PB82-260837
River Basin Validation of the Water Quality
Assessment Methodology for Screening
Nondesignated 208 Areas. Volume I
Nonpoint Source Load Estimation
Midwest Research Inst.
Kansas City, MO
Prepared for
Environmental Research Lab,
Athens, GA
May 82
U.S. DEPARTMENT OF COMMERCE
National Technical Information Service
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EPA 600/3-82-057a
May 1982
RIVER BASIN VALIDATION OF THE WATER QUALITY ASSESSMENT
METHODOLOGY FOR SCREENING NONDESIGNATED 208 AREAS
Volume I: Nonpoint Source Load Estimation
by
Michael J. Davis, Michael K. Snyder, and John W. Nebgen
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
Grant No. R806315-01-0
Project Officer
Robert B. Ambrose
Technology Development and Applications Branch
Environmental Research Laboratory
Athens, Georgia 30613
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS. GEORGIA 30613
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/3-82-057a
2.
3. RECIPIENT'S ACCESSION-NO.
ORD Report
4. TITLE AND SUBTITLE
River Basin Validation of the Water Quality Assessment
Methodology for Screening Nondesignated 208 Areas
Volume I: Nonpoint Source Load Estimation
5. REPORT DATE
May 1982
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Michael J.
Davis, Michael K. Snyder, and John W. Nebgen
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110
10. PROGRAM ELEMENT NO.
ACUL1A
11. CONTRACT/GRANT NO.
R806315-01-0
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory—Athens GA
Office of Research and Development
U.S. Environmental Protection Agency
Athens, Georgia 30613
13. TYPE OF REPORT AND PERIOD COVERED
Final, 9/79-11/81
14. SPONSORING AGENCY CODE
EPA/600/01
15. SUPPLEMENTARY NOTES •
River Basin Validation of the Water Quality Assessment Methodology for Screening
Nondesignated 208 Areas, Volume II: Chesapeake-Sandusky Nondesignated 208 Screening
16. ABSTRACT
Methodology Demonstration.
In earlier work under the .sponsorship of EPA, loading functions were developed
by Midwest Research Institute (MRI) for estimating the quantities of different diffuse
loads entering receiving waters from nonpoint sources and a screening methodology was
produced by Tetra Tech, Inc., for assessing water quality problems in areas not covere
under Section 208 of the Federal Water Pollution Control Act Amendments of 1972. The
two methods had never been applied together under realistic conditions, however, to
demonstrate how the combined techniques might be used for identification of water
quality problems in U.S. rivers.
In this volume, the successful application of the MRI-developed nonpoint loading
procedures under field conditions in five river basins is described and the compati-
bility of these procedures with the 208 screening methodology is demonstrated.
Volume II describes the application of the Tetra Tech-developed nondesignated 208
screening methodology to the same river basins. The basins in which the assessment
techniques were used were the Sandusky River in Ohio and four Chesapeake Bay Basins .
(Patuxent, Chester, Occoquan, and Ware Rivers).
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report}
UNCLASSIFIED
21. NO. OF PAGES
15?
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (9-73)
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NOTICE
Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
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FOREWORD
As environmental controls become more costly to implement and the
penalties of judgment errors become more severe, environmental quality
management requires more efficient analytical tools based on greater knowledge
of the environmental phenomena to be managed. As part of this Laboratory's
research on the occurrence, movement, transformation, impact, and control of
environmental contaminants, the Technology Development and Applications
Branch develops management and engineering tools to help pollution control
officials achieve water quality goals through watershed management.
In earlier work sponsored by EPA, water quality assessment techniques
were developed for characterizing pollution problems in nondesignated 208
areas and loading functions were developed for estimating quantities of
different pollutants entering receiving water bodies from nonpoint sources.
It appeared that these two tools used in concert might provide an adequate
set of methods for screening nondesignated 208 areas by simple hand calcula-
tion procedures. This report describes the application of both methods to
the identification of water quality problem areas in several river basins
in the United States.
David W. Duttweiler
Director
Environmental Research Laboratory
Athens, Georgia
n i
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ABSTRACT
In earlier work under the sponsorship of EPA, loading functions were
developed by Midwest Research Institute (MRI) for estimating the quantities
of different diffuse loads entering receiving water bodies from nonpoint
sources and a screening methodology was produced by Tetra Tech, Inc., for
assessing water quality problems in areas not covered under Section 208 of
the Federal Water Pollution Control Act Amendments of 1972. The two methods
had never been applied together under realistic conditions, however, to
demonstrate how the combined techniques might be used for identification of
water quality problems in U.S. rivers.
In this volume, the successful application of the MRI-developed nonpoint
loading procedures under field conditions in five river basins is described
and the compatibility of these procedures with the 208 screening methodology
is demonstrated. Volume II describes the application of the Tetra Tech-devel-
oped nondesignated 208 screening methodology to the same river basins. The
basins in which the assessment techniques were used were the Sandusky River
in Ohio and four Chesapeake Bay Basins (Patuxent, Chester, Occoquan, and
Ware Rivers).
This report was submitted in fulfillment of Grant No. R806315-01-0 by
Midwest Research Institute under the sponsorship of the U.S. Environmental
Protection Agency. The report covers the period September 1979 to March 1981,
and work was completed as of November 1981.
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CONTENTS
Figures ix
Tables x
1. Introduction 1
1.1 Background 1
1.2 Purpose and Scope 2
1.3 Format and Organization 3
2. Demonstration of Methods 6-
2.1 Demonstration Example: The Sandusky River Basin .... 6
2.1.1 Character of the Basin 6
2.1.2 Nonpoint Load Estimation Methodology - An
Overview 9
2.1.3 Rural Nonpoint Sources 12
2.1.3.1 Parameter Evaluation 12
2.1.3.1.1 .Rainfall Factor (R) 13
2.1.3.1.2 Soil Erodibility Factor (K) . 15
2.1.3.1.3 Slope Factors (L and S) . . . 16
2.1.3.1.4 Cover Factor (C) 17
2.1.3.1.5 Support Practice Factor (P) . 23
2.1.3.1.6 Land Use Within the Sandusky
Basin 24
2.1.3.1.7 Nutrients 29
2.1.3.1.7.1 Determination of
Enrichment
Ratio 30
2.1.3.1.7.2 Nutrient Concen-
tration. ... 30
2.1.3.1.7.3 Rainfall
Nitrogen ... 32
2.1.3.1.8 Sediment Delivery Ratio ... 33
2.1.3.2 Load Determination 34
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2.1.4 Urban Nonpoint Sources 37
2.1.4.1 Combined Sewer Overflows—Methodology. . 37
2.1.4.2 Character of the Urban Areas of the
Sandusky River Basin 40
2.1.4.3 Load Estimation 41
2.1.5 Nonpoint Source Impacts on Water Quality 48
2.2 Demonstration Example: The Chester River Basin 48
2.2.1 Character of the Basin 48
2.2.2 Nonpoint Load Estimation Methodology - An
Overview 51
2.2.3 Rural Nonpoint Sources 51
2.2.3.1 Parameter Evaluation 51-
2.2.3.1.1 Rainfall Factor 51
2.2.3.1.2 Soil Erodibility Factor (K) . 53
2.2.3.1.3 Slope Factors (L and S) . . . 53
2.2.3.1.4 Cover Factor (C). ...... 54
2.2.3.1.5 Support Practice Factor (P) . 57
2.2.3.1.6 Land Use Within the Chester
. River Basin 57
2.2.3.1.7 Nutrients 58
2.2.3.1.8 Sediment Delivery Ratio .... 59
2.2.3.2 Load Determination 59
2.2.4 Nonpoint Source Impacts on Water Quality 60
2.3 Demonstration Example: Patuxent River Basin 60
2.3.1 Character of the Basin 60
2.3.2 Nonpoint Load Estimation Methodology—An
Overview 61
2.3.3 Rural Nonpoint Sources 63
2.3.3.1 Parameter Evaluation 63
2.3.3.1.1 Rainfall Factor (R) 63
2.3.3.1.2 Soil Erodibility Factor (K) . 63
2.3.3.1.3 Slope Factors (L and S) . . . 66
2.3.3.1.4 Over Factor (C) 66
2.3.3.1.5 Support Practice Factor (P) . 68
VI
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2.3.3.1.6 Land Use Within the Patuxent
River Basin 68
2.3.3.1.7 Nutrients 71
2.3.3.1.8 Sediment Delivery Ratio ... 72
2.3.3.2 Load Determination 72
2.3.4 Urban Nonpoint Sources 73
2.3.5 Nonpoint Source Impacts on Water Quality 73
2.4 Demonstration Example: Ware River Basin 74
2.4.1 Character of the Basin 74
2.4.2 Nonpoint Load Estimation Methodology—An
Overview 74
2.4.3 Rural Nonpoint Sources 76
2.4.3.1 Parameter Evaluation 76
2.4.3.1.1 Rainfall Factor (R) 76
2.4.3.1.2 Soil Erodibility Factor (K) . 77
2.4.3.1.3 Slope Factors (L and S) . . . 77
' ' 2.4.3.1.4 Cover Factor (C). 77
2.4.3.1.5 Support Practice Factor (P) . 79
2.4.3.1.6 .Land Use Within the Ware
River Basin 79
2.4.3.1.7 Nutrients 79
2.4.3.1.8 Sediment Delivery Ratio ... 79
2.4.3.2 Load Determination 79
2.4.4 Nonpoint Source Impacts on Water Quality 80
2.5 Demonstration Example: Occoquan River Basin 80
2.5.1 Character of the Basin 80
2.5.2 Nonpoint Source Load Estimation Methodology-An
Overview 81
2.5.3 Rural Nonpoint Sources 83
2.5.3.1 Parameter Evaluation 83
2.5.3.1.1 Rainfall Factor (R) 83
2.5.3.1.2 Soil Erodibility Factor (K) . 84
2.5.3.1.3 Slope Factors (L and S) . . . 84
2.5.3.1.4 Cover Factor (C) 84
2.5.3.1.5 Support Practice Factors (P). 86
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2.5.3.1.6 Land Use With the Occoquan
River Basin 87
2.5.3.1.7 Nutrients 87
2.5.3.1.8 Sediment Delivery Ratio ... 88
2.5.3.2 Load Determination 88
2.5.4 Urban Nonpoint Sources 88
2.5.5 Nonpoint Source Impacts on Water Quality 89
3. Determination of Nutrient Fluxes in Streams, With Case
Studies of the Potomac and Susquehanna Rivers 90
3.1 Introduction 90
3.2 Methodology 90
3.3 Case Study: Potomac River Basin 95
3.4 Case Study: Susquehanna River Basin 105
3.5 Discussion 114
4. Discussion and Conclusions. 117
4.1 Data Availability 117
'4.2 Value of Parameter Refinement 118
4.3 Sensitivity Analysis 119
4.4 Level of Effort Required in an Application 121
4.5 Verification of the Load Estimation Procedures 121
4.6 Future Applications . 121
4.7 Use of the Load Estimation Methodology in Specialized
Applications 122
4.8 Attainment of the Goals of the Study 122
4.9 Impact of Methodological Shortcomings on an Assessment . 123
References 125
Appendix I. P Factors, Slopes, and Slope Lengths Used in Chapter 2 . 128
Appendix II. Soil and Nutrient Loss Calculations for Chapter 3. ... 143
Appendix III. English to Metric Conversions for Volume I 145
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FIGURES
Number Page
2-1 Sandusky River Basin 7
2-2 Chester River Basin and Subbasins 50
2-3 Patuxent River Basin and Subbasins 62
2-4 Ware River Basin and Subbasins 75
2-5 Occoquan River Basin and Subbasins 82
3-1 Potomac River Basin 97
3-2 Susquehanna River Basin 106
IX
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TABLES
Number Page
2-1 Sandusky River Subbasins 8
2-2 Evaluation of EI3o for the Sandusky Basin 14
2-3 Average K Factor Values by Subbasin for the Sandusky 16
2-4 Agricultural Data for Counties in the Sandusky Basin in 1975. 17
2-5 Estimated Areas of Principal Crop Rotations by County in the
Sandusky River Basin for 1975 19
2-6 Determination of Cover Factor for Corn After Soybeans by Crop
Stage 20
2-7 Cover Factors for Corn for the Sandusky Basin 21
2-8 Cover Factors for Soybeans for the Sandusky Basin 21
2-9 Cover Factors for Grain for the Sandusky Basin 22
2-10 ' Cover Factors by Event and by Crop for the Sandusky Basin . . 23
2-11 Land Capability Classes and Land Uses 25
2-12 Land Uses in the Sandusky Basin 28
2-13 Soil Nutrient Concentrations in the Sandusky River Basin. . . 31
2-14 Resolution Associated with Important Parameters in Rural
Nonpoint Analysis 34
2-15 Estimated Stream Loading Rates for the Average Sandusky
Event, Sediment Delivery Ration =0.1 36
2-16 Estimation of Loads Associated with Sanitary Sewage in Dry
Weather Flow 38
2-17 Sandusky River Basin Urban Areas 41
2-18 Street Loading Rates for Bucyrus 42
2-19 Estimated Annual Loads from Combined Sewer Overflows 42
2-20 Estimated Annual Loads in Stormwater Runoff, Not in CSOs. . . 43
2-21 Estimated Total Annual Urban Loads 43
2-22 Comparison of Load Estimates for Bucyrus 45
2-23 Average Unused Capacity of Plants During Dry Flow Period. . . 45
2-24 Components of Combined Sewer Overflow Loads for Bucyrus ... 46
2-25 Comparison of CSO Loads for Bucyrus 48
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TABLES (Cont'd)
Number Page
2-26 Chester River Subbasins 49
2-27 Evaluation of EI30 for the Chester Basin 52
2-28 Average K Factor Values by Subbasin and County for the
Chester River 53
2-29 Agricultural Data for Counties in the Chester River Basin
for 1973 55
2-30 Cover Factors for Various Intervals for the Chester River
Basin 56
2-31 Soil Nutrient Concentrations in the Chester River Basin ... 58
2-32 Estimated Stream Loading Rates for the Average Chester
Event, Sediment Delivery Ratio = 0.1 60
2-33 ' Patuxent River Subbasins 61
2-34 Evaluation of EI30 for the Patuxent Basin 64
2-35 Average K Factor Values by Subbasin for the Patuxent Basin. . 65
2-36 Agricultural Data for Counties in Patuxent River Basin in
1973 : . 66
2-37 Estimated Areas of Principal Crop Rotations by County in
Patuxent River Basin in 1973 67
2-38 Cover Factors (C) for Crops by Rainfall Intervals and
Counties for the Patuxent River Basin 69
2-39 Soil Nutrient Concentrations in the Patuxent River Basin. . . 71
2-40 Estimated Stream Loading Rates for the Average Patuxent
Event, Sediment Delivery Ratio = 0.1 72
2-41 Estimated Annual Loads for Urban Runoff: Patuxent River
Basin 73
2-42 Ware River Subbasins 74
2-43 Evaluation of EI30 for the Ware River Basin 76
2-44 Agricultural Data for Glouchester County (Ware River Basin),
Virginia for 1977 78
XI
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TABLES (Cont'd)
Number Page
2-45 Cover Factors (C) by Rainfall Interval for the Ware River
Basin 78
2-46 Estimated Stream Loading Rates for the Average Ware Event;
Sediment Delivery Ratio =0.1 80
2-47 Occoquan River Subbasins 81
2-48 Evaluation of EI30 for the Occoquan Basin 83
2-49 Agricultural Data for Counties in the Occoquan River Basin
in 1973 " 85
2-50 Estimated Areas of Principal Crop Rotations by County in the
Occoquan River Basin for 1973 86
2-51 Cover Factors (C) by Rainfall Interval and by Crop for the
Occoquan River Basin '." 86
2-52 Soil Nutrient Concentrations in the Occoquan River Basin. . . 88
2-53 Estimated Stream Loading Rates for the Average Occoquan
Event - 89
2-54 Estimated Annual Loads from Urban Runoff: Occoquan River
Basin 89
3-1 Potomac River Basin Subwatersheds 98
3-2 Suspended Sediment Discharges, Potomac River 99
3-3 Estimates of Average Annual Phosphorus Flux at Great Falls
From Nonpoint Sources 103
3-4 Sensitivity of Annual Phosphorus Flux Estimates to Streambank
and Gully Erosion 105
3-5 Susquehanna River Basin Subwatersheds . . . 107
3-6 Suspended Sediment Discharge at Harrisburg 108
3-7 Estimates of Average Annual Phosphorus Flux Into Reservoir
Behind Safe Harbor Dam Ill
3-8 Estimates of Rural Nonpoint Source Flux of Phosphorus at the
Mouth of the Susquehanna 113
3-9 Estimates of Average Annual Phosphorus Fluxes 116
xn
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TABLES (Cont'd)
Number Page
1-1 Practice Factors by Land Use and Land Capability Class for
Each County 129
1-2 Slope Length in Land Resource Area by Land Capability Class . 141
1-3 Slope in Land Resource Area by Land Capability Class 142
II-l Land Resource Areas Included in Study 144
III-l English to Metric Conversion Factors 145
xm
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CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
In August 1977 the U.S. Environmental Protection Agency (EPA) published a
document entitled "Water Quality Assessment—A Screening Method for Nondesig-
nated 208 Areas" (EPA-600/9-77-023). This document is a compendium of tech-
niques designed to aid in the assessment of water quality problems in areas
other than those covered under Section 208 of the Federal Water Pollution
Control Act Amendments of 1972. Designated 208 areas are generally character-
ized by high concentrations of urban or industrial discharges, whereas nondes-
ignated 208 areas may encompass a wider spectrum of human activities and, hence,
a larger set of water quality, conditions.. These include agriculture and silvi-
culture, as well as industrial and municipal activities. As a result, methods
to assess water quality in nondesignated 208 areas must include not only the
capability to predict impacts from point sources but also impacts from diffuse
or nonpoint sources.
In the above EPA document, Tetra Tech, Inc., brought together a number of
methods designed to accommodate both urban and non-urban nonpoint sources, as
well as municipal and industrial point sources of pollutants. In addition to
the assessment of effluent water quality, the methodology provided for system-
atic routing of these pollutants through rivers and streams, impoundments,
and estuary systems. All algorithms were designed to be used as hand calcula-
tion tools.
In 1976 Midwest Research Institute (MRI) developed a document entitled,
"Loading Functions for Assessment of Water Pollution from Nonpoint Sources,"
for the U.S. Environmental Protection Agency (EPA-600/2-76-151). The loading
functions described therein are used to estimate the quantities of different
1
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diffuse loads that enter receiving water bodies. These methods do not route
pollutants through the receiving waters, however.
It appeared that the use of these two tools in concert might provide an
adequate set of methods for screening nondesignated 208 areas by simple hand
calculation procedures. The methods developed by MRI for analysis of diffuse
sources of water pollution and the parallel methodology developed by Tetra Tech
had never been applied together in an actual field situation. Thus, this study
represents an application of both methods under realistic situations for the
purpose of demonstrating how the methodologies may be used for identification
of water quality problem areas in nondesignated 208 areas.
The vast majority of the data used in making the calculations in Volume I
were in English units. Metric equivalents were not included in the text and
tables because direct conversion of each English unit would produce numbers
that are awkward and undesirable. Conversion of the Universal Soil Loss Equa-
tion (USL'E) as a whole is more appropriate (Wischmeier and Smith, 1978). For
the convenience of the reader, English to metric conversion factors for units •
used within this report are included in Appendix III.
1.2 PURPOSE AND SCOPE
The primary goal of this study is to demonstrate Midwest Research Insti-
tute's loading functions and Tetra Tech's nondesignated 208 screening proced-
ures under authentic field situations. The demonstration is designed to sub-
ject the procedures to a wide range of data availability, water quality
parameters, and hydrologic/hydraulic situations. In addition to the primary
goal, there are several secondary goals. They are:
1. Provide a report demonstrating the 208 screening methodology to be
used as a guide by planners.
2. Show the degree of compatibility between the nonpoint loading analy-
sis and the 208 screening methodology.
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3. Develop firmer insight into the strengths and weaknesses of the non-
point loading methodology.
4. Evaluate the sensitivity of nonpoint load estimates to varying degrees
of data availability.
5. Determine how critical or necessary the quality and quantity of non-
point source details are with regard to reliably modeling instream processes
as they are affected by nonpoint loading.
6. Demonstrate strengths and weaknesses of the 208 screening methodology.
It is worth emphasizing that the goal of this effort is to provide a dem- "
onstration of existing techniques—not to engage in methodology development.
While some new approaches are incorporated in this study, by and large the
approach follows that which has been documented earlier". The new approaches
were adde'd to overcome gaps in the existing methodology which would have re-
stricted this demonstration.
1.3 FORMAT AND ORGANIZATION
This report consists of two volumes. Volume I is a discussion of the
application of MRI's nonpoint load assessment methodology to a number of river
basins. Volume II considers the application of the nondesignated 208 screening
methodologies developed by Tetra Tech to these same basins. The nonpoint source
load estimates given in Volume I are used as inputs to the calculations involv-
ing wet weather conditions that are presented in Volume II. The two volumes
are organized similarly; the river basins are considered in the same order in
both. There is cross-referencing in this volume to portions of Volume II so
that the interested reader can see how results obtained in Volume I are used
in the second volume.
Even though there is this connection between the two, each volume can
stand alone as separate demonstrations of the different methodologies being
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examined. That is, a reader whose primary interest is water quality assess-
ments in water bodies, and who is not concerned with estimation of nonpoint
source loads, can use Volume II independently of Volume I. Similarly, a
reader interested only in estimating nonpoint source loads would need Vol-
ume II only if concerned with how these loads are used in assessing impacts
on water quality, or if interested in examining how such use can say something
about the reliability of the nonpoint load estimates.
The nonpoint source loading functions used are not described in detail in
either volume. Such information is available in the EPA report by McElroy
et al. (1976). Only modifications or extensions of the loadings functions
are considered in detail here. Loading functions were developed for a variety
of sources, not all of which are examined in this demonstration. The water-
sheds considered allowed application of the methodologies for agriculture and
urban areas. Other nonpoint sources such as river drainage, feedlots, and
waste disposal areas were not examined.
