. Don
cf
1"
Slope
(%)
5
—
—
—
3
—
4
15
7
25
30
10
40
50
—
—
5.T fac-
tor
Tons
5
—
—
3
—
5
2
5
__
—
5
2
5
—
NA
NA
—
—
6. Estimate Soil Losses for Selected Cropping Systercs
Sm. grain
Hay
per acre
4
—
—
—
—
NA
—
NA
NA
NA
—
—
NA
NA
NA
—
NA
NA
—
—
Irrig.
Hay
er year
2
__
—
1
1
NA
NA
—
—
NA
NA
NA
—
NA
NA
—
—
Range-
land
POOR
NA
—
—
—
NA
—
NA
4.0
0.5
—
—
1.0
0.5
0.5
—
NA
NA
—
—
Range-
land
GOOD
NA
—
—
—
—
NA
—
NA
0.5
0.1
—
—
0.25
0.1
0.1
—
NA
NA
—
—
LO
CO
ON
I/ Tons soil 10S3-6T
-------
STATE
LRA
UTAH
47
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachment to EVT-2
l.Capabil- :2. Dominant Soil :3.Dora
ity Class: :Slope
and : :Length
Subclass . .
• •
• »
(ft)
I :Ustic Torrifluvents flra (I):1200
He .-Genola 1, 1-2% (I) .1200
Us :Ustic Torrifluvents colm (I). 600
IIw freen River 1, 0-1% (I)"- 300
He JRedfield sil, 0-1% (I) ".1200
Hie Quaker sicl, 1-2% (I)." 1200
Ills ^nabella si, 1-2% (I)j 500
lllw -Ephraim sicl (I)IlOOO
IIIc [Rasband 1, 1-3% (I)! 1200
IVo [flirdow vfsl, 4-8% '. 600
1Vs ianpete gfsl, 2-5% (I)." 600
IVw kovich 1 (I)j 500
IVc Bridie Calcixerolls flm J 600
Ve : :
ifq, .Fluvaquents . 600
Vs : :
Vc " *
Vie jlenefer sil, 10-25% j 500
Vlw ;Fluvaquents • 600
Vis :Lizzant stl, A-20Z : 500
Vic flitch sil, 0-2% : 200
Vile :Flygare 1, 40-60% . 500
VIIs :Daybell 1, 40-65% . 500
VIIw ioganeab cl, 1-3% : 600
Vnc JRedfield sil, 0-1Z .1200
Vllle iGullied land I/ . 40
VIIIs :Rock outcrop .
VII Iw JRlverwash 2J \ 100
VIIIc : :
4. Dora
of
It
Slope
(%)
1
2
1
1
1
2
2
2
2
4
5
2
2
2
25
2
15
2
50
50
2
1
2
1
5.T Fac-
tor
Tons
5
5
3
4
5
5
2
2
3
5
1
1
2
1
3
1
2
5
3
2
1
5
5
1
G. Estimate Soil Losses for Selected Cropping Systems
*
WHEAT : ROTATED
FALLOW :CROP
(Dry Crop) (I".)
per acre per year
8.0
9.3
i ..•)
2.1
1.7
1.7
1.6
1.7
2.5
1.6
2.9
ASTURE
.2
.1
GOOD
RANGE
1.5
1.2
.5
2.0
.5
1.0
2.5
1.0
1.0
3.0
.6
POOR
RANGE
2.0
1.7
1.0
3.0
1.0
2.0
3.0
1.0
1.0
3f\
.0
If
.5
CO
I/ Tons soil loss 100
2J Tons soil loss 75T
-------
STATE COLORADO
LRA
(Includes Res. Area 49)
Form 1W. Dominant Soil, L, S, and T Factors
and Estimated Tons Soil -Lost to Erosion for
Selected Cropping Systems
USDA - SCS
Attachnent to EVT-2
1. Capabil-
ity Class
and
Subclass
I
He
11s
IIw
He
JJIe
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vw
Vs
Vc
Vie
VIw
Vis
Vic
me
VIIs
VI Iw
VIIc
VlUe
VIIIs
VIIIw
VIIIc
2.. Dominant Soil
Fruita loam, 0-1% (I)
Fruita loam, 1-3% (I)
Billings scl, 0-1% (1)
NA
NA
HOlderaann si, 3-5%
Colonne. cl, 0-2% (I)
Uncompahgre si, 0-3%
Collbran 1, 0-3%
Bostwick si, 5-10% (I)
Christianburg c, 0-2% (I)
Yempa 1, 0-1% (I)
Cerro cl, 0-3%
NA
Big Blue 1, 0-5%
Valmont kcl, 1-5%
Anvik 1, 0-3%
Evanston 1, 5-20%
Gas Creek grsl, 0-3%
Gateview kl, 2-8%
Evanston 1, 1-5%
Perlin grl, 5-45%
Carbol vrsl, 15-60%
Vasquez stsl, 5-20%
Williams 1, 0-5%
Badland !_/
Rock outcrop
MA
NA
NA
3 .Dom
Slope
Length
(it)
800
600
1200
_ —
500
1000
1000
500
300
1200
1200
500
1200
1000
300
500
1000
500
800
1000
800
300
1200
50
50
NA
NA
4.Doo» 5.T Fac-:6. Estimate Soil Losses for Selected Cropping Systems
% tor :pasture
Slope :and
:i4ayland
(',o) Tons per acre
i 5 :i
2 5 :2
1 5 .2
.
4 5 ".NA
1 5 '.2
1 5 \2
2 5 .NA
8 5 '.5
1.5 °2
1 5 1.25
2 5 ".NA
1 5 !-25
3 5 *.NA
2 5 INA
10 5 .' NA
1 2 ". NA
4 5 I NA
3 5 1 NA
20 5 INA
35 1 I NA
10 5 ; NA
3 5 1 NA
30 NA ; NA
50 NA j NA
NA NA | NA
NA NA " NA
•
Dry
Beans
er year
—
— _
—
1(5
NA
NA
5
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Pry rSogar
Wheat :Beets
:and
:Corn
.4
*C
— — ^
• .
:4
~~ :
20 \5
NA ".5
NA .'NA
10 "NA
NA J5
NA. |5
NA :.NA
10 |NA
NA JNA
NA ;NA
NA : NA
*
NA "NA
NA :NA
NA :NA
NA :NA
NA : NA
NA :NA
NA : NA
NA : NA
NA :NA
NA :NA
NA : NA
NA : NA
•
Rdnge
POOR
Cond.
NA
\l A
r*rt
VI A
NA
NA
WA
5.0
NA
NA
2.0
NA
NA
NA
5.0
0.5
0.5
0.5
5n
. u
0.5
1.0
1.0
5.0
0.5
0.5
1.0
NA
NA
NA
NA
Kdnge
GOOD
Csmd.
MA
NA
MA
Jv/V
VI A
fJA
NA
NA
0.25
NA
NA
0.25
NA
NA
NA
0.25
0.1
0.25
0.25
Oe;
. J
0.1
0.25
0.25
0.5
0.1
0.1
0.2.5
MA
NA
NA
NA
oo
oo
I/ Tons of soil loss—8T
-------
APPENDIX F
REPRODUCTION OF "PESTICIDE RESIDUE LEVELS IN SOILS, FY1969-
NATIONAL SOILS MONITORING PROGRAM*
* Published in Pesticides Monitoring Journal, (3(3):194-228, December 1972,
389
-------
PESTICIDES IN SOIL
Pesticide Residue Levels in Soils, FY1969—National Soils Monitoring Program
G. B. Wiersma1, H. Tai1, and P. F. Sand'
ABSTRACT
This report is a summary of the FY 1969 results of the Na-
tional Soils Monitoring Program, an integral part of the
National Pesticide Monitoring Program (NPMP). Pesti-
cide residues in cropland soil for 43 Stales and noncropland
soil for il States are reported. Tables for each State five
the number of samples collected, arithmetic means and
ranges of residue levels detected, and the percent of sites
with detectable residues. In addition, for selected pesticides
and various States and State groupings, a frequency distri-
bution of pesticide residues was determined. Use records for
FY 1969 are given by the pesticides used, the percent of
sites treated, the average application rates, and the average
amounts applied per site. Comparisons are made between
residue levels in different land-use areas.
Introduction
The National Soils Monitoring Program is an integral
part of the National Pesticide Monitoring Program
(NPMP), which was initiated as a result of a recom-
mendation made by the President's Science Advisory
Committee in its report of 1964 entitled "Use of Pesti-
cides" that the appropriate Federal agencies "develop
a continuing network to monitor residue levels in air,
water, soil, man, wildlife, and fish." The NPMP as
originally designed was described in the first issue of
the Pesticides Monitoring Journal (/), and a revised
description to reflect certain program realignments and
1 Pesticides Regulation Division. Office of Pesticide Programs, Environ-
mental Protection Agency, Washington. D. C. 10460.
5 Pesticides Regulation Division. Office of Pesticide Programs. Environ-
mental Protection Agency, Mississippi Teit Facility, Bay St. Louis,
Miss. 395:0.
5 Plant Protection and Quarantine Programs. Animal and Plant Health
Inspection Service. U.S. Department C'f Agriculture. Hyalt»ville. Md.
20782.
194
other changes was published in the June 1971 issue of
this Journal (2).
The objectives of the NPMP are to determine levels and
trends of pesticides in the various components of the
environment (2). The establishment of baseline or back-
ground levels of pesticide residues through the NPMP
will provide a basis for comparison of subsequently
identified pesticide residue levels in an environmental
component.
The Panel on Pesticides Monitoring of the Working
Group on Pesticides (2) listed five bases for concern to
be used in evaluating pesticide residue levels in the
various environmental components. They are:
(1) any concentration of a pesticide known to he
potentially harmful;
(2) increasing trends;
(3) exceeding standards;
(4) recognition of adverse effects on humans; and
(5) erratic variability (a statistically oriented observa-
tion that is potentially common to each stratum
sampled).
The results of this study serve to establish a baseline
of pesticide residues in cropland and noncropland soils
at a particular point in time (FY 1969). The present data
and all future data will be evaluated using applicable
criteria included in the five bases of concern outlined
above.
Sampling Procedures and Methods
In general, sampling techniques involved in this study
were the same as those described by Wiersma, Sand,
and Cox (.?).
PESTICIDES MONITORING JOURNAL
390
-------
In FY 1969, cropland soil was sampled in every State
except Alaska, Hawaii, Kansas, Minnesota, Montana,
Oregon, and Texas. Noncropland was sampled in 11
Slates—Arizona, Georgia, Idaho, Iowa, Maine, Mary-
land, Nebraska, Virginia, Washington, West Virginia,
and Wyoming. Samples collected in FY 1969 included
both soil and mature crops and/or those ready for
harvest; however, results of crop analyses arc not pub-
lished in this report.
A nalytical Procedures
ORGANOCHLORINE AND ORGANOPHOSPHOROUS
COMPOUNDS
A subsample of soil weighing 300 g, wet weight, was
placed in a 2-qt fruit jar with 600 ml of 3:1 hexane-
isopropanol solvent. The jars were sealed and rotated
for 4 hours. After rotation, the soil was allowed to
settle, and 200 ml of the extract solution was filtered
into a 500-mI separatory funnel. Isorpropanol was re-
moved with two washings of distilled water, and the
remaining solution was then filtered through a funnel
containing glass wool and anhydrous sodium sulfate
(NajSO.,). Further cleanup was normally not required
before analysis.
Gas-Liquid Chromatography
Analyses were performed on gas chromatographs
equipped with tritium foil electron affinity detectors for
.organochlorine compounds and thermionic or flame
photometric detectors for organophosphorous com-
pounds. A dual-column system employing polar and
nonpolar columns was utilized to identify and confirm
pesticides. Instrument parameters were as follows:
Columns: Glass, 183 cm long by 6 mm, o.d., and 4 mm, i.d.,
with one of the following packings:
3% DC-200 on 100/120 mesh Gas Chrom Q or 9%
QF-I on 100/120 mesh Gas Chrom Q
Carrier gas: 5% methane-argon at a flow rate of 80 ml/min
Temperatures: Detector 200* C
Injection port 250* C
Column QF-1 166° C
Column DC-200 I70"-I75° C
When necessary, confirmation of residues was made by
thin layer chromatography or p-values. The lower limit
of detection was 0.01 ppm. The average recovery rate
for all pesticides was 100% (with a ±10% error); the
data were corrected for recovery and also adjusted to
a dry-weight basis by determining the moisture content
on a separate portion of each sample using the oven
drying method.
ATRAZINE
After a 4-hour Soxhlet extraction of a 50-g subsample
of soil with 25 ml of water and 300 ml of mcthanol,
the sample extract was transferred to a 1-liter scparalory
funnel and 200 ml of water added. The sample extract
VOL. 6, No. 3, DECEMHCR 1972
was partitioned three times with a portion of 150 ml
of freon 113 for each partitioning. The freon 113 frac-
tions were combined and concentrated to incipient
dryness. The sample was then dissolved in hexane,
adjusted to a 5-ml volume, and injected into a gas-
liquid chromatograph.
Gas-Liquid Chromatography
A thermionic flame detector with rubidium sulfate
coating on a helix coil was used. Instrument parameters
were as follows:
Column: Glass, 183 cm long by 6 mm, o.d., and 4 mm,
i.d., packed with 3X Versamid 900 on 100/120
mesh Gas Chrom Q
Carrier gas: Helium
Detector fuel gas: Oxygen (200-300 ml'min);
Hydrogen (20-30 ml, min)
Temperatures: Detector 240* C
Injection port 2W C
Column 2A)' C
Confirmation was made using a DC-200 column at
180° C and a Coulson detector (reductive mode) at
the following temperature settings: pyrolysis tube—850°
C, transfer line—220r- C. and block—220C C.
The minimum detection limit was 0.01 ppm, and re-
covery was about 100%.
2,4-D
Analyses were made following the procedure developed
by Woodham et a!. (4). The analvtica! method involved
a dicthyl ether extraction of acidified soil, an alkali
wash to remove interfering substances, and an esleri-
fication procedure using 10% boron trichloride in 2-
chloroethanol reagent. The 2-chloroethyl ester of 2.4-D
was then analyzed by gas chromatography. The minimum
detection limit was 0.01 ppm, and the average recovery'
was 85%. Results were corrected for percent recovery'.
ARSENIC
Arsenic was determined by atomic absorption spectro-
photornetry. The soil sample was first extracted with
9.6N hydrochloric acid (HCL) and reduced to trivalent
arsenic with stannous chloride. The trivalent arsenic
was partitioned from HCL solution to benzene, then
further partitioned into water for the absorption meas-
urement. A Perkin-Elmer Model 3O3 instrument was
used, and absorbance was measured with an arsenic
lamp at 1972 A with argon as an aspirant to an air-
hydrogen flame. The minimum detection limit was 0.1
ppm, and the recovery value for arsenic averaged 70%.
Results were corrected for percent recovery.
Results
The data in this report are for soils only (both crop-
land and noncropland) and include results for all Slates
195
391
-------
sampled in the study. Caution should be exercised when
interpreting the arithmetic means presented in the tables,
because pesticide residue data are not normally distrib-
uted, and the arithmetic means for pesticide residues
tend to be greater than the corresponding median. There-
fore, they cannot be considered an indication of the
central tendency of the data. Information accompanying
the arithmetic means in this report such as the percent
occurrence, ranyc of detected residues, and number of
observations can aid in evaluating the arithmetic mean.
RESIDUES—ALL STATES
Table 1 presents a summary of pesticide residues in
cropland soils for all 43 States sampled. Percent occur-
TABLE 1.—Summary of pesticide residues in cropland soil from 43 Stales—FY 1969
COMPOUND
Aldrin
Arsenic
Atrazine
Carbophenothion
Chlordanc
2,4-D
DCPA (Dacthal*)
o,p'-DDE
p,p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
DEF
Diazinon
Dicofol
Dieldrirt
Endosulfan (I)
EndosuLfan (II)
Endosul/an sul/ate
Endrin
EndrLn aldehyde
Endria kctone
EthJOD
Heptachlor
HepLachlor epoxide
Isodrin
Lindane
Malathion
Metho.xychJor
Ethyl pa/athion
PCNB
o,p'-TDE
P.P'-TDE
Toxaphere
Trifiuralin
NLAIHER OF
SAMPLES
ANALYZED'
1.729
1.725
199
66
1.729
(88
1.729
1.729
1.729
1.729
1.729
1.729
1,729
66
1.729
1.729
1.729
1.729
1,729
1.729
1.729
1.729
66
1,729
1,729
1.729
1,729
66
1,729
66
1.729
1.729
1,729
1.729
1,729
NUMBER OF
POSITIVE
SAMPLES
189
1.713
28
1
ISI
3
1
79
429
243
384
451
1
2
9
480
J
9
11
39
1
9
1
68
139
11
15
2
1
7
1
49
265
73
60
PEHCENT
POSITIVE
SITES ;
10.9
99.3
14.1
1.5
8.7
1.6
O.I
4.6
24.8
14.1
22.2
26.1
O.I
3.0
0.5
27.8
0.3
0.5
0.6
2.3
0.1
0.5
1.5
3.9
8.0
0.6
0.9
3.0
0.1
10.6
O.I
2.8
15.3
4.2
3.5
MEAN RESIDUE
LEVEL
(PPM)
0.02
6.43
0.01
<0.01
0.04
<0.01
-------
rencc of residues is based on the number of sites with
residues greater than or equal to the sensitivity limit.
The data for atrozinc, 2,4-D, and the organophosphatcs
arc not truly comparable with those determined for the
organochlorincs or arsenic, because analyses for atrazine
and 2,4-D were made only when use records indicated
that they had been applied—199 and 188 times, respec-
tively, and analyses for organophosphatcs were per-
formed on only 66 of the 1,729 samples.