This volume is divided into four chapters; the first chapter is the intro-
duction. Chapter 2 discusses the demonstration of nonpoint loading techniques
as applied to five watersheds. In the first application (to the Sandusky River
Basin in Ohio) there is considerable discussion of the approach used. The dis-
cussions of the remaining applications are more abbreviated. It is, therefore,
recommended that the interested reader carefully examine Section 2.1 for infor-
mation on details and on the philosophy used in this study. Chapter 3 is a
discussion of an approach, based on the loading function, for estimating nutri-
ent fluxes in streams. The content of Chapter 3 is independent of the remain^
der of Volume I and also has no connection with Volume II. Finally, Chapter 4
contains a general discussion and conclusions based on the demonstration.
Following the development of the nonpoint loading functions by McElroy
et al. (1976), EPA supported the development of a computer program called the
"Nonpoint Calculator" (Davis et al., 1979). This is a programmed version of
the original loading functions with some modifications and updating. It is
intended for rapid application of the, loading functions and is tied to a
national data base at the county level of resolution. Use of the program and
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data base facilitates preliminary nonpoint source load assessments for large
areas. The nonpoint calculator program was used to obtain the load estimates
presented in this volume. Such use eliminated a considerable amount of tedious
effort. The loading functions applied in this effort are intended for use in
desktop calculations. In applications such as those described here, which cover
many counties, the use of the computer program is advisable, however. All modi-
fications in the approach used by the nonpoint calculator as compared with the
original loading functions are noted in this volume, where appropriate.
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CHAPTER 2
DEMONSTRATION OF METHODS
2.1 DEMONSTRATION EXAMPLE: THE SANDUSKY RIVER BASIN
2.1.1 Character of the Basin
The Sandusky River (Figure 2-1) is located in northwestern Ohio and dis-
charges into the western end of Lake Erie. The basin, not including Green
Creek, has an area of about 1,399 sq miles. The major land use is agricul-
ture, and corn and soybeans are the principal crops. The river falls 520 ft
in elevation from its source to its mouth and has a length of about 120 miles,
giving an average fall of about 4 ft/mile.
The southern two-thirds of the basin is rather flat and is characterized
by broken ridges (end moraines) left by glaciers during their retreat from the
area. These ridges may be several miles wide and as high as 50 ft. The north-
ern one-third of the basin is also rather flat or gradually rolling. As might
be expected given such topography, the streams in the basin tend to be sluggish.
The basin receives an average of 34 in. of rain each year; the heaviest-
rainfall occurs during spring and early summer. The climate is humid conti-
nental with warm summers.
Figure 2-1 shows how the basin has been divided into subbasins for pur-
poses of analysis. County boundaries are also shown. Table 2-1 provides some
information on each of the subbasins.
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/
c
/ (Uppoi
I Little Tymochlee Creak
' S~^->
.^l
I WYA
r^=^
^.
.MOOT COUNTY
MARION COUNTY
y
TYx-n" CRAWFO
Yr " ~ MARIOI
MARION COUNTY
SANDUSKY RIVER BASIN
Figure Z-~\. Sandusky River Basin.
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TABLE 2-1. SANDUSKY RIVER SUBBASINS
Area
(mile2)'
Length of
principal .
streams (miles)
Paramour Creek
Broken Sword Creek
Rock Run
Sycamore Creek
Honey Creek
Upper Little Tymochtee Creek
Lower Little Tymochtee Creek
Warpole Creek
Spring Run
Tymochtee Creek (without tributaries)
Rock Creek
Willow Creek
East Branch Wolf Creek
Wolf Creek (without East Branch)
Muskellunge Creek
Indian Creek
Sandusky River:
Crawford County
Wyandot County
Seneca County
Sandusky County
Total area
27.8
9.6
94.
10.
64.
179.0
49.6
31.4
20.
30.
170.
34.8
5.7
84.
73.
46.
,6
.1.
3
5
5
,7
11.8
108.2
166.6
79.5
49.5
1,339.0
47.
7.
23.
82.
26.
24.4
10.5
15.8
104.4
22.6
5.0
31.0
23.
18.
1
,3
7.5
29.8
45.4
32.6
22.4
Source: Cross, William P., Drainage Areas of Ohio Streams, Supplement
to Gazetteer of Ohio Streams, Ohio Department of Natural
Resources, Ohio Water Plan Inventory, Report 12a, Columbus,
Ohio, 1967.
Source: Ohio Department of Natural Resources, Gazetteer of Ohio Streams,
Ohio Water Plan Inventory, Report 12, 1960.
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2.1.2 Nonpolnt Load Estimation Methodology - An Overview
Several pollutant sources were considered in the analysis: runoff from
rural lands; urban runoff; and combined sewer overflows. Average annual loads
for urban runoff and combined sewer overflows were estimated for each of the
major urban areas in the basin. The approach used is discussed in section
2.1.4. Feedlots were found to be an unlikely source of significant pollutant
loads in the basin and were not considered in detail.
The most complex portion of the analysis involves pollution arising from
the rural land surface. Estimates of pollutant loads were made on a subbasin
basis. In making the calculations, the counties in the basin were assumed to "
be homogeneous in terms of land use (i.e., all land uses evenly distributed
throughout the county) since land use information was not available below the
county level of detail at the time the analysis was done. The assumption of
homogeneity is not valid for many of the subbasins, but subdivision was neces-
sary to determine areas delivering loads to particular stream segments.
Nonpoint loads from the.land surface are delivered during and following
rainstorm events that produce run'off. At such times the streams involved are
usually experiencing high flow conditions. Since it is desirable to compare
instream water quality changes with those expected based on the pollutant loads
calculated, it is necessary to estimate loading rates during events. Longer
term averages, while useful for some purposes, will not allow such comparisons
to be made.
Based upon flow levels and the availability of water quality data, storm
periods were selected by the Tetra Tech team for which nonpoint source loads
were to be determined. It was necessary that the same periods be examined by
those concerned with the load estimates and those examining instream effects.
The periods selected were chosen so as to avoid the possibility of snowmelt
occurring, since the methodology does not consider the erosion produced by
melting snow. In the case of the Sandusky this consideration resulted in the
analysis of events following only in the April to September time interval.
-------
The rural nonpoint loads were determined using the assumption that pol-
lutants are associated with sediment. Sediment loads were estimated with the
Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) used along
with a delivery ratio. The USLE is intended to predict annual average soil
loss. If applied to a particular storm event, it predicts the soil loss ex-
pected on average from many such events. It will not predict the loss from
the individual event primarily because antecendent conditions are not consid-
ered. In the present study, event loads were estimated by averaging over a
series of many storms of different characteristics. As indicated above, these
storms were selected to assure consistency between the two phases of this ef-
fort: load estimation and instream effects.
Soil losses were estimated for each event in the series of storms, and
these values were then averaged to give an average soil loss per event for the
series of events studies. Whereas the estimates of soil loss per individual
event will not provide good predictions of losses for those individual events,
averaging over a series of events, with varying antecedent conditions, will
tend to yield a representative average for the series, assuming the series of
events is of sufficient length and covers a variety of antecedent conditions.
This average soil loss is used to determine average pollutant loads for the
series of events. These average loads can then be used along with average
flows for the series of events to obtain estimates of instream concentrations,
which may in turn be compared with observed values for such conditions. Be-
cause of the need to provide appropriate load estimates for the water quality
analysis, the approach just outlined was used. Extending the analysis to ob-
tain results concerning the frequency with which particular events occur coulci
be an interesting addition but not considered since it did not relate directly
to the primary goal of demonstration of the methodology.
Annual sediment delivery ratios were estimated based on available data.
The average delivery ratio for the series of events studies was then assumed
to be equal to the annual average value, although this tends to underestimate
the delivery ratio for the average event. An average delivery ratio was esti-
mated using measured sediment discharge near the mouth of the Sandusky and
estimated average annual soil loss for the basin.
10
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The calculations of soil loss for the counties were carried out using the
nonpoint calculator (Davis et al., 1979). Initially, calculations were made
using parameter values contained in the associated national data base. The
estimates were then refined using improved parameters. This parameter refine-
ment was carried out for the R, C, and K factors.* Land use characteristics
were determined based on county agricultural statistics combined with land use
data contained in the national data base.
A difficulty encountered in applying the methodology was the lack of
available adequate land use information on the spatial scale needed for sub-
watersheds. The U.S. Corps of Engineers has land use information containing a
very high level of resolution, but such information was not available in time ~
to use in this study.
K factors were determined for soil associations within the basin, and
then K values, averaged across soil associations, were found by subbasin. C
values were determined for each of the primary crops in each of the principal
crop rotations used. Values were determined for the time of year correspond-
ing to each event.
Using event-related parameter values for R and C, soil loss was determined
for each event studied. These losses were then averaged to give an average
loss per event in the series. This value, along with the average delivery
ratio for the basin, was used to estimate sediment loads to the streams in the
basin.
Nutrients (phosphorus and nitrogen) were assumed to be transported along
with the sediment; the enrichment was estimated using a relation developed by
Menzel (1980). (See section 2.1.3.1.7.) Estimated enrichment on an event-by-
event basis was determined. These values, weighted by soil losses, were then
used to find an effective average enrichment ratio. Little variation was found
* See section 2.1.3.1 for definitions of these factors.
11
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among average ratios for different locations in the basin. Both phosphorus
and nitrogen were considered in the application since they are both dealt with
by the loading functions. It is recognized, however, that much of the nitrogen
is transported in a soluble form. That is, it is not associated with sediment.
Therefore, although results are presented for nitrogen for the sake of complete-
ness, they are not expected to be accurate.
2.1.3 Rural Nonpoint Sources
2.1.3.1 Parameter Evaluation
Determination of rural nonpoint source loads requires knowledge of a num-
ber of relevant factors. Soil loss is found using the USLE (Wischmeier and
Smith, 1978). That equation is given by:
A = RKLSCP
where A = soil loss in T/acre/year*
R = rainfall factor
K = soil credibility factor
L = si ope-length factor
S = si ope-steepness factor
C = cover and management factor
P = support practice factor
Sediment yield is then given by:
Y = yA (T/acre/year)*
where y = sediment delivery ratio
Loads of sediment-associated pollutants are determined using expressions
of the form
Y = f-r-[P]-Ys (T/acre/year)*
where f = availability factor
r = enrichment ratio
[P] = soil concentration of pollutant
The unit given is tons/acre/year. Actually, the time interval is the
same as that for which the R in the USLE is evaluated. This is typically
an average year. In this study an average event is used and the units
become T/acre/average event.
12
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In addition, for nitrogen, input from rainfall is considered. Since some of
the factors are a function of land use, land use statistics must be available.
The following sections describe in detail how the various parameters in
the above equations were evaluated for the Sandusky River Basin.
2.1.3.1.1 Rainfall Factor (R)
For a particular rainstorm the rainfall factor is defined as the product
of the total storm energy (E) and the maximum 30-minute rainfall intensity
(I30) for the storm (Wischmeier and Smith, 1978). To determine E, the storm
periods were first divided into intervals with approximately uniform rainfall
intensity in each. E was calculated for each interval using the following ex-
pression: *
E = (916 + 331 log10I)-(Rainfall in interval, in inches)
where I = rainfall intensity in in/h during an interval. The values of
E for each interval were summed to give a total E for the storm.
Average annual R can be determined from maps. Annual values are the sum of
storm values for EI30 for a year, averaged over a 22-year period.
Values for EI30 were determined using the above definition for the events
of interest. Rainfall records from Fremont, Ohio, were used to find EI30. A
better approach would have been to consider the nonhomogenei.ty of rainfall stl-
tistics over the basin; however, the effort involved would have been consider-
ably increased. It was assumed that on the average, rainstorm characteristics
are the same basinwide and that averaging EI30 values at any point would give
the same values.
The expression applied only for intensities less than 3 in/h. For higher
intensities I is the expression is set equal to 3 in/h.
13
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Table 2-2 shows the periods analyzed, the total rainfall at Fremont, and
the value of EI3o for each interval at Fremont. To obtain EI3o it was neces-
sary to analyze the actual rainfall records,* since published data provide
rainfall only at 1-h intervals. For the three events with nonzero EI30, for
which the records were of sufficient quality to estimate EI3o, the average
value of EI3o is 12.1. Without the single largest value of EI3o, the average
is 8.4. The storm periods studied are distributed throughout the interval of
April to September.
TABLE 2-2. EVALUATION OF EI30 FOR THE SANDUSKY BASIN
Total rainfall
at Fremont (inches)
EI3o for Fremont
May 16-22, 1969
July 5 - 11, 1969
March 31 - April 6, 1970 - -
June 14 - 21, 1970
May 4-10, 1971
April 15-22, 1972
May 12-18, 1972
June 18-24, 1973
June 30 - July 6, 1973
March 29 - April 5, 1974
August 29 - September 4, 1975
July 6-12, 1976
March 31 - April 6, 1977
May 2 - 8, 1977
April 17-23, 1978
May 19-25, 1978
Total
3.41
1.42
3.96
0.97
2.69
• 0.90
0.76
0.75
0.92
2.04
0.17
0.87
1.97
1.64
22.5
23.7
poor records
7.5
56.8
2.5
13.3
2.6
1.6
4.6
. 6.6
9.2
A. 0
1.9
8.9
poor records
18.7
157.9
Rainfall records were obtained from the National Climatic Center, Asheville,
North Carolina.
14
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Based on maps of R, the average annual value for the Sandusky Basin is
roughly 125. The average annual value decreases somewhat from the southern to
the northern end of the basin. Based on an annual average of 125, the average
storm with an El of 12.1 produces about 10% of the annual soil loss.
2.1.3.1.2 Soil Erodibility Factor (K)
A weighted soil credibility factor (K) for the subbasins in the Sandusky
River Basin was calculated using three sources of information:
1. Generalized county maps showing the soil associations in the county
(Ohio Department of Natural Resources).
2. A breakdown of the soil series comprising the soil associations in a
county (Ohio Department of Natural Resources).
3. 'A list of K factors for each soil series (U.S. Department of Agri-
culture, 1979).
A planimeter was used to determine the proportion of a subbasin occupied
by a soil association. An approximate K value for each soil association was
determined by multiplying the K values of each soil series in a particular soil
association by the respective fraction with which they occurred in the associ-
ation, and then summing these numbers to obtain the weighted K. Each soil
association contains a number of minor soils for which only a composite per-
centage of occurrence is given; all minor soils were assumed to have equal ~
representation when the soil association K value was calculated.
The Blount-Pewamo Association in Crawford County can be used as an example.
The Blount series has a K of 0.43 and comprises 35% of the association. The
Pewamo series has a K of 0.24 and comprises 25%. The composite of minor soil
series is 40% and five minor series are listed. The average K for the five
minor series is 0.38. The weighted K for the association is the sum of the
products of the K values for each series and the fraction of the total which
each series comprises which gives 0.36.
15
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The weighted K for a subbasin is simply the sum of the products of the
soil association average Ks and the fractional areas of each association in
the subbasin. Table 2-3 shows average values obtained in this manner for each
of the subbasins (by county). Richland and Huron Counties were not included
because of the small areas involved.
TABLE 2-3. AVERAGE K FACTOR VALUES BY SUBBASIN FOR THE SANDUSKY
Average K values by county
Subbasin Sandusky
Paramour Creek
Broken Sword Creek
Rock Run
Sycamore Creek
Honey Creek
Upper Little Tymochtee
Creek
Lower Litte Tymochtee
Creek .
Warpole Creek
Spring Run
Tymochtee Creek (without
tributaries)
Rock Creek
Wil low Creek
East Branch Wolf Creek 0.22
Wolf Creek (without 0.23
East Branch)
Muskel lunge Creek 0.29
Indian Creek 0.20
Sandusky River 0.28
Seneca
_
-
-
-
0.39
-
-
-
0.38
-
0.38
0.37
0.35
0.34
0.32
0.28
0.33
Wyandot
_
0.39
0.38-
0.37
0.38
0.36
0.35
0.38
0.36
0.37
-
-
-
-
-
-
0.36
Crawford Hardin Marion
0.36
0.35
-
0.37
0.36
0.36 0.36
. - -
- - -
_
0.38 0.34
_
_
- .
_
-
_
0.37 - 0.34
2.1.3.1.3 Slope Factors (L and S)
Slopes and slope lengths were obtained for the nonpoint calculator data
base. That data base contains slopes and slope length by Land Resource Area
(LRA) (Austin, 1972). The basin is located in LRAs 99 and 111: the Erie-Huron
Lake Plain and the Indiana and Ohio Till Plain. LS was determined by the non-
point calculator using the algorithm described in the documentation for that
16
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program (Davis et al., 1979). The slopes and slope lengths used are tabulated
in Appendix I.
2.1.3.1.4 Cover Factor (C)
Since soil loss estimates for particular events are needed, it is neces-
sary to estimate values for the cover factor at the time of the runoff events.
Cropland is the primary source of sediment, so it is necessary to know which
crops are present, what rotations they are in, and what stage in the rotation
applies at the time of the rainfall event.
There are four principal crops in the basin: soybeans, corn, wheat and
oats. These crops, plus hay, are likely to be in one of seven different rota-
tions. The approach for determining the quantity of each crop in each rotation
is based on the analysis by Logan (1978). The total acreages of each crop were
obtained from county agricultural statistics. Crop statistics from 1969 to
1977 were' examined. The year 1975 was selected as typical of that interval.
(Land use considerations are discussed in section 2.1.3.6 below.) Table 2-4
shows county statistics for 1975 for the basin. (Huron, Richland, and Hardin
Counties are not considered in this analysis since they constitute such small
portions of the basin.) Based upon county crop data and discussions with
county extension agents, the predominant cropping practices within each county
in the basin were established and some assumptions made as to crop acreage dis-
tributions. These are:
TABLE 2-4. AGRICULTURAL DATA FOR COUNTIES IN THE SANDUSKY BASIN IN 1975
Crop area (103 acres)
County
Sandusky
Seneca
Wyandot
Crawford
Marion
Corn
55.2
64.5
57.5
57.8
67.7
Soybeans
63.3
91.8
73.9
57.4
79.6
Wheat
32.0
52.1
48.3
31.0
31.6
Oats
7.5
14.3
6.4
10.8
7.2
Hay
10.7
16.0
10.0
11.8
8.7
Total
168.7
238.7
196.1
168.8
194.8
Source: Ohio Agricultural Research and Developement Center, Ohio
Agricultural Statistics 1970-75. Research Bulletin 1106,
Wooster, Ohio, 1978.
17
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1. All wheat is in a corn-soybean-wheat rotation: C Sb W.
2. Fifty percent of the hay harvested is in the rotations: M C Sb W (M
meadow).
3. All oats are planted in the spring after beans: C Sb 0 W.
4. Any remaining corn and soybeans after No. 3 above are in C Sb rota-
tion.
5. Any remaining corn or soybeans after No. 4 above are in continuous
corn or soybeans.
6. Fifty percent of hay harvested is in permanent pasture.
The seven crop rotations predominantly used in the basin are:
C Sb W
M C Sb W
C Sb 0 W
C Sb
Continuous C
Continuous Sb
Permanent pasture
The crop statistics shown in Table 2-4 plus the assumptions given above-
are sufficient to determine the areas in each of the seven rotation patterns.
These areas are determined as follows:
1. Area inCSbOW=4x area in oats.
2. Area in M C Sb W = 4 x (0.5 x area in hay).
3. Permanent pasture = 0.5 x area in hay. .
18
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4. Area in C Sb W = 3 x (area in wheat - area in oats - 0.5 x area in
hay).
5. Area in C Sb = 2 x (lesser of area in C or area in Sb - area in
wheat).
6. If area in Sb > area in C, area in continuous Sb = area in Sb minus
area in C.
7. If area in C > area in Sb, area in continuous C = area in C minus
area in Sb.
The results are shown in Table 2-5.
TABLE 2-5. ESTIMATED AREAS OF PRINCIPAL CROP ROTATIONS BY COUNTY
IN THE SANDUSKY RIVER BASIN FOR 1975
Area in rotation (103 acres)
County
Sandusky
Seneca
Wyandot
Crawford
Marion
C Sb W
57.6
89.4
110.7
42.9
60.0
M C Sb W
21.2
32.0
20.0
23.6
17.6
C Sb 0 W
30.0
57.2
25.6
43.2
28.8
C Sb Cont. C
46.4
24.8
18.4
52.8 0.4
72.2
Cont. Sb
8.1
27.3
16.4
11.9
Permanent
pasture
5.3
8.0
5.0
5.9
4.4
Note: C = corn;
Sb = soybeans;
W = wheat;
M = meadow.
Cover factors were determined using the results presented by Wischmeier
and Smith (1978). Results are presented there in terms of each crop and the
crop it follows in rotation. Application of the results requires knowledge
about the development of each crop throughout the period of interest, namely
19
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April through September. Table 2-6 provides an example of the determination
of the cover factor for corn after soybeans. Similar tables were developed
for each crop in each rotation (e.g., corn after hay, corn after wheat, beans
after corn, beans after beans, corn after beans, oats after beans, wheat after
corn, wheat after beans, etc.).
TABLE 2-6. DETERMINATION OF COVER FACTOR FOR CORN AFTER SOYBEANS BY CROP STAGE
Soil loss
Time interval
March to mid- April
Mid- April to late April
Late April to mid-May
Mid-May to mid- June
Mid-June to late July
Late July to mid-October
Mid-October to mid-April
Period3
4
F
SB
I
2
3
4
ratio
0.38
0.47
0.78
0.65
0.51
0.30
0.37
Line used
Table 5-C
110
110
110
110
110
110
Comment
Assume 40% mulch
Plowed with moldboard
Plant
10 to 50% canopy cover
50 to 75% canopy cover
90% canopy at harvest
Source: Wischmeier and Smith (1978), pp. 18-26; assumes good productions,
spring-plowed with moldboard, and conventional tillage.
a F = rough fallow; SB = seed bed; 1 = establishment; 2 = development;
3 = maturing crop; 4 = residue or stubble (see Wischmeier and Smith
for complete definitions).
Ratio of loss from crop stage as compared to clean-tilled, continuous
fallow. These values are cover factors for the time interval indicated.
c Line used from Table 5 or 5-C of Wischmeier and Smith.
The cover factors (soil loss ratios) for each crop in each rotation were
then weighted by the fraction of the total crop in each rotation to give an av-
erage cover factor for each time interval for each crop. Tables 2-7 through
2-9 show the results for corn, soybeans, and grain (wheat and oats).