Elemental arsenic residues were found most frequently,
with 99.3% of the sites having detectable residues and
a mean level of 6.4 ppm. It is probable that most of this
arsenic was from natural sources, although agricultural
sources cannot be ruled out at this time.
The most widely distributed organochlorine pesticide
was dieldrin, with 27.8% of the sites having detectable
residues, followed by DDTR residues (a compilation of
all members of the DDT group) found at 26.1% of the
sites; aldrin, found at 10.9%; and chlordane, found at
8.7%. DDTR had the highest mean residue level, with
0.31 ppm found in cropland soils. With the exception
of individual members of the DDT group, the other
organochlorincs had average residues ranging from
<0.01 to 0.07 ppm.
Based on the 66 samples analyzed for organophos-
phates, ethyl parathion was detected 10.6% of the time,
with a mean residue level of 0.06 ppm. Malathion and
diazinon were each detected 3.0% of the time, with
mean residue levels of 0.01 and <0.01 ppm, respectively.
In the 188 samples analvzed for 2,4-D and other
chlorophcnoxy herbicides, 2,4-D was the only one de-
tected; 2.4-D was found in 1.6% of 188 samples
analyzed, with a mean residue level of <0.01 ppm.
Atrazine was detected in 14.1% of the 199 samples
analyzed, with a mean residue level of 0.01 ppm—the
highest me;m residue of the herbicides detected. Tri-
fluralin was detected in 3.5% of the 1,729 samples, with
a mean residue level of <0.01 ppm.
The residues found in noncropland soils for the 11
States sampled are presented in Table 2. The mean
arsenic residue level was 5.0 ppm, occurring in 98.5%
of the samples. DDTR was detected in 16.1% of the
noncropland soils at levels ranging from 0.01 to 0.62
ppm, with a mean level of 0.01 ppm. With the excep-
tion of members of the DDT group, dieldrin was the
most widely distributed pesticide, occurring in 4.07^ of
the samples, with residues ranging between 0.01 to 0.09
ppm and a mean residue level of <0.01 ppm.
RESIDUES— INDIVIDUAL STATES
The pesticide residue summaries for cropland by in-
dividual States are given in Table 3. and similar results
are shown for noncropland in Table 4. It would be
impractical to attempt to comment on the results for
each State: therefore, in order to facilitate summariz-
ing the data. Figs. 1. 2. and 3 are presented. These are
for three of the most- commonly occurring residues—
arsenic, DDTR. and dieldrin. Means for each pesticide
in each State uere calculated, and distribution of these
averages are indicated on the corresponding Figures.
TABLE 2.—Summary of pesticide residues in noncropland soil from 11 Stales—FY 1969
COMPOUND
Aldrin
Arsenic
Chlordane
o.p'-DDE
p.p'-DDE
o,p'-DDT
P,P'-DDT
DDTR
Dicofol
Dieldrin
Hcptachlor epoxide
p.p'-TDE
Toxaphene
NUMBER OF
SAMPLES
ANALYZED '
199
198
199
199
199
199
199
199
199
199
199
199
199
NUMBER OF
POSITIVE
SAMPLES
1
195
3
1
27
7
18
32
2
8
2
6
1
PEP.CEVT
POSITIVE
SITES -
OS
98.5
1.5
0.5
13.6
, 3J
9.1
16.1
1.0
4.0
1.0
3.0
0.5
MEAN RESIDVE
LEATL
(PPM)
<0.01
5.01
<0.01
<0.0l
0.01
<0.01
0.01
0.01
<0.01
<0.01
-------
KEY
p 1 No Sample
.01 ppm 10
.03 ppm
.03 ppm to
.06 ppm
FIGURE 1.—Arsenic residues in cropland soil
The class intervals for the keys accompanying each
Figure were obtained in the following manner: The
range of residues for the Nation was obtained, and the
highest value was converted to a logarithm. This value
was then divided by the number of desired classes. The
resulting intervals were added to obtain the class bound-
aries which, in turn, were converted to the untrans-
formed dimensions. Essentially, this took advantage of
the fact that most residue data are logarithmically distrib-
uted.
Distribution of arsenic residues across the United States
is presented in Fig. 1. The highest residue levels were
found in the New England States (Connecticut, Maine,
Massachusetts, New Hampshire, Rhode Island, and
Vermont), Arkansas, Kentucky, New York, North
Dakota, Ohio, and Pennsylvania; these individual States
and the New England States had mean residues of
arsenic >8.4 ppm. The remaining residues were distrib-
uted primarily in the 2.0 to 8.4 ppm range, with
Wyoming and Florida having less than 2.0 ppm. Those
States left blank were not sampled.
The distribution of DDT residues (DDTR) is shown in
Fig. 2. Once again, the key indicates the range of residues
for each of the class intervals. A similar map for diel-
drin residues is presented in Fig. 3.
198
The mean residue levels, the percent positive sites, and
the range of residue levels for the 12 States with the
highest arsenic residues are shown in Table 5.
Residue data for the five States with the highest DDTR
residues are presented in Table 6. Although Michigan
had a mean residue of 2.09 ppm and a range of 0.01 to
78.36 ppm, only 23.5% of the samples had detectable
residues, indicating that the residues were not widely
distributed. By contrast, Mississippi had a mean residue
of 2.06 ppm with 89.7% of its sites having detectable
residues and a narrower range (0.03 to 13.14 ppm). Al-
though the range was narrower, pesticide residues were
more widely distributed in Mississippi than in Michigan.
The seven States wth the highest dieldrin residues are
listed in Table 7. The highest mean residue level, 0.11
ppm, was found in Illinois, with 61.3% of the sites hav-
ing detectable residues. In general, the other six States
tended to have mean residues approximating one an-
other, 0.06, 0.07, or 0.08 ppm.
PESTICIDE USE RECORDS
When soil samples were collected, an attempt was made
to determine what pesticides had been used on the sites
for the year of sampling. The summary tables for the
use records show the percent of times a pesticide was
PESTICIDES MONITORING JOURNAL
394
-------
ppm 10
ppm
51.0 ppm
FIGURE 2.—DDTR residues in cropland soil
KEY
j j No Sampk
'.'.•.i.'.l
;,;.;;;:;y <2.o PPm
2 0 ppm lo
<4.1 ppm
4.1 ppm lo
8.4 ppm
58.4 ppm
FIGURE 3.—Dicldrin residues in cropland soil
VOL. 6, No. 3, DECEMBER 1972
199
395
-------
used, the average application rate expressed in pounds
per acre of the active ingredients, and the average
amount applied per site. The average amount per site
was determined by dividing the total amount of active
ingredient of a pesticide used by the total number of
sites surveyed.
Table 8 shows 130 different pesticides reported to have
been used on cropland in the year of sampling. Those
most commonly used were atrazine, captan, 2,4-D,
malathion, and methylmercury dicyandiamide. Technical
DDT was used on 3.44% of the sites, aldrin on 4.16%
of the sites, and dieldrin on 1.19% of the sites.
On noncropland sites 2,4-D, malathion, and mirex were
reported to have been used (Table 9). However, these
should not be considered the only pesticides used on
noncropland sites. In general, records of treatment of
noncropland sites are less accurate than those kept for
cropland. The breakdown of pesticide usage by in-
dividual States for cropland and noncropland soils,
respectively, are shown in Tables 10 and 11. Of the
43 States with cropland soil analyzed, use records for
4 showed no pesticides used on the sampling sites:
Nevada (2 sites); New Hampshire (2 sites); Vermont
(5 sites); and Wyoming (17 sites). Of the 11 States
with noncropland soil analyzed. 8 reported no pesticides
used on the sampling sites; Arizona (43 sites); Iowa
(7 sites); Maine (11 sites); Maryland (3 sites); Virginia
(14 sites); Washington (11 sites); West Virginia (9 sites);
and Wyoming (37 sites).
Because of the number of States and pesticides presented
in Tables 10 and 11, it is difficult to make all possible
comparisons between the use patterns indicated and
the detected residues shown in Tables 3 and 4. There-
fore, comparisons have been restricted to those States
having the highest residues as shown in Figs. 1, 2, and 3
(arsenic, DDTR, and dieldrin, respectively).
Table 12 compares those States having the highest
arsenic residues with the average amount applied per
site and the percent of sites which reported using an
arsenic compound. The amount of arsenic applied did
not seem to be directly related to the amount detected
in the soil. Arkansas, Kentucky, North Dakota, and
Ohio reportedly used no arsenic compounds, whereas
New England, New York, and Pennsylvania reported
using sodium arsenite and lead arsenate. The application
rates were below the detected residue levels, and the
percent of times used was below the percent of times
residues were detected. It also must be considered that
the application rates were for the active ingredients of
sodium arsenite and lead arsenate, and not for elemental
arsenic alone. A fair assumption would be that most
arsenic residues delected in cropland soils probably
resulted from natural levels of arsenic.
200
A similar comparison for the five States with the high-
est DDTR residues is found in Table 13. It is interesting
to note that use records for four of the States listed
(California, Michigan, Mississippi, and South Carolina)
indicate that the amount applied was less than the mean
level detected in the soil. Also, in all five States, the per-
cent of sites positive for DDTR was approximately three
or four times greater than the percent of sites reportedly
treated with DDT. Unlike arsenic, the residues of DDTR
could only result from the use of DDT either in the year
of sampling or in previous years.
Table 14 lists the seven Stales with the highest dieldrin
residues. In most cases, the average amount of aldrin/
dieldrin applied approximated the mean residue of diel-
drin detected in the soil, but ihe percent of sites re-
portedly treated with dieldrin or aldrin was always con-
sideably less than the percent of sites with dieldrin
residues. This wider distribution of dieldrin residues,
when compared to use records for the year of sampling,
probably indicates residues from previous years.
PESTICIDE FREQUENCY DISTRIBUTION
The statistics discussed thus far, namely the mean, the
range, and the percent of sites at which residues were
detected, do not describe their distribution. To describe
this distribution, probit analysis was used. The residue
levels were ranked from lowest lo highestB accumulated,
and the percentages computed. The residues were trans-
formed to logarithms, the percentages to probits, and
the relationship between the logarithms of the residues
and the probits of the accumulated percentages was
calculated by regression analysis. The computer program
used was that of Daum. (5); the theory and techniques
as applied in the cited reference were modified slightly.
The residue levels at the fiftieth percentile point (median)
for the individual pesticides in soil for each State along
with the upper and lower 95% fiducial limits are
presented in Table 15. For example, in the State of
Alabama, the fiftieth percentile point (median) for
arsenic was 4.09 ppm. Thus, 50% of the sites had
residues less lhan 4.09 ppm. The upper and the lower
fiducial limits of the residues establish the 95% confi-
dence interval about the residue value for the fiftieth
percentile. It should be noted that the mean for a
particular State is not the same as the fiftieth percentile
point (median) from the frequency distribution. For
example, the mean level of arsenic for Alabama was 6.1
ppm, while the frequency distribution indicated 4.09
ppm for the fiftieth percentile point. This is an example
of the fact that residue data are not normally distributed
and the mean and median arc not identical.
Not all pesticides arc shown for all States. A cutoff point
of six or more pairs of observations was used to eliminate
PESTICIDES MONITORING JOURNAL
396
-------
situations where (here were too few observations to
calculate a reliable distribution. Space did not permit
printing tables showing distribution of pesticide residues
for percentiles other than ihe fiftieth.
CROPPING REGIONS ANALYSIS
The data were grouped by counties into various crop-
ping regions, and these are shown in Tables 16 and 17.
The boundaries for the various cropping areas were
based on a major land-use map of the United States
compiled by F. J. Marschner of the U.S. Department
of Agriculture, Bureau of Agricultural Economics, 1950.
No effort was made to make a land-use division within
counties. This resulted in a good definition of the larger
land-use areas such as the corn belt and cotton-growing
areas. The land in the United States was grouped into
several major land-use areas—corn, cotton, general
farming, hay, small grain, vegetables, and fruit. In some
cases, two areas overlapped. Irrigated land was deter-
mined from information obtained at the time of sample
collection in this study.
It is of interest to make a few individual comparisons
between the cropping regions and the national means.
For example, note that in. the corn region, aldrin oc-
curred 23.5% of the time (Table 17) with a mean residue
level of 0.05 ppm (Table 16). However, nationally,
aldrin only occurred 10.9% of the time with a mean
level of 0.02 ppm (Table 1), an indication of the
heavier use of aldrin in the corn region. But, in the corn
region, the mean residue level of DDTR was 0.14 ppm
which is well below the national mean of 0.31 ppm.
The vegetable and fruit cropping region had the high-
est level of DDTR, over two times higher than the next
highest cropping region and over six times, higher than
the national mean for DDTR. This might result from a
high use of DDT in various orchard operations. The
next highest residue was found in the cotton and vege-
table region, with approximately equal amounts de-
tected between them. The rest of the amounts of DDT
in the cotton and general farming, general farming,
hay and general farming, and irrigated land were simi-
lar t) one another. The two areas with the least amount
of DDTR in the soil were the corn and small grains
cropping regions.
The corn, vegetable, and vegetable and fruit cropping
regions had the heaviest residues of dieldrin. Residues
of dieldrin in the other cropping regions were either
equal to or below the mean residues detected for all
States (Table 1).
The cotton cropping region had the highest toxaphene
residues. The cotton and general farming and general
farming cropping regions had residue levels of about
half those detected in the cotton cropping region,
A cknowledgment
It is not possible to list, by name, all the people who
contributed to this study; however, special mention is
made of the staff at the Monitoring Laboratory, Mis-
sissippi Test Facility, Bay St. Louis, Miss., who proc-
essed and analyzed the samples for chemical residues and
contributed immeasurably to this study and of the in-
spectors from the Animal Plant Health Inspection
Service (APHIS) who collected the samples. Finally,
recognition is due Dr. Edwin Cox, Biometrical Services
Staff, USDA, for the sample allocation procedures and
to Dr. Richard Daum of the Animal Plant Health In-
spection Service, USDA, for the probit analyses.
See Appendix for chemical namct ana compounds discussed in this
paper.
LITERATURE CITED
(/) Pestic. Monit. J. 7967. 1(0:1-22.
(2) Pestic. Monit. J. 1971. 5(1):35-71.
(3) Wiersma. C. B., P. F. Sand, and E. L. Cox. 1971. A
sampling design to determine pesticide residue levels in
soils of the conterminous United States. Pestic. Monit J.
5(l):63-66.
(4) Woodham, D. W., W. G. Mitchell, C. D. Lojtis, and
C. W. Collier. 1971. An improved gas chromatographic
method for the analysis of 2,4-D free acid in soil. J.
Agric. Food Chem. 19(1): 186-188.
(5) Daum, R. L., 1970. Revision of two computer programs
for probit analysis. Bull. Entomol. Soc. Am. 16:10-15.
VOL. 6, No. 3, DKCKMBER 1972
397
-------
TABLE 3.—Pesticide residues in cropland soil from 43 States—t- r ty(>y
COMPOUND
NUMBER OP
SAMPLES
ANALYZED »
NUMBER OP
POSITIVE
SAMPLFI
PERCENT
POSITIVE
SITES'
MEAN RESIDUE
LEVEL
(PPM)
RANGE or
DrrtcTED RESIDUES
(PPM)
ALABAMA
Arienic
Chlordane
o,p'-DDE
p.p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
Dieldrln
Endrin
Heplachlor
Heptachlor epoxldc
Lindane
o,p'-TDE
P,p'-TDE
Toxaphene
Trifiuralin
23
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
23
3
1
19
16
20
20
i
2
2
3
2
1
13
6
7
100.0
13.6
4.6
86.4
72.7
90.9
90.9
22.7
9.1
9.1
13.6
9.1
4.6
59.1
27.3
31.8
6.11
CM
<0.01
0.17
0.09
0.78
1.13
0.01
•CO.OI
<0.01
<0.01
<0.01
-------
TABLE 3.—Pesticide residues in cropland soil from 43 Stales—FY J969—Continued
COMPOUND
NUMBER OF
SAMPI.KS
ANALYZED'
NUMBER or
Posmve
SAMPLES
PERCH NT
PosmvE
SfTES'
MEAN RESIDUE
LEVEL
-------
TAU1.E 3.—Pesticide residues in cropland soil from 43 States—FY 1969—Continued
COMPOUND
N'lIMBFR Of
SAMPLES
ANALYZED »
NUMBER op
POSITIVE
SAMPLES
PtRCEM
POSITIVE
SITES'
MEAN RXSIDUT
LEVEL
(PPM)
FLORIDA—Continued
GEORGIA
RANOE OF
DETECTED RESIDICI
(PPM)
Diazinon
Dicldrin
Endrin
Endrin aldehyde
Endrin ketonc
Elhion
Heptachlor
Heptachlor epoxide
Ethyl parathion
o,p'-TDE
p.p'-TDE
Toxaphcne
Trifluralin
5
18
18
18
18
5
18
18
5
18
18
18
18
1
7
2
1
1
1
1
3
2
1
II
2
1
20.0
38.9
11.1
5.6
5.6
20.0
5.6
16.7
40.0
5.6
61.1
11. 1
5.6
0.03
0.08
0.03
<0.01
-------
TABLE 3.—Pesticide residues in cropland soil front 4S Stales—FY 1969—Continued
COMPOUND
NUMBER OF
SAMPLES
ANALYZT.D '
NUMBER OF
POSITIVE
SAMPLES
PMCCXT
SlIFS'
Mt»s RESIDUE
LEVEL
(PPM)
RA^GE OF
DCTTCUD Rrsii'vt
(PPM)
ILLINOIS— Continued
Dieldrin
Heptachlor
Heptachlor epoxide
Isodrin
o,p'-TDE
p.p'-TDE
Trifluralin
142
142
142
142
142
142
142
87
31
36
2
1
5
2
61.3
21.8
25.4
1.4
0.7
3.5
1.4
0.11
0.03
0.02
<0.01
<0.01
<0.01
6-107.i5
0.01-1.55
O.W-630
0.01-0.18
0.01-0.05
0.01-0.34
o.oi-o.eo
o.oi-ox:
0.01-0.97
O.OI-OJ3
0.01-0.02
0.10
0.01-0.50
0^)2-0.08
KENTUCKY
Aldrin
Arsenic
Chlordane
o.p'-DDE
P.P'-DDE
o,p'-DDT
P.P'-DDT
DDTR
Dieldrin
Heplachlor
Heplachlor tpoxide
Isodrin
o,p'-TDE
P.p'-TDE
31
31
31
31
31
31
31
31
31
31
31
31
31
31
8
31
4
1
5
3
6
6
17
2
1
2
1
4
25.8
100.0
12.9
3.2
16.1
9.7
19.4
19.4
54.8
6.5
3.2
6.5
3.2
12.9
0.03
(.41
0.02
-------
TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
INUMBtK OF
SAMPLES
ANALYZED »
NUMBER OF
POSITIVE
SAMPLES
PERCEVT
PosmvE
Snt$»
MEAN RF.SIDUE
Lrvu.