Examination of Table 2-2 shows that the events can be placed into five
time groups: early April, late mid-April, May, late June to early July, and
September.
20
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TABLE 2-7. COVER FACTORS FOR CORN FOR THE SANDUSKY BASIN
County
Sandusky
Seneca
Wyandot
Crawford
Marion
Average
March to
mid- April
0.20
0.12
0.20
0.25
0.30
0.21
Mid- April
late April
0.41
0.39
0.43
0.44
0.45
0.42
Late April to
mid-May
0.68
0.64
0.69
0.72
0.74
0.69 ':
Mid-May to
mid- June
0.58
0.55
0.59
0.61
0.62
0.59
Mid-June to
late July
0.45
0.43
0.46
0.47
0.49
0.46
Late July to
October
0.27
0.25
0.27
0.28
0.29
0.27
TABLE 2-8. COVER FACTORS FOR SOYBEANS FOR THE SANDUSKY BASIN
County
Sandusky
Seneca
Wyandot
Crawford
Marion
Average
March to
mid-April
0.37
0.37
0.37
0.37
0.37
0.37
Mid- April to
late April
0.40
0.41
0.41
0.39
0.40
0.40
Late April to
mid- June
0.66
0.68 i
0.67
0.64
0.66
0.66
Mid- June to
mid-July
0.57
0.59
0.58
0.56
0.57
0.57
Mid-July to
early August
0.42
0.44
0.43
0.41
0.42
0.42
Early August to
October
0.22
0.24.
0.23
0.21
0.22
0.22
-------
TABLE 2-9. COVER FACTORS FOR GRAIN FOR THE SANDUSKY BASIN
ro
County
Sandusky
Seneca
Wyandot
Crawford
Marion
Average
March
0.34
0.34
0.34
0.34
0.34
0.34
April
0.35
0.36
0.35
0.36
0.35
0.35
Early May to
mid-May
0.19
0.20
0.17
0.22
0.19
0.19
Mid-May to
mid-June
0.17 .
0.18 ;
0.16
0.19
0.17
0.17
Mid-June to
mid-July
0.09
0.10
0.07
0.10
0.09
0.09
Mid- July to
August 1
0.06
0.06
0.07
0.06
0.06
0.06
August 1 to
late September
0.07
0.07
0.07
0.07
0.07
0.07
-------
Perusal of Tables 2-7 through 2-9 indicates that in most cases, C does not vary
much from county to county within the basin for given crops and given time in-
tervals. These observations allow the soil loss calculations to be consider-
ably simplified by using a limited number of C values. Table 2-10 indicates
average values for use for each group of events.
TABLE 2-10. COVER FACTORS BY EVENT AND BY CROP
FOR THE SANDUSKY BASIN
Group of Events
Early April
Mid to late April
May
Late June to early July
Early September
Corn
a -
0.42
0.64
0.46
0.27
Crop
Beans
0.37
0.40
0.66
0.57
0.22
Grain
0.35
0.35
0.18
0.09
0.07
Use values given in Table 2-7.
In addition, long-term average values for C for the major crops were de-
termined. This involved averaging over counties and rotations. The averaged
soil loss ratios were then weighted by the fraction of El in each time interval
to give values for C. The average values are 0.39 for corn and soybeans and
0.10 for small grains.
The final results of the C factor evaluation are in Table 2-10. These
values will be used below along with the other factors to determine soil loss
by event.
2.1.3.1.5 Support Practice Factor (P)
Conservation needs were identified during the 1967 conservation needs in-
ventory (CNI) on a county-by-county basis. These data were used to develop
P factor values for the nonpoint calculator data base. Those values for the
23
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counties in the Sandusky Basin were used in this analysis and are given in
Appendix I. The P values are defined in the data base as a function of land
use and land capability class. The land uses and capability classes used are
listed in Table 2-11.
It has been assumed that no significant change in conservation treatment
needs occurred from the time of the 1967 CNI to the period of interest for this
study, 1969 to 1978. This assumption was necessary since the information
needed to update the description of practices was not readily available.
2.1.3.1.6 Land Use Within the Sandusky Basin
Since detailed land use data for the Sandusky Basin were not available in
time for this study, the land use data used were based on county agricultural
statistics augmented by data from the nonpoint calculator data base. The prin-
cipal assumption used in the analysis was that land use within each county is
homogeneous. This is apparently a reasonable assumption. However, it is very
likely unfair to assume that a uniform distribution of land use can be applied
to many of the smaller subbasins studied.- Therefore, loads estimated for these
small subbasins could be in considerable error if the land use in the subbasin
differs substantially from average.
The land use data base for the nonpoint calculator considers 16 land uses
and the 29 land capability classes and subclasses (Table 2-11) and was devel-
oped on a county-by-county basis using the 1967 CNI results for each state.
Preliminary calculations using the data base with no updating indicated that
the primary sources of soil loss were land uses 1 through 3~agricultural uses.
Since these were the critical areas, an effort was made to update the land use
information in these categories.
Land use changes have occurred over the study period of nearly 10 years.
It would be a considerable effort to account for such changes each year. In-
stead, a typical year having land use patterns similar to the average over the
period was chosen. Soybeans and corn are the dominant crops; and Sandusky,
24
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TABLE 2-11. LAND CAPABILITY CLASSES* AND LAND USES
Twenty-nine land capability classes
Class I-
Class II
Class III
Class IIw
Class He
Class Hie
Class Ills
Class IIIw
Class IIIc
Class IVe
Class IVs
Class IVw
Class IVc
Class Ve
Class Vs
Class Vw
Class Vc
' Class Vie
Class Vis
Class VIw
Class Vic
Class VIIs
Class VIIs
Class VIIw
Class VIIc
Class VHIe
Class VIIIs
Class VIIIw
Class VIIIc
Sixteen land uses
Corn and sorghum
Other row crops
Close-grown crops
Summer fallow
Rotated hay and pasture
Hay only
Conservation use only
Temporarily idle
Orchards
Open, formerly cropped
Pasture
Range
Other farmland
Other non-farmland
CNI commercial forest
CNI noncommercial forest
The classification below follows that used in the Conservation
Needs Inventory (CNI).
Land capability classes are defined as follows:
Class I. Soils have few limitations that restrict their
use.
Class II. Soils have moderate limitations that reduce the
choice of plants or require moderate conservation
practices.
(continued)
25
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TABLE 2-11. (continued)
Class III. Soils have severe limitations that reduce the
choice of plants, require special conservation
practices, or both.
Class IV. Soils have very severe limitations that reduce
the choice of plants, require very careful manage-
ment, or both.
Class V. Soils are subject to little or no erosion but have
other limitations, impractical to remove, that
limit their use largely to pasture, range, forest,
or wildlife food and cover.
Class VI. Soils have severe limitations that make them gen-
erally unsuited to cultivation and limit their
use largely to pasture or range, forest, or wild-
life food and cover.
Class VII. Soils have very severe limitations that make them
unsuited to cultivation and that restrict their
use..largely to pasture or range, forest, or wild-
life food and cover.
Class VIII. Soils and landforms have limitations that preclude
their use for commercial plants and restrict their
use to recreation, wildlife, or water supply, or
to aesthetic purposes.
Subclasses describe a grouping of soils within one class having
similar kinds of limitations. Four kinds of limitations are recognized
and are designated and defined as follows:
"e" shows that the main limitation is risk of erosion.
"w" shows that water in or on the soil interferes with plant growth
or cultivation.
"s" shows that the soil is limited because it is shallow, droughty,
or stony.
"c" shows that the chief limitation is climate that is too cold or
too dry.
26
-------
Seneca, Wyandot, Crawford, and Marion are the major counties in the basin. Av-
erage acreages of corn and soybeans were determined for each of these counties
for the 1969 to 1977 interval. These averages correspond closely to the acre-
ages for 1975, so 1975 was used in the analysis.
Table 2-12 shows average land use (1969 to 1977), land use for 1975, and
the 1967 CNI data for the counties. Since the nonpoint calculator data base
(1967 CNI data) breaks each land use category into areas by land capability
class, which is compatible with the way the parameters in the soil loss equa-
tion are defined in the nonpoint calculator, it was decided to modify all new
land use information into a form compatible with the original data base. To
do this required several assumptions: (a) the ove.rall distribution of land by
capability class does not change; (b) when land uses are changed the land
shifted from one use to the other is taken from each capability class in pro-
portion to the amount in that class; (c) if land use additions for uses 1 to 3
exceed land available in categories labeled "conservation use only" or "tempo-
rarily idle," land is shifted from "open, formerly cropped," or forest lands
given in the 1967 CNI. With the exception of Sandusky County, agricultural
land uses (uses 1 to 3) increased from 1967 to 1975. Therefore, shifts from
the other categories to crops are necessary. There are no data indicating
which land uses were shifted, but the shifts chosen seem reasonable.
The specific land use changes are outlined below by county.
Sandusky County: There was a 4,800-acre decrease of corn acreage from
1967 to 1975, along with a 15,000-acre decrease of soybeans and a 11,600-acre
increase of close-grown crops. All the decrease in corn acreage was assumed
to be shifted to close-grown crops, and the balance of the decrease in soybean
acreage was assumed to go to the temporarily idle category.
Seneca County: The decrease in corn production was shifted to soybeans,
and land from the conservation use category was shifted to accommodate the in-
creased production of soybeans and close-grown crops.
27
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TABLE 2-12. LAND USES IN THE SANDUSKY BASIN
ro
CO
Other row crops
Corn (soybeans)
Sandusky County
Average
1975
1967-CNI
Seneca County
Average
1975
1967-CNI
Wyandot County
Average
1975
1967-CNI
Crawford County
Average
1975
1967-CNI
Marion County
Average
1975
1967-CNI
53.4
55.2
60.0
65.6
64.5
73.5
57.1
57.5
54.0
54.9
57.8
60.9
66.3
67.7
66.0
66.7
63.3
78.3
92.9
91.8
77.2
71.6
73.9
50.8
60.9
57.4
41.0
76.5
79.6
44.9
• 103 acres
Close-grown crops Conservation
(oats and wheat) use only
39.5
27.9 21.9
66.4
52.0 32.1
54.7
31.8 21.7
41.8
31.6 16.1
38.8
35.1 13.4
Temporarily
idle
-
1.0
-
0.3
-
12.8
-
-
-
3.1
Note: Hyphens indicate no data available.
-------
Wyandot County: The large increases in crop production were accommodated
by shifts from conservation use, temporarily idle, open formerly cropped, and
forestlands.
Crawford County: Decreased corn production allowed a shift to soybeans.
Land from conservation use and rotated hay and pasture were shifted to soybean
and small grain production.
Marion County: Corn area was left unchanged; all temporarily idle, con-
servation use, open formerly cropped, and the decreased area in rotated hay and
pasture were shifted to soybeans and small grains.
The land use shifts were somewhat arbitrary. However, the total land area
was held constant and the total area in each capability class was also main-
tained constant. When crop production increased, land .was added from what
seemed to be the most available category. The fact that .the primary soil
losses come from the cropped areas means that the most sensitive issue is the
area of crops used. The other land uses from which land might be drawn have
low soil losses and the final results are not sensitive to the actual land use
from which cropland is taken.
Because the portions of Hardin and Richland counties contained in the
basin are very small fractions of the total areas of those counties, it does
not seem appropriate to try to update land uses in those areas. The assump-
tion of homogeneous land use throughout the counties would probably not apply
to those small areas. The 1967 CNI data were used for the two counties.
2.1.3.1.7 Nutrients
In addition to the information need to determine soil loss, three other
items are needed to determine loads of nutrients, namely the soil concentra-
tion of the nutrient, the enrichment ratio, and, for nitrogen, the input due
to rainfall.
29
-------
.2.1.3.1.7.1 Determination of Enrichment Ratio
The following relationship between the enrichment ratio for nutrients and
soil loss has been developed by Menzel (1980):
In (r) = 2 - 0.2 In (A)
where r = enrichment ratio
A = soil loss in kg/ha for individual events
The relationship predicts r for actual events; the higher the soil loss,
the less the enrichment. The relationship appears to be valid over a wide
range of soil and vegetative conditions. Annual.average enrichment ratios
estimated using the expression should be accurate to within a factor of 2.
Values of r were determined for each event for each county. An average
soil loss weighted value of r. (r = Z(rA)/A) was determined.for each county.
The average value of r for the counties is 1.96 with a standard deviation of
0.08. Because of the small variation from county to county and the rather un-
certain nature of the value for r, a value of r equal to 2 has been used in
the analysis. This average value used with average soil loss and soil nutri-
ent concentrations will provide an estimate of the total nutrient load for the
actual series of events studied.
2.1.3.1.7.2 Nutrient Concentrations
Soil nutrient concentrations were estimated or obtained for each county
in the basin. Table 2-13 shows the values used. Total soil phosphorus con-
centrations for the Sandusky Basin are in the range 600 to 700 ppm;* a value
of 650 ppm was used for calculations here. It is worth noting that the non-
point calculator data base has a value of 660 ppm for total soil phosphorus
concentration for the basin.
* Personal Communication with Terry Logan, Soil Scientist, Department of
Agronomy, Ohio State University, Columbus, Ohio, 1979.
30
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TABLE 2-13. SOIL NUTRIENT CONCENTRATIONS IN THE SANDUSKY
RIVER BASIN
Average nutrient concentration in soil (ppm)
County
Available phosphorus'
Total nitrogen
Sandusky
Seneca
Wyandot
Crawford
Marion
Huron
Richland
Hardin
30
20
23
23
23
27
23
20
1,880
1,880
1,860
1,960
1,790
1,950
1,950
1,940
Source: Logan, Terry J., "Levels of Plant Available Phos-
phorus in Agricultural Soils in the Lake Erie
Drainage Basin," Army Corps of Engineers, Lake
Erie Wastewater Management Study, December 1977.
Total soil phosphorus concentrations average
650 ppm (see Text).
b Source: Calculated using Jenny's Equation (Jenny, 1930);
Ten percent of total nitrogen is assumed to be
available.
Concentrations of available phosphorus were obtained from Logan (1977).
Total nitrogen was determined using an expression developed by Jenny (1930).
His equation relates soil nitrogen concentration to temperature, precipita-
tion, and relative humidity:
where
CS(NT)
where C$(NT) =
P
RH
SVP.
0.55e-0-08T(l-e-°-005H)
concentration of soil nitrogen, g/100 g
annual average temperature, °C
(I - -\
u 100'
precipitation, mm/year
relative humidity, %
saturated vapor pressure at given temperature, mm of Hg
31
-------
SVPt and T are related by (McElroy et al., 1976):
SVP = 10[9'2992 - 23607(273 + T)]
Using county portions of each subbasin, soil concentrations of the nutri-
ents were determined for each subbasin. Values for BOD5 were estimated based
on the nitrogen concentration. Organic matter concentrations of 20 times NT
were assumed. This procedure gives organic matter concentrations of about 4%
in the soil, which appears reasonable based on the limited data available in
soil surveys. However, the basin is predominantly agricultural, and the oxi-
dation of the organic matter in such areas should reduce the organic matter
concentrations; hence, the estimate based on 20 times NT is probably high.
BOD5 was estimated as 10% of organic matter, or 2Ny. Ten percent of total
soil nitrogen was assumed to be available.
2.1.3.1.7.3 Rainfall Nitrogen
Nitrdgen contained in rainfall was estimated from the data presented in
McElroy et al. (1976). A loading rate of 2.7 Ib/acre/year was used. It was
assumed that such loading is distributed .throughout the year in proportion to
rainfall: Since the average annual rainfall is 30.25 in. at Fremont and since
the series of events studied had a total of 22.5 in., a value of 2.0 Ib/acre
is estimated for the series of events studied.
For the Sandusky Basin, based on annual precipitation and annual discharge,
30% of the rainfall leaves the basin as runoff. Assuming that 20% of the runoff
is in overland flow;* 6% of the N in rainfall appears in the runoff. Assuming
Following McElroy et al. (1976) it is assumed that only that portion of
rainfall nitrogen contained in overland flow reaches a stream. The frac-
tion of runoff which follows the overland route varies from basin to basin
and storm to storm. Proper evaluation of the fraction of the runoff which
is in overland flow would require the detailed examination of hydrograph
for the basin (actually, for various parts of the basin) for the events of
interest. Because of the level of effort involved in determining the
amount of overland flow and because the loading functions being applied
are not well suited to estimation of nitrogen loading, it was merely as-
sumed that only a fraction of runoff was in the form of overland flow and
20% was used arbitrarily.
32
-------
that 25% of the nitrogen in overland flow is lost gives an average per event
load (based on 14 events) of 0.0064 Ib/acre.
2.1.3.1.8 Sediment Delivery Ratio
Sediment discharge data- are available for a sampling location near Fremont.
An area of 1,251 sq miles lies above that location, and the annual average sed-
iment discharge at that location is 226,000 tons/year based on 6 years of U.S.
Geological Survey data. Using this information along with an estimate of the
annual average soil loss in the basin, an average basinwide sediment delivery
ratio may be estimated.
Using an average rainfall factor of 125 along with the updated average K
and C values and 1975 land use information gives a gross annual soil loss for
the basin (1,339 sq miles) of 2.23 x 106 tons/year.
Mildner (1978) estimates that 7% of the total sediment yield of the Maumee
River is from streambank erosion. The Maumee is located near the Sandusky, and
a similar relationship will be assumed to'apply there as well.
Extrapolating the sediment discharge data to the entire watershed (1,339
sq miles), using the soil loss given above, and assuming that 7% of the sedi-
ment discharged originated in streambank erosion, gives a sediment delivery
ratio for the basin equal to 0.10. This is an estimate of the average annual
basinwide ratio based on limited sediment discharge data.
It would be preferable to have delivery ratios within each subbasin and
by season or month. Unfortunately such information is not available. Use of
a basinwide delivery ratio for subbasins will tend to result in an underesti-
mation of delivery ratios for subbasins in upland areas. Apparently, the ef-
ficiency of delivery of sediment is rather uniform in the basin (David Baker,
personal communication), so the use of the basin average is a reasonable as-
sumption throughout the basin. There is no similar simple approach for dealing
with any seasonal variations in sediment delivery.
33
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In the calculations in the following section a delivery ratio of 0.10 will
be used.
2.1.3.2 Load Determination
The various factors involved in determination of pollutant loads have been
discussed in the preceding section. Ideally, the parameters should be evalu-
ated at field scale. However, the data and time limitations and the overall
philosophy of the approach being pursued are inconsistent with such an effort.
Table 2-14 shows the spatial level of resolution associated with various impor-
tant factors in the study. Because the analysis is built around the USLE and
because the USLE is intended for use at field scal.e, the high level of aggrega-
tion used in determining the various parameters results in inaccuracies in the
analysis compared with what would be expected if all computations have been made
at the field level. Although decreasing the reliability of the results, such
inaccuracies are unavoidable if a screening approach is used.
TABLE 2-14. RESOLUTION ASSOCIATED WITH IMPORTANT PARAMETERS
IN RURAL NONPOINT ANALYSIS
Parameter
Level of spatial resolution used
Rainfall factor (R)
Soil credibility factor (K)
Slope factors (LS)
Cover factor (C)
Support practice factor (P)
Areas in particular land use
Delivery ratio
Soil nutrient concentrations
Rainfall nitrogen input
Enrichment ratio
River basin
Subbasin
LRA (by land capability class)
County, but averaged to river basin
(by land use)
County (by land use and capability class)
County
River basin (annual value)
County and river basin
River basin
River basin
LRA = Land resource area
34
-------
Table 2-14 shows that except for the K and LS factors, all information re-
lates to the county or river basin scale. Therefore, the only difference be-
tween unit area loads determined for the subbasins results from differences in
the Ks. Given the data available, the analysis does not really distinguish be-
tween various portions of the basin in terms of unit area loads.
However, a definite distinction is made between loading rates per river
mile in different subbasins. Unit area loads were determined for each subbasin.
The subbasin areas given in Table 2-1 were then used to find total loads by
subbasin. This subbasin load was divided by the length of the stream in the
subbasin (as given in Table 2-1) to obtain a total average event load per unit
length of stream channel in each subbasin. This load per length of channel was
multiplied by the delivery ratio to obtain the quantity of material actually de-
livered to the stream channel. (Note that the basinwide average delivery ratio
is being used for each subbasin. See section 2.1.3.1.8 above for a discussion
of this point.
Table 2-15 shows the results obtained, by subbasin, for the average event
considered. For the entire basin, the sediment load is 3.0 x 104 ton/event,
the total phosphorus load is 7.9 x 104 Ib/event, and the available phosphorus
load is 3.0 x 103 Ib/event.
Livestock feeding operations in the Sandusky River Basin are located al-
most exclusively in confined, surfaced, partially covered feedlots that have
runoff control.* A number of environmentally sound practices are used for the
treatment and disposal of the feedlot runoff (Livestock Waste Facilities Hand-
book, 1975). Consequently, its contribution as a pollutant source to surface
streams in the basin is minimal. Runoff from feedlots was therefore neglected
in the analysis.
Personal Communication with David Reed, Extension Livestock Specialist,
Northwest Ohio Livestock Extension Office, Ohio State University,
Defiance, Ohio, May 1, 1979.
35
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TABLE 2-15. ESTIMATED STREAM LOADING RATES FOR THE AVERAGE SANDUSKY
EVENT, SEDIMENT DELIVERY RATIO =0.1
Ib/mile/event
tons/mile/event Phosphorus
Subbasin
Paramour Creek
Broken Sword Creek
Rock Run
Sycamore Creek
Honey Creek
Upper Little Tymochtee
Creek
Lower Little Tymochtee
Creek
Warpole Creek
Spring Run
Tymochtee Creek
(without tributaries)
Rock Creek'
Willow Creek
Wolf Creek, East Branch
Wolf Creek (without
East Branch)
Muskellunge Creek
Indian Creek
Sandusky River
Above Grass Run
(Crawford County)
Below Grass Run,
Sediment
58
49
49
67
43
43
40
65
54
45
28
19
45
50
35
15
88
120
Total
150
130
130
170
110
120
110
170
140
120
73
49
120
130
94
37
220
300
Available
5.4
4.5
4.6
6.2
3.7
3.6
.
3.6
6.0
4.8
4.2
2.2
1.5
3.5
4.1
3.7
1.4
8.1
11.0
BOD5
900
770
730
1,000
660
660
600
960
800
680
430
300
670
750
520
220
1,300
1,700
Nitrogen
Total
460
400
370
510
340
340
310
490
410
350
220
160
350
390
220
120
680
860
Available
56
47
42
63
42
41
35
56
48
41
27
20
45
51
38
19
84
100
above Mexico
(Wyandot County)
Below Mexico, above
Wolf Creek (Seneca
County)
Below Wolf Creek
to the mouth
(Sandusky 'County)
37
28
97
74
3.0
3.4
560 290
360 190
38
30
36
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2.1.4 Urban Nonpoint Sources
Two types of pollutant sources in urban areas are considered: stormwater
runoff going directly to a receiving water and combined sewer overflows (CSO).
The first of these sources was considered in the development of the loading
functions; the second was not. It is, therefore, necessary to outline a meth-
odology for estimating loads from CSOs. After the approach has been given, es-
timates of pollutant loads from the urban areas in the Sandusky Basin will be
presented.
2.1.4.1 Combined Sewer Overflows—Methodology
Most of the urban areas in the basin have combined sewers as a part of
their collection systems. For many of these areas, combined sewers constitute
a majority of the systems. In addition, for the majority of the wastewater
treatment plants, the present. average flow is near the design flow. Therefore,
in the Sandusky Basin, CSOs (or bypasses) are common in many systems.
During dry weather flow (DWF), material settles to the bottom of a com-
bined sewer. This material is scoured during periods of stormwater runoff.
The scoured material, the stormwater, and sanitary sewage are lost during CSOs.
The scoured material is quite significant in terms of pollutant loads; as will
be shown below, the pollutant loads from sanitary sewage are probably rather
small in comparison to the loads associated with scoured material.
Since many of the combined sewers overflow and bypasses appear to be com-
mon in the basin, large runoff events will remove most material settled in the
sewers. Therefore, the annual load of pollutants for an urban area from CSOs
(or bypasses) is given by:
\ /pollutant 1 oad \ /pollutant load \
nnnan
(pollutant) = n° H in 1 + / settled during \ + / associated with \ (1)
> \ ?ormwater/ I °WF ^ "oured/ lloss of San1tary
\by high flow / \sewage /
37
-------
The settled load for the dry weather period will be assumed to be 10% of
that associated with the DWF (Heaney et al., 1976). During the course of the
year, all of this settled load will be assumed to be scoured by wet weather
flow, and lost.
Table 2-16 shows the characteristics of "average" raw sewage. Based on an
average per capita flow for an urban area (included nonresidential uses) of 150
gal. per capita per day, Table 2-16 also shows average annual per capita loads
of various pollutants. The last column of the table shows average annual per
capita pollutant loads assumed to be lost in CSOs due to scouring.
TABLE 2-16. ESTIMATION OF LOADS ASSOCIATED WITH SANITARY SEWAGE
IN DRY WEATHER FLOW
Parameter
Suspended solids
BOD5
Total nitrogen (as N)
Total phosphorus (as P)
. . . -
Average
concentration
(mg/L)
200
200
40
10
Annual per
capita load
based on
150 gpcd flow
(Ib/yr)
91
91
18
4.7
10% of
annual
per capita
load
(Ib/yr)
9.1
9.1
1.8
0.47
a gpcd = gallons per capita per day.
The pollutant loads in stormwater will be estimated using curb loading
rates for pollutants along with the assumption that the fraction of the storm-
water transported by and lost from combined sewers during CSOs equals the frac-
tion of the collection system that uses combined sewers.
38
-------
The solids loading rate is assumed to account for all surface-related
sources in the urban area. Therefore, the load per curb-mile includes inputs
from nonstreet sources. The street loading rate is probably an underestimate
of the total loading rate.
Loss of sanitary sewage during bypassing will be estimated as follows. It
will be assumed that 70 bypasses per year occur and that each lasts 5 h. This
is equal to the annual average (350 h/year) for many plants (EPA, 1976) and ap-
pears reasonable based on data for Bucyrus (Burgess and Niple, 1969). The por-
tion of raw sewage lost during bypassing is related to the ratio of treatment
plant capacity to average DWF. For a ratio of one, the loss is 67% (EPA, 1976).
Since most plants in the Sandusky Basin have a ratio near one, 67% is used
here. Bypassing for 350 h with a loss of 67% corresponds to 2.7% of annual
DWF. Therefore, the pollutant load associated with loss of raw sewage will be
estimated by:
/pollutant load \ /annual per capitaX /portion of \
jassociated with ( = 0.027 [load of pollutant \ /population\ /collection U2)
iloss of sanitary! I in DWF - from J Vserved / I system using )
'sewage ) \Table 2-16 / \combined sewers/
The total load of pollutants lost during CSOs can then be found by com-
bining the above results with Eq. (1):
(3)
annual
pollutant
load in
CSOs
= fl_ls + f (population)
storm-
water
load
/annual DWF>
pollutant
, load per
\capita /
(0.1 +
load
scoured
0.027)
raw
sewage
lost
= f (LI + (0.127) (population) (annual DWF pollutant load per capita)
where f = fraction of system served by combined sewers
L = annual load of pollutant per curb-mile
1 = number of curb-miles
The important assumptions that have been made are:
39
-------
1. Ten percent of DWF pollutants settle in sewers.
2. All settled pollutants are scoured and lost during the course of a
year.
3. The loss of sanitary sewage during overflows is 67% of DWF, and over-
flows occur 350 h/year.
4. It is implicitly assumed that all stormwater is discharged via sepa-
rate or combined sewers.
The methodology just outlined will provide estimates of annual loads from
CSOs. Estimation of loads on an event basis is more difficult. If event-
related loads were required for screening purposes, the most likely approach
would be to use average volumes of overflows and average pollutant concentra-
tions during the overflow to estimate storm loads.
2.1.4.2 Character of the Urban Areas of the Sandusky River Basin
Table 2-17 presents a brief description of the urban areas within the
basin. The table provides estimates of the population and street lengths for
each area, the portion of the collection systems using combined sewers, and an
indication of how close to capacity the basin's treatment plants are operat-
ing.
The largest cities in the basin are Bucyrus, Crestline, Fremont, Tifin,
and Upper Sandusky (see Figure 2-1 for locations). These cities have a com-
bined population of 69,000. For these cities, CSO-associated loads will be
found using the procedure outlined in the preceding section. Carey is rather
small, and since not all information needed is available, CSOs will be neg-
lected and only direct stormwater runoff will be considered.
40
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TABLE 2-17. SANDUSKY RIVER BASIN URBAN AREAS
City
Carey
Crestline
Bucyrus
Upper Sandusky
Tiffin
Fremont
a Source: City
Source: Ohio
Estimated
population
3,575
6,000
13,500
6,000
23,000
20,500
officials.
EPA, "State
Estimated
street
miles
20
19
60
22
67
75
Water Quality
Percent system
having combined
sewers
Part
20%
84%
39%
60%
90%
I Management Plan,
For STP: avg.
annual flow/
design flow
0.61
0.93
1.11
1.00
0.80
0.73
Sandusky
River Basin," Part II, no date, preliminary report.
In all calculations, none of the stormwater runoff will be assumed to be
treated. . It is assumed that all stormwater runoff enters a body of water di-
rectly.
Table 2-18 characterizes the street loading rates for the City of Bucyrus.
Since these values are undoubtedly more typical of the Sandusky Basin than the
average loading rates given in the loading functions report (McElroy et al.,
1976) they are used here. It is worth noting, however, that the solids loading
rate in Table 2-18 is about 2.4 times the "average" given in the loading func-
tions report for the northeast region. The loading rates for the other contami-
nants are smaller than the average values.
2.1.4.3 Load Estimation
Table 2-19 presents estimates of annual loads from CSOs for the urban areas;
Table 2-20 gives estimates for loads in stormwater but not in CSOs; and Table 2-21
presents the sum of the two. Results in Table 2-19 were obtained using Eq.(3)
and results in Table 2-20 were found using the standard screening approach,
load = (l-f)Lls.
41
-------
TABLE 2-18. STREET LOADING RATES FOR BUCYRUSC
Total
BOD5
Total
Total
Parameter
solidsb
nitrogen (as N)
phosphorus (as P)
Loading
Ib/curb-mile/day
690
1.4
0.61
0.04
rates
tons/curb-mi 1 e/year
126
0.26
0.11
0.007
a Source: Sartor, J. D., and G. B. Boyd, "Water Pollution Aspects of
Street Surface Contaminants," EPA-R2-72-081, November 1972.
p. 142.
b Source: A particle size distribution was given by Sartor and Boyd
(p. 4E); 26% of.the solids are classified as. silt, and 74%
are classified as sand (above 43 |jm in size).
TABLE 2-19. ESTIMATED ANNUAL LOADS FROM COMBINED
SEWER OVERFLOWS
City
Bucyrus
Carey
Crestline
Fremont
Tiffin
Upper Sandusky
Pollutant
Suspended
solids
3,400
0
260
4,500
2,700
580
loads
BOD5
92
0
10
142
101
18
(tons/year)
NT PT
24 4
0 0
2 0.5
37 6
25 5
5 0.8
42
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TABLE 2-20. ESTIMATED ANNUAL LOADS IN STORMWATER
RUNOFF, NOT IN CSOs
City
Bucyrus
Carey
Crestline
Fremont
Tiffin
Upper Sandusky
Pollutant
Suspended
solids
620
1,300
990
490
1,800
880
loads
BOD5
5
10
8
4
14
7
(tons/year)
N, PT
2 0.1
4 0.3
3 0.2
2 0.1
6 0.4
3 0.2
TABLE 2-21.- ESTIMATED TOTAL ANNUAL URBAN LOADS
City
Bucyrus
Carey
Crestline
Fremont
Tiffin
Upper Sandusky
Pollutant
Suspended
solids
4,000
1,300
1,300
5,000
4,500
1,500
loads
BOD5
97
10
18
146
115
25
(tons/year)
N, P,
26 4
4 0.3
5 0.7
39 6
31 5
8 1
43
-------
The suspended solids in the portion of the load associated with the run-
off from street surfaces were determined based on the assumption that only the
silt portion of the solids was transported in suspension. The larger particles
are unlikely to stay in suspension and cannot be compared directly with the
suspended solids load in sanitary sewage. The size distribution of the solids
for Bucyrus was used for all the cities, i.e., 26% of the solids were assumed
to be suspended. All the BOD, N, and P loads were assumed to be delivered to
a receiving water.
Data are available for pollutant loads in CSOs for Bucyrus for 1969.
Table 2-22 compares values for annual loads estimated from measurements made
in the city with those estimated here. As Table-2-22 shows, the agreement
between "measured" and estimated values is rather good for BOD5, NT, and Py,
especially considering the character of the screening methodology used. The
table also shows that the methodology appears to overpredict the solids load-
ing.
The assumption concerning the high frequency of CSOs in the basin can be
viewed in the following light. Assume that an urban area of 500 acres contri-
butes 0.1 in. of runoff to the combined sewers. That volume is 1.38 x 106
gal. If this runs off in 12 h, the flow is 2.7 MGD,* on average. Such
numbers are not unreasonable for a modest storm in any of the areas. This
flow may be compared to the unused capacity of the region's wastewater treat-
ment plants. Table 2-23 shows estimated unused capacities of the plants. The
portion of the system that would contribute to overflows (percent combined
sewers) is also shown. Note that 2.7 MGD is well above any of the capacities
available. Therefore, overflows or bypasses should be common in all the cities.
To illustrate the relative importance of the various sources of pollutants
to the CSO load estimates, Table 2-24 shows the contribution of each source
for the pollutants considered for Bucyrus.
MGD = million gallons per day.
44
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TABLE 2-22. COMPARISON OF LOAD ESTIMATES FOR BUCYRUS
Parameter
Loads
Estimate based on
observations of CSQs
in Bucyrus in 1969
Estimate for CSOs
based on loading rates
Annual volume of CSOs (MG)
Suspended solids (T/yr)
BOD5 (T/yr)
NT (T/yr)
P| (T/yr)
350
700
175.
19b
4.9
3,400
92
24
4
Source: Burgess and Niple, Ltd., "Stream Pollution and Abatement from
Combined Sewer Overflows," FWQA, 1969.
Based on average flow concentrations given in the Burgess and Niple report.
TABLE 2-23. AVERAGE UNUSED CAPACITY OF PLANTS DURING DRY FLOW PERIOD
City
Average
flow
(MGD)
Average
flow/
capacity
Capacity
(MGD)
Unused
capacity
(MGD)
Percent
combined
sewers
Bucyrus 1.9
Crestline 0.56
Fremont 5.1
Tiffin 2.7
Upper Sandusky 1.7
1.11
0.93
0.8
0.73
1.0
-1.9
0.6
6.4
3.7
-1.7
0
0.04
1.3
1.0
0
84
19
90
60
39
45
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TABLE 2-24. COMPONENTS OF COMBINED SEWER OVERFLOW LOADS FOR BUCYRUS
Loads (tons/yr)
Parameter
Suspended solids
BOD5
NT
PT
Scoured
material
52
52
10
2.7
Stormwater
3,300
26
11
0.7
Sanitary
sewage lost
14
14
3
0.7
Total
3,400
92
24
4
As the table shows, stormwater dominates the suspended solids load; the
scoured material is most important for BOD and P; and stormwater and scoured
material both contribute substantially to the nitrogen load. The loss of sani-
tary sewage is a relatively minor addition in all cases.
The sensitivity of the results to the assumptions used in the analysis can
be inferred from Table 2-24:
1. Since loss of sanitary sewage is a fairly minor component of the load,
the overall results are not sensitive to any of the assumptions made concerning
this source.
2. Since the stormwater component provides essentially 100% of the sus-
pended solids load, the solids loading rate and the portion of the solids as-
sumed to be suspended are critical factors in the analysis.
3. The nitrogen load obtained is sensitive to the nitrogen loading rate
used to estimate stormwater load and to the assumptions related to the settling
and later scouring of DWF pollutants. Fifty percent changes in the loading
rates or the settling/scouring portion of DWF pollutants produce about a 20%
change in the total load (for Bucyrus).
46
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4. Since the input of scoured material appears to be the most important
source of phosphorus and BOD, the estimate of loads of these materials is sen-
sitive to the quantity of DWF pollutants which settle and are scoured. A 50%
variation in this quantity would change PT and BOD loads for Bucyrus by about
30%.
Finally, it is interesting to compare the results obtained using measured
street loading rates for Bucyrus with those obtained using average rates for
the region as given in the screening methodology. For the Northeast, the aver-
age solids loading rate is 291 Ib/day/curb-mile. Based on the average pollut-
ant concentrations for the region, the loading rates for BOD5, total N, and
total P are 5.8, 2.4, and 0.28 Ib/day/curb-mile,-respectively. Using these
values for Bucyrus gives the results shown in Table 2-25.
As Table 2-25 indicates, using average rather than measured loading rates
results in improved estimates-for suspended solids and BOD5 .(compared to meas-
ured loads in CSOs) and poorer results for N and P. The suspended solids cal-
culation assumed the same fraction of silt for both calculations and is based
on measurements made in Bucyrus. It can be concluded that, for the four param-
eters studied, using average values for loading gives results as good as those
using local rates. Therefore, using regional values in this case provides
rather accurate results. However, two factors should be kept in mind. First,
the suspended solids load calculation used information on the particle size
distribution obtained for measurements in Bucyrus. Such size distribution
information will not normally be available in the application of the method-
ology. Second, although it appears in this case that average values provide
results as good as measured local values for loading rates, it would seem wis-
est to always use local information when available. Nevertheless, the fact
that the average values provide reasonable results increases their credibil-
ity.
47
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TABLE 2-25. COMPARISON OF CSO LOADS FOR BUCYRUS
Load based on Load based on
Load based on measured street average street
observations in loading rates loading rates
Parameter Bucyrus in 1969 for Bucyrus for region
Suspended solids 700 3,400 1,500
(T/yr)
BOD5 (T/yr)
NT (T/yr)
PT (T/yr)
175
19
4.9
92
24
4
170
56
8.3
a Source: Bucyrus and Niple, Ltd., "Steam Pollution and Abatement from
Combined Sewer Overflows" (FWQA, 1969).
2.1.5 Nonpoint Source Impacts on Water Quality
Estimates of instream concentrations of sediment, phosphorus, and nitro-
gen, based on the loading rates estimated in the preceding sections, are given
in sections 3.2.9 and 3.2.10 of Volume II of this report. Calculation of in-
stream concentrations of these constituents provides the best means available
for verifying the nonpoint load estimation procedures. The results presented
in Volume II indicated that the loading estimates obtained for sediment and
phosphorus for this basin appear reasonable.
2.2 DEMONSTRATION EXAMPLE: THE CHESTER RIVER BASIN
2.2.1 Character of the Basin
The Chester River is located in the Delmarva Peninsula onthe eastern
shore of Chesapeake Bay. The headwaters are in Delaware, but most of the' basin
48
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is located in Kent and Queen Annes Counties in Maryland. The area of the basin
is approximately 440 sq miles. The major land use is agriculture, and corn and
soybeans are the major crops. Some small grains (wheat and barley) are also
grown.
The relief of the basin is low. The uplands have elevations of 80 to
100 ft, and the lower basin elevation ranges from sea level to about 60 ft.
Average annual precipitation in the basin is about 43 in. , and is fairly
uniformly distributed throughout the year. Normally, the wettest month is
August (4.9 in.) and the driest month is February (2.9 in.).
For purposes of analysis, the Chester River -Basin has been divided into
six subbasins as indicated in Figure 2-2 and Table 2-26. The numerical desig-
nation of the basins corresponds to those defined by the Maryland Department
of Natural Resources (DNR). The Maryland DNR includes the Wye River Basin (to
the south) as part of the Chester River Basin, and the designations 1 to 4 are
subbasins of the Wye. The Wye River Basin was not included in any analysis of
the Chester River Basin.
TABLE 2-26. CHESTER RIVER SUBBASINS
Subbasin
5 Chester River
6 Langford Creek
7 Corsica River
8 Southwest Creek
9 Chester River
10 Chester River
Area
(acres)
40,943
24,878
24,511
34,517
35,527
114,207
Length of
principal
streams (miles)
27
32
31
39
8
63
Total
274,583
49
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tn
o
NEW CASTLE
1 COUNTY
\
QUEEN ANNE COUNTY
COUNTY BOUNDARY
WATERSHED BOUNDARY
SUB-BASIN BOUNDARY
Figure 2-2. Chester River Basin and Subbasins.
-------
2.2.2 Nonpoint Load Estimation Methodology - An Overview
The procedure used for the Chester River Basin is essentially the same as
that used for the Sandusky. The details of the approach are not repeated here.
Since the Chester River Basin does not contain any significant sources of urban
runoff, urban nonpoint sources are not considered.
2.2.3 Rural Nonpoint Sources
2.2.3.1 Parameter Evaluation
2.2.3.1.1 Rainfall Factor . .
No recording rain gauges are located in the Chester River Basin. The
nearest such gauges are located 25 to 30 miles away at Wilmington, Delaware,
Perry Point, Maryland (near.the mouth of the Susquehanna), Baltimore, Maryland,
and Federalsburg, Maryland. There are four nonrecording gauges in the basin.
Since rainfall records with at least 30-min time resolution are needed to
estimate R, it was necessary to use one of the stations outside the basin for
the analysis. However, use of a distant station reduces the accuracy of the
results.
Table 2-27 shows the periods analyzed (selected for compatibility with
the study presented in Volume II) and the results for Wilmington. Wilmington
was chosen arbitrarily for use in the study; the other stations mentioned above
would have served equally well. The results in Table 2-27 were obtained using
data obtained from the National Climatic Center in Asheville, North Carolina.
For the 14 periods analyzed, the average value of EI3o is 14.6, or 11.4
without the largest single value of EI36. The periods studied are distributed
throughout April to September, with half occurring in July and August. The
periods are usually 1 to 2 days long and precede selected 7-day high flow periods
for the Chester. Occasionally, additional small amounts of rainfall occurred
later in the 7-day period, but this rainfall was neglected.
51
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TABLE 2-27. EVALUATION OF EI30 FOR THE CHESTER BASIN
Total rainfall
at Wilmington
(in.) EI3o for Wilmington
June 18-19, 1967
August 3-4, 1967
August 24-25, 1967
July 27-29, 1969
August 9, 1969
April 14-15, 1970
April 20, 1970
September 11-13, 1971
June 22-27, 1972
July 13, 1972
June 2, 1974
July 13, 1975
July 20, 1975
September 22-24, 1975
May 8-9, .1978
1.55
2.94
1.00
3.71
0.56
2.26
0.82
4.92
Hurricane Agnes - neglected
0.94
0.32
2.61
1.12
3.77
1.30
8.3
56.0
3.1
22.9
2.2
8.0
2.3
37.2
3.6
0.25
30.0
12.2
13.7
4.2
Total 27.9 204.0
Total rainfall at Wilmington for the events analyzed was 27.9 in., while
the total rainfall at Chesterton (in the Chester Basin) for the same period
was about 38 in. The correlation coefficient of the rainfall (in each period)
at Wilmington and Chesterton for the 14 events is 0.62. For Chester and
Millington (also in the basin) the correlation coefficient (11 events) is 0.85,
and for Wilmington and Millington the correlation coefficient (11 events) is
0.80.
The average annual value for R for the Chester Basin is in the range 190
to 200. An average storm with an El equal to 14.6 produces about 7% of the
annual soil loss. An El value of 14.6 has been used in the analysis.
52
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2.2.3.1.2 Soil Erodibility Factor (K)
The weighted soil credibility (K) factors for the Chester River Basin were
established from soil association maps from the State of Maryland and from pub-
lished soil surveys of the counties in the basin. The K factors by county and
subbasin are shown in Table 2-28. In this analysis, the weighted average K
was assumed to represent all the soils within the fraction of a subbasin within
a county.
TABLE 2-28. AVERAGE K FACTOR VALUES BY SUBBASIN AND
COUNTY FOR THE CHESTER RIVER
Subbasin
5
• 6
7
8
9
10
Kent,
Md.
0.40
0.38
-
-
0.35
0.36
K values
Queen Annes,
Md.
0.36
-
0.34
0.32
0.28
0.30 '
by county
Kent, New Castle,
Del. Del.