("M)
RASCE OF
DriTCTEo Rtsn»-tj
<«M)
LOUISIANA
Aldrin
Arsenic
Chlordane
o.p'-DDE
p,p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
Dieldrio
Endrin
Endrin ketone
p,p'-TDE
Toxaphene
Trifluralin
27
27
27
27
27
27
27
27
27
27
27
27
27
27
5
26
1
2
12
9
13
13
10
1
1
9
4
1
18.5
963
3.7
7.4
44.4
33J
48.2
48.2
37.0
3.7
3.7
33J
14.8
3.7
<0.0l
2.15
-------
TABLE 3.—Pesticide residues in cropland soil from 43 States—FY 1969—Continued
COMPOUND
NUMBFR OP
SAMPLES
ANALYZED '
NUMBER OP
POSITIVE
SAMPLES
PEXCEHT
POSITIVE
SITES'
MEAN RESIDUE
LEVEL
(PPM)
RANGE OF
DrrtcrtD REjmiTS
(PPM)
MICHIGAN—Continued
Dicldrin
Endosulfan (I)
Endosulfaji sulfale
Endrin
p,p'-TDE
51
51
51
51
51
11
2
2
1
5
21.6
3.9
J.9
2.0
9.8
0.05
0.01
0.02
<0.01
0.65
0.01-1.01
0.03-0.24
0.25-0.94
0.01
0.02-31.43
MISSISSIPPI
Arsenic
o,p'-DDE
p,p'-DDE
o,p'-DDT
p,p'-DDT
DDTR
Dieldrin
Endrin
Endrin kelone
Lindane
o.p'-TDE
p,p'-TDE
Toxaphenc
Trifluralin
30
29
29
29
29
29
29
29
29
29
29
29
29
29
30
9
26
22
26
26
10
1
1
2
2
20
14
6
100.0
31.0
89.7
75.9
89.7
89.7
34.5
3.5
3.5
6.9
6.9
69.0
48.3
20.7
5.70
0.01
0.31
0.20
1.36
2.06
0.01
0.01
<0.01
<0.01
0.03
0.15
0.78
0.02
1.10-16.90
0.01-0.08
0.01-1.43
0.02-1 J 5
0.01-9 .28
0.03-13.14
0.02-0.10
0.19
0.11
0.01-0.04
0.33-0.49
0.01-0.81
0.10-8.80
0.02-0 .25
MISSOURI
Aldrin
Arsenic
Chlordane
p,p'-DDE
o,p'-DDT
p,p'-DDT
DDTR
Dieldrin
Endrin
Heptachlor
Heptachlor epoxide
Isodrin
Toxaphene
Trifluralin
82
81
82
82
82
82
82
82
82
82
82
82
82
82
18
80
6
.1
2
3
3
26
|
5
5
1
1
5
22.0
98.8
7J
3.7
2.4
3.7
3.7
31.7
1.2
6.1
6.1
1.2
1.2
6.1
0.05
5.99
0.03
<0.01
<0.01
-------
TABLE 3.—Pesticide residues in cropland soil jrom 43 Slates—FY 1969—Continued
COMPOUND .
NUMBER of
SAMPLES
ANALYZED '
NtrMBEK OF
POSITIVE
SAMPLES
PEHCTST
PosmvE
SITES'
MBAS RESIDUE
LZVEL
(PPM)
NTEVADA
Arsenic
Arsenic
p.p'-DDE
DDTR
2
2
100.0
2.32
RANGE OK
DETECTED RESIOVFJ
(?m)
t. 77-2.86
NEW HAMPSHIRE
2
2
2
2
1
I
100.0
50.0
50.0
5.35
0.02
0.02
1J 1-9.3 8
0.03
0.03
NEW JERSEY
Arsenic
o,p'-DDE
P.p'-DDE
o,p'-DDT
p,p'-DDT
DDTR
Dieldrin
Endosulfan (II)
Endosulfan sulfate
Heptachlor epoxide
Lindunc
Ethyl parathion
o,p'-TDE
p.p'-TDE
5
5
5
5
5
5
5
5
5
5
5
I
5
5
5
1
2
I
2
2
2
2
100.0
20.0
40.0
20.0
40.0
40.0
40.0
20.0
20.0
20.0
20.0
100.0
20.0
40.0
11.72
<0.01
0.17
0.06
0.24
0.55
0.05
<0.01
0.02
-------
TABLE 3.—Pesticide residues in cropland soil from 43 Stata—FY 1969—Continued
COMPOUND -
NUMDCR OF
SAMPIIS
ANALYZED '
NUMBER OF
POSITIVE
SAMPLES
PmcF.vt
Posrrivt
SITE s-
MEAS RFSIOLC
LtVFL
(PPM)
RANCT OF
DtTTCItO RFSl^VTi
(TPM)
NORTH CAROLINA—Continued
o.p'-DDE
p,p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
Dieldrin
Endrin
Heptachlor
Hcplachlor cpoxide
Isodrin
Ethyl paralhlon
o,p'-TDE
p,p'-TDE
Toxaphene
Trifiuralin
3!
31
31
31
31
31
31
31
31
31
6
3)
3t
31
31
6
22
14
19
22
10
2
2
4
1
1
11
19
7
2
19.4
71.0
45.2
61.3
71.0
32.3
6.5
6.5
12.9
3.2
16.7
35.5
61.3
22.6
6.5
-C0.01
0.08
0.07
0.2S
0.53
0.08
<0.01
<0.01
<0.01
<0.01
-------
TABLE 3.—Pesticide residues in cropland soil from 43 State-,—FY 1969—Continued
COMPOUND
NUMBER OF
SAMPLES
ANALYZED >
NUMBER OP
POSITIVE
SAMPLES
PERCENT
POSITIVE
SITES3
MLAV RESIDLT
LEVLL
(PPM)
RtSGt OF
DETECTED RESIDVTS
<«M)
OKLAHOMA— Continued
Dieldrin
Heptachlor epoxide
p,p'-TDE
Trifluralin
64
64
64
64
2
1
2
I
3.1
1.6
3.1
1.6
<0.01
-------
TABLE 3.—Pesticide residues in cropland soil from 43 Stales—FY 1969—Continued
COMPOUND
NUMBER OK
SAMPLFS
ANALYZED '
NUMBER OF
POSITIVE
SAMPLFS
PERCENT
POSITIVE
Sms'
MEAN RISIDL-E
.LEVEL
(PPM)
RANGE or
DrnCTtD RcMDLtS
(PPX)
SOUTH DAKOTA—Continued
e.p'-DDT
p.p'-DDT
DDTR
Dieldrin
Heptachlor
Hepuchlor epoxide
Lindane
P.r'-TDE
106
106
106
106
106
106
106
106
2
2
4
9
1
3
3
'
1.9
1.9
3.8
8.5
0.9
2.8
2.8
0.9
-------
TABLE 3.—Pesticide residues in cropland soil from 43 States—FY 1969—Continued
COMPOUND
Aldrln
Arcenic
2.4-D
o,p'-DDE
p,p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
Dieldrin
o,p'-TDE
P.P'-TDE
Toxaphene
Trifluralin
NUMBE« OP
SAMPLES
ANALYZED >
NUMBEI OP
POSITIVE
SAMPLE]
PERCENT
POSITIVE
SITES =
MEAS RtsiotT
LENXL
(PPM)
RANGE or
DETEOTO RESiocts
(PPM)
WASHINGTON
45
45
6
45
45
45
45
45
45
45
45
45
45
I
45
1
2
10
6
10
11
8
1
3
1
1
4.4
100.0
16.7
4.4
22.2
13.3
22.2
24.4
17.8
2.2
6.7
2.2
2.2
<0.01
2.61
<0.01
-------
TABLE 4.—Pesticide residues in noncroplanJ soil from 11 Slates—FY 1969
COMPOUND
NUMBFH OF
SAMPLES
ANALYZED '
NUMBER OF
PosmvB
SAMPLES
PEKCCST
POSITUT
Sms«
MEAS RESIDLT
LivtL
("M)
RANGE or
Drrtcito RESIDLTJ
(MM)
ARIZONA
Arsenic
Chlordane
p,p'-DDE
p,p'-DDT
DDTR
Dieldrin
Arsenic
p,p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
Dieldrin
p,p'-TDE
44
44
44
44
44
44
44
1
8
1
8
I
100.0
2J
18.2
2J
18.2
2.3
6.63
<0.01
-------
TABLF. 4.—Pesticide residues in noncropland soil from II States—FY 1969—Continued
COMPOUND
NUMBER OF
SAMPLES
ANALYZF.O '
NUMRER OF
Posmve
SAMPLKS
PtRCENT
POSITIVE
Sm$»
MEAS RiSIDVE
LEVEL
(rrM)
RANGE or
DLILCILO RIJIDULS
(rrn)
NEBRASKA— Continued
DDTR
Dicofol
Dieldrin
Heplachlor epoxide
19
19
19
19
3
2
2
1
15.8
10.5
10.5
5.3
<0.01
0.02
<0.01
<0.01
0.01-0.07
0.10-0^9
0.01
OJO\
VIRGINIA
Arsenic
p.p'-DDT
DDTR
Dieldrin
p,p'-TDE
10
13
13
13
13
10
3
3
2
1
100.0
23.1
23.1
15.4
7.7
4.07
0.01
0.01
0.01
<0.01
OJO-12.42
0.03-0.07
0.03-OX»
0.03-0.09
0.02
WASHINGTON
Arsenic
p,p'-DDE
p,p'-DDT
DDTR
21
21
21
21
21
3
2
3
100.0
14.3
9.5
I4J
6.94
<0.01
<0.01
<0.01
1.58-54.17
0.01-0.02
0.01
0.01-0.03
WEST VIRGINIA
Arsenic
p,p'-DDE
p.p'-DDT
DDTR
Dieldrin
p,p'-TDE
6
8
8
8
8
8
6
100.0
12.5
12.5
123
12.5
12.3
5.16
-------
TABLE 5.—Arsenic residue data for the 12 Stales having the highest residue levels—FY 1969
STATE
Arkansas
Kentucky
New England »
New York
North Dakota
Ohio
Pennsylvania
NUMBFR op
SAMPLES
ANALYZED
47
31
19
37
158
69
29
PERCENT
POSITIVE
SITES «
100.0
100.0
100.0
94.6
100.0
100.0
100.0
MEAN RESIDUE
LEVEL
(PPM)
9.0
8.4
10.2
9.4
8.5
11.2
10.8
RANGE OP
DETECTED RESIDLTS
(rrM)
». 7-28.2
2.6-12.8
1.0-14.1
1.2-0.9
1.0-37.5
U-4IJ
3.0-64.9
1 Percent based on number of sites with residues greater than or equal to the sensitivity limits.
1 Connecticut, Maine. Massachusetts, New Hampshire, Rhode Island, and Vermont.
TABLE 6.—Pesticide residue data for 5 States having the highest DDTR residue levels—FY 1969
STATE
Alabama
California
Michigan
Mississippi
South Carolina
NUMBER OF
SAMPLES
ANALYZED
22
65
51
29
17
PERCENT
PosmvE
SITES'
90.9
84.6
23.5
89.7
88.2
MEAN RESIDUE
LEVEL
(PPM)
1.13
1.47
2.09.
2.06
1.17
RANCH OF
DETECTED RESIDUES
("V.)
0.05-8.08
0.01-4 1.81
O.OI-78J6
0.03-13.14
0.01-4.78
Percent based on number of sites wilh residues greater than or equal to the sensitivity limits.
TABLE 7.—Residue data for the 7 States with the highest dieldrin residue levels—FY 1969
STATE
Florida
Illinois
Iowa
Kentucky
North Carolina
Virginia/West Virginia
NUMBER OF
SAMPLES
ANALYZtD
18
142
151
31
31
27
PERCENT
PpsmvE
SITES1
38.9
61.3
53.6
54.8
32.3
25.9
MEAN RESIDUE
LEVEL
(PPM)
0.08
0.11
0.06
0.06
0.08
0.07
RANGE OF
DETECTED RESIDUES
(PPM)
0.01-0.52
0.01-1.^2
0.01-0.42
0.01-0.65
0.01-1.53
0.01-1.60
1 Percent based on number of sites with rebiducs greater than or equal to the sensitivity limits.
VOL. 6, No. 3, DECEMBER 1972
215
411
-------
TABLE 8.—Summary of pesticides used in FY 1969 on cropland for all 43 Slates
ALL STATES—1,664 SITES
COMPOUND
Aldrin
Amibcn
Aramite
Atrazinc
Azinphosmethyl
Azodrin
Bacillus Ihuringienjjj
Barban
Beneiin
Benzene hcxachloride
Bidrin
Binapacryl
Bordeaux mixtures
Cacodylic acid
Captan
Carbaryl
Carbophcnothion
CDAA
Cercsan I.
OrcSHii M
Ceresan icJ
Chevron RE-5353
Chlordane
Chlorobenzilale
Chlnroncb
Chloroxuron
Chromophon
CIPC
Copper carbonate
Copper oxide
Copper oxychloride suifale
Copper-8-quinolino!a!e
Copper sulfalc
Cotoran
2,4-D
2,4-DB
Dalapon
DDT technical
DEF
Demcton
Diazinon
Dicamba
Dichlone
Dichloropropajie
Dichloropropene
Dichlorprop
Dicofol
Dicldtin
Difolalan
Dimelan
Dimethoatc
Dinitrobutylphenol
Diniirocresol
Dinocop
Dioxathion
Diphenamid
Diquat
Disulfotun
PERCENT
OF
SlTTS
TREATED
4.16
2.14
0.12
7.66
0.59
042
0.12
0.12
0.18
0.06
0.24
0.06
0.06
0.06
11.16
1.72
0.18
0.89
1.25
1.48
1.84
0.30
0.12
0.12
0.36
0.30
0.06
0.12
0.06
0.18
0.12
0.06
0.36
0.48
15.14
0.89
0.42
3.44
0.59
0.18
1.96
0.30
0.12
0.06
0.36
0.06
0.42
1.19
0.06
0.06
0.12
0.95
0.06
0.12
0.12
0.24
0.06
1.72
AVERA&E
APPLI-
CO ION
RATE
(IB/ACRE)
1.25
1.07
2.35
1.88
1.70
2.07
9.50
0.17
1.36
3.00
0.18
2.12
0.50
001
0.12
3 64
1.83
1.78
0.01
0.01
0.01
1.72
3.10
1.31
0.05
1.65
0.15
1.50
0.60
4.23
4.68
0.01
13.53
0.74
0.54
0.4?
2.12
5.56
\.f>6
0.59
1.22
0.39
2.00
54.43
70.07
2.00
2.12
0.17
0.01
0.01
0.75
3.78
3.00
0.22
2.60
2.19
O.S3
1.77
AVERAGE
AMOUNT
APPLIED
PI-.R Snn
(LB/ACRE)
00522
0.0229
0.0028
0.1442
0.0101
0.0086
0.0113
0.0002
0.0024
0.0018
0.0004
0.0013
0.0003
0.0000
0.0133
0.0627
0.0033
0.0158
0.0002
0.0001
0.0003
0.0051
0.00? 7
0.0016
0.0002
0.0049
0.0001
0.0018
0.0004
0.0075
0.0056
0.0000
0.0482
0.0035
0.0825
0.0042
0.0088
0.1915
0.0099
0.0011
0.0240
0.0012
0.0024
0.0323
0.2496
0.0012
0.0088
0.0021
0.0000
0.0000
0.0009
0.0359
0.0018
0.0003
0.0031
0 0052
00005
0.0305
COMPOUND
Dnhane M-45
Diuron
DSMA
Endosulfan (1)
Endrin
EPN
EPTC
Elhion
Elh>lene dibromidc
Falore
Ferbam
Folex
Hepiachlor
Herbiian
Hexachlorobenzenc
Lead arsenate
Lindane
Linuron
Malathion
Maleic hydrazidt
Maneb
MCPA
Methox>chlor
Methyl demcton
Meth>lmcrcury
dicyandiajnide
Methylrnercury nitrilc
Mevinphos
Mirex
Monuron
MSMA
Nabam
Nalcd
Nitralin
Nitrate
Korea
NPA
Oxyde me lo nme Lay!