-
-
-
0.28 0.30
2.2.3.1.3 Slope Factors (L and S)
Slopes and slope lengths were obtained from the data based used for the
nonpoint calculator. These factors have been established for each land capa-
bility class within each Land Resource Area (LRA). The Chester River Basin is
contained within two LRAs, LRA 149 (Northern Coastal Plain) and LRA 153
(Atlantic Coast Flatwoods). Values for the LS factor were calculated based on
these slopes and slope lengths. The slopes and slope lengths are tabulated in
Appendix I.
53
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2.2.3.1.4 Cover Factor (C)
Cover factors are dependent on the cropping patterns of the basin or sub-
basin under study. Since cropland is the primary source of sediment, crop
acreages and cropping rotation for a given year must be known. State agricul-
tural statistics were used to establish crop acreages in each county, and
county extension agents provided information on cropping rotations. Uniform
distribution of crop acreage and rotation within each county was assumed.
The analysis of agricultural practices in the Chester River Basin was done
for the year 1973. This year was chosen because it was a typical recent year,
i.e., rainfall and crop yield were approximately aormal. The acreages of prin-
cipal crops for 1973 are shown in Table 2-29. Acreages of crops within various
crop rotations were determined from contacts with county agents. Cover factors
for each crop throughout the growing season were determined in the same manner
as described in detail for.the Sandusky River Basin in Section 2.1.3.1.4 of
this volume.
The cropping patterns in the Chester'River Basin are:
1. All small grain is in rotation with corn and soybeans.
2. No meadow is in rotation.
3. No winter cover crop; stubble is left on the field.
4. Some corn and soybeans are in continuous production.
There are four major cropping rotations for corn, soybeans, and small
grain (wheat and barley). These practices are:
1. Four-year rotation of corn-corn-small grain-soybeans = 4 x acres in
small grain. This practice is used in Kent County, and Queen Annes County,
Maryland; and Kent County, Delaware.
54
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TABLE 2-29. AGRICULTURAL DATA FOR COUNTIES IN THE
CHESTER RIVER BASIN FOR 1973
County
Kent, Md.
Queen Annes, Md.
Kent, Del.
New Castle, Del.
Crop area (103
Corn Soybeans Wheat
50.0 29.0 8.5
55.0 38.0 13.3
52.0 65.0 11.0
23.0 30.0 10.0
acres)
Barley
4.0
5.8
8.5
4.5
Total
91.5
112.1
136.5
67.5
Areas in rotation (103 acres)
County
Kent.-Md.
Queen Annes, Md.
Kent, Del.
New Castle, Del.
CCSgSb CC
-50.0 25.0
76.4 16.8
78.0 ' 13.0
8.5
SbSb
16.5
18.9
45.5
15.5
CSgSb
-
-
-
43.5
CCSgSb = Corn-corn-small grain-soybean (4-yr rotation).
CC = Continuous corn.
SbSb = Continuous soybean.
CSgSb = Rotation corn-shiall grain-soybean (3-yr rotation).
55
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2. Three-year rotation of corn-small grain-soybeans.= 3 x areas in small
grain; used for New Castle County, Delaware.
3. Continuous corn = area in corn minus (2 x area in small grain) for
4-year rotation corn-corn-small grain-soybeans; or = area in corn minus area
in small grains for 3-year, rotation in corn-small grain-soybeans.
4. Continuous soybeans = area in soybeans minus area in small grain for
all rotations.
The acreages for those cropping practices are also shown in Table 2-29.
From the agronomic (growth stage, cropping practice) data describing each
crop, it is possible to determine C factors for corn, soybeans, and small
grains at specific times of the year. The methods used .are described in Sec-
tion 2.1.3.1.4 of this report. Since the nonpoint loads are generated by
rainfall events, a series of time intervals were developed using the periods
in Table 2-27 and the C factors for these time intervals. These C factors are
given in Table 2-30.
TABLE 2-30. COVER FACTORS FOR VARIOUS INTERVALS
FOR THE CHESTER RIVER BASIN
Interval
Mid-April-early May
Early May- late May
Late May- late June
Late June- late August
Late August-mid-October
a Kent, Maryland; Queen
New Castle, Delaware.
Corn3
0.29
0.29
0.26
0.21
0.18
Annes,
Corn
0.39
0.39
0.34
0.26
0.22
Maryland; and
Crop
Soybeans Small Grains
0.18
0.18
0.25
0.19
0.14
Kent, Delaware.
0.16
0.09
0.06
0.07
0.07
56
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When the C factors were evaluated, it was noted that the values for corn
in New Castle County, Delaware, were significantly different from those for
the other counties in the basin. Therefore, corn grown in New Castle County
was assigned a separate C value.
2.2.3.1.5 Support Practice Factor (P)
Lacking needed information on current conditions, it was assumed that no
significant changes occurred in the cropping practice factor (P) between 1967
and 1979. Therefore, P factors based on the 1967 Conservation Needs Inventory
(CNI) were used for the Chester River Basin. These values are tabulated in
Appendix I. -
2.2.3.1.6 Land Use Within the Chester River Basin
Land use changes have occurred in the basin; from 1967.to 1973 there was
an increase in the acreage of corn and soybeans and a reduction in the acreage
of small grains and pasture. Crop acreages in the 1967 CNI were shifted to re-
flect the acreages reported in the agricultural statistics of 1973, the base
year for the analysis. The approach used followed that described for the
Sandusky.
Specifically the following changes were made:
Kent County, Maryland: 15,700 acres of corn were shifted to soybeans;
6,300 acres of small grain were shifted to soybeans; and 7,600 acres of small
grain were shifted to pasture.
Queen Annes County, Maryland: 10,000 acres of corn were shifted to beans;
3,800 acres of corn were shifted to pasture; 4,000 acres of small grains were
shifted to soybeans; and 4,400 acres of small grain were shifted to pasture.
Kent County, Delaware: 15,270 acres of corn were shifted to pasture; and
1,755 acres of corn were shifted to small grain.
57
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New Castle County, Delaware: 1,700 acres of small grain were shifted to
soybeans; 8,700 acres of corn were shifted to beans; and 1,900 acres of "Con-
servation Use Only" were shifted to beans.
2.2.3.1.7 Nutrients
Soil nutrient concentrations were obtained for each county in the basin
from soil scientists in the area and from soil test results where possible.
In most cases, only values for available phosphorus and nitrogen could be ob-
tained. Total phosphorus was estimated to be in the range of 100 to 200 ppm
based on regional soil data. This range represents total phosphorus content
of virgin soils. Total phosphorus content of agricultural soils is probably
higher due to fertilizers. We used a value of 200 ppm for total phosphorus
concentration. Since nitrogen is required in the total form for purposes of
load estimation, the Jenny equation was used to estimate total soil nitrogen
levels. The values for available soil phosphorus and total.soil nitrogen for
the counties in the Chester River Basin are shown in Table 2-31.
TABLE 2-31. SOIL NUTRIENT CONCENTRATIONS IN
THE CHESTER RIVER BASIN
Average nutrient concentration in soil (ppm)
County Available phosphorus a Total nitrogen
Kent, Md.
Queen Annes, Md.
Kent, Del.
New Castle, Del.
24
25
25
24
1,550
1,200
1,590
1,630
a Total phosphorus is approximately 200 ppm.
Available nitrogen is assumed to be 10% of total.
58
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Enrichment ratios for the nutrients in soils were evaluated using the
method described for the Sandusky Basin. The value obtained for the Chester
River Basin is 1.97 + 0.11; a value of 2.0 was used for making the load esti-
mates.
Finally, nitrogen in rainfall was estimated to be 0.0045 Ib/acre/event us-
ing the methods described in Section 2.1.3.1.7.
2.2.3.1.8. Sediment Delivery Ratio
There are no regular, long-term measurements of sediment discharge in the
Chester Basin. Discussions with the Soil Conservation Service and the United
States Geology Survey (USGS) did not reveal any information on sediment yields
in the basin or any evidence of the existence of regression relationships for
predicting sediment yield based on watershed variables. - There is, therefore,
no dependable method available for determining sediment delivery ratios in the
basin. Gross soil loss was determined and loads found using a delivery ratio
of 100%. The Chester Basin has an area of 440 sq miles and is very flat. It
would be expected that the annual delivery ratio for such a basin would be low,
probably below 10%. A delivery ratio of 0.1 (10%) was used for the analysis in
Volume II.
Steambank and gully erosion have been neglected in the analysis. In por-
tions of the basin shoreline erosion is a serious problem. The sediment loads
determined in this report underestimate the total sediment load in the basin to
the extent that such other sources were neglected.
2.2.3.2. Load Determination
The nonpoint loads determined for the Chester River Basin are presented
in Table 2-32.
59
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TABLE 2-32. ESTIMATED STREAM LOADING RATES FOR THE AVERAGE
CHESTER EVENT, SEDIMENT DELIVERY RATIO =0.1
Ib/mile/event
ton/mil e/event Phosphorus
Subbasin
5
6
7
8
9
10
Sediment
46
27
17
18
130
42
Total
36
21
13
14
102
33
Available
4.5
2.7
1.6
1.7
12.8
4.1
BOD5
490
310
170
190
1,620
510
Nitrogen
Total
250
160
90
100
820
260
Available
32
19
13
14
102
33
2.2.4 Nonpoint Source Impacts on Water Quality
Inst'ream concentration estimates for sediment, nitrogen, phosphorus, and
BOD5 for the Chester River are presented in Section 3.3.6.2 of Volume II.
Since point source loads appear to be of minor importance in the basin, the
nonpoint loads estimated in the preceding section are the primary pollutant
inputs for the basin.
2.3 DEMONSTRATION EXAMPLE: PATUXENT RIVER BASIN
2.3.1 Character of the Basin
The Patuxent River is located in Maryland between Washington, D.C.,
and Baltimore and drains into the western side of Chesapeake Bay. The drain-
age basin area covers about 930 sq miles. Approximately half of the basin is
forested, 35% is agriculture, and the remainder is suburban. Principal crops
of the basin are corn, soybeans, tobacco, and small grains.
The headwaters of the Patuxent area are in the Piedmont region (Howard
and Montgomery counties), and the river flows through the Atlantic Coastal
Plain (Anne Arundel, Calvert, Charles, Prince Georges, and St. Marys counties).
60
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Average -precipitation throughout the basin is about 43 in. annually, with
about 25% of the annual total coming in July and August. The driest months
are December and February.
The Patuxent River Basin has been divided into subbasins (Figure 2-3) as
defined by the State of Maryland. The acreages of the subbasins and lengths of
principal streams are shown in Table 2-33.
TABLE 2-33. PATUXENT RIVER SUBBASINS
Subbasin
1
2
3
4
5
6
7
8
Total
Area
(acres)
197,738
67,106
. .. 70,594
47,552
66,463
35,894
36,169
49,480
570,996
Length of principal
streams (miles)
121
34
59.
22
61
22
28
49
2.3.2 Nonpoint Load Estimation Methodology—An Overview
The procedure used for nonpoint load estimation for the Patuxent River
Basin is essentially the same as that used for the Sandusky. The details of
the apporach are not repeated. Both urban and rural nonpoint sources are con-
sidered in the basin.
61
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COUNTY BOUNDARY
WATERSHED BOUNDARY
SUB-BASIN BOUNDARY
Figure 2-3. Patuxent River Basin and Subbasins,
62
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2.3.3 Rural Nonpoint Sources
2.3.3.1 Parameter Evaluation
2.3.3.1.1 Rainfall Factor (R)
There are several recording rain gauges near the basin in Beltsville,
Unionville, Baltimore, and Leonardtown. Beltsville is the most centrally lo-
cated of the gauges and was used in the analysis.
Table 2-34 shows the periods analyzed (selected for compatibility with
the study presented in Volume II) and the results for Beltsville. Data for
Beltsville were obtained from the National Climatic Center.
For the 16 periods with data the average value of EI30 is 22.2 (.average
annual value is about 220);. without the two largest events., the average is
14.8. The events are distributed through the period April to September; about
half occurred in April and May.
An attempt was made to determine the uniformity of rainfall over the
basin by comparing rainfall totals for Beltsville with those at Leonardtown.
For the periods studied, there are 10 intervals having data at both stations.
The average rainfall event at Beltsville was about 2 in., while Leonardtown
had about 1.5 in. No attempt was made to adjust for any nonuniformity in
rainfall over the basin.
2.3.3.1.2 Soil Erodibility Factor (K)
The soil association maps and soil surveys for the Patuxent River Basin
were analyzed to determine weighted averages of K factors for the soil loss
continuation. The resulting K factors for subbasin within specific counties
of the basin are presented in Table 2-35.
63
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TABLE 2-34. EVALUATION OF EI30 FOR THE PATUXENT BASIN
Total rainfall
at Beltsville3
(in.) EI30 for Beltsville
May 6-7, 1967
August 23-25, 1967
May 27-28, 1968
June 27-July 3, 1968
April 14-15, 1970
May 14-20, 1970
July 9-10, 1970
July 20-23, 1970
April 6-7, 1971
May 13-16, 1971
May 28- June 3, 1971
July 1, 1971
August 2-4, 1971
September 11-12, 1971 .. .
April 1-2, 1973
April 25-27, 1973
May 27-28, 1973
September 23-29, 1975
April 1-7, 1976
April 5-11, 1977
May 14-20, 1978
1.45
4.63
2.22
2.65
2.6
2.94
1.07
1.80
3.55
2.15
1.1
2.01
3.05
2.1
2.7
1.0
3.7
75.3
6.5
22.4
4.2
No data
56.3
1.0
2.0
16.8
6.3
11.2
46.1
73.2
18.5
9.5
2.8
Data problems
Data problems
Data problems
Data problems
Data prior to April 1973 are from the Beltsville Plant
Station 5. Those after that date are from Beltsville.
Generally, the difference between the two locations seems
small.
64
-------
TABLE 2-35. AVERAGE K FACTOR VALUES BY SUBBASIN FOR THE PATUXENT BASIN
CTl
in
Average K values by county
Subbasin Anne Arundel
1 0.37
2 0.34
3
4 0.34
5 0.32
6
7
8
Calvert Charles Howard Montgomery
0.34 0.38
0.33
_
0.37
0.35
0.36
0.36 0.37'
0.35 0.37
Prince Georges
0.33
0.34
0.33
0.34
-
-
-
_
St. Marys
0.31
-
-
-
-
-
-
_
-------
2.3.3.1.3 Slope Factors (L and S)
Slopes and slope lengths for the IRAs of which the Patuxent River Basin
is a part were obtained from the national data base associated with the non-
point calculator. These values were used to obtain slope and slope length
factors for the basin. The data used included that for LRA 148 (Northern
Piedmont) and LRA 149 (Northern Coastal Plains) and are tabulated in Appendix I.
2.3.3.1.4 Over Factor (C)
The year 1973 was chosen as the base year for defining land use conditions
for the Patuxent River Basin. The areas of principal crops for that year are
presented in Table 2-36.
TABLE 2-36. AGRICULTURAL DATA FOR COUNTIES IN PATUXENT RIVER BASIN
IN 1973
Crop area-(103 acres)
County
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
Corn
9.0
7.0
11.0
14.0
21.0
8.0
13.0
Wheat
0.8
0.8
2.5
2.5
5.7
2.2
3.1
Tobacco
4.6
4.5
5.5
-
-
4.2
6.2
Soybeans
2.0
1.5
5.0
-
0.7
3.0
9.5
Barley
0.1
0.4
0.2
3.0
3.0
0.1
1.6
Total
16.5
14.2
24.2
19.5
30.4
17.5
33.4
Conversations with county agents indicated the following cropping patterns:
1. Continuous corn.
2. Continuous tobacco with a winter cover crop.
3. Corn-soybeans in 2-year rotation.
66
-------
4. Corn-small grains (wheat, barley) in 2-year rotation.
5. Tobacco-tobacco-cover (rye) in 3-year rotation.
6. Small grain-cover in an annual rotation.
7. Corn-corn-small grain-small grain in 4-year rotation.
8. Corn-corn-cover-corn in 4-year rotation.
These patterns were used in conjunction with 1973 agricultural statistics
to estimate acreages of crops grown in the rotations. The results of this
analysis are presented in Table 2-37.
TABLE 2-37. ESTIMATED AREAS OF PRINCIPAL CROP ROTATIONS BY COUNTY
IN PATUXENT RIVER BASIN IN 1973
Crop area (103 acres)
County
Anne Arundel
Calvert
Charles
Howard-
Montgomery
Prince Georges
St. Marys
CC TcT
6.1 4.6
4.3 4.5
3.3
14.0
-
2.7
5.0
CSb
4.0
3.0
10.0
-
1.4
6.0
19.0
CSg
1.8
2.4
5:9
-
-
4.6
7.0
CCCoCo TTCo
-
5.5
-
11.6
4.2
2.4
SbCo CCSgSg
-
-
5.5
17.4
-
CC = Continuous corn.
TcT = Tobacco-winter cover-tobacco (annual rotation).
CSb = Corn-soybean (2-yr rotation).
CSg = Corn-small grain (2-yr rotation).
CCCoCo = Corn-corn-cover-cover (4-yr rotation).
TTCo = Tobacco-tobacco-Cover crops (3-yr rotation).
SbCo = Soybeans-cover crops (annual rotation).
CCSgSg = Corn-corn-small grain-small grain (4-yr rotation).
The C factors for the various crops and rotations were developed for each
of the counties within the basin. These factors were then matched with
rainfall intervals established using the periods given in Table 2-34. In
doing so, it was found that significant variations in C factor occurred from
67
-------
county to county within the basin. Therefore, each crop in each county was
assigned a C value for each rainfall interval. These C factors are given in
Table 2-38.
2.3.3.1.5 Support Practice Factor (P)
The 1967 CNI contains information pertaining to land on which conservation
practices have been applied. Lacking more current information, it was assumed
that conservation practices did not change significantly between 1967 and 1973.
The P values used are tabulated in Appendix I.
2.3.3.1.6 Land Use Within the Patuxent River Basiji .
The changes in cropping patterns as reflected in differences between the
1967 CNI and the 1973 base year were accommodated by shifting acreages within
the various counties. Specific changes were:
Anne Arundel County: 1,490 acres of row crops (soybeans or tobacco) were
shifted to corn; 5,960 acres of row crops and small grains were shifted to
pasture.
Calvert County: 3,170 acres of row crops were shifted to corn; 13,400
acres of row crops and small grains were shifted to pasture.
Charles County: 560 acres of small grains were shifted to corn; 200
acres of small grains were shifted to row crops; and 2,140 acres of small
grains were shifted to pasture.
Howard County: 880 acres of row crops were shifted to corn; 1,930 acres
of row crops and small grains were shifted to pasture.
Montgomery County: 1,500 acres of corn were shifted to row crops; 1,000
acres of corn were shifted to small grains; 4,800 acres of pasture were shifted
to small grains.
68
-------
TABLE 2-38. COVER FACTORS (C) FOR CROPS BY RAINFALL INTERVALS AND
COUNTIES FOR THE PATUXENT RIVER BASIN
Rainfall interval /county
1.
2.
3.
4.
Early April -mid- April
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
Mid- April -late May
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
Late May- late June
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
Late June-late July
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
Corn
0.14
0.14
0.21
0.07
0.06
0.18
0.80
0.68
0.68
0.73
' 0.60
0.52
0.71
0.78
0.59
0.19
0.62
0.52
0.45
0.61
0.66
0.45
0.45
0.48
0.41
0.36
0.47
0.51
Small
grain
0.19
0.19
0.19
0.16
0.16
0.19
0.21
0.14
0.14
0.14
0.12
0.12
0.14
0.16
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.03
0.07
0.03
0.03
0.03
0.03
0.03
C factor for crop
Tobacco Soybeans
0.02
0.02
0.02
0.07
0.07
0.05
"0.02
0.26
0.26 .
0.26
0.07
0.07
0.46
0.17
0.30
0.30
0.30
0.66
0.66
0.51
0.20
0.25
0.25
0.25
0.58
0.58
0.38
0.17
Row crops
0.04
0.03
0.04
-
-
0.06
0.05
0.20
. 0.21
0.17
-
-
0.30
0.11
0.41
0.39
0.47
-
-
0.57
0.40
0.35
0.33
0.41
-
-
0.46
0.42
69
-------
TABLE 2-38. (continued)
C factor for crop
Rainfall interval/county
Small
Corn grain Tobacco Soybeans Row crops
5. Late July-early August
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
6. Early August-mid-September
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges
St. Marys
0.45
0.45
0.48
0.41
0.36
0.37
0.51
0.26
0.26
0.28
0.24
0.21
0.27
0.29
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.07
0.14
0.14
0.14
0.42
0.42
0.20
0.10
-
0.32
0.32
0.32
0.28
0.28
0.42 •••• -
0.39
0.22
0.21
0.27
-
-
0.29
0.29
0.31
0.31
0.30
-
-
0.34
0.32
70
-------
Prince Georges County: 1,800 acres of corn were shifted to pasture;
6,700 acres of row crops were shifted to pasture; and 1,450 acres of small
grains were shifted to pasture.
St. Marys County: 830 acres of corn were shifted to pasture; 180 acres
of row crops were shifted to pasture; and 3,200 acres of small grains were
shifted to pasture.
2.3.3.1.7 Nutrients
Concentrations of nutrients in the Patuxent Basin soils were sought from
regional soil scientists and from soil test results. Information relating to
total nutrient content was not readily obtainable; most information reflected
available nutrient concentrations. The concentrations of available phosphorus
are shown in Table 2-39. Total phosphorus was estimated from general soils
information for the region... For purposes of nonpoint load estimates, a value
of 200 ppm phosphorus was used for the coastal region (Anne Arundel, Calvert,
Charles, and Prince Georges counties); and 500 ppm for the Piedmont region
(Howard and Montgomery counties).
TABLE 2-39. SOIL NUTRIENT CONCENTRATIONS IN THE
PATUXENT RIVER BASIN
County Available phosphorus Total nitrogen
Anne Arundel
Calvert
Charles
Howard
Montgomery
Prince Georges,
St. Marys
28
29
24
20
20
24
26
1,580
1,480
1,540
1,800
1,660
1,630
1,480
a Total phosphorus is estimated to be 200 ppm in the coastal
region and 500 ppm in the Piedmont.
Available nitrogen is assumed to be 10% of total.
71
-------
Table 2-39 also shows the total concentration of nitrogen in soils.
These values were obtained from the Jenny equation.
Enrichment ratios for nitrogen were determined using the method described
in section 2.1.3.1.7. The average value for the seven counties in the
Patuxent River Basin is 1.62 + 0.08. An enrichment ratio of 1.6 was used.
The input of rainfall nitrogen was also estimated using procedures
outlined in section 2.1.3.1.7. The value estimated for the Patuxent
River Basin is 0.0059 Ib/acre/event.
2.3.3.1.8 Sediment Delivery Ratio
Based on available information, sediment delivery ratios for the Patuxent
Basin cannot be quantified. A value of 0.1 was arbitrarily chosen, for
estimating nonpoint loads. ...
2.3.3.2 Load Determination
The estimated nonpoint loads for the basin are shown in Table 2-40.
TABLE 2-40. ESTIMATED STREAM LOADING RATES FOR THE AVERAGE PATUXENT
EVENT, SEDIMENT DELIVERY RATIO =0.1
Ib/mile/event
Subbasin
1
2
3
4
5
6
7
8
ton/mi le/event
Sediment
98
104
67
114
60
101
97
67
Phosphorus
Total
65
58
37
70
75
163
153
108
Available
8.5
7.5
4.8
8.6
4.4
6.5
6.1
4.3
BOD5
940
1,050
700
1,180
660
1,160
1,050
750
Nitrogen
Total
480
540
360
600
340
590
530
380
Available
57
65
42
72
39
68
58
44
72
-------
2.3.4 Urban Nonpoint Sources
Two significant urban centers are located in the Patuxent River Basin:
Bowie (population 41,000) and Laurel (population 14,500). Neither community
has combined sewers, so there are no combined sewer overflows in the basin.
Public Works officials in Bowie and Laurel were contacted to obtain
lengths of streets in the two municipalities. These values were used in
conjunction with deposition rates of solids, BOD5, available nitrogen, total
nitrogen, available phosphorus, and total phosphorus. Estimated annual loads
for these pollutants in urban runoff are shown in Table 2-41.
TABLE 2-41. ESTIMATED ANNUAL LOADS FOR URBAN RUNOFF:
PATUXENT RIVER BASIN
Towns:
Bowie; population = 41,000; curb-miles = 260
Laurel; population = 14,500; curb-miles = 70
Loading Rate Loads (ton/yr)
Constituent
Solids
BOD5
Available nitrogen
Total nitrogen
Available phosphorus
Total phosphorus
(Ib/curb mile/day)
5.90
3.6
0.25
0.60
0.06
0.12
Bowi e
28,000
170
12
29
3
6
Laurel
7,500
46
3
8
1
2
2.3.5 Nonpoint Source Impacts on Water Quality
Instream concentration estimates for nitrogen and phosphorus for the
Patuxent River, based on the loads estimated above, are presented in Section
3.4.4.2.2 of Volume II. No water quality data were available for comparison.
73
-------
2.4 DEMONSTRATION EXAMPLE: WARE RIVER BASIN
2.4.1 Character of the Basin
The Ware River is the smallest of the four river basins evaluated in the
Chesapeake Bay phase of the study. Its area is only about 62 sq miles. The
basin is located entirely within Gloucester County, Virginia, off Mojack Bay,
adjacent to Chesapeake Bay. The land consists mainly of forest and swamps
with small areas of agricultural development. There are no urban areas in the
basin. The largest town in the basin is Gloucester, with a population of 700.
For analysis the basin has been divided into.four subbasins. Table 2-42
gives the areas of the subbasins and the lengths of the principal streams.
Figure 2-4 shows the basin and subbasins.
TABLE 2:42. WARE RIVER SUBBASINS
Subbasin
Cow Creek
Beaver Dam Swamp
Fox Mill Run
Ware and Wilson Creek
Total
Area-
(acres)
3,164
16,214
10,678
9,491
37,547
Length of principal
streams (miles)
5
22
14
16
The average annual rainfall for the Ware Basin is about 45 in., and the
wettest months are July and August. Dry months are October through December.
2.4.2 Nonpoint Load Estimation Methodology—An Overview
The same nonpoint source assessment procedure which was applied to the
Sandusky River Basin was used for the Ware. The Ware River Basin contains no
significant urban sources of nonpoint pollution.
74
-------
GLOUCESTER COUNTY
\
\
X
N
North River
Mobjack
Bay
COUNTY BOUNDARY
WATERSHED BOUNDARY
—- SUB-BASIN BOUNDARY
Figure 2-4. Ware River Basin and Subbasins.
-------
2.4.3 Rural Nonpoint Sources
2.4.3.1 Parameter Evaluation
2.4.3.1.1 Rainfall Factor (R)
There are no recording rain gauges in the Ware River Basin. The nearest
station with recoverable data is at Williamsburg, Virginia, about 12 miles
southwest of the basin. Table 2-43 shows the periods analyzed (based on
compatibility with the study presented in Volume II) and the EI30 values
calculated for Williamsburg. The data were obtained from the National
Climatic Center.
TABLE 2-43. EVALUATION OF EI30 FOR THE WARE RIVER BASIN
Total rainfall
at Williamsburg (in.) EI30 for Williamsburg
May 7, 1967
August 23-25, 1967
May 27- June 2, 1968
August 3-September 1969
April 23-24, 1970
April 23-29, 1972
May 19-25, 1972
June 21-23, 1972
April 8-14, 1973
April 26-May 2, 1973
June 23-29, 1974
July 24-27, 1974
June 17, 1974
September 4-10, 1974
July 12-18, 1975
September 1-7, 1975
September 23-29, 1975
April 1-7, 1976
May 1-7, 1976
September 16-22, 1976
April 4-5, 1977
April 24, 1977
April 24-25, 1972 '
August 17-18, 1972
August 24, 1977
1.15
3.2
No data
No data
No data
No data
Data problems
2.4
No data
No data
No data
3.3
0.8
No data
Data problems
No data
No data
No data
Data problems, late April
No data
1.6
0.9
0.9
1.4
2.7
7.1
28.7
13.9
30.7
4.2
6.4
2.9
2.8
13.0
42.8
Total 152.5
76
-------
As can be seen from the entries in Table 2-43, the rainfall data for
Williamsburg are plagued with problems. As a result, there is a considerably
more uncertainty in the definition of a rainfall event for the Ware Basin than
for the other basins studied. Better data may have been available for
Norfolk, Virginia. However, Norfolk is about 50 miles from the Ware Basin.
Therefore, we chose to use data from Williamsburg, which is closer.
For the 10 storm periods with data, the average value of EI30 is 15.3;
without the largest event, the average is 12.2. The events are distributed
over the period April through September, and the most erosive rainfall occurs
in late summer.
2.4.3.1.2 Soil Erodibility Factor (K)
The weighted K factor for the soils in the Ware River Basin is 0..25. This
value was established by estimating areas of particular soil, series within soil
associations and using the acreage as the weighting factor to determine average
K values for the associations. Soil credibility was fairly uniform throughout
the basin; hence, the single K value for all the soils.
2.4.3.1.3 Slope Factors (L and S)
The slopes and slope lengths for LRA 149 as developed by the Soil Conser-
vation Service were used to estimate LS values. LRA 149 (Northern Coastal
Plain) includes Gloucester County and the Ware River Basin. Values for slopes
and slope lengths are tabulated in Appendix I.
2.4.3.1.4 Cover Factor (C)
The cover factors (C) were developed for specific crops based upon crop-
ping patterns and rotations. The base year for the nonpoint analysis is 1977.
The areas of principal crops (corn, soybeans, wheat and barley) in Gloucester
County for 1977 were presented in Table 2-44, along with areas of the crop ro-
tations. Crop acreage was assumed to be uniformly distributed throughout the
county and approximately 28 percent of the county is in the Ware Basin.
77
-------
TABLE 2-44. AGRICULTURAL DATA FOR GLOUCESTER
COUNTY (WARE RIVER BASIN),
VIRGINIA FOR 1977
Crops
(acres)
Corn
Soybeans
Wheat
Barley
Total
Rotations
Continuous corn
Corn-soybeans (2-yr rotation)
Corn-small grains-soybeans
(2-yr rotation)
7,300
6,500
1,400
1,100
16,300
800
8,000
7,500
The crop rotations were used to establish C factors for various stages of
growth for corn, soybeans, and small grains (wheat and barley). Cover factors
are tabulated in Table 2-45.
TABLE 2-45. COVER FACTORS (C) BY RAINFALL INTERVAL FOR THE WARE
RIVER BASIN
Group of events
Corn
Soybeans
Small grain
Early April to mid-April
Mid-April to mid-May
Mid-May to mid- June
Mid-June to mid-July
Mid-July to mid-August
Mid-August to mid-September
0.46
0.77
0.64
0.50
0.24
0.24
0.08
0.15
0.38
0.38
0.30
0.13
0.02
0.02
0.02
0.05
0.05
0.05
78
-------
2.4.3.1.5 Support Practice Factor (P)
The practice factors (P) based on information for Gloucester County as re-
ported in the 1967 CNI were used for the Ware River Basin. More current infor-
mation was not available. The P values used are given in Appendix I.
2.4.3.1.6 Land Use Within The Ware River Basin
The changes in land use based on comparison of the 1967 CNI and the 1977
agricultural statistics data for Gloucester County are the following: 1,000
acres of row crops (soybeans) shifted to corn; 1,800 acres of row crops shifted
to pasture; and 300 acres of small grain shifted to pasture.
2.4.3.1.7 Nutrients
Total soil phosphorus concentration in soils in the Ware River Basin is es-
timated to be about 200 ppm. Of this total, 38 ppm is present as available
phosphorus.
Total soil nitrogen as estimated by the Jenny equation is 1,410 ppm.
Total nitrogen in rainfall is estimated to be 0.0027 Ib/acre/event.
2.4.3.1.8 Sediment Delivery Ratio
No data are available for estimating a sediment delivery ratio. A value
of 0.1 was used in the analysis.
2.4.3.2 Load Determination
Estimates of nonpoint loads are given in Table 2-46.
79
-------
TABLE 2-46. ESTIMATED STREAM LOADING RATES FOR THE AVERAGE WARE
EVENT; SEDIMENT DELIVERY RATIO =0.1
Ib/mile/event
tons/mile/event Phosphorus
Subbasin
Cow Creek
Beaver Dam Swamp
Fox Mill Run
Ware and Wilson
Creek
Sediment
5.1
5.9
6.1
4.7
Total
4.7
5.8
5.8
4.7
Available
0.9
1.1
1.1
0.9
BOD5
71
83
85
66
Ni
Total
37
43
45
35
trogen
Available
6
6
6
5
2.4.4 Nonpoint Source Impacts on Water Quality
Instream concentration..estimates for sediment, nitrogen, phosphorus, and
BOD5 are presented in Section 3.5.5 of Volume II. These concentration
estimates are based on the loads for rural nonpoint sources presented above.
2.5 DEMONSTRATION EXAMPLE: OCCOQUAN RIVER BASIN
2.5.1 Character of the Basin
The Occoquan River is located in Northern Virginia. The drainage area is
approximately 480 sq miles; the river discharges into the Potomac River below
the Washington, D.C. metropolitan area. The basin is located entirely within
LRA 148—Northern Piedmont.
The principal land uses of the Occoquan River Basin are agriculture and
forestry. There are significant areas of urban development at Manassas and
Manassas Park. A major urban area—Fairfax, Virginia—is located on the north-
ern periphery of the basin. Much of the area around these communities is low
density surburban housing.
80
-------
The Occoquan River is dammed near its confluence with the Potomac. The
reservoir behind the dam is used as a major public water supply for the
Washington, D.C.-Northern Virginia area.
The average annual rainfall for the area is about 41 in. (Washington,
D.C.) and is fairly evenly distributed throughout the year. The wettest
months are July and August, and driest months occur in winter.
For purposes of evaluation, the Occoquan River Basin has been divided
into five subbasins. Figure 2-5 is a map delineating the Occoquan Basin and
subbasins. Areas of the subbasins and lengths of principal streams are given
in Table 2-47.
TABLE 2-47. OCCOQUAN RIVER SUBBASINS
Area (acres) Length of principal streams (miles)
Kettle Run
Cedar Run
Broad Run
Bull Run
Occoquan
Total
15,104
115,463
42,178
82,190
52,022
306,957
16
37
22
43
38
2.5.2 Nonpoint Source Load Estimation Methodology—An Overview
As for the other basis the procedure used for load estimation in the
Occoquan was the same as described for the Sandusky. Details may be found in
Section 2.1. Both urban and rural nonpoint sources exist in the Occoquan
Basin.
81
-------
T// Dulles Airporl
N
00
NJ
FAIRFAX
COUNTY
BULL RUN'SUB-BASIN
\ / /
«_^l *^^(—«v^ I 1 ^.^ f '
~~~\ SUB-BASIN
, \ ~^r^
} JCEDAR RUN
( 'SUB-BASIN
\ l
COUNTY BOUNDARY
WATERSHED BOUNDARY
SUB-BASIN BOUNDARY
^J^
Figure 2-5. Occoquan River Basin and Subbasins.
-------
2.5.3 Rural Nonpoint Sources
2.5.3.1 Parameter Evaluation
2.5.3.1.1 Rainfall Factor (R)
The recording station at The Plains, Virginia, was chosen for use in de-
termining EI3o values for the Occoquan River Basin. There are several stations
around the basin, but none was found to be very satisfactory in terms of data
availability, e.g., length of record, completeness of records, etc. Thus, the
station at The Plains represents the best choice of a bad lot.
Table 2-48 shows the periods for which rainfall data were analyzed for
Plains. Data were obtained from the National Climatic Center in Asheville,
North Carolina. Events were selected based on their compatibility with the
analysis presented in Volume II.
TABLE 2-48. EVALUATION OF EIjjo FOR THE OCCOQUAN BASIN
Rainfall at The Plains (in.)
El
30
May 13-16, 1971
April 21-24, 1971
May 20-26, 1972
June 21-27, 1973
April 25-27, 1973
May 27-28, 1973
May 11-12, 1974
June 2-8, 1974
August 31-September 6, 1975
March 31-April 1, 1976
September 15-16, 1976
Total
2.4 11.0
1.8 10.3
Data problems
Hurricane Agnes - neglected
2.3
1.3
1.8
1.6
2.3
13.7
No data
No data
7.0
6.2
21.9
13.6
12.0
82.0
Note: Average of seven events = 11.7;
Average without largest = 10.0
83
-------
For the seven events for which data were available during the period 1971
to 1976, the average EI30 is 11.7. Average annual EI30 value for the basin is
about 200. The events are scattered throughout the growing season. However,
data gaps are frequent in the summer months. These gaps led to the definition
of a rainfall interval encompassing the period mid-June to early October, which
also encompasses a major segment of the crop growing season. Thus, when the
rainfall intervals are integrated with crop growing data to ascertain appropri-
ate factors for the Occoquan Basin, there is difficulty in providing accu-
rate C factors for all periods.
2.5.3.1.2 Soil Erodibility Factor (K)
Soil surveys were available for only three of the four counties in the
Occoquan Basin. It was determined that the survey for Fauquier County could
be used to provide a representative K value for the entire Occoquan Basin.
Analysis of the county soil survey maps showed that K values for the pre-
dominant series varied from 0.28 to 0.43. However, when the K values were
weighted according to area! extent of specific soil series, it was found that
both the predominant and average K value for the Fauquier County was 0.32. A
K value of 0.32 was used for estimating soil loss from the entire Occoquan
River Basin.
2.5.3.1.3 Slope Factors (L and S)
Slopes and slope lengths for LRA 148 were obtained from the nonpoint cal-
culator data base. These values are tabulated in Appendix I. More appropriate
data were not readily available.
2.5.3.1.4 Cover Factor (C)
The base year chosen for the analysis of the Occoquan River Basin is 1973.
The areas of crops grown in the Occoquan Basin in 1973 are presented in Table
2-49. A large part of the corn grown in the region is used for silage.
84
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TABLE 2-49. AGRICULTURAL DATA FOR COUNTIES IN THE
OCCOQUAN RIVER BASIN IN 1973
Crop area (103 acres)
County Corn for grain Corn for silage Soybean Wheat Barley Total
Fairfax Virtually no cropping; all nonurban land assumed to be pasture
Fauquier 14.1 14.1 1.1 3.0 2.2 34.5
Loudoun 22.0 4.0 0.8 5.3 3.0 35.1
Prince William 3.3 1.7 - 0.9 0.4 6.3
Six crop rotation patterns were identified through contact with county
agents. These are:
1. Continuous corn (annual rotation).
2. Corn-small grain (wheat and barley) (2-yr rotation).
3. Soybean-small grain (2-yr rotation).
4. Corn-corn-small grain (3-yr rotation).
5. .Corn-corn-soybean (3-yr rotation).
6. Corn-small grain-cover (rye)-cover (4-year rotation).
These rotation patterns were used in conjunction with the 1973 data to
generate estimates of acres of crops in the rotations. These estimates are
presented in Table 2-50.
The crop rotation data were used to generate C factors for each rotation
as a function of stage of growth. These factors were then integrated with the
rainfall event intervals and are tabulated in Table 2-51.
As discussed earlier, the rainfall data for the Occoquan River Basin are
not good. The interval from mid-June to early October is much too long to
calculate accurate C factors. During this period rapid crop growth occurs
which directly affects C values.
85
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TABLE 2-50. ESTIMATED AREAS OF PRINCIPAL CROP ROTATIONS BY COUNTY
IN THE OCCOQUAN RIVER BASIN FOR 1973
Areas in rotation (103 acres)
County CC CCSg SbSg CSg CCSb
Fauquier 20.0 12.3 2.2
Loudoun 16.1 - - 16.6 2.4
Prince William 3.7 - - 0.9
CSgCoCo
1.7
CC = Continuous corn (annual rotation).
CCSg = Corn-corn-small grain (3-yr rotation).
SbSg = Soybean-small grain (2-yr rotation).
CSg = Corn-small grain (2-yr rotation).
CCSb = Corn-corn-soybean (3-yr rotation).
CSgCoCo = Corn-small grain-cover-cover (4-yr rotation).
TABLE 2-51. COVER FACTORS (C) BY RAINFALL INTERVAL AND BY CROP
FOR THE OCCOQUAN RIVER BASIN
Group of Events
Early April to mid-April
Mid- April to late April
Late April to late May
Late May to mid- June
Mid-June to early October
Corn
0.12
0.17
0.17
0.16
0.13
Soybeans
(Loudoun)
0.25
0.25
0.25
0.19
0.16
Crop
Soybeans
(Fauquier)
0.19
0.19
0.09
0.03
0.26
Small
grain
0.14
0.14
0.08
0.05
0.20
2.5.3.1.5 Support Practice Factors (P)
The 1967 CNI contains the latest data concerning implementation of conser-
vation practices on cropland. Lacking more current information, conservation
practice factors for 1967 were assumed to be the same in 1973 for purposes of
estimating soil loss. P values are tabulated in Appendix I.
86
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2.5.3.1.6 Land Use Within The Occoquan River Basin
The changes in cropping patterns as reflected in the differences between
the 1967 CNI and the 1973 base year were accommodated by shifting acreages
within the various counties. The following land use shifts were made:
Fairfax County: 1,800 acres of corn were shifted to pasture; 430 acres
of row crops were shifted to pasture; and 730 acres of small grain were shifted
to pasture. (Note: Although there were agriculture products reported for
Fairfax County in 1973, the portion of the county in the Occoquan Basin pro-
duced none of these.)
Fauquier County: 6,200 acres of small grain were shifted to corn; 5,000
acres of pasture were shifted to corn; and 1,100 acres of pasture were shifted
to other row crops.
Loudon County: 800 acres of corn were shifted to row crops; 5,400 acres
of corn were shifted to pasture; and 6,100 acres of small grain were shifted
to pasture.
Prince William County: 210 acres of row crops were shifted to small
grain; 890 acres of pasture were shifted to small grain; and 2,470 acres of
pasture were shifted to corn.
2.5.3.1.7 Nutrients
The available soil phosphorus as reported in soil test information and
the total soil nitrogen as calculated from the Jenny equation are presented in
Table 2-52. The total phosphorus concentration in soil is estimated to be 480
ppm from information in the nonpoint calculator data base.
The nutrient enrichment ratio was found to be 2.14 + 0.08. The enrich-
ment ratio used for estimating nonpoint loads was 2.1.
Nitrogen in rainfall was estimated to be 0.0052 Ib/acre/event.
87
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TABLE 2-52. SOIL NUTRIENT CONCENTRATIONS IN THE OCCOQUAN RIVER BASIN
Average nutrient concentration in soil -(ppm)
County Available phosphorus Total nitrogen
Fairfax
Fauquier
Loudoun
Prince William
34
20
22
27
1,640
1,750
1,640
1,520
? Total phosphorus is approximately 420 ppm.
Available nitrogen is assumed to be 10% of total.
2.5.3.1.8 Sediment Delivery Ratio
Two sediment delivery ratios were arbitrarily used in the evaluation of
the Occoquan River Basin--0.1 and 0.2. This basin has greater physiographic
relief than the other Chesapeake basins, and the sediment delivery ratio may
be larger than those of the other basins-. Sediment delivery ratios of both
0.1 and 0.2 were used in the Occoquan evaluation in Volume II. Details of the
analysis are presented there. The information needed for determining delivery
ratios for the basin was not available.
2.5.3.2 Load Determination
The nonpoint loads estimated for the Occoquan River Basin are presented
in Table 2-53. The two sets of entries reflect the use of the two delivery
ratios (0.1 and 0.2) used in the analysis.
2.5.4 Urban Nonpoint Sources
Urban nonpoint annual lands for the principal municipalities in the
Occoquan River Basin are presented in Table 2-54. These loads were
estimated by multiplying street curb-miles for the cities (Manassas and
Manassas Park) by deposition rates as reported by Sartor and Boyd (1972).