Ethyl parathior;
Methyl paralhJon
PCNB
PCP
Phcnylmercury urea
Phorate
Phosphamidon
Picloram
PMA
Polyram
Promelo ne
Propanil
Propa2ine
Ramrod
Ro-Nm
Roundup
R.tndox T
Silvex
Simajine
Smielrync
Sodium arsenitc
Sodium chlorate
PEHCTNT
OF
SITES
TREATTO
j 0.30
'•"
j 0.36
j 0.48
| 0.48
] 0.06
i OJ6
! 034
1 0.12
0.06
0.06
0.06
1.96
; 0.06
I 0.06
0.06
1 0.65
j 0.77
7.54
OJ6
OJO
1.07
:.20
0.06
5.46
OM
036
0.24
0.06
0.48
0^4
030
0.36
1.13
0.12
036
0.18
1.84
3.03
0.42
0.06
0.06
0.65
0.12
0.12
0.18
0.06
0.06
0.42
0.06
1J7
0.12
0.12
0.12
0.12
0.12
O.C«
0.24
0.06
AVEKAGE
ArpLi-
CATION
RATE
(L8/ACXE)
5.82
0.93
1.52
1.11
2.21
1.50
2.65
2.06
K.62
2.00
9.12
1.50
0.33
10.00
0.01
3.80
0.03
0.73
0.17
1.43
2.14
OJ3
0.04
1.50
0.01
0.01
1.4S
0.01
1.60
1.21
1.18
1.62
0.76
64.58
0.4«
1.01
0.40
1.44
3.07
1.59
1JO
0.01
2.17
0.13
0.63
0.06
10.40
2.00
3.96
2.00
1.45
1.88
0.78
0.90
0.63
2.07
2.00
5.25
6.00
AVERAGE
AMOLVT
Ar?Lito
fa. SITE
(L8/ACHE)
0.0173
0.0105
0.0054
0.0033
0.0105
0.0009
0.0094
O.CK>29
0.0174
0.00 11
0.0054
0.000?
O.CO65 '
O.OOfC
0.0000
1 0.0013
1 0.0002
I 0.0056
j 0.0127
0.0051
0.0064
0.0035
0.0008
O.CX»*
O.f'X-6
O.COOO
O.C053
O.COOO
0.0010
0.0058
0.0042
O.OOiS
0.0027
0.7266
0.0006
OJX336
0.0007
0.0272
0.0929
0.0066
0.0009
OJJOOO
O.OK2
0.0002
0.0007
0X1001
0.0062
0.0012
0.0165
0.0012
0.0198
0.0022
0.0009
0.0011
0.0007
0.0025
0.0012
0.0125
0.0036
216
PESTICIDES MONITORING JOURNAL
412
-------
"ABLE 8.—Summary of pesticides uactl in FY 79(59
on cropland for all 43 States—Continued
TABLE 10.—Summary of pesticides used in FY
on cropland by State—Continued
COMPOUND
Slrobane
Sulfur
2,4,5-T
TCA
TUE technical
Telradifon
Thiram
Toxaphenc
Trichlorofon
Trifluralin
Vernolale
Zineb
Ziram
PERCENT
op
SITES
TREATED
0.12
0.71
0.18
0.06
0.36
0.12
1.07
1.90
0.06
4.33
0.53
0.18
0.06
AVI RAGE
APPLI-
CATION
RATE
(LO/ACRC)
16.50
34.00
0.83
2.00
2.31
0.50
0.03
9.87
0.80
0.76
1.29
4.90
0.80
AvtRAGE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
0.0196
0.2423
0.0015
0.0012
0.0082
0.0006
0.0003
0.1876
0.0005
0.0327
0.0069
0.0087
0.0005
TABLE 9. — Summary of pesticides used in FY 1969
on noncropland for all 11 States
ALL STATES— 195 SITES
COMPOUND
2,4-D
Malalhion
Mirtx
SITES
TREATED
0.51
0.51
0.51
AVERAGE
APPLI-
CATION
RATE
(LB/ACRE)
2.00
0.61
0.01
AVERAGE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
0.0103
0.0031
0.0001
TABLE 10.— Summary of pesticides used in
FY 1969 on cropland by State
COMPOUND
PERCENT
SITES
TREATED
AVERAGE
APPLI-
CATION
(LB/ACRE)
AVERAGE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
ALABAMA— 23 SITES
A/odrin
Benzene hexachloride
Captan
Carharyl
Ctrcsan M
Copier sulfalc
Cotoran
DDT technical
DEF
Diuron
DSMA
Kndrin
P.l'N -
4J5
4.35
21.74
4.35
4.35
8.70
4.35
39.13
4.35
8 70
8.70
4.35
8.70
4.35
0.84
3.00
0.04
0.40
0.01
36.08
1.50
10.73
1.50
0 35
0.95
1.00
1.20
1.50
0.0365
0.1304
0.0083
0.0174
0.0004
3.1374
0.0652
4.2000
O.OM2
0030-t
0.0826
0.0435
0.1043
0.0652
COMPOUND
PERCENT
OF
SITES
TREATED
AVT-RAGC
APPLI-
CATION
RATE
ILB ACRE)
AVMAteE
AMOUN1
APPLIED
PL« SITE
(LB 'ACRE)
ALABAMA— 23 SITES— Comir.ued
Malalhion
Ethyl paralhion
Meth>l paralhion
MSMA
Phoratc
Promelrync
Thiram
Toxaphenc
Trifluralin
Vernolale
8.70
4.35
52.17
8.70
4.35
4J5
4.35
17J9
47.83
8.70
2.50
1.00
3.42
MO
1.00
2.00
0.0:
J.45
0.61
1.05
0.2174
O.CK35
1.7848
0.1304
0.0435
0.0870
0.0009
06000
0.2913
0.0913
ARIZONA— 9 SITES
Azodrin
Captan
Ceresan L
Demeton
Dicldrin
Diuron
Endosulfan (I)
Naled
Ethyl paralhion
Methyl parathiin
PCNB
Phorate
Strobane
Toxaphene
Trifluralin
11.11
11.11
11.11
11.11
11.11
11.11
11.11
11.11
22.22
44.44
11.11
11.11
11.11
22.22
6.25
0.6944
0.0!
0.01
0.13
0.01
1.00
2.00
0.50
5.50
2.75
1.50
15.00
2.00
1.12
0.0011
O.OOil
0.0144
O/X'1 1
o.nn
0.22:2
0.0556
1.2222
i.::i-i
0.1667
1.6667
0.2222
0.2500
ARKANSAS— 45 SITES
Aldrin
Captaji
Ceresan M
Chloroxuron
2,4-D
2,4-DB
DEF
Disulfoton
Diuron
DSMA
Endrin
Linuron
NPA
Nitralin
Methvl paralhion
Propanil
2.4.5-T
Thiram
Trifluralin
2.22
13.33
2.22
6.67
2.22
2.22
2.22
6 67
2.22
2.22
4.44
2.22
8.89
6.67
2.22
2.22
4.44
4.44
4.44
15.56
0.25
0.03
0.01
1.00
0.05
1.75
1.00
1 58
0.01
0.7}
3.00
12.00
0.94
054
0.44
12-00
530
O.S8
0.03
0.79
0.0056
0.0036
0.0002
O.W«7
0.0011
0.0389
0.0222
0 10<6
0.0002
0.0167
0.1333
0.2667
O.OS33
0.0362
0.0098
0.2667
0.24-U
0.03
-------
TABLE 10.—Summary o/ pesticides used in FY 1969 on cropland by Stale—Continued
COMPOUND
PERCENT
OF
SITES
TREATED
ANI.RACE
APPLI-
CATION
RATC
ILB'ACRE)
AVCKACE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
CALIFORNIA— 66 SITES— Continued
Azinphosrnelhyl
Bacillus thuringiensis
Bcnefin
Binapacryl
Bordeaux mixtures
Captan
Carbaryl
Carbophenothion
Ceresan red
Chlordanc
2,4-D
DDT technical
Dinzinon
Dicliloropropcnc
Dichlorprop
Dicofol
Dinicthoatc
Dioxathion
Diphcn.imid
Disulfnton
Diuinn
Dithanc M-45
Endosulfan (1)
Ethion
Malalhion
MCPA
Mevinphos
Nabam
Naled
Ethyl parathion
Melhyl parathion
Propanil
Simazine
Simetryne
Sulfur
Tctradifon
Toxaphenc
Trichlorofon
Trifluralin
3.03
3.03
1.52
1.52
1.52
1.52
6.06
3.03
3.03
1.52
3.03
13.64
6.06
4.55
1.52
7.58
1.52
3.03
1.52
3.03
1.52
3.03
7.58
3.03
4.55
3.03
9.09
1.52
6.06
6.06
4.55
3.03
1.52
1.52
7.58
3.03
6.06
1.52
6.06
0.48
9.50
1 .83
2.12
0.50
2.30
10.76
1.75
0.01
5.00
0.63
2.82
0.99
8.67
2.00
2.59
1.00
?..60
2.62
4.00
0.75
5.00
0.99
1.38
1.65
0.76
1.48
3.50
1.90
2.03
6.10
3.63
3.75
2.00
35.79
0.50
9.75
0.80
0.88
0.0145
0.2879
0.0277
0.0321
0.0076
0.0348
0.6521
0.0530
0.0003
0.0758
0.0189
0.3844
0.0603
0.3939
0.0303
0.1962
0.0152
0.0788
0.0397
0.1212
0.0114
0.1515
0.0750
0.0417
0.0750
0.0230
0.1345
0.0530
0.1152
0.1230
0.2773
0.1098
0.0568
0.0303
2.7115
0.0152
0.5908
0.0121
0.0530
COLORADO— 60 SITES
Aldrin
Carbaryl
Ccresan M
2,4-D
2,4-DB
Endrin
Malalhion
Ethyl parathion
Picloram
PMA
3.33
1.67
1.67
10.00
1.67
5.00
1.67
1.67
1.67
1.67
0.08
1.00
0.01
0.51
0.70
0.33
0.60
0.25
1.00
0.15
CONNTCT1CUT— 2 SITUS
Atrazinc
50.00
0.0027
0.0167
0.0002
0.0508
0.0117
0.0167
0.0100
0.0042
0.0167
0.0025
1.2500
COMPOUND
PnCENT
OF
Srrti
TlEATEO
AVEHAGE
AJPLf-
CATION
RATB
(L«/ACM)
AVEAACE
AMOUNT
ATTLtLa
PER Sm
(IB/ACRE)
DELAWARE— 3 SITES
Captan
Lindanc
33J3
33-33
0.04
0.08
0.0133
0.0267
FLORIDA— 15 SITES
Atrazinc
Azinphosmethyl
Captan
Carbophenoihion
Chlorober.zil2ie
Copper oxide
Copper ox>chloridc
sulfatc
2,4. D
Dalapon
DDT technical
Diazinon
Dichloroproperw
Dicofol
Ethion
Ferbam
Mirex
Ethyl parathion
Methyl parathion
Sulfur
2,4,5-T
TDE technical
Toxaphenc
Zineb
6.67
6.67
6.67
6.67
13.33
6.67
6.67
6.67
6.67
6.67
6.67
667
6.67
13.33
0.80
2.50
7.50
2.00
1J1
730
8.00
130
1.70
7.00
2.90
194.40
1.50
2.75
6.67 1 9.12
6.67
13.33
£.67
6.67
6.67
6.67
6.67
13.33
0.01
2.85
10.00
•46.50
0.75
10.00
2.00
635
0.0533
0.1667
0.5003
0.1333
0.1753
0.5000
03333
O.IOO)
0.1133
0.4667
0.1933
129500
0.1000
0.3667
0.6080
0.0007
0--BCO
0.6667
3.1000
0.0500
0.6667
0.1333
0.8733
GEORGIA— M SITES
Atrazinc
Aiodrin
Benefin
Captan
Cercsau red
Copper OTvcrUoriCte
sulfate
Copper sulfate
2,4-D
DDT technics!
Disulfoton
Folex
Malathion
Maleic h)drazid<
Methoxychlor
Mirex
Ethyl parathion
Methyl parathien
PCNB
Sulfur
Thirarn
Toxaphene
Trifluralin
7.14
3.57
7.14
39.29
10.71
3.57
337
10.71
21.43
3J7
3.57
21X3
7.14
2SJ7
3.57
3.57
U.29
3.57
7.14
10.71
17.86
7.14
3.00
4.00
1.12
0.08
0.01
1J6
2.72
030
14.18
2.00
130
0.34
2.41
0.02
0.01
1.00
1.84
10.00
40 20
0.04
14.45
1.25
0.2143
0.1429
0.0800
0.0307
0.001!
0.04S6
0.097!
0.0536
3.0396
0:0714
0.0536
0.0732
0.1725
0.0046
0.0004
0.0357
0^632
0.3571
2 8714
0.0043
:.'AL
414
-------
TABLE 10. Summary of pesticides used in FY 1969 on cropland by Slate—Continued
COMPOUND
PERCENT
OF
SITES
TREATED
AvfRAGE
APPU-
CHIION
RATE
(LB/ACRE)
AVI RACK
AMOUNT
APPLltD
PLR Sire
(LR/ACRE)
IDAHO— 33 SITES
Captan
Ccresan M
Ceresan L
CIPC
2.4-D
2,4-DB
DDT technical
Dieldrin
Diqual
EPTC
Hcrxachlorobenzene
Ro-Nect
Trifluralin
12.12
6.06
15.15
3.03
12.12
3.03
fi fifi
O.UO
3.03
3.03
3.03
3.03
6.06
6.06
0.01
0.01
0.01
2.00
2.12
0.50
ft ^n
U. J\t
0.01
0.83
0.38
0.01
1.87
0.56
0.0015
0.0006
0.0015
0.0606
0.2576
0.0152
n film
U.UJUJ
0.0003
0.0252
0.0115
0.0003
0.1136
0.0342
ILLINOIS— 141 SITES
Aldrin
Amibcrt
Atrazinc
Caplan
Carbaryl
CDAA
Ceresan red
Ceresan L
Chevron RE-5353
2,4- D
2,4-DD
Diazinon
Dieldrin
Heplachlor
Linuron
Malathion
Methoxychlor
Ramrod
Roundup
Silvex
Thiram
Trifluralin
Vernolale
19.15
7.80
9.22
49.65
0.71
7.80
0.71
0.71
1.42
20.57
2.13
3.55
2.13
9.93
1.42
39.72
10.64
7.80
0.71
0.71
0.71
2.84
0.71
1.52
0.91
2.19
0.06
4.80
1.51
0.01
0.06
2.56
0.42
0.35
1.86
0.23
0.46
0.66
0.03
0.01
1.22
0.07
0.25
0.01
0.97
0.37
0.2914
0.0707
0.2023
0.0317
0.0340
0.1176
0.0001
0.0004
0.0363
0.0864
0.0075
0.0659
0.0050
0.0455
0.0094
0.0104
0.0011
0.0955
0.0005
0.0018
0.0001
0.0277
0.0026
INDIANA— 75 SITES
AJdrin
Amiben
Atrazinc
Caplan
Carbaryl
CDAA
Ceri'san I.