88
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TABLE 2-53. ESTIMATED STREAM LOADING RATES FOR THE AVERAGE OCCOQUAN EVENT
(Ib/mile/event)
Subbasin
Sediment1
Phosphorus
Total Available
BODc
Nitrogen
Total Available
Sediment delivery ratio =0.1
Kettle Run
Cedar Run
Broad Run
Bull Run
Occoquan
21
77
46
36
25
42
160
90
81
54
2.4
7.0
4.4
5.0
3.8
290
1,130
680
500
330
150
580
350
260
170
20
73
44
35
24
Sediment delivery ratio =0.2
Kettle Run
Cedar Run
Broad Run
Bull Run
Occoquan
41
153
92
73
50
84
320
180
160
110
4.7
13.9
8.8
10.0
7.6
580
2,270
1,350
990
660
290
1,150
690
510
340
34
130
78
60
40
Sediment units are ton/mile/event.
TABLE 2-54. ESTIMATED ANNUAL LOADS FROM URBAN RUNOFF: OCCOQUAN RIVER BASIN
Towns: Manassas Park - Population = 8,500; curb-miles = 28
Manassas - Population = 14,000; curb-miles = 102
Rate
Constituent
Ob/curb-mile/day)
Loads (tons/yr)
Manassas Park
Manassas
Solids
BOD5
Total nitrogen
Available phosphorus
Total phosphorus
590
3.6
0.25
0.06
0.12
3,000
18
1.3
0.3
0.6
11,000
67
11.2
1.1
2.3
2.5.5 Nonpoint Source Impacts On Water Quality
The water quality impacts associated with the nonpoint source loads esti-
mated in the preceding sections are considered in Sections 3.6.2 to 3.6.4 of
Volume I.
89
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CHAPTER 3
DETERMINATION OF NUTRIENT FLUXES IN STREAMS, WITH CASE
STUDIES OF THE POTOMAC AND SUSQUEHANNA RIVERS
3.1 INTRODUCTION
Determination of long-term average quantities of nutrients discharged by
a stream is not an easy task. However, it is important because of the poten-
tial impact of these nutrients on estuaries or lakes into which the stream
might flow. A number of approaches could be used to .determine nutrient
fluxes, the most reliable being the monitoring of the stream's nutrient con-
centration levels over a long period of time. Resource managers often need
estimates of important environmental quantities but are not able to afford the
long delay associated with extensive experimental work. This report describes
an approach, along with two applications, for obtaining estimates of fluxes of
sediment-associated nutrients in river basins. The approach is intended to
provide useful results with only a minimum amount of new information. It is
not designed to give definitive answers but rather to provide assistance in
obtaining upper and lower limits on the average nutrient flux of a stream.
The procedure used to estimate nutrient flux is described in section 3.2.
Section 3.3 reports the results of its application to the Potomac and
Susquehanna River Basins.
3.2 METHODOLOGY
This section presents a procedure for estimating fluxes of sediment-
associated nutrients in river basins. To make such estimates using the ap-
proach outlined, gross soil loss "must be determined for a number of subbasins
90
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in the watershed, and measured values of sediment yield for the basin must be
available. The procedure for determining nutrient fluxes is the following:
1. Divide the watershed into subbasins.
2. Determine the soil loss in each subbasin using the Universal Soil Loss
Equation (USLE) (Wischmeier and Smith, 1978).
3. Determine the sediment discharge for the basin based on measurements;
a long-term average is preferred (near 20 years).
4. Estimate gross nutrient loads for each subbasin based on gross soil
loss and the soil concentration of the nutrient.
5. Assuming that the nutrient moves in association-with sediment, with a
possible enrichment, limits on the nutrient flux from the watershed may be es-
timated based on the constraint that the sediment discharge for the basin
equals the average measured discharge.
The soil loss for each subbasin is found using the USLE. The average
sediment discharge Y for the entire basin can then be estimated by:
where Y. = soil loss for subbasin i
v. = sediment delivery ratio for subbasin i
The sediment delivery ratios are generally unknown. An average value for
the entire basin is given by:
91
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where Ysm = measured, average sediment discharge for the basin (sediment
originating from sheet and rill erosion)*
The nutrient is assumed to be associated with the sediment; nutrient discharge
Y is given by:
where [x.] = the soil concentration of nutrient x in subbasin i
r. = the enrichment ratio for x.
The enrichment ratio is the ratio between the nutrient concentration in the
eroded sediment and that in the undisturbed soil. It accounts for the fact
that nutrients are associated more (on a per unit volume basis) with the
smaller soil particles than with the larger ones and that there is a prefer-
ential erosion and transport of the smaller particles.
Using an expression such as that given above for Y , it is possible to de-
/\
termine the nutrient flux from the watershed. To do so it is first necessary
to evaluate the parameters in the equation. The sediment delivery ratios, Y.,
are particularly difficult to estimate. What is proposed here is a procedure
that allows upper and lower limits on the nutrient flux, Y , to be determined
based on a knowledge of the other factors in the above equation for Y . In
/\
addition, an average nutrient flux can be estimated using the basinwide average
value for y and y.
The physical argument for assigning upper and lower limits to the nutrient
flux is essentially the following. Some portions of a watershed may yield
larger quantities of nutrient per unit of sediment discharge than other por-
tions. The larger than average values are due to higher soil concentrations
Sediment discharge associated with sheet and rill erosion is equal to to-
tal sediment discharge minus sediment discharge originating from other
sources such as streambank and gully erosion and roadbank erosion.
92
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of the nutrient and possibly to a higher degree of enrichment than exists
elsewhere. The upper limit on the nutrient discharge would be established by
assuming that all sediment comes from those parts of the watershed yielding the
highest relative amounts of nutrient per unit of sediment. These regions would
be assumed to supply the sediment needed to provide the sediment discharge ob-
served for the basin. In essence, such regions would be assumed to have high
delivery ratios; delivery ratios elsewhere would be zero. This arrangement
would provide the maximum possible nutrient flux from the basin since there
would be no other region or combination of regions within the basin which
could provide a higher flux, given that the sediment discharge for the total
watershed is fixed.
An additional requirement concerning the estimation of the maximum flux
is that the product y.r. in the expression for Y would always be less than
or equal to 1 for each subbasin. If y.r. exceeds 1, Y .could be larger than
I I /\
the total nutrient contained -in the soil. This limitation on y.r. means that
in finding an upper limit for Y , delivery ratios cannot be increased without
considering the enrichment that occurs.
A similar argument can be used to determine a lower limit on nutrient
flux. To find a lower limit, those areas in the basin providing the least
nutrient flux per unit of sediment discharge are assumed to provide the sedi-
ment discharge from the basin. These areas have the lowest values of soil
concentration of the nutrient and enrichment ratios.
The above argument for setting upper and lower limits on nutrient flux
may be expressed more succinctly mathematically. The upper limit on nutrient
flux is found by maximizing the expression above for Y subject to a constraint
/\
on sediment discharge for the basin. Similarly, the lower limit on nutrient
flux may be found by minimizing Y subject to the same constraint.
/\
The upper (lower) limit on Y is found as follows:
/\
93
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maximize 2y-r.[x.]Y.
(minimize) n
subject to ZYlY. = Y
sm
where 0 ^ y = l/r-
The constraint requires that the sediment discharge for the basin be equal to
the measured value; y. is less than or equal to l/r. since if it were not,
y.r. could be greater than 1, which would mean that more nutrient is delivered
than originally existed in the soil.
This procedure requires no assumptions concerning the sediment delivery
ratios. It will determine those delivery ratios which maximize or minimize
the nutrient flux. The critical assumptions are (a) that the nutrient is
transported with the sediment., (b) that there is no net uptake or loss of nu-
trient in the stream, and (c) that enrichment ratios can be defined.
To clarify these assumptions, some discussion of sediment transport in a
watershed is necessary. Sediment movement in a watershed may be considered to
be composed of three stages: the initial dislodging of soil particles; upland
transport and deposition of sediment before a stream channel is reached; and
channel transport. It is assumed that there is no long-term accumulation of
sediment in a stream channel. Long-term deposition of sediment is assumed to
occur in the upland stage of sediment transport. The sediment delivery ratio
accounts for he efficiency of such upland transport. Nutrients move with the
eroded soil and are enriched. The enrichment ratio specifies the degree of en-
richment that occurs between point of origin and stream channel. After the nu-
trient is in a stream channel, part or all of it may change into a soluble
form. However, it is assumed that no net long-term losses or additions of the
nutrient occur in the stream channel. If from a long-term perspective, sedi-
ment and nutrients can be treated as conservative constituents while being
transported by streams, then the enrichment ratio (r) and the assumption con-
cerning sediment-nutrient association need apply only before the nutrient
reaches a stream channel.
94
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Such assumptions mean that the analysis described is only appropriate for
determination of very long-term average nutrient fluxes. It is possible to
apply it to short time periods, but such an application will encounter diffi-
culties. First, for a given short time period there may be losses of sediment
or nutrient in the channel or additions from the channel. Second, the soil
losses determined using the USLE are long-term estimates.
An average value of nutrient flux can be determined assuming that the av-
erage delivery ratio for the nutrient is the same as for sediment in the basin.
Then,
For the approach just outlined, the differences between the estimated av-
erage value and the estimated upper and lower limits on nutrient flux are de-
termined by the degree of homogeneity of the product r.[x.] throughout the
basin. For a completely homogeneous basin, the average value and the upper
and lower limits are the same.
Since the approach described assumes that the nutrient yield is correlated
with sediment yield, it will not be a valuable method for dealing with nitrogen.
However, it should be of value in estimating fluxes of total phosphorus. (The
procedure will also be of value for use with any other constituents which are
associated with sediment, not just nutrients.)
The method described has been applied to the Potomac and Susquehanna River
Basins. Results are presented in the following section.
3.3 CASE STUDY: POTOMAC RIVER BASIN
The methodology outlined in the preceding section has been applied to the
Potomac River to estimate the nonpoint-source-associated phosphorus flux from
the basin at the Fall Line. The Fall Line separates the coastal plain from the
Piedmont Plateau and crosses the Potomac at Great Falls near river mile 126.
The river below the Fall Line is mostly tidal, and the head of tidewater is at
Little Falls (river mile 117).
95
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Figure 3-1 is a map of the Potomac River Basin showing the outline of the
basin and the boundaries of eight subbasins into which the watershed has been
divided. The entire basin has an area of 14,670 sq miles. Of this area,
11,430 sq miles are above Great Falls.
Long-term (October 1960 to present) suspended sediment records are avail-
able at Point of Rocks, Maryland, at river mile 159.5 (6 miles upstream from
the Monocacy River) and at Jug Bridge on the Monocacy River (16.9 miles up-
stream from the mouth). The drainage areas above these points are 9,651 and
817 sq miles, respectively. The combined areas constitute 91.6% of the Potomac
Basin above Great Falls.
The sediment that is delivered to the tidal portion of the river is appar-
ently deposited there. Schubel and Carter (1976) indicate that the Potomac
estuary is a net sink for sediment from the Chesapeake Bay. Therefore, the
estimates made will be only..for the nontidal portion of the .basin.
Table 3-1 provides information on the eight subbasins shown in Figure 3-1.
Using the USLE, estimates were made of gross soil loss in each subbasin along
with gross loss of phosphorus, based on the soil concentration of the nutrient.
Table 3-1 contains the areas for which land use information was available. The
land use data are from the 1967 Conservation Need Inventory (CNI) and represent
land use in the basin at that time. All calculations with the USLE used county
portions of subbains which were then combined in each subbasin to give totals
for the subbasins. Calculations were done with a procedure and data base de-
scribed by Davis and Nebgen (1979); these are briefly discussed in Appendix I.
Comparing the areas in Table 3-1 with the basin areas given earlier indi-
cates that 82% of the entire basin is inventoried, including 87% of the area
above Great Falls. Inventoried land excludes federally owned land, urban
areas, and small water areas.
Table 3-2 shows annual suspended sediment discharge data for the Potomac
at Point of Rocks and the Monocacy at Jug Bridge. The 17-year average dis-
charges at these two locations are 1.073 x 106 tons/year and 0.183 x 106 tons/
year, respectively.
96
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UD
PENNSYLVANIA
MARYLAND
River Basin Boundary
Sub-basin Boundary
Figure 3-1. Potomac River Basin.
-------
TABLE 3-1. POTOMAC RIVER BASIN SUBWATERSHEDS
Ui
00
1
2
3
4
5
6
7
8
(1)
Subbasin
Below Great Falls
Occoquan River
Above Washington
Monocacy River
Shenandoah River
Opequon Creek
South branch Potomac
North of river
Total
(2)
Inventoried
area
(miles2)
1,620
480
860
1,050
2,120
880
2,560
2,460
12,030
(3) (4)
Subbasin avg.
Soil loss soil phos-
(tons/yr x phorus cone.
10-6)
4.
1.
3.
5.
12.
3.
5.
10.
47.
7
4
2
6
0
7
9
5
o :
(ppm)
260
500
440
430
300
300
300
300
(5)
Avg. soil
phosphorus
loss (lb/
acre/yr)
2.
4.
4.
7.
5.
3.
2.
4.
4
4
9
2
3
9
2
0
(6)
Phosphorus
loss (tons/
yr x 10-3)
1.
0.
1.
2.
3.
1.
1.
3.
15.
2
7
4
4
6
1
8
2
4
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TABLE 3-2. SUSPENDED SEDIMENT DISCHARGES, POTOMAC RIVER a
Water year
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
Total
Sediment discharge, Potomac
River at Point of Rocks (tons)
1,104,726
858,417
1,107,089
895,966
652,176
443,946
1,129,158
.734,893
156,865
1,270,933
1,375,032
2,435,621
1,533,788
1,036,640
1,595,278
528,908
1,377,571
18,237,007
3 Source: Water Resources Data for Maryland
Sediment discharge, Monocacy
River at Jug Bridge (tons)
123,299
146,480
113,026
131,503
106,851
113,690
155,032
. ... 107,761
72,507
314,684
185,449
455,552 .
240,378
160,676
311,531
146,532
222,654
3,107,605
and Delaware, U.S.
Geological Survey.
99
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Assuming that the average annual sediment discharge per unit area from above
both of these locations can be used to estimate sediment output for the 962-sq
mile area located above Great Falls but not above the sediment gauging station,
an annual average value at Great Falls of 1.372 x 106 tons/year is obtained.*
Using the gross soil loss values from Table 3-1 for the area above Great Falls
along with this sediment discharge gives an average sediment delivery ratio for
the Potomac Basin above the Fall Line of 0.034, a value that is not unreasonable
based on the size of the basin.
It should be noted that the 1967 land use information corresponds to a
time period near the middle of the period of record for suspended sediment.
Since land use changes during the 1961 to 1977 period are not considered, 1967
values are reasonable ones to use for average values.
Assuming that phosphorus moves in association with sediment (at least in
the upland phase of transport), the approach can be applied to the Potomac to
estimate limits for the annual flux of phosphorus at Great Falls.
The constraints on phosphorus flux are established by the estimated sedi-
ment discharge at Great Falls and the measured value for the Monocacy River.
For the Monocacy River, the annual sediment discharge at Jug Bridge is 0.183 x
106 tons/year. This is for a drainage area of 817 sq miles. The gross soil
loss for the entire Monocacy watershed (i.e., subbasin 4) (1,210 sq miles es-
timated for Table 3-1 assuming 87% inventoried) is 5.6 x 106 tons/year. There-
fore, assuming that sediment yield is uniform throughout the watershed, the
sediment delivery ratio, y4, for the Monocacy is:
v - (1.210/817) (0.183 x 106) _
Y4 5.6 x 106 ~
The contribution of bedload to the total sediment discharge has been ne-
glected. At Point of Rocks the streambed is bedrock; the suspended load
is less than about 10% sand and the suspended solids concentration is be-
low 1,000 mg/L even during high flow periods. Neglect of any bedload
contribution seems to be a very good assumption.
100
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Also, the sediment discharge at Great Falls is 1.372 x 106 tons/year. The
minimum phosphorus flux is then given by:
8
minimize I y-r.[P.]Y.
1=3 n ^ 1
8
subject to X y.Y. = 1.372 x 106
1=3 1 n
and y = 0.048
4
where 0 S y. S 1/r.
Minimum soil phosphorus concentrations are in subbasins 5 to 8. In prin-
ciple, subbasin 5 (the Shenandoah River) can satisfy the sediment constraint
along with the Monocacy so that:
y Y .+ y Y = 1.372 x 106
44 55
= 1.372 x 106 - (1.210/817) (0.183 x 106)
ys 12.0 x iob •
= 0.092
y =y = y =y =0
3678
So, assuming that r = 2 for all subbasins,* the minimum P flux is:
Y_ = 2y [P ]Y + 2y [P ]Y
Pmin 444555
= 890 tons/year
The values of [P.]Y. are taken from Column (6) of Table 3-1.
A maximum P flux (with r = 2 in all subbasins) can be found in the same
manner:
The use of r = 2 is somewhat arbitrary. McElroy et al. (1976, p. 106) re-
port values from 1.5 to 3.4. The value could be assigned more accurately
by determining r on an event-by-event basis over many years and then av-
eraging.
101
-------
8
maximize I Y-r.[P.]Y.
1=3 1 ] 1 1
8
subject to I y.Y. = 1.372 x 106
i=3 1 n
and Y = 0.048
4
where 0 ^ Y- = l/r-
Maximum soil phosphorus concentrations are in subbasins 3 and 4. Sub-
basin 4 is the Monocacy, for which an accounting has already been made. Sub-
basin 3 can supply sufficient sediment to satisfy the constraint at Great
Falls.
Y Y + Y Y = 1.372 x 106
33 44
_ 1.372 x 106 - (1,210/816) (0.183 x 106)
Ys 3.2 x 10b
Y = 0.34
3
and Y = Y = Y = Y = 0
5678
The maximum P flux is:
Yn = 2Y [P ]Y + 2Y [P ]Y
Pmax 333 444
so Y = 1,200 tons/year
An average value of Y can be estimated by:
Y = v 2 r [P ]Y
P i=3 i i i '
assuming that phosphorus is delivered in the same manner as sediment, with only
an enrichment r. With r = 2 in each subbasin,
Y = (0.034)(2)(13,500)
= 920 tons/year
102
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The calculations concerning the P flux at Great Falls are summarized in
Table 3-3.
TABLE 3-3. ESTIMATES OF AVERAGE ANNUAL PHOSPHORUS FLUX AT
GREAT FALLS FROM NONPOINT SOURCES
Tons/yr
Upper limit equal to gross phosphorus loss
(subbasins 3 to 8) 13,500
Maximum flux with r = 2 1,200
Flux using basin average delivery ratio (0.034)
and r = 2 920
Minimum flux with r = 2 890
Flux using basin average delivery ratio (0.034)
and no enrichment 460
As Table 3-3 shows, the long-term average annual flux into the Potomac es-
tuary from nonurban, nonpoint sources is in the range 890 to 1,200 tons/year
(if r = 2). This assumes that phosphorus is associated with sediment, that the
relationship between sediment and phosphorus is determined by the soil concen-
tration of phosphorus, that a uniform enrichment of phosphorus by a factor of
2 occurs during transport, and that sediment contributions from other than
sheet and rill erosion are negligible.
Measurement of long-term phosphorus fluxes on the Potomac are not avail-
able. However, an estimate of the nonpoint-source phosphorus flux at Great
Falls for calendar year 1966 has been published (Jaworski, 1969). This esti-
mate is for a very short period, and the estimate of the portion of the flux
from nonpoint sources is based on a consideration of a single agricultural
watershed (Catoctin Creek) and a single forested watershed (Patterson Creek)
with an extrapolation from these measurements to the entire upper basin.
Therefore, it would not be surprising if the 1966 results differed substan-
tially from the estimate made here for a long-term average.
103
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For the calendar year 1966, Jaworski reports 0.470 x 106 tons of sediment
at Point of Rocks and 0.118 x 106 tons at Jug Bridge on the Monocacy. Using
these numbers gives an estimate of suspended sediment discharge equal to
0.642 x 106 tons at Great Falls. The estimated total land-use-related phos-
phorus flux was 8,610 Ib/day as P04, which is equivalent to 512 tons/year as
phosphorus.
The estimates in Table 3-3 are for a long-term average sediment discharge
at Great Falls of 1.372 x 106 tons/year. If the phosphorus yield in a given
year (compared to the long-term average) varies in proportion to sediment
yield, the estimated P flux for 1966 would be:
Y = 0.642 x 1Q6 fg2(n = 43Q
Yp1966 1.372 x 10te (^U) 4
-------
TABLE 3-4. SENSITIVITY OF ANNUAL PHOSPHORUS FLUX ESTIMATES
TO STREAMBANK AND GULLY EROSION3
Percentage of sediment discharge at
Great Falls that originates in
streambank and gully erosion
(tons/yr)
0 5 10 15 20
Maximum flux with r = 2 1,200 1,140 1,080 1,020 960
Flux with basin average
delivery ratio and r = 2
Minimum flux with r = 2
920
890
870
850
830
" 810
780
760
740
710
Assumes no nutrient contributed by streambank and gully erosion.
The portion of the sediment originating in streambanks and gully erosion
is probably within the range shown in Table 3-4; estimates of this type of ero-
sion for the Potomac Basin have not been located. However, a study by the Soil
Conservation Service (1977) estimates that the streambank contribution to total
sediment discharge is 14% for a 220-sq-mile area in the Monocacy Basin. That
report also uses values of 5 to 14% for other basins studied for the Baltimore
Regional Planning Council. Therefore, the results in Table 3-4 indicate the
order of magnitude of the expected influence of streambank erosion on estimated
phosphorus flux.
3.4 CASE STUDY: SUSQUEHANNA RIVER BASIN
The phosphorus flux for the Susquehanna has also been studied. The
Susquehanna Basin is more difficult to analyze than the Potomac because of a
series of reservoirs on the river near its mouth which influence sediment and
nutrient transport into the Chesapeake Bay and because the period of record for
sediment discharge measurements for the stream is rather short.
Figure 3-2 is a map of the Susquehanna and indicates the nature of the
seven subbasins into which the basin has been divided. The river drains an
area of 27,580 sq miles. Table 3-5 gives data on the subbasins used.
105
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NEW YORK
PENNSYLVANIA
River Basin
Boundary
__ Sub-Basin
Boundary
PENNSYLVANIA
" MARYLAND "
Figure 3-2. Susquehanna River Basin.
106
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TABLE 3-5. SUSQUEHANNA RIVER BASIN SUBWATERSHEDS
o
-g
(1) (2)
Inventoried
area
1
2
3
4
5
6
7
Subbasin
Susquehanna, above
Athens, Pa.