2.4-D
DDT technical
Dicldrin
Difolatan
Heptachlor
Malathion
Mcthoxychlor
10.67
5.33
13.33
26.67
1.33
1.33
2.67
10.67
1.33
1.33
1.33
5.33
17.33
4.00
1.11
0.85
1.79
0.01
1.60
1.07
0.01
0.35
0.01
0.01
0.01
0.32
0.01
0.01
0.1187
0.0453
0.2393
0.0027
0.0213
0.0143
0.0003
00373
0.0001
0.0001
0.0001
0.0172
0.0017
0.0004
COMPOUND
PERCENT
OP
SITES
TXEATEO
A v« ACE
APPLI-
CATION'
RATE
(LB/ACHE)
Avrr.AGE
AMOL'VT
APPLIfD
PER Sm
(LB/AOe)
INDIANA— 75 SITES— Continued
Mcthjlmercury
dicyandiamide
Ramrod
Roundup
rrifluralin
Zineb
2.67
2.67
1.33
2.67
1.33
0.01
1.40
1.50
0.75
1.60
0.0003
0.0373
0.0200
0.0200
0.0213
IOWA— 151 SITES
Aldrin
Amiben
Alrazine
Captan
Carbaryl
CDAA
f~ht>\jri-tn PP <1O
*_ncvrOn K tOJjJ
2,4-D
Diazinon
Dicamba
Dieldrin
Heptachlor
Lindane
Ethyl parathion
Phoratc
Ramrod
Randox T
Thiram
Trifluralin
8.61
8.61
10.60
2.65
0.66
1.32
n «
U.oo
20.53
6.62
0.66
0.66
4.64
1.32
0.66
1.32
3.97
0.66
0.66
2.65
0.68
1.12
2.00
0.03
1.00
1.50
IfVl
.w
0.62
1.19
0.50
0.15
0.35
0.06
OJ2
0.95
2.02
0.40
0.06
0.47
0.0587
0.0966
0.2123
0.0003
0.00«
0.0199
fi C«"1A£
U.wvO
0.1278
0.0791
0.0033
0.0010
0.0164
0.0009
0.00:i
0.0126
0.0301
0.0026
0.0004
0.0125
KENTUCKY— 31 SITES
Aldrin
Atrazine
2,4-D
Dalapon
DDT technical
EPTC
9.68
19.35
3.23
3.23
3.23
3.23
2.00
1J3
0.50
1.50
3.00
1.50
0.1935
0.2581
0.0161
0.04*4
0.096S
0.0484
LOUISIANA— 27 SITES
Aldrin
Captan
Carbaryl
Ccresan L
Cotoran
2,4-D
Dalapon
DDT technical
DEF
Dimetan
M.ilaihion
Mcihylmcrcury
dicyandiamidc
Mcth> Iniercury nitrilc
MS.MA
Nitr.nc
22.22
3.70
3.70
3.70
3.70
11.11
3.70
7.41
3.70
3.70
3.70
3.70
3.70
3.70
22.22
0.08
0.25
12.00
0.01
1.00
1.58
2.00
23.25
9.00
0.01
1.00
0.01
0.01
1.50
72.00
0.0178
0.0093
0.4-U4
0.0004
0.0370
0.1759
0.0741
1.7222
OJ333
O.OXM
0.0370
0.0004
0.0004
0.0556
16.0000
VOL. 6, No. 3, DECEMBER 1972
719
415
-------
TABLE 10.—Summary of pesticides used in FY 1969 on cropland by Staff—Continued
COMPOUND
PERCENT
or
SlTFS
TREATED
AVLRM.E
APPLI-
CATION
RATE
(LB/ACRE)
AVCKACE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
LOUISIANA— 27 SITES— Continued
Methyl parathion
Propanil
Silvex
Strobane
TCA
Toxaphcne
Trifluralin
7.41
11.11
3.70
3.70
3.70
3.70
3.70
7.20
3.17
1.00
18.00
2.00
75.00
1.00
0.5333
0.3519
0.0370
0.6667
0.0741
2.7778
0.0370
MAINE — 8 SITES
Dalapon
Dinitrobutylphcnol
Disulfolon
Malalhion
Mancb
Sodium arsenite
12.50
37.50
25.00
12.50
12.50
25.00
4.90
1.37
8.50
1.00
0.70
8.80
0.6125
0.5125
2.1250
0.1250
0.0875
2.2000
MARYLAND— 13 SITES
Atraiinc
Captan
2,4-D
Dieldrin
Lindane
Malalhion
Melhoxychlor
Thiram
30.77
30.77
15.38
7.69
15.38
23.08
7.69
7.69
1.26
0.03
0.54
0.01
0.01
0.01
0.01
0.01
0.3885
0.0100
0.0838
0.0008
0.0015
0.0023
0.0008
0.0008
MASSACHUSETTS— 2 SITES
Carbaryl
Dinilrobulylphenol
Disulfoton
Dilhanc M-45
Malcic hydrazidc
Oxydemetonmethyl
Ethyl paratbion
50.00
50.00
50.00
50.00
50.00
50.00
50.00
0.83
3!06
1.50
12.40
2.32
0.25
0.53
0.4150
1.5300
0.7500
6.2000
1.1600
0.1250
0.2650
MICHIGAN— SI SITES
Atrazinc
Azinphosmcthyl
Cap tan
CDAA
Ceresaji red
C1PC
Chloroxuron
2.4-D
DDT technical
Dinitroburylphenol
Diuron
EPTC
Herbisan
Malalhion
Mcllioxychloi
11.76
1.96
1.96
1.96
1.96
1.96
3.92
9.80
1.96
1.96
I Q£
1 .70
1.96
1.96
3.92
'•"
1.49
8.00
0.01
6.00
0.01
1.00
2.63
0.53
1.50
11.25
*> f\n
/.uu
2.00
10.00
0.50
o.ni
0.1753
0.1569
0.0002
0.1176
O.CO02
0.0196
0.1029
0.0524
0.0294
0.2206
0.0392
0,0392
0.1961
0.0198
0.0002
COMPOL'SD
PWCEST
OP
SITES
TREATED
AVEKAGE
APfLI-
CAT1ON
RATE
(u/AOte)
AvtHAGE
AMOUNT
ArpLiEo
Pt« SfTE
(L3/AO.E)
MISSISSIPPI— 29 SITES
Azinphosmethyl
Azodrin
Bidrin
Captan
Ccresan M
Ceresan red
Ceresan L
Chloroncb
Cotoran
DDT technical
DEF
Disulfoton
Diuron
DSMA
Endrin
Linuron
Malathion
McthoxvchJor
Mirex
MSMA
Norta
NiLralin
Methyl parathion
PCNB
Sodium chlorate
Toxaphenc
Trifluralin
Vernolate
J.45
6.90
6.90
24.14
3.45
27.59
3.45
J7.24
13.79
31.03
20.69
31.03
17.24
10.34
005
0.76
0.03
0.09
0.01
0.02
0.01
0.06
0.47
3.47
0.82
0.05
0.63
0.70
3.45 ! 2.00
3.<5
6.90
3.45
6.90
1034
3.45
13.79
4138
10.34
3.45
34.48
37.93
3.45
0.42
1.40
0.01
0.01
1.44
OJ3
0.99
2.14
0.13
6.00
7.50
0.85
OJO
MISSOURI— 81 SITES
Aldrin
Amiben
Atrazine
Bidrin
Captaji
Ceresaji M
2.4-D
2,4-DB
Diazinon
Dinitrobutylphenol
Heplachlor
Linuron
NPA
Methyl parathion
Propazine
Ramrod
Trifluralin
VernoUle
4.94
4.94
12.35
1.23
103
1.23
11.11
2.47
1.23
2.47
1.23
2.47
2.47
1.23
103
103
7.41
2.47
1.65
1.15
1.87
0.10
0.01
0.01
0.77
005
0.93
0.53
0.19
OJ7
1.07
0.50
2.00
1.10
0.91
1J8
O.OOS6
0.0524
0.0024
0.0210
0.0003
0.0045
0.0003
0.0100
0.0652
1.0759
0.1690
0.0152
0.1079
0.0728
0.0690
0.0145
0.0969
0.0003
0.0007
O.KF6
0.0114
0.1372
08W1
0.0131
0.2069
2.5f62
OJ241
0.0103
0.0815
0.0568
0.2315
0.0012
0.0001
0.0001
0.0856
0.0062
0.0115
0.0131
0.0023
0.0091
0.0264
0.0062
0.0247
0.0136
0.0673
0.0340
NEBRASKA— 103 SITES
Amiben
Alrazinc
Captan
Ceresan red
0.97
4.85
17.48
0.97
2.50
0.82
0.04
0.01
0.0243
C.04CO
0.0069
0.0011
220
PESTICIDES MONITORING JOURNAL
416
-------
TABLE 10.—Summary of pesticides used in FY 1969 on cropland by Stale—Continued
COMPOUND
PERCENT
or
S1TK3
TREATED
AVERAGE
APPLI-
CATION
RATE
(LB/ACRE)
AVERAGE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
NEBRASKA— 103 SITES- Continued
Ccrcsan L
Chevron RE-5353
2.4-D
Diazinon
Dieldrin
Disulfoton
EPTC
Malathion
Methoxychlor
Methylmercury
dicyandiamide
Nabani
Noica
Ethyl paralhion
Phorate
Ramrod
Thirani
0.97
1.94
14.56
4.85
3.88
0.97
0.97
17.48
1.94
4.85
0.97
0.97
3.88
0.97
0.97
4.85
0.01
1.25
0.44
0.98
0.01
0.22
3.00
0.0 1
0.01
0.01
0.01
0.60
0.50
0.90
0.83
0.03
0.0001
0.0243
0.0644
0.0476
0.0004
0.0021
0.0291
0.0017
0.0002
0.0005
0.0001
0.0058
0.0194
0.0087
0.0081
0.0014
NEW JERSEY-5 SITES
2,4-l>
Monuron
Ethyl parathion
Sulfur
40.00
20.00
20.00
20.00
0.31
1.60
0.54
9.00
0.1240
0.3200
0.1080
1.8000
NEW MEXICO— 10 SITES
Azodrin
Carbaiyl
DDT technical
Diuron
Ethyl parathion
Toxaphen«
10.00
10.00
10.00
20.00
10.00
10.00
1.50
2.50
1.00
1.12
2.50
1.00
0.1500
0.2500
0.1000
02250
0.2500
0.1000
NEW YORK— 38 SITES
Atrazine
Azinphosmethyl
Captan
Carbaryl
Copper sulfate
2,4-D
Dalapoit
DDT technical
Demeton
Diazinon
Dichlone
Dinilrobutylphenol
Diuron
Endosulfan (1)
Lead arscnate
Malathion
MCPA
Methoxychlor
Mcthylmrrcury
dicyandiamide
23.68
5.26
13.16
5.26
2.63
7.89
2.63
5.26
2.63
2.63
2.63
5.26
5.26
5.26
2.63
5.26
5.26
2.61
10.53
1.36
1.15
0.87
1.55
3.00
0.21
2.50
135
0.04
1.00
2.20
15.22
2.40
0.95
3.80
0.01
0.33
0.01
0.01
0.3211
0.0605
0.1150
0.0816
0.0789
0.0166
0.0658
0.0711
0.00 11
0.0263
0.0579
0.8013
0.1263
0.0500
0.1000
0.0005
0.0176
0.0003
0.0011
COMPOUND
PERCENT
OF
SITES
TREATED
AVLJUGE
APPLI-
CATION
(IB/ACRE)
NEW YORK— 38 SITES— Continued
Nabam
Nitrate
Oxyde me ton methyl
Ethyl parathion
Phosphamidon
Sodium arserute
2.63
7.89
2.63
5.26
2.63
2.63
2.40
26.17
0.15
0.45
0.15
0.90
AVERACE
APPLIED
PER SITE
(t! ICRE)
0.0632
2.0658
0.0039
0.0239
0.0039
0.0237
NORTH CAROLINA— 29 SITES
Aldrin
Atrazine
Carbaryl
Ceresan red
Chromophon
Copper carbonate
Copper-8-quinoliro'.ate
2.4-D
2,4-DB
DDT technical
Diazinon
Dicamba
Dichloropropene
Dieldrin
Dinitrobutyl phenol
Diphenamid
EPTC
Ethylene dibromic't
Lindane
Maleic hydrazJde
Ethyl parathion
Methyl paratbion
Phorate
Sulfur
TDE technical
Thiram
Toxaph«ne
Trifluralin
Vemolale
6.90
6..90
20.69
3.45
3.45
3.45
3.45
20.69
3.45
13.79
13.?9
3.45
3.45
6.90
3.45
6.90
<
:>AJ
3:45
10.34
1.75
2.75
1.43
0.1207
0.1897
0.2966
0.10 i 0.0034
0.15 0.0052
0.60
0.01
1.00
0.07
0.70
1.12
20.00
1.25
1.50
1.07
_
600
00!
0*7
6.90 0.50
3.45
6.90
3.45
10.34
6.90
6.90
3.45
6.90
0.83
1.13
11.10
0.26
0.01
8.65
0.57
I.S5
0.0207
0.0003
0.2079
0.0024
0.0962
0.1545
0.0414
0.6897
0.0866
0.0517
0.0741
Tj.ljT?
O.IT069
O.OT03
O.W26
0.0345
0.02S6
0.0793
OJS2S
O.C272
O.CO07
0.5966
0.0197
0.1276
NORTH DAKOTA— 159 SITES
Barban
CapUn
Ceresan M
Ceresan red
. Ceresan L
2.4-D
Dicamba
Disulfoton
Endrin
Heptachlor
Lindane
Malalhion
Maneb
MCPA
Methylmercury
dicyandiamide
1.26
0.63
0.63
2.52
5.03
42.14
1.89
0.63
0.63
1.26
1.89
0.63
O.M
5.66
41.51
0.17
0.01
0.01
0.01
0.0 1
0.40
0.03
3.00
0.25
0.04
O.C2
C.01
1.50
0.30
0.0!
0.0021
0.0001
0.0001
0.0003
0.0005
0.1673
0.0016
0.0189
0.0016
0.0005
O.OWW
0.0001
0.0094
0.0171
P.0042
VOL. 6, No. 3. DECEMBER 1972
221
417
-------
TABLE 10.—Summary of pesticides used in FY 1969 on cropland by State—Continued
COMPOUND
PERCENT
OF
SITES
TREATED
AVERAGE
APPLI-
CATION
RATE
(LB/ACRE)
AVEPACE
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
NORTH DAKOTA— 159 SITES— Continued
Phenylmercury urea
PMA
Polyram
0.63
1.26
0.63
0.01
0.01
10.40
0.0001
0.0001
0.0654
OHIO— 66 SITES
Aldrin
Amiben
Atiazine
Captan
Ceresan M
Copper sulfate
2,4-D
Dalapon
Diazinon
Dichlonc
Dicldrin
Dinocap
Dithanc M-45
Linuron
Matalhion
Maneb
Melhylmercury
dicyandiamide
NPA
PCP
Piclorarrt
Randox T
Sulfur
TDE technical
Trifluralin
Ziram
6.06
3.03
12.12
12.12
1.52
1.52
19.70
1.52
1.52
1.52
1.52
1.52
1.52
1.52
10.61
3.03
1.52
1.52
1.52
1.52
1.52
1.52
1.52
1.52
1.52
3.00
1.75
1.20
0.02
0.01
1.60
0.44
1.50
0.50
1.80
0.01
0.01
0.30
0.75
0.01
0.75
0.05
2.27
1.50
0.25
1.40
25.00
0.80
1.00
0.80
0.1818
0.0530
0.1455
0.0021
0.0002
0.0242
0.0859
0.0227
0.0076
0.0273
0.0002
0.0002
0.0045
0.0114
0.0011
0.0227
0.0008
0.0344
0.0227
0.0038
0.0212
0.378S
0.0121
0.0152
0.0121
OKLAHOMA— 45 SITES
Cacodylic acid
Captan
Carbaryl
Ceresan M
Ceresan red
Chloroneb
2,4-D
2,4-DB
Dieldrin
Dimethoate
Dinitrobutyl phenol
Disulfoton
Falone
Methylmercury
dicyandidmidc
Nitrate
Ethyl paraihion
Methyl paraihion
PCNB
Phosphamidon
Thiram
Trifluralin
1.54
4.62
1.54
20.00
12.31
1.54
6.15
4.62
4.62
1.54
1.54
7.69
1.54
1.54
10.77
3.08
12.31
1.54
1.54
1.54
4.62
0.01
0.01
0.30
0.01
0.01
0.01
0.86
0.50
0.01
0.50
7. .00
0.58
2.00
0.01
16.64
0.75
0.65
0.01
0 1 2
0.01
1.10
0.0002
0.0005
0.0046
0.0020
0.0012
0.0002
0.0531
0.0231
0.0005
0.0077
0.0308
0.0445
0.0308
0.0002
1.7923
0.0231
0.0800
0.0002
0.0018
0.0002
0.0508
COMPOL'SD
PEKCT^T
OF
SITES
TREATED
PENNSYLVANIA— 31
Atrarine
Azinphosmeih> 1
Captan
Carbaryl
Chlordane
Copper sulfate
2,4-D
DDT technical
Dicofol
Diniuobutyl phenol
Dinitrocresol
Dinocap
Diuron
Lindane
Linuron
Maneb
Methyl demeton
NiLrate
Ethyl paraihion
Phoratc
Simazine
Sodium arscnite
Trifluralin
I9J5
3.23
3.23
3.23
3.23
3.23
16.13
9.68
3.23
3.23
3.23
3.2J
3.23
3.23
333
3.23
3.23
3.23
6.45
3.23
3.23
3.23
3.23
AVWAGE
APPLI-
CATION
FUTE
(l«/AC«E)
SITES
1.57
0.50
0.01
0.32
1.20
1.70
0.92
0.83
0.42
0.82
3.00
0.44
0.32
0.02
1.00
7.00
1.50
100.00
0.45
12.50
0.40
:.— 1 SITE
Carbaryl
DDT technical
Disulfoton
Dithane M-45
EPTC
Oxydemetonmethy 1
100.00
100.00
100.00
100.00
100.00
100.00
0.80
2.00
2.00
6.40
5.00
0.80
0.8000
2.0000
2.0000
6.4000
5.0000
0.8000
SOUTH CAROLINA— n SITES
Azodrin
Carbaryl
2,4-D
DDT technical
DEF
Demeton
Diuron
MSMA
Nabam
Ethyl paraihion
Meth>! parathion
Phorale
TDE technical
Toxaphene
Trifiuralin
5.88
17.65
11.76
29.41
5.88
5.88
5.88
5.88
5.88
11.76
11.76
5.88
5.88
17.65
35.29
0.40
7.19
0.40
2.46
0.20
1.60
0.72
0.45
1.20
0.51
5.10
0.20
2.25
6.17
0.21
0.0235
1J^82
0.0471
0.7229
0.0118
0.0941
0.0424
0.0265
0.0706
0.0600
0.6000
0.0118
0.1324
1.0894
0,0753
SOUTH DAKOTA— 106 SITES
Atrazine
Captan
Carbar>I
1.89
10.38
0.94
1.40
0.01
1.05
0.0264
0.0010
0.0099
222
PESTICIDES MONITORING JOURNAL
418
-------
TABLE 10.—Summary of pesticides used in FY 1969 on cropland by Slate—Continued
COMPOUND
PERCENT
OP
SITES
TREATED
AVERAGE
APPLI-
CATION
RATE
(I.B/ACRE)
AVERACB
AMOUNT
APPLIED
PER SITE
(LB/ACRE)
SOUTH DAKOTA— 106 SITES— Continued
Ceresan M
2.4-D
Dalapon
Dieldrin
Heptachlor
Llndanc
Malathion
MCPA
Methoxychlor
Methylmercury
dicyandiamide
Phorate
Ramrod
Thiram
0.94
26.42
0.94
1.89
3.17
0.94
6.60
4.72
3.77
10.38
0.94
0.94
0.94
0.01
0.47
0.74
0.01
0.02
0.01
0.01
0.20
0.01
0.01
0.60
1.00
0.01
0.0001
0.1230
0.0070
0.0002
0.0007
0.0001
0.0007
0.0095
0.0004
0.0010
0.0057
0.0094
0.0001
TENNESSEE— 28 SITES
Atrazinc
Bidtin
Captan
Ceresan M
Ceresan red
Cotoran
2,4-DB
Disulfoton
Diuron
Linuron
Malathion
Methyl mercury
dicyandiamide
MSMA
Nitrate
Nitralin
PCNB
Trifluralin
21.43
3.57
10.71
7.14
3.57
7.14
7.14
3.57
7.14
7.14
3.57
3.57
3.57
7.14
3.57
3.57
14.29
1.98
0.54
0.01
0.01
0.01
0.78
0.43
0.01
0.06
0.75
0.01
0.01
0.46
250.00
0.15
0.01
0.38
0.4250
0.0193
0.0011
0.0007
0.0004
0.0557
0.0307
0.0004
0.0046
0.0536
0.0004
0.0004
0.0164
17.8571
0.0054
0.0004
0.0543
UTAH— 12 SITES
Dichloropropene
HeptachJor
8.33
8-53
180.00
0.34
15.0000
0.0283
VIRGINIA— 20 SITES
Atrazine
Azinphosmeihyl
Carbaryl
Copper oxide
2,4-D
2,4-DB
DDT technical
Diazinon
Dinitrobutylphenol
Diphenamid
Disulfoton
Ethylcnc dibromide
Dichloropropane
5.00
5.00
5.00
10.00
10.00
5.00
5.00
5.00
5.00
5.00
10.00
5.00.
5.00
4.00
2.00
2.75
2.60
1.12
0.20
2.00
0.50
1.50
4.00
6.EO
23.24
54.43
0.2000
0.1000
0.1375
0.2600
0.1125
0.0100
0.1000
0.0250
0.0750
0.2000
0.6800
1.1620
2.7215
COMPOUND
PEKCTNT
OP
Sms
TREATED
AVTJUCE
APPLI-
CATION
RATE
(LB/ACRE)
VIRGINIA— 20 SITES— Continued
Malathion
Methoxychlor
Ethyl parathion
Phorate
Sulfur
Vernolate
5.00
5.00
5.00
5.00
5.00
5.00
0.95
1.00
6.00
3.00
57.00
2.40
AVEXACE
AMOUNT
APPLIED
PER SITE
(Le'ACur. i
0.0475
0.0500
0.3000
0.1500
2.JOO
0.1200
WASHINGTON— 2 SITES
Ceresan L
2,4-D
50.00
50.00
0.01
1.00
0.0050
OJOOO
WEST VIRGINIA— 5 SITES
Azinphosmeth}!
Ethyl parathion
20.00
20.00
O.JO
1.50
0.1000
0.3000
WISCONSIN— 68 SITES
Atrazine
Ceresan red
2,4-D
Ramrod
TrifluraJin
29.41
1.47
2.94
1.47
1.47
2.61
0.01
0.75
2.00
2.00
0.7644
0.0001
0.0t21
OXC94
0.0294
NOTE: Of the 43 Slates with cropland soil analyzed, use records for
4 showed no pesticides used on the sampling sites: Nevada
(2 sites); New Hampshire (2 sites); Vermont (5 sites); and
Wyoming (17 sites).
TABLE 11. — Summary of pesticides used in FY 1969
on noncropland, by Stale
COMPOUND
PERCENT
OF
SITES
TREATED
AVERAGE
APPLICATION
RATE PER
SrrE
TREATED
(La -'ACRE)
AVERAGE
AMOUNT
APPUED
PER SITE
(IS/ACHE)
GEORGIA— 15 SITES
Mircx
6.67
0.01
0.0007
IDAHO— 26 SITES
Malathion
3.85
0.61
0.0135
NEBRASKA— 19 SITES
2.4-D
5.26
2.00
0.1053
NOTE: Of ihe 11 Stales »ilh noncropland soil analyzed. 8 reported no
pesticides used on the sampling sites: Arizona (43 sites); Iowa
(7 Nilcil; Maine (11 siles); Maryland (3 sites); Virginia (14
sites); Washington (11 siles); West Virginia (9 sites); and
W>oming (37 sites).
VOL. 6, No. 3, DECEMBER 1972
223
419
-------
TABLE n.-—Comparison of residues detected with use records for 12 Slates with hightft arsenic residues. FY 1969
STATE
Arkansas
Kentucky
New England •
New York
North Dakota
Ohio
Pennsylvania
AVERAGE
AMOUNT APPI ito
(LB/ACKE)
«0.13
PERCENT
OF SUES
TREATED
4.4
No Arsenic Compounds Used
•0.88
'0.12
10.0
5.3
No Arsenic Compounds Used
No Arsenic Compounds Used
•0.08
3.2
MEAN RESIDUE
LEVEL
(PPM)
«.o
8.4
10.2
9.4
8.5
11.2
10.8
PtlCEST
Posrnvt
SITES'
100.0
100.0
100.0
94.6
100.0
100.0
100.0
Percent based on number of sites with residues greater lhan or equal to the sensitivity limits.
Connecticut, Maine. Massachusetts, New Hampshire, Rhode Island, and Vermont.
Calculated for DSMA.
Calculated for sodium arsenite.
Calculated for sodium arsenile and lead arsenale.
TABLE 13.—Comparison of residues detecled with use records for 5 States with highest DDTR residues, FY 1969
STATE
Alabama
California
Michigan
Mississippi
South Carolina
AVERAGE
AMOUNT APPLIED
(LB/ACRE)
4.20
0.38
0.03
1.07
0.72
PERCENT
OF SITES
THE* TED
39.1
13.6
2.0
31.0
29.4
MEAS RESIDUE
Li VII.
-------
TABLE 15
—Fiftieth pcrccnlilc value tor pesticide residues in cropland soil including the 95% confidence intenal by Stale
PESTICIDE
UPPE»
LIMIT
(PPM)
FIFTIETH
PERCENTILE
RESIDUE LEVEL
(PPM)
LOWER
LIMIT
(PPM)
ALABAMA
Arsenic
p.p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
p,p'-TDE
4.42
0.10
0.03
0.27
0.48
0.02
4.09
0.07
0.02
0.21
0.38
0.01
3.76
0.04
0.01
0.15
0.29
0.01
ARKANSAS
Arsenic
p,p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
Dicldrln
p,p'-TDE
Toxaphene
Arsenic
o.p'-DDE
p.p'-DDE
o.p'-DDT
P.p'-DDT
DDTR
Dieldrin
o.p'-TDE
P.P'-TDE
Toxaphene
7.42
0.02
0.01
0.04
0.10-
0.00
0.01
0.15
7.26
0.02
0.01
0.03
0.09
0.00
0.01
0.04
CALIFORNIA
4.02
0.00
0.05
0.01
0.04
0.14
0.00
0.00
0.02
0.02
3.92
0.00
0.04
0.01
0.03
0.13
0.00
0.00
0.01
0.01
7.10
0.02
0.00
0.03
0.08
0.00
0.01
0.00
3.83
0.00
0.04
0.00
0.03
0.12
0.00
0.00
0.01
0.00
COLORADO
Arsenic
4.26
4.20
4.15
FLORIDA
Arsenic
Chlordane
P.P'-DDE
o.p'-DDT
p.p'-DDT
DDTR
P.P'-TDE
0.64
0.05
0.03
0.01
0.07
0.10
0.02
0.58
0.03
0.02
0.00
0.05
0.08
0.01
0.53
0.02
0.01
0.00
0.03
0.06
0.00
GEORGIA
Arsenic
P.P'-DDE
o,p'-DDT
p.p'-DDT
DDTR
p.p'-TDE
Toxaphcne
1.96
0.06
0.01
o.n
0.30
n.oi
0.36
1.88
0.05
0.01
0.01
0.23
0.01
0.28
1.80
0.04
0.01
0.09
0.17
0.01
0.18
PESTICIDE
UPPEK
LIMIT
(PPM)
FIFTIETH
Put CE STILE
RESIDL-£ LEVEL
(PPM)
IDAHO
Arsenic
p.p'-DDT
DDTR
2.85
0.00
0.01
2.53
0.00
0.00
LOWT»
LIMIT
(PPM)
2.21
O.CO
0.00
ILLINOIS
AJdrin
Arsenic
Chlordanc
DDTR
Dieldrin
Heptachlor
Heptachlor epoxide
0.00
6.28
0.01
0.00
0.0?
0.00
0.00
0.00
6.20
0.01
0.00
0.02
0.00
0.00
0.00
6.13
0.00
0.00
0.02
0.00
0.00
INDIANA
Aldrin
Ar«nic
Dieldrin
0.00
7.24
0.00
0.00
7.03
0.00
0.00
6.?:
0.00
IOWA
Aldrin
Arsenic
Atrazine
Chlordane
P.p'-DDE
p.p'-DDT
DDTR
Dieldrin
Heptachlor
Heplachlor epoxide
0.00
5.86
0.01
ao.oi
0.00
0.00
0.00
0.02
0.00
0.00
0.00
5.~&
0.00
0.01
0.00
0.00
0.00
0.01
0.00
0.00
o.w
5.71
0.00
0.00
0.00
0.00
0.00
0.01
c.oo
0.00
KENTUCKY
Aldrin
Arsenic
Dieldrin
0.00
8.45
0.01
0.00
7^9
0.01
0.00
7JO
0X0
LOUISIANA
Arsenic
p.p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
Dieldrin
1.80
0.01
0.01
0.02
0.02
0.01
1.65
0.00
0.00
0.01
0.01
0X11
1.51
0.00
0.00
0.00
0.01
0.00
MICHIGAN
Arsenic
p.p'-DDE
DDTR
Dieldrin
4.92
0.00
0.00
0.00
3.83
0.00
0.00
0.00
2.93
0.00
0.00
0.00
VOL. 6, No. 3, DECEMBER 1972
225
421
-------
TABLE 15.—Fiftieth percvntile value for pesticide residues in cropland soil including the 95% confidence Interval
by Stale—Continued
PESTICIDE
UrPEK
LIMIT
(PPM)
FlFTIKTH
PERCESfllE
RESIDUE LEVEL
(PPM)
LOWE*
LIMIT
(PPM)
MtD-ATLANTIC STATES GROUP'
Arsenic
5.87
5.34
4.83
MISSISSIPPI
Arsenic
p.p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
P.P'-TDE
Toxaphene
4.86
0.11
0.06
0.36
0.67
0.03
0.24
4.68
0.09
0.06
0.29
0.55
0.02
0.16
4.51
0.08
0.05
0.24
0.45
0.01
0.09
MISSOURI
Aldrin
Arsenic
Dieldrin
0.00
5.43
0.00
0.00
5.05
0.00
0.00
4.69
0.00
NEBRASKA
Arsenic
Chlordane
p,p'-DDE
DDTR
Dicldrin
4.73
0.01
0.00
0.00
0.00
4.56
0.00
0.00
0.00
0.00
4.40
0.00
0.00
0.00
0.00
NEW ENGLAND STATES GROUP1
Arsenic
p,p'-DDE
p.p'-DDT
DDTR
P.P'-TDE
6.39
0.02
0.05
0.04
0.01
5.71
0.01
0.02
0.02
0.0 1
5.09
0.00
0.00
0.01
0.00
NEW YORK
Arsenic
p.p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
Dieldrin
p.p'-TDE
6.34
0.00
0.00
0.01
0.00
0.00
0.00
6.12
0.00
0.00
0.00
0.00
0.00
0.00
5.90
0.00
0.00
0.00
0.00
0.00
0.00
NORTH CAROLINA
Arsenic
p,p'-DDE
o.p'-DDT
p,p'-DDT
DDTR
Dieldrin
o.p'-TDE
3.42
0.03
0.02
0.05
0.18
0.01
0.03
3.07
0.03
0.01
0.03
0.13
0.00
0.02
2.77
0.02
0.01
0.02
0.08
0.00
0.01
PESTICIDE
Vrrn
LIMIT
<»M)
FlPTUTH
PUttNTlie
RESIDUE LEVEL
(mi)
LOWM
LIMIT
(PfM)
NORTH CAROLINA— Continued
p.p'-TDE
Toxaphene
0.03
0.16
0.02
0.07
0.01
0.01
NORTH DAKOTA
Arsenic
p.p'-DDT
DDTR
6.82
0.00
0.00
6.79
0.00
0.00
6.75
0.00
0.00
OHIO
Aldrin
Arsenic
DDTR
Dieldrin
0.00
7.85
0.00
0.00
0.00
7.58
0.00
0.00
0.00
7J2
0.00
0.00
OKLAHOMA
Arsenic
2.19
2.11
2.02
PENNSYLVANIA
Arsenic
p.p'-DDE
P,p'-DDT
DDTR
Dieldrin
p.p'-TDE
7.22
0.00
0.00
0.00
0.01
0.00
6.65
0.00
0.00
0.00
0.00
0.00
6.15
0.00
0.00
0.00
0.00
0.00
SOUTH CAROLINA
Arsenic
p.p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
p.p'-TDE
1.98
0.08
0.04
0.18
OJI
0X6
1.82
0.06
0.03
0.13
0.15
0.04
I.6S
0X3
0.02
0.08
0.06
0.02
SOUTH DAKOTA
Arsenic
Dieldrin
3.86
0.00
3.76
0.00
3.68
0.00
TENNESSEE
Arsenic
P.p'-DDE
P.P'-DDT
DDTR
7.18
0.00
0.01
0.01
7.00
0.00
0.00
0.01
6.81
0.00
0.00
0.00
226
PESTICIDES MONITORING JOURNAL
422
-------
TABLE 15.-
-Fifliclh perccntilc value for pesticide residues in cropland soil including tlie 95c,i confidence interval
by Stale—Continued
PESTICIDE
UPPF.R
LIMIT
(PPM)
FIFTIETH
PFRCENTILP,
RrsiDUC LEVEL
(PPM)
LOWER
LIMIT
(PPM)
VIRGINIA AND WEST VIRGINIA
Arsenic
p.p'-DDT
DDTR
Hcptachlor epoxide
2.90
0.0 1
0.02
0.01
2.72
0.00
0.01
0.00
2.56
0.00
0.01
0.00
WASHINGTON
Arsenic
p.p'-DDT
DDTR
2.30
0.00
0.00
2.22
0.00
0.00
2.14
0.00
0.00
WESTERN STATES GROUP'
Arsenic
p,p'-DDE
3.57
0.01
3.40
0.00
3.23
0.00
PESTICIDE
UPPEX
LIMIT
(PPM)
FIFTIETH
PEUCCNTILE
RESIDUE LEVEL
(PPM)
LOWEK
LIMIT
(PPM)
WESTERN STATES GROUP— Continued
P.p'-DDT
DDTR
Arsenic
DDTR
Dieldrin
0.00
0.01
0.00
0.01
0.00
0.00
WISCONSIN
3J3
0.00
0.00
3.14
0.00
0.00
2.97
0.00
0.00
WYOMING
Arsenic
0.92
0.85
0.78
1 Includes Delau-are, Maryland, and New Jersey.
"" Includes Connecticut, Maine, Massachusetts, New Hampshire. Rhode
Island, and Vermont.
3 Includes Arizona. Nevada. New Mexico, and Utah.
TABLE 16.—Mean pesticide residues in ppin in soil /or various cropping regions, FY 1969
COMPOUND
Aldrin
Arsenic
Atrazine
Carbophenothlon
Chlordane
2,4-D
DCPA
o.p'-DDE
P,p'-DDE
o.p'-DDT
p.p'-DDT
DDTR
DEF
Diazinon
Dicofol
Dieldrin
Endosulfan (I)
Endosulfan (II)
Endosulfan sulfate
Endrin
Endrin aldehyde
Endrin ketone
Etliion
Ethyl parathion
Heptachlor
Heptachlor epoxide
Isodrin
Lindane
Malathion
Methoxychlor
PCNB
o.p'-TDE
p.p'-TDE
Toxaphene
Trifluralin
CORN
0.05
7.44
0.02
0.09
—
<0.01
0.01
0.01
0.06
0.14
—
-------
TABLE 17.—I'emnt til sites with detectable [>c.*liciJc residues in ppm in toil for \arious cropping reckons, FY 1969
COMPOUND
Aldrin
Arsenic
Atrazine
Caibophenothion
Chlordane
2.4-D
DCPA
o,p'-DDE
p,p'-DDE
o,p'-DDT
P.p'-DDT
DDTR
DUF
Diazinon
Dicofol
Dieldrin
Endosulfan (I)
Endosulfan (11)
Emlmulfan sulfate
Endn'n
Endrin ftldehyde
Endrin V clone
Ethion
Ethyl narathion
Htptachlor
Htptachlor epoxide
Isodrin
Lindane
Malathion
Methoxychlor
PCNR
o.p'-TDF.
p.p'-TDE
Toxaphene
Trifluralin
CORN
23.5
100.0
14.5
14.5
—
0.5
9.5
3.0
7.7
10.9
—
06
41.8
0.3
0.2
03
0.3
—
—
8.6
13.3
1.2
0.3
—
—
03
3.3
0.2
2.0
Corros
6.4
1000
1.8
—
15.6
69.7
51.4
66.1
72.5
—
—
—
24.8
—
—
—
7.3
--
2.8
—
0.9
—
—
. .
_
—
0.9
47.7
22.9
12.8
COTTON
AND
GI.NIRAL
FAR MINI;
r
0.9
93.4
2.6
—
5.2
44.8
25.0
43.1
47.4
—
—
.
14.7
—
—
—
2.6
—
0.9
-.
1.7
3.4
—
2.6
—
—
2.6
25.9
12.1
7.8
GENKRAL
FARMING
6.6
99.4
_
7.2
14.3
—
10.8
46.4
27.1
42.2
49.4
0.5
—
25.3
—
—
3.6
—
—
10.0
1.8
7.8
1.8
1.2
0.6
10.8
35.5
10.2
6.0
M«Y AND
GENERAL
2.1
99.3
—
5.5
—
2.8
21.4
10.3
16.5
22.8
—
0.7
15.2
0.7
0.7
1.4
—
_
—
1.4
4.1
—
0.7
—
2.1
11.7
IRRIGATED
6.5
99.1
—
11. 1
0.9
19.4
58J
33J
53.7
60.2
—
12.5
5.6
39.8
1.8
5.6
5.6
II. 1
—
2.8
6.3
12.5
0.9
13.0
—
i.8
*
—
13.0
38.0
12.0
93
SvlALL
GlLAINS
0.6
99.4
8.3
0.9
1.7
—
—
5.8
3.0
5.8
6.1
—
—
7.0
—
—
—
0.9
—
—
_
0.9
—
0.6
—
—
1.8
03
\
1 VEGETABLE
I.I
98.9
!
4.3
i —
—
: 5.3
; 38.3
: 23.4
31.9
39.4
—
—
23.4
—
1.1
1.1
3.2
—
I.I
4J
—
3.2
I.I
—
5J
27.7
1.1
2.1
! FH.IT
3.0
93.9
; 21.2
—
15.1
(0.6
36.4
57.6
; 63.6
—
—
21.2
—
—
—
6.1
3.0
3.0
3.0
12.1
—
—
—
6.1
45.4
6.1
3.0
NOTE; Blank = not analyzed; — = not detected.
228
PESTICIDES MONITORING JOURNAL
424
-------
APPENDIX G
PESTICIDE PROPERTIES: PERSISTENCE, SOLUBILITY,
LEACHABILITY, RUNOFF
425
-------
Organochlorine Insecticides
Phosphate Insecticides
Chlordane
DDT
BHC, Dieldrin
Heptachlor, Aldrin, Metabolites
i i i i i L
Diazinon
PffifiBSffliaB
Di-Syston
EUjjgg
Phorate
Malathion, Parathion
i _ i
0 1
234
Years
Urea, Triazine, and Picloram Herbicides
02 46 8 10 12
Weeks
Benzole Acid and Amide Herbicides
Propazine, Picloram
^'gaiBS*asBBJ8a^ai3i™Bagig!^
Simazine
Atrazine, Monuron
Linuron, Fenuron
gggj
Prometryne
i i i i i i i i i
2,3,6-TBA
Bensulide
agss^KSffljjj^j
Diphenamide
pmBaiBi
Ami ben
CDAA, Dicamba
0 2 4 6 8 10 12
Months
0 2 4 6 8 10 12 14 16 18
Months
Phenoxy, Toluidine, and Nitrile Herbicides Carbamate and Aliphatic Acid Herbicides
2,4,5-T
OiJljSSIISiB
Dichlobenil
TCA
Dalapon, CIPC
I PC, EPIC
Barban
_L
_L
_L
6 0 2 4 6 8 10 12
Weeks
Figure G-l. Persistence of individual pesticides in soils.
Source: Kearney, P. C., and C. S. Helling, "Reactions of Pesticides in Soils,"
Residue Reviews. Vol. 25 (1969).
426
-------
Table G-l. PERSISTENCE OF PESTICIDES AND THEIR
DEGRADATION PRODUCTS IN SOIL
Pesticide and
Degradation Products
Organochlorine
Insecticides:
Chlordane
DDT
Endosulfan
Application Rate
10 ug/liter
Six rates ranging
0.625 to 20 Ib/acre
Normal rates
10 Ib/acre/year
1 to 2 Ib/acre
20 Ib/acre/year
1 to 2-1/2 Ib/acre
10 Ib/acre
1 Ib/acre
100 ppm
High rate
Normal
10 to 20 Ib/acre
10 to 20 Ib/acre
25 Ib/acre
2 Ib/acre
Type of Soil
or Water
Natural river water
Loam soil
Soils
Normal agricultural
soils
Sandy clay soil
Soils
Sandy clay soil
Soils
Silt loam soil
Maine forest soil
Soil
Sandy loam
Soil
Normal agricultural
soils
Soil
Soil
85 soil types
Soil
Persistence Time
20 to 8 weeks
14.3 months
9 to 13 years
5 years
4 years
1 to 6 years
4 years
4 to 30 years
15 years
30 years
4 years
17 years
3 years
4 years
> 4 years
> 10 years
8 years
96 days
Comments
85% remains
507. remains
257. remains
25 to 01 remains
Half life
5% remains
Half life
5% remains
10.6% remains
Persistence
227. remains
39% remains
36% remains
25 to OT. remains
Persistence
Persistence
44% remains
No detectable
amounts remain
427
-------
Table G-l. (Continued)
Pesticide and
Degradation Products
Toxaphene
Organophosphorus
Insecticides:
Carbopheriothion
Diazinon
Dimethoate
Ethion
Guthion
frlalathion
Application Rate
20 Ib/acre/year
140 ppm
50 ppm
100 ppm
2 to 4 Ih/acre
3 Ib/acre
High application
rates
Normal
2 kg/hectare
2 to 4 Ib/acre
3 pg/liter
2 kg/hectare
4 to 6 Ib/acre
2 to 6 Ib/acre
50 Ib/acre
Normal
Type of Soil
or Water
Sandy clay soil
Soil
Sandy loam
Sandy loam
Fine sandy soil
Different types
of soils
Soil
Soil
Submerged tropical
soil
Normal agricultural
soils
Sandy loam soil
Loam soil
Fine sandy soil
Silt loam soil,
sandy loam soils
Loam soil
Fine sandy soil
Fine sandy soil
Loam soil
Normal agricultural
Persistence Time
4 years
> 6 years
11 years
14 years
6 to 8 months
20 weeks
26 weeks
< 40 days
50 to 70 days
12 vieeks
1 month
10 months
6 to 8 months
1 month
2 months
2 to 3 months
6 to 8 months
5 months
1 veek
Contnents
Half life
Persistence
507. remains
45% remains
5% remains
< 87. remains
Persistence
Very low levels
remain
6 to 27, remains
25 to 0% remains
~ 51 remains
5% remains
< 10% remains
307. remains
257. remains
< 10% remains
43 to 23% remains
64 to 13% remains
25 to 0% remains
soils
428
-------
Table G-l. (Continued)
Pesticide and
Degradation Products
Methyl parathion
Parathlon
Paraoxon
p-Nitrophenol
Amlnoparathion
Phorate
Herbicides:
Amltrole
Application Rate
5 Ib/acre
20 rag/kg
31.4 Ib/acre
31.4 Ib/acre
1 Ib/acre
5 Ib/acre
Normal
20 ppm
20 pptn
20 pptn
3 Ug/g
10 ppm
Normal
2 to 8 Ib/acre
2 to 10 IbAacre
Type of Soil
or Water
Silt loam soil
Sand-clayey soil
Sandy loam soil
Sandy loam soil
Silty clay loam soil
Soil
Silt loam soil
Normal agricultural
soils
Sandy loam soil
Silt loam soil
Silt loam soil
Silt loam soil
Silt loam soil and
sandy loam soil
Sandy loam soil
Normal agricultural
soils
Sandy loam soil
Fine sandy soil
Moist loam field soil
Soil
Persistence Time
8 days
7 to 11 days
4 years
16 years
2 months
5 years
3 months
1 veek
4 weeks
1 day
16 days
2 days
1 month
68 days
2 weeks
1 to 2 weeks
2 months
3 to 5 weeks
30 days
Comments
3. 11 remains
Complete
decomposition
31 remains
0.17. remains
No detectable
amounts
Persistence
3% remains
Persistence
< 51 remains
< 101 remains
No residues
detected
No residues
detected
Complete breakdown
501 remains
25 to 07. remains
< 21 remains
< 121 remains
Persistence
207. remains
429
-------
Table G-l. (Continued)
Pesticide and
Degradation Products Application Rate
20 ppm
3 to 18 Ib/acre
8.9 Ib/acre
Atrazine 2 to 10 kg/hectare
1 to 100 ppm
1 and 2 Ib/acre
Normal
2 Ib/acre
2 to 4 Ib/acre
2 to 3 Ib/acre
3 to 8 Ib/acre
3.2 to It Ib/acre
2,4-D Normal
0.5 to 3 Ib/acre
4 Ib/acre
3.6 Ib/acre
10 Ib/acre
1.5 kg/hectare
Type' of Soil
or Water
Soil
Soil
Soil
Loam soil
Four Hawaiian soils
Soils
Normal agricultural
soils
Soil
Soil
Soil
Soil
Soil
Normal agricultural
soils
Moist loam soil
Peat soils
Clay loam soil
Several soil type
Podsolic soil
Persistence Time
7 veeks
1 to 3 months
4 to 5 months
4 months
34 days
> 200 days
10 months
17 months
4 to 7 months
4 to 7 months
12 months
4 to 8 months
1 month
1 to 4 weeks
4 to 18 weeks
2 months
2 to 14 weeks
2 to 7 weeks
Comments
Persistence
Residual phyto-
toxicity
Residual phyto-
toxicity
32 to 62% remains
15 to 307. remains
Persistence
25 to 0%, remains
Persistence
Residual phyto-
toxlcity
Residual phyto-
toxicity
Residual phyto-
toxicity
Residual phyto-
toxicity
25 to 07. remains
Persistence
Persistence
Persistence
Persistence
Complete
detoxification
430
-------
Table G-l. (Continued)
Pesticide and
Degradation Products
Application Rate
Average
4 to 40 Ib/acre
5 Ib/acre
Type of Soil
or Water
Soil
Soils
Soils
Persistence Time
1 month
1 month
1 month
Comment e
Persistence
Residual phyto-
toxlclty
Residual phyto-
toxiclty
Dacthal
Dalapon
Diphenamid
Recommended rates
50 ppra
50 ppm
Normal
5 to 40 Ib/acre
7.4 to 20 Ib/acre
20 Ib/acre
6 to 8 Ib/acre
Recommended rates
Normal
3 to 4 Ib/acre
3 Ib/scre
3.75 Ib/acre
Most soil types
100 days
43 different Range from
California soils 2 to 8 weeks
Soils
veeks
Different types of 4 to 5 weeks
soils (20 to 27Z
moisture)
Normal agricultural 8 weeks
soils
Moist loam field eoil 10 to 60 days
Soils 1 month
Soils
Soils
Most soil types
Normal agricultural
soils
Soils
Soils
Soils
3 to 4 months
1 to 2 months
3 to 6 months
8 months
10 to 12 months
3 months
< 3 months
Average half life
Total dlsapperance
to 667. remaining
No phytotoxicity
No residue remains
25 to OT. remains
Persistence
Residual phyto-
toxicity
Residual phyto-
toxicity
Residual phyto-
toxicity
Average persistence
25 to 07. remains
Residual phyto-
toxicity
Residual phyto-
toxicity
Residual phyto-
toxicity
431
-------
Table G-l. (Continued)
Pesticide and
Degradation Products Application Rate
Dlurcm Normal
1 to 3 Ib/acre
10 to 40 Ib/acre
1 to 2 Ib/acre
3.6 to 4 Ib/acre
1 to 2 Ib/acre
2 Ib/acre
DNBP 6 to 9 Ib/acre
16 Ib/acre
8 Ib/acre
12 Ib/acre
0.05 Ib/acre
DMOC 4 kg/hectare
50 ppm
MCPA 1/2 to 3 Ib/acre
Type of Soil
or Water
Normal agricultural
soils
Moist loam field soil
Moist loam field soil
Clay loam and silt
loan soils
Soils
Soils
Soils
Moist loam field
soil
Soil
Soil
Soil
Soil
Soil
Soil
Mosit loam field
soil
Persistence Time
8 months
3 to 6 months
6 to 24 months
18 to 20 weeks
5 to 7 months
4 to 8 months
15 months
3 to 5 weeks
4 to > 8 weeks
6 months
> 5 months
> 3 months
28 weeks
7 days
1 to 4 weeks
Comments
25 to 07. remains
Persistence
Persistence
Persistence
Residual phyto-
toxiclty
Residual phyto-
toxicity
Residual phyto-
toxicity
Persistence
Persistence
Residual phyto-
toxicity
Residual phyto-
toxicity
Residual phyto-
toxicity
< 0.01 ppm remains
Persistence
Persistence
Normal
Normal agricultural
soil
3 months
25 to 07. remains
Pyrazon
4 ppm
Soil
6 to 7 months
Almost disappeared
432
-------
Table G-l. (Continued)
Pesticide and
Degradation
Simazine
Application Rate
Recommendation rates
3.6 ]b/acre
1 to 4 Ib/acre
10 to 40 Ib/acre
2 Ib/acrc
3 kg/hectare
Normal
lype o£ Soil
or Viater
Soils
Clay loam soil
Mois.t loam field soil
Moist loam field soil
Soil
Soil
Soil
Normal agricultural
soil
Persistence Time
3 to 6 months
60 days
3 to 6 months
6 to 24 months
17 months
24 weeks
11 weeks
1 year
Comments
Average persistence
Persistence
Persistence
Persistence
Persistence
157. activity remains
Total decompositior
25 to 0% remains
Sodium arsenitc
Sodium chlorate
Sutan
TCA
2 to 5 Ib/acre
0.45 to 4.5 Ib/acrc
4 Ib/acrc
3.2 to 4.0 Ib/acre
Recommendation ratos
450 to 1,200 Ib/acre
300 Ib/acre
Recommendation rates
15 Ib/acre
Soil
Soil
Soil
Soil
Soils
Hoist loam field
soil
Soil
Several soils
Soil
Soils
12 months
3 to 7 months
18 months
4 to 14 months
5 years
6 to 12 months
> 1 year
1.5 to 3 weeks
42 to 64 days
5 weeks
Residual phyto-
toxlcity
Residual phyto-
toxlcity
Residual phyto-
toxicity
Residual phyto-
toxicity
Phytotoxicity
Persistence
Persistence
Half life
Peruistence
No phytotoxicity
433
-------
Pesticide and
Degradation Products
Trlfluralin
Other Pesticides:
Captan (fungicide)
Naban (fungicide)
Ziram (fungicide)
Table G-l. (Concluded)
Application Rate
40 to 100 Ib/acre
Normal
Type of Soil
or Water
Persistence Time
Moist loam field soil 50 to 90 days
Normal agricultural 12 weeks
soil
soils
Well distributed in
soil
100 ppm
8 to 60 Ib/acre
12.5 to 67 Ib/acre
16 to 30 Ib/acre
1 and 2 Ib/acre
0.75 Ib/acre
Normal
Soils
Soils
Soils
Soils
Soils
Normal agricultural
1 to 3 months
7 to 12 months
4 months
> 200 days
10 to 12 months
6 months
Residual phyto-
toxlcity
Residual phyto-
toxlclty
Residual phyto-
toxlclty
Persistence
10 to 157. remains
25 to 07. remains
Fumus sandy soils
Soil
Soil
Soil
Soil
3 weeks
1 to 2 days
65 days
> 20 days
> 35 days
Half decay value
Half life
Persistence
Persistence
Persistence
Source: "The Effects of Agricultural Pesticides in the Aquatic Environment,"
Irrigated Croplands, San Joaquin Valley, Office of Water Programs,
Environment, American Chemical Society, Washington, D.C.
434
-------
Table C-2. PESTICIDE PROPERTIES
LO
Ln
Solubility
Pesticide
Alachlor
Propanil
Trlfluralin
Dalapon - Na
MPCA
2,4-D
2,4,5-T
Carbaryl
Ma lath ion
Naled
Dime thoate
Fenthion
Diaz inon
Ethlon
Azinphos-raethyl
Phosphomidon
Mevinphos
Methyl ?a rath ion
Parathion
DDT
BHC
Chlordane
Heptachlor
Toxaphene
Aldrin
Dleldrln
Endrin
Captan
Benomyl
Zlneb
Ha neb
Hancozeb
Methyl Bromide
(ppm) at
210
140
< 1
500,000
825
620
278
NA
120
2,000
50,000
55
40
2
25
mis .
mix.
25
9
insol.
10
insol.
0.01
0.0.4
0.011
0.11
0.16
3.3
0
v. slight
v. slight
v. slight
slight
(°C)
30
30
30
30
30
30
30
30
25
20
22
30
30
30
20
20
20
20
25
20
20
20
25
20
Volatil-
ization
Index
3
2
2
1
1
I
1
3.5
2
4
2
2
3
1.5
NA
2.5
3.5
3
3
1
3
2
3
4
1
1
1
2
3
1
1
1
NA
Leaching
Index
1.5
1.5
1.5
4
2
2
2
2
2.5
3
2.5
2
2
1.5
1.5
3.5
3.5
2
2
1
1
1
1
1
1
1
1
1
2.5
2
2
2
nil
Surface
Water
minor
nog
no
yes
yes
yes
yes
low
low
poss .
high
low
poss .
low
nil.
high
high
low
low
low
nil.
nil.
nil.
ell.
no
no
no
low
no
low
low
no
no
runo £f
Sed.
minor
neg
low
yes
yes
yes
yes
low
poss .
low
poss .
poss .
poss .
poss.
low
poss.
nil.
low
poss.
high
minor
NA
NA
poss.
high
high
low
NA
no
low
slight
poss .
no
Degrada t Ion
rapid
1-2 days
807./yr
60 days
rap id
rap id
f. rapid
mod . -rapid
rapid
v. rapid
low In water
moderj te
mod . rapid
rapid
rapid
NA
rapid
v. rapid
moderate
v. slow
v. slow
slow
slow
slow
slow
s low
5 low
rapid
NA
rapid
NA
rapid
rapid
Persistence
little
little
moderate
little
4-6 weeks
4 weeks
3 months
), - 7-10 days
< 2 weeks
> • 4 hr
3-4 days
low, 4 months
4-6 weeks
907. In 30 days
low
NA
3-12 hr
not persist.
not persist.
v. persist.
v. persist.
x - 1 yr
^ » 1 yr
v. persist.
v. persist.
v. persist.
persist.
i = 2 weeks
NA
not
1-2 weeks
not
x - 55 days
Carry-over
none
none
poss ib ie
no
0
0
0
0
0
0
0
0
0
0
< 107,/yr
0
0
0
0
v. high
yes
yes
yes
poss .
v. high
v. high
high
0
NA
0
0
0
0
Source: Pesticide Manual, R. von Rumker and F. Moray, USAID, August 1972.
Volatilization Index
1 - volat. loss
2 - volat. loss
3 - volat. loss
4 - volat. loss
< 0.1 kg/ha/yr
2
5
> 10
Leaching Index
Distance of travel through loam
soil profile under 150 cra/yr rain-
fall
1 - movement of < 10 cm
2 • movement of 10-20 era
3 » movement of 20-50 cm
4 - movement of < 50 cm
-------
Table G-3. SELECTED EXPERIMENTAL AND FIELD DATA ON PESTICIDE RESIDUES AND LOSSES IN RUNOFF
No.
1
2
3
4
5
6
Location and
vear
North Carolina
(1968)
Baton Rouge,
Louisiana
West, Texas
Coshocton, Ohio
(1971-1972)
Castans, I ova
(1967-1970)
Pesticide
used
2,4-D
Picloram
2,4,5-T
Endrln
Trifluralin
Propazine
Atrazine
Dieldrin
Carbofuran
(broadcast)
Carbofuran
(band)
Diazinon
Propachlor
Propachlor
Atrazine
Atrarine
Atrazine
Application
rate
(Ib/acre)
.
-
-
_
_
-
-
5
4.83
3.71
1
4
4
3
3
2.2
Soil class
.
-
Mhoon clay loam
Pullman
Sllty
C lay loam
Muskingham silt
loam
Muskingham silt
loam
Muskingham silt
loam
_
Surface contour
Ridge
Surface contour
Ridge
Haggerstown silty
clay
Maximum
concentration Concentration 7. Loss of
in runoff after in runoff after pesticide in
first storm (ppb) stated time (ppb) sediment
1,882
4,187
681
1.06 (24 hr) 0.46 (72 hr)
40 - -
50 - -
40 - -
20 4 (3 years) 2.20
1,398 (25 days) 5 (239 days)
13,678 (28 days) 3 (119 days)
677 (30 days)
82 (4 days)
780 (7 days) - 0.65
200 (37 days) - 0
-
4,910 (7 days) - 3.4
0.15
1,390 - 0>w
< 200 (1 month)
% Loss
(total)
.
-
-
^
_
-
-
2.27
0.9
0.5
0.1
2.6
0.23
~ 0
14.0
2.0
2.56
.
Reference
Kearney—
Villia-
Axes?.7
Caro et al-'
Caro et a!
I-7
U!
Caro et al-
Ritter et
Ritter et
Ritter et
Ritter et
H.llZ/
.£'
.ll/
all/
all/
10
Watklnsville, Atrazine
Georgia (1965)
Watkinsville, 2,4-D
Georgia (1967)
Waynesville, 2,4-D
North Carolina
(1968-1970)
2.2
Cecil sandy loam
Cecil sandy loam
700 (96 hr)
1,200
1,900
2,500
Dothan, Alabama
Mobile, Alabama
Atrazine (807.) 1.88
Dichlobenil 12.0
(507.)
Atrazine (807.) 3.76
Dichlobenil 12.0
(807.)
1.95
0.36
1.93
2.33
2.0 White et all/
4.0 Barnet
6.44
2.72
13.3
9.94
10/
—
Shee
^/
Bailey et al-
Balley et «1—-
-------
Table G-3. (Concluded)
No.
11
12
Location and
vear
(1971)
(1965)
(1965)
Pesticide
used
Aldrin
Dieldrln
DDT
DDT
Endrin
Endosulfan
Application
rate
(Ib/acre)
1.5
1.5
1.5
731 g/ha
289 g/ha
351 g/ha
Soil
Maury
Maury
Maury
class
silt loaai
silt loam
silt loam
-
-
-
Maximum
concentration
in runoff after
first otorm (pob)
20 (1 day)
45 (1 day)
4 (1 day)
82 (1 day)
50 (1 day)
2.2 (1 day)
Concentration
In runoff after
stated time (ppb)
20 (7 days)
45 (7 days)
4 (7 days)
-
-
-
7. Loss of
pesticide In
sediment
7.3
6.2
6.3
-
-
-
7. loss
(total)
11.0
11.2
6.8
1.39
0.85
0.35
Reference
Haarv2/
Epstein and CT
11
-p-
u>
—I
-------
REFERENCES TO TABLE G-3
1. U.S. Environmental Protection Agency, "A Catalog of Research in
Aquatic Pest Control and Pesticide Residues in Aquatic Environ-
ments," May 1972.
2. Axe, J.A., A. C. Mathers, and A. F. Wiese, "Disappearance of Atra-
zine, Propazine, and Trifluralin from Soil and Water," 22nd Annual
Meeting of the Southern Weed Science Society, Proceedings, 21-23
January 1969.
3. Sheets, T. J., W. L. Rieck, and J. F. Lutz, "Movement of 2,4-D,
2,4,5-T, and Picloram in Surface Water," Southern Weed Science
Society- Proceedings (1972).
4. Caro, J. H., H. P. Freeman, D. E. Glotfelty, B. C. Turner, and
W. M. Edwards, "Dissipation of Soil-Incorporated Carbofuran in
the Field," J. Agr. Food Chem., £1(6):1010-1015 (1973).
5. Bailey, G. W., et al., "Herbicide Runoff from Four Coastal Plain Soil
Types," EPA Report No. EPA-660/2-74-017, April 1974.
6. Ritter, W. F., H. P. Johnson, W. G. Lovely, and M. Molnau, "Atra-
zine, Propachlor, and Diazinon Residues on Small Agricultural
Watersheds. Runoff Loses, Persistence, and Movement," Environ.
Sci. Technol., 8(l):38-42 (1974).
7. Hall, J. K., M. Pawless, and E. R. Higgins, "Losses of Atrazine in
Runoff Water and Soil Sediment," J. Environ. Quality, 1(2):172-
176, April/June 1972.
8. White, A. W., A. B. Barnett, B. G. Wright, and J. H. Holladay,
"Atrazine Losses from Fallow Land Caused by Runoff and Erosion,"
Environ. Sci. Technol., l(9):740-744 (1967).
9. Haan, G T., "Movement of Pesticides by Runoff and Erosion," Tr. ASAE,
14_(3):445, May-June 1971.
10. Barnett, A. P., E. W. Hauser, A. W. White, and J. H. Holladay, "Loss
of 2,4-D. Wash-Off from Cultivated Fallow Land," Weeds, 15:133-
137 (1967). ~~
11. Epstein, E., and W. J. Grant, "Chlorinated Insecticides in Runoff Water
as Affected by Crop Rotation," Soil Sci. Am. Proc., 32(3):423,
May-June 1968. "
438
-------
APPENDIX H
STATISTICS OF DEICING SALT APPLICATION ON HIGHWAY
AND TOLLWAYS IN THE UNITED STATES
439
-------
Table H-l. APPLIED SALTS AND APPLICATION RATES TO HIGHWAYS AND TOLLWAYS IN
THE UNITED STATESfL/
NaCl
Applied
Salt
1965-1966
State
Northeastern States
Maine
New Hampshire
Vermont
Massachusetts
Connec ticut
Rhode Island
j> New York
o
Pennsylvania
New Jersey
De laware
Maryland
Virginia
North-Central States
Ohio
West Virginia
Kentucky
Indiana
Illinois
Michigan
Wisconsin
Minnesota
North Dakota
1^000 MT
76.2
75.0
75,4
109.1
67.7
42.6
222.5
292.1
15.9
2.5
40.7
29.7
464.0
31.2
36.6
101.0
113.7
122.7
101.6
361.0
1.8
1^000 tons
84.0
82.7
83.1
120.3
74.6
47.0
245.3
322.0
17.5
2.8
44.9
32.7
511.0
34.4
40.3
111.3
125.3
135.3
112.0
398.0
2.0
Application Rate
Per
(kg/km)
210
221
510
401
299
423
187
182
176
385
471
268
268
96
210
499
201
162
141
129
2
Snowday
(Ib/mile)
747
787
1,810
1,422
1,058
1,506
665
647
625
1,400
1,675
948
950
339
743
1,772
712
576
498
459
6
Applied
CaCl2
Salt
1965-1966
(MT)
907
490
454
5,312
7,258
907
3,538
31,265
2,899
744
422
14,607
10,886
11,308
907
4,130
5,017
3,341
2,812
12,701
907
(tons)
1,000
540
500
5,855
8,000
1,000
3,900
34,463
3,195
820
465
16,101
12,000
12,465
1,000
4,552
5,530
3,683
3,100
14,000
1,000
Application
Rate
Per Snowday
(kg/km) (
2.5
1.4
3.1
19.5
31.9
9.1
3.0
19.5
32.1
116.0
4.9
132.0
6.3
34.7
5.2
20.4
8.9
4.4
3.9
4.5
0.8
Ib/raile)
8.9
5.1
10.9
69.2
113.0
32.1
10.6
69.2
114.0
410.0
17.4
467.0
22.3
123.0
18.4
72.5
31.4
15.7
13.8
16.1
2.9
-------
Table H-l. (Continued)
NaCl
Applied
Salt
1965-1966
State
Southern States
Arkansas
Tennessee
North Carolina
Mississippi
Alabama
Georgia
£ South Carolina
Louisiana
Florida
West-Central States
Iowa
Missouri
Kansas
South Dakota
Nebraska
Colorado
Southwestern States
Oklahoma
New Mexico
Texas
1,000 MT
1.1
N.A.
15.0
0.9
N.A.
0.007
N.A.
0.3
0.0
30.3
9.9
13.5
0.9
3.3
4.1
2.1
10.0
2.3
1,000 tons
1.2
16.5
1.0
0.008
0.37
33.4
10.9
14.9
1.0
3.6
4.5
2.3
11.0
2.5
Application Rate
Per
(kg/km)
410
170
1
144
27
46
113^
H3k/
53
113*'
85,
113*
Snowday
(Ib/mile)
1,447
606
4
510
97
164
400 -/
400^
188
400 -7
301
400-
Applied
CaCl2
Salt
1965-1966
(MT)
352
1,610
227
272
20
2,064
1,674
588
907
363
136
N.A.
N.A.
0.0
(tons)
388
1,775
250
300
22
2,275
1,845
648
1,000
400
150
Application Rate
Per Snowday
(kg/km) (Ib/mile)
44.0 156
37.5 133
9.9 35
4.5 16
2.0 7
0.8 3
0.3 1
1.7 6
-------
Table H-l. (Concluded)
Nad
State
Applied Salt
1965-1966
1,000 MT 1,000 tons
Application Rate
Per Snowday
(kg/km) (Ib/mile)
2.3
0.8
1.0
0.7
0.7
20.0
2.6
31.3
0.1
28.1
0.4
6
2
14
1
2
113^
70
113^
2,802
113 y
20
8
50
4
6
1,320
400^
246
400^
10,040
400^7
CaCl.
Applied Salt
1965-1966
(MT)
Applicaf-ion Rate
Per S .iowday
(tons) (kg/lea) (Ib/mile)
Western States
Washington 2. 1
Idaho 0.7
Montana 0.9
Oregon 0.6
Wyoming 0.6
California 18.1
^ Nevada 2.4
^ Utah 28.4
Arizona 0. 1
District of Columbia 25.5
Alaska 0.4
Hawaii
a/ Sources: Field, R., E. J. Struzeski, Jr., H. E. Masters, and A. N. Tafuri,
Water Pollution and Associated Effects from Street Salting, U.S.
Environmental Protection Agency, Report EPA-R2-73-257, May 1973.
Hanes, R. E., L. W. Zelazny, and R. E. Blaser, Effects of Deicing Salts
on Water Quality and Biota, Highway Research Board, National Co-
operative Highway Research Program Report 91 (1970).
b_/ Recommended application rate - Salt Institute.
N.A. - Not available.
36
907
58
150
145
N.A.
N.A.
N.A.
9
41
94
0.0
40
1,000
64
165
160
10
45
104
0.0
0.1
2.8
0.8
0.3
0.3
4.5
0.3
10
3
1
1
16
-------
Table H-2. MILEAGE OF TREATED HIGHWAYS AND TOLLWAYS, AND MEAN ANNUAL
SNOWDAYS BY STATE
State
Northeastern States
Maine
New Hampshire
Vermont
Massachusetts
Connecticut
Rhode Island
New York
Pennsylvania
New Jersey
Delaware
Maryland
Virginia
North-Cental States
Ohio
West Virginia
Kentucky
Indiana
Illinois
Michigan
Wisconsin
Minnesota
North Dakota
Southern States
Arkansas
Tennessee
North Carolina
Mississippi
Alabama
Georgia
South Carolina
Louisiana
Florida
Single-Lane
Kilometers
Treated
x 1,000-'
12.1
11.3
7.4
15.1
15.1
8.4^
59.4
89.0
12.9
1.3
10.8
22.2
173 . lk/
27.2
34.9
25.3
62.9
37.8
40.0
186.0^
111. 8^
N.A.
N.A.
12.2
5.3
0.1
7.2
N.A.
N.A.
0.0
Single-Lane
Miles
Treated
x 1,000-'
7.5
7.0
4.6
9.4
9.4
5.2^
36.9
55.3
8.0
0.8
6.7
13.8
107.6^
16.9
21.7
15.7
39.1
23.5
25.0
115. 6^
69. 5^
N.A.
N.A.
7.6
3.3
0.1
4.5
N.A.
N.A.
0.0
Mean Annual
Snowdays — '
30
30
20
18
15
12
20
18
7
5
8
5
10
12
5
8
9
20
18
15
10
3
3
3
1
1
1
1
1
0
443
-------
Table H-2. (Concluded)
State
West-Central States
Iowa
Missouri
Kansas
South Dakota
Nebraska
Colorado
Southwestern States
Oklahoma
New Mexico
Texas
Western States
Washington
Idaho
Montano
Oregon
Wyoming
California
Nevada
Utah
Arizona
District of Columbia
Alaska
Hawaii
Single-Lane
Kilometers
Treated
x 1,000
21.1
51.5
41.7
96.9^/
123.9^
3.9
N.A.
11.7
N.A.
24.6
16.1
3.2
29.8
20.3
9.7
N.A.
20.4
N.A.
1.3
N.A.
0.0
Single-Lane
Miles
Treated
x 1,000
13.1
32.0
25'9u/
60.2^
77. 0-/
2.4
N.A.
7.3
N.A.
15.3
10.0
2.0
18.5
12.6
6.0
N.A.
12.7
N.A.
0.8
N.A.
0.0
Mean Annual
Snowdays
10
7
7
10
10
20
3
10
3
15
20
20
20
20
5
10
20
10
7
23
0
a/ Source: Hanes, R. E., L. W. Zelazny, and R. E. Blaser, Effects of
Deicing Salts on Water Quality and Biota, Highway Research
Board, National Cooperative Highway Research Program Report
91 (1970).
b/ MRI estimates.
£/ Source: U.S. Department of the Interior, Geological Survey, The
National Atlas of the United States (1970).
N.A. - Not available.
444
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1 . REPORT NO.
EPA-600/2-76-151
3. RECIPIENT'S ACCESSIOONO.
4. TITLE AND SUBTITLE
LOADING FUNCTIONS FOR ASSESSMENT OF WATER POLLUTION
FROM NOIIPOINT SOURCES
5. REPORT DATE
Hay 1976 (ISSUING DATE)
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
A.D. McElroy, S.Y. Chin, J.'1. Nebgen, A. Aleti,
and F. W. Bennett
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
l JIB 617
11. CONTRACT/GRANT NO.
68-01-2293
12. SPONSORING AGENCY NAME AND ADDRESS
Agriculture and Nonpoint Sources Management Division
Office of Research and Development
U.S. Environmental Protection Agency
Washington , D.C. 20460
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA-ORD
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Methods for evaluating the quantity of water pollutants generated from nonpoint
sources including agriculture, silviculture, construction, mining, runoff from urban
areas and rural roads, and terrestrial disposal are developed and compiled for use in
water quality planning. The loading functions, plus in some instances emission
values, permit calculation of nonpoint source pollutants from available data and
information.
Natural background was considered to be a source and loading functions were
presented to estimate natural or background loads of pollutants.
Loading functions/values are presented for average conditions, i.e., annual
average loads expressed as metric tons/hectare/year (tons/acre/year). Procedures
for estimating seasonal or 30-day maximum and minimum loads are also presented. In
addition, a wide variety of required data inputs to loading functions, and delinea-
tion of sources of- additional information are included in the report.
The report also presents an evaluation of limitations and constraints of various
methodologies which will enable the user to employ the functions realistically.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
COSATI Field/Group
Runoff, Hydrology, Soil Erosion,
Salinity, Nitrogen, Phosphorus, Water-
sheds, Pesticides, Urban Areas, Mining,
Agricultural Wastes, Sediments, Construc-
tion, Surface Water Runoff, Nutrients,
Radioactivity
Nonpoint Pollution,
Loading Functions,
Natural Background,
Cropland, Runoff
Modeling, Groundwater
Pollution, Coliforms,
Heavy Metals
13B
13. DISTRIBUTION STATEMENT
Release to Public
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
465
20. SECURITY CLASS (This page}
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
445
U. S GOVERNMENT PRINTING OFFICE: 1976-657-695/5'(25 Region No. 5-I
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