Chemung River
Susquehanna, Athens,
Pa. to Sunbury, Pa.
West branch Susquehanna
Juniata River
Susquehanna, Sunbury,
Pa. to Safe Harbor
Dam
Safe Harbor to mouth
Total
(miles2)
4
2
3
6
3
4
_1
25
,720
,540
,490
,610
,190
,010
,100
,660
(3) (4)
Subbasin avg.
Soil loss soil phos-
(tons/yr x phorus cone.
10-6)
4.
3.
7.
10.
12.
25.
7.
72.
7
2
6
7
5
9
6
2
(ppm)
510
500
410
310
300
340 ,
460
(5)
Avg. soil
phosphorus
loss (lb/
acre/yr)
1.
2.
2.
1.
3.
6.
9.
6
0
8
6
7
9
8
(6)
Phosphorus
loss (tons/
yr x
2.
1.
3.
3.
3.
8.
3.
26.
10-3)
4
6
1
3
8
9
5
6
-------
Calculations were done in the same manner as for the Potomac (see Appendix II
for a discussion). Again, 1967 CNI data were used to represent land use.
About 93% of the basin is inventoried. The noninventoried areas are urban
land, small bodies of water, and federal land.
Suspended sediment discharge data are available at Harrisburg,
Pennsylvania. The area drained above Harrisburg is 24,100 sq miles or 87% of
the Susquehanna Basin. Data are available from only 1971 to the present so
they cannot be classified as long term. Table 3-6 gives the annual sediment
discharge information for Harrisburg. In 1972, the result of Hurricane Agnes
was an unusually large sediment discharge from the basin.
TABLE 3-6. SUSPENDED SEDIMENT DISCHARGE AT HARRISBURG
Sediment discharge
Water year (tons)
1971 1,418,761
1972 10,395,082
1973 3,603,250
1974 2,282,870
1975 3,976,008
1976 2,255,148
1977 3.426.936
Total 27,358,055
(16,962,973
without 1972)
a Source: Water Resources Data for Pennsylvania,
Volume 2, Susquehanna and Potomac
River Basins (Part 2, Water Quality
Data), U.S. Geological Survey.
108
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Neglecting the discharge for 1972 gives an annual-average discharge of 2.83 x
106 tons/year at Harrisburg (6-year average).*
Williams and Reed (1972) have studied sediment yields in the Susquehanna
Basin. Based on sediment yields in various portions of the basin, they esti-
mate a total discharge of about 3 x 106 tons/year, neglecting trapping in res-
ervoirs near the mouth of the river. Based on the work of Williams and Reed,
the portion of the basin below Harrisburg but above Safe Harbor Dam (largely
in the Lowland Piedmont) has an average sediment yield of about 200 tons/sq
mile. Below Safe Harbor, the basin (in the Upland Piedmont) has an average
sediment yield of about 300 tons/sq mile. Assuming that the sediment yield
below Harrisburg and above Safe Harbor is 200 tons/sq mile, and using the es-
timated sediment discharge at Harrisburg given above, results in an estimate
of 3.3 x 106 tons/year sediment discharge into the Safe Harbor reservoir.
The procedure outlined..earlier and applied to the Potomac has been used to
estimate a range of possible values for phosphorus flux into the Safe Harbor
reservoir. Estimates have also been made for the phosphorus input into the
Chesapeake Bay; however, the uncertainties involved are considerably increased.
From Table 3-5 the estimated soil loss above Safe Harbor is about 64.6 x
106 tons/year. The 3.3 x 106 tons/year estimate for sediment discharge there-
fore implies a delivery ratio of 0.051 for the portion of the basin above Safe
As was done for the Potomac, the contribution of bedload to sediment dis-
charge was neglected for the Susquehanna. At Harrisburg the suspended
sediment concentration is well below 1,000 mg/L even during high flow pe-
riods, and the suspended material is mostly silt and clay; less than about
10% is sand. The streambed is bedrock. Based on these considerations,
the correction for bedload should be very small (Strand, 1975). The Dis-
trict Office of the U.S. Geological Survey in Harrisburg estimates that 5
to 10% of total load is bedload. Therefore, neglect of bedload for the
river seems justified.
109
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Harbor. Estimating sediment delivery to the Chesapeake Bay is more difficult
because of the three dams near the mouth of the river: Safe Harbor, Holtwood,
and Conowingo. Williams and Reed (1972) discuss the sediment trapping effi-
ciencies of the reservoirs; therefore, some estimate of sediment delivery to
the Bay can be made. However, it is difficult to assess the trapping effec-
tiveness for nutrients, which would be expected to be less influenced by the
reservoirs than is the sediment. A second complication is that the hurricane
in 1972 resulted in sediment removal from the reservoirs, which may have al-
tered their trapping efficiencies.
Minimum soil phosphorus concentrations for the Susquehanna are in sub-
basins 4 and 5, and maximum concentrations are in.subbasins 1 and 2. Each of
these subbasins has sufficient soil loss to satisfy the sediment discharge re-
quirement at Safe Harbor (see Table 3-5). However, for either Subbasin 1 or 2,
satisfying the sediment requirement would result in delivery ratios above 0.5;
and, therefore, yr > 1.0 (if r = 2). Hence, the maximum phosphorus flux must
be determined by combining subbasins 1 and 2. The maximum and minimum phos-
phorus fluxes into the reservoir are the following (with r = 2).
Maximum P flux = (4,000)(2) tons/year
= 3,300 tons/year
•3 O y 1f)6
Minimum P flux = ^Q 7 x 10« (3, 300) (2) tons/year
= 2,000 tons/year
where the calculations were made for subbasins 1 and 2 combined (maximum flux)
and for subbasin 4 (minimum flux).
For a delivery efficiency for phosphorus equal to the basin average sedi-
ment delivery ratio, the phosphorus flux would be 2,400 tons/year. Table 3-7
summarizes phosphorus flux estimates at Safe Harbor.
110
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TABLE 3-7. ESTIMATES OF AVERAGE ANNUAL PHOSPHORUS
FLUX INTO RESERVOIR BEHIND SAFE
HARBOR DAM
Tons/yr
Upper limit equal to gross phosphorus 23,100
loss above dam
Maximum flux with r = 2 3,300
Flux with basin average delivery ratio 2,400
(0.051) and r = 2
Minimum flux with r = 2 2,000
Flux with basin average delivery ratio 1,200
(0.051) and r = 1
Using the results of Williams and Reed (1972), an estimate can be made of
sediment delivered to the Bay. For the purposes of these calculations, it will
be assumed that the trapping efficiencies of the reservoirs are as given by
Williams and Reed. If sediment removed from the reservoirs has increased their
trapping effectiveness, then the results presented here are overestimates.
Williams and Reed state that Safe Harbor reservoir is in a state of man-induced
dynamic equilibrium with 106 tons of sediment deposited each year and the same
amount being removed by dredging. They indicate that Holtwood has been in a
state of dynamic equilibrium since the 1940's. Finally, they use a trapping
efficiency of 17% for Conowingo Dam. To estimate sediment discharge at the
mouth of the river, it will be assumed that the sediment yield in the area be-
low Safe Harbor is 300 tons/sq mile/year and that all the material passes
through or is deposited in Conowingo Reservoir. (Actually, some of the land
is below Conowingo, but this consideration will be neglected.)
The area below Safe Harbor is estimated at 1,200 sq miles. Therefore,
the sediment discharged from this area is:
111
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(1,200)(300)(0.83) = 3.0 x 105 tons/year
The input to Safe Harbor is estimated at 3.3 x 106 tons/year. If 106
tons/year are removed at Safe Harbor and 17% of the remaining 2.3 x 106 tons/
year are deposited in Conowingo, 1.9 x 106 tons/year reach Chesapeake Bay. To
this must be added the contribution of 3.0 x 10s tons/year from below Safe Har-
bor, giving a total discharge to the Bay of 2.2 x 106 tons/year. (Williams and
Reed estimate 1.8 x 106 tons/year.)
The sediment yield of 300 tons/sq mile/year assumed to apply below Safe
Harbor is equivalent to a sediment delivery ratio of 0.047 for that subbasin.
If phosphorus is delivered at this same efficiency (with r = 2), then the load
added below Safe Harbor is 330 tons/year.
Since some portion of the phosphorus will be in a. soluble form after
reaching a stream and since, the particulate phosphorus wi.ll.be preferentially
associated with smaller sized particles, there will be less relative loss of
phosphorus in the reservoirs than there will be loss of sediment. The phos-
phorus downstream from each reservoir will be further enriched, but it is dif-
ficult to estimate this additional enrichment. However, if it is assumed that
no phosphorus is lost in the reservoirs or if it is assumed that the loss is
proportional to sediment loss, then upper and lower limits on phosphorus export
may be obtained.
If phosphorus is unimpeded by the dams, then the maximum flux (with r = 2)
at the mouth would be 3,300 + 330 = 3,600 tons/year. If phosphorus were trapped
with the same efficiency as sediment, then about 30% of the input to Safe Harbor
would be lost and about 17% of the phosphorus reaching Conowingo would be
trapped. Since the minimum input to Safe Harbor (with r = 2) was estimated at
2,000 tons/year, 30% removal would leave 1,400 tons/year. Adding the 330 tons/
year input below Safe Harbor and applying the 17% loss at Conowingo gives 1,400
tons/year to the Bay. Therefore, the input to the Bay should be in the range
of 1,400 to 3,600 tons/year. Table 3-8 summarizes the results for the nonpoint
loads of phosphorus reaching the Bay. The uncertainty in phosphorus flux for
the Susquehanna is considerably larger than for the Potomac, primarily because
of the difficulty of assessing how phosphorus will move through the reservoirs.
112
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TABLE 3-8. ESTIMATES OF RURAL NONPOINT SOURCE FLUX OF
PHOSPHORUS AT THE MOUTH OF THE
SUSQUEHANNA
Tons/yr
Upper limit equal to gross phosphorus loss 26,600
in basin
Maximum flux (r = 2, no loss in reservoirs) 3,600
Minimum flux (r = 2, P follows sediment in 1,400
reservoirs)
Flux using basin average delivery ratio
(0.051) and r = 2
No loss in reservoirs 2,700
P follows sediment in reservoirs 1,700
There are a number of impoundments and lakes in the upper portion of the
Susquehanna Basin. However, these control a very small portion of the basin,
about 700 sq miles of the 24,100 sq miles above Harrisburg. Their influence
has been neglected.
As in the case of the Potomac, contributions of sediment from streambank
and gully erosion have been neglected because of lack of data required to esti-
mate their importance. The work by the Soil Conservation Service cited in the
discussion of the Potomac suggests the magnitude of streambank erosion which
might be expected in the lower portion of the Susquehanna. The phosphorus
fluxes estimated here would be reduced in proportion to the contribution of
sediment discharge arising from streambank erosion, again assuming negligible
phosphorus input from that source. If the Soil Conservation Service results
are indicative of conditions in the Susquehanna Basin, the influence of stream-
bank erosion is small.
113
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3.5 DISCUSSION
Minimum and maximum phosphorus fluxes were determined for both of the
basins studied. These values should not be considered absolute upper and
lower bounds on the fluxes. What the bounds do indicate, however, is the
range of expected values if the assumptions used in the analysis are correct
and if the various factors used were accurately estimated. It is worth con-
sidering how the results depend upon the factors involved in the analysis.
The important factors which must be known in order to carry out the analy-
sis are: (a) the nutrient enrichment ratio, (b) the soil concentration of the
nutrient, (c) the soil loss, and (d) the sediment discharge from the basin.
The flux estimate is directly proportional to all these except soil loss.
Interestingly, the result is quite insensitive to uncertainties in the esti-
mates of soil losses. The..soil loss is involved in the analysis only to the
extent that it determines the area of the basin needed to satisfy the sediment
discharge requirement, which is important only if it should be near the size of
the subbasins having maximum or minimum soil nutrient concentrations. For ex-
ample, if the subbasin having maximum soil concentration had a small total soil
loss, then it might be necessary to use the two (or more) subbasins with the
highest soil concentrations of the nutrient to satisfy the sediment discharge
requirement. Adding subbasins with lower soil concentrations would lower the
maximum flux estimated. If the soil loss estimate were very inaccurate or if
there were very large differences in soil nutrient concentrations between sub-
basins, this effect could become important. But it is not important for the
basins studied here.
Therefore, the result is that the bounds that have been set on phosphorus
flux are not sensitive to errors in the soil loss calculations. In addition,
the results require JTO information on sediment delivery ratio. Hence, two of
the most troublesome factors in the analysis play only minor roles in reaching
the results. On the other hand, the results are quite sensitive to soil nu-
trient concentration, enrichment ratio, and sediment discharge. Knowledge of
114
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the last factor is based on field measurements, and the only assurance of a
high level of accuracy is a substantial period of sampling. Nutrient concen-
tration and enrichment ratio are quantities that would benefit from refinement.
An example of possible errors in the bounds given is the following. If
soil loss estimates in the two basins are underestimates of any amount, then
use of correct but larger values would have no influence on the result given.
If the results are overestimated by as much as 40 to 50%, there is still no
influence. Larger overestimates would result in decreases in the upper bounds
given. Therefore, any conceivable uncertainties in soil loss calculations
could do no more than decrease the upper bounds already set. If the enrichment
ratios are in error by 25%, soil concentrations by- 25%, and sediment discharge
by 20%, the possible errors in the upper and lower limits would be about 90%
and 55%, respectively. There is a possibility that some errors would be com-
pensating and that errors in soil nutrient concentrations would not be uniform
throughout the basin. Since .the errors given for the various parameters are
not unreasonable for the basins studied and for the analysis described here,
they can be used to set rough limits on fluxes with uncertainties due to errors
included.
The results given in Table 3-9 imply that the flux estimates are rather
uncertain. This uncertainty is primarily due to the fact that the analysis
used readily available data, and little effort at refinement was made. Con-
siderable improvement would be expected if an effort were made to better eval-
uate the important parameters and to better define the possible errors in each.
The primary purpose of this work was to outline a methodology for assign-
ing upper and lower limits to nutrient export from a watershed. The applica-
tions presented are not highly refined ones; in fact, they should only be
viewed as a first step used to illustrate the approach. As indicated, are a
number of ways in which the applications could be improved. Probably the most
promising would be to use more refined values for phosphorus concentration in
the soil.
115
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TABLE 3-9. ESTIMATES OF AVERAGE ANNUAL PHOSPHORUS FLUXES1
Tons/yr
Minimum flux
Location
Potomac at Great Falls
Susquehanna into reser-
Best
estimate
890
2,000
Lower limit
.possible
with errors
400
900
Maximum flux
Best
estimate
1,200
3,300
Upper limit
possible
with errors
2,300
6,200
voir behind Safe
Harbor
Susquehanna at mouth
No loss in reservoirs
P follows sediment in
reservoirs
1,400
630
3,600
6,800
Basis for calculations: Best estimates are taken from maximum and minimum
flux values in Tables 3-3, 3-7, and 3-8. Upper and lower limits assume
25% errors in enrichment ratio and soil phosphorus concentration and a 20%
error in sediment discharge and no compensation among errors. Results do
not vary with errors in soil loss unless 40 to 50% over-estimates have
been made (which would decrease maximum fluxes) and do not depend upon any
underestimation of soil loss.
It would be very interesting to apply the methodology outlined here to a
basin for which both long-term sediment and phosphorus yield data are avail-
able. If the basin were smaller than the one considered here, a more detailed
analysis would also be possible.
116
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CHAPTER 4
DISCUSSION AND CONCLUSIONS
This report describes the application of a water quality screening meth-
odology to a number of river basins of various sizes and characteristics.
Volume I is concerned with the estimation of nonpoint source loads of pollut-
ants in those basins. The primary goal of the application is to demonstrate
an existing methodology, not to develop new techniques. Nevertheless, some
small modifications and extensions to the existing approach have been made.
The details of the application and the presentation of the modifications to
the original methodology are presented in Chapters 2 and 3. There are a num-
ber of general issues related to the demonstration of the methodology which
deserve additional attention or comments. These will be considered here.
4.1 DATA AVAILABILITY
Application of the methodology requires a large volume of data in spite
of the relative simplicity of the overall approach. Therefore, a major prob-
lem in this study and probably in any other application is data availability.
A screening analysis should by its nature not require the generation of sig-
nificant quantities of data. Those data that are used in the analysis should
already be available and should require a minimum of manipulation prior to use.
For example, the Sandusky Basin has been well studied, but there was a definite
lack of applicable data readily available for this study. Generally, the data
which were available were aggregated to the county level, e.g., land use infor-
mation. Also, as might be anticipated, in all the basins there was a problem
in estimating sediment delivery ratios. Long-term sediment yield data are not
available for the basins; therefore, average delivery ratios cannot be properly
estimated. Furthermore, there is a lack of the sediment yield data needed to
117
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define variability within the basins. Finally, there is, generally, no way to
estimate how average delivery ratios might vary with the season. Use of a
single average value for the delivery ratio can result in a considerable over-
or underestimation of loads for particular subbasins and seasons. This diffi-
culty is nearly universal. It is, in fact, a problem of less concern in the
Sandusky Basin than for most basins since some sediment measurements are avail-
able, and since the efficiency of delivery is thought to be relatively uniform
throughout the basin. Information on pollutant loading rates and pollutant
characteristics in urban areas is also not readily available. It was purely
a matter of chance that actual measurements were available for use in one of
the urban areas (Bucyrus) in one of the basins (Sandusky).
4.2 VALUE OF PARAMETER REFINEMENT
The most important parameter refinements involved land use data and the
R, K, and C factors in the..universal soil loss equation.. ..The national data
base used provides R values by Land Resource Area (LRA). These can easily be
replaced by values which more nearly represent each county. Such values are
available in Wischmeier and Smith (1978)! For example, in the Sandusky, the
change was from an annual value of 150 to 125. (Of course, individual event
values were calculated for use in the demonstration.) Cover (C) factor values
were also changed by the refinement process. The C values used for the indi-
vidual counties reflect changes in the stage of crop growth, which is an im-
provement over the average annual C values in the data base. The level of
resolution of soil credibility values was improved from the LRA level (in the
data base) to the county level. In some cases, resolution was at the subbasin
level. As much as a 20 to 40% decrease in credibility value was noted for some
subbasins in the Sandusky due to refinements in the K values. Land use changes
in that basin mostly increased soil loss in the interval between 1967 (data
base) and the base year used in the individual basin calculations.
On balance then, as compared to the data base values, the refinements for
the Sandusky led to decreases in R, K, and C and, therefore, to a decrease in
annual soil loss over that which would be obtained using the data base. Land
118
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use changes partially offset the decrease. Again, using the Sandusky Basin as
an example, in six out of the eight counties, average soil losses decreased by
20% or more due to the refinements. Given the level of effort required to pro-
duce the refinements and the inherent inaccuracy in the approach, the use of
the data base is a very cost-effective approach. For annual soil loss calcula-
tions in the basins studied, the original data base will provide useful results
(as compared to the refined values) if one merely modifies the R factors for
each county and accounts for the major change in cropland. That is, a signifi-
cant improvement is possible in this case with only a limited amount of effort.
Certain problems occurred in the attempt to improve the estimates for some
of the parameters as already noted. A particular problem was estimating sedi-
ment delivery rates, a problem that was exacerbated in the case of individual
subbasins. Reasonable estimates of delivery ratios are essential for accurate
estimates of sediment delivered to a stream. Lacking a general approach to the
problem, the delivery ratio..issue will continue to frustrate many applications
of the methodology. Although less difficult, problems also occurred with other
parameters as well. The LS and the P factors in the USLE were not modified and
were used directly from the existing national data base. Improvement of the
estimates used requires substantial information on topography and soil conser-
vation practices in each basin. As already noted, data on the loading rates
for pollutants on city streets are generally lacking and recourse must be made
to tabulated, crude averages.
4.3 SENSITIVITY ANALYSIS
A screening methodology such as is being considered in this study involves
numerous simplifications and assumptions. In the case of urban nonpoint loads,
the important matter of sensitivity to assumptions was considered in section
2.1.4.3. As noted, the major problem centers on determination of street load-
ing rates and use of an annual average approach. Rural nonpoint loads have not
yet been discussed from the point of view of sensitivity to the various factors
involved in the analysis. The most important fact to recall in the case of
rural nonpoint sources is that the various factors used in determining sediment
119
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or nutrient loads (except rainfall inputs) are multiplied together to obtain
the final result. Therefore, uncertainties in the factors are multiplied.
For example, for sediment loads, a 20% error in each factor involved in deter-
mining the load gives approximately a 300% error in the load, assuming no com-
pensation among the errors. Similar errors in the case of nutrient calculation
yield a total error of about 400%. Since most of the factors cannot be deter-
mined with an error of less than 20%, the possible error in the results can be
quite large unless there is compensation among the errors. This fact indicates
that the results obtained are always rather uncertain.
Uncertainties in land use information in the present application relate
primarily to the resolution of the information. -Agricultural land use data
are generally available; however, they are at the county level of resolution.
Therefore, specifying land use conditions in a subbasin may be difficult. The
primary need in land use data is for accurate specification of the cropland—
its area and type of crop... .Land use affects pollutant .lo.ad calculations
through the C factor in the USLE. In an application in which a pollutant load
is needed for a subbasin which covers a fraction of a county and in which land
use and other data are available only at the county level of resolution or
lower, the loads may be grossly overestimated. This could occur in a subbasin
for which a higher than average fraction (for the county) is cropped, for which
slopes are steeper than average, or for which there are no conservation prac-
tices applied (P = 1). Poor resolution of needed data can result in substan-
tial errors for particular locations within a basin.
Results are also somewhat sensitive to errors in describing agricultural
practices in a basin. The significance of errors in practices relates primar-
ily to the problem of timing of agricultural operations and, therefore, the
degree of cover on the ground at particular times.
In summary, errors in results are directly proportional to errors in the
various parameters used in the analysis since they are multiplicative. Assess-
ment of sensitivity to errors in the description of practices or land use, or
to the degree of resolution in the available data is an involved exercise which
120
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will yield results that vary considerably from basin to basin. Such variabil-
ity is anticipated because of different rainfall patterns and the degree of
homogeneity of land use among basins.
4.4 LEVEL OF EFFORT REQUIRED IN AN APPLICATION
Application of the nonpoint load estimation methodology to basins such as
those examined in this study should require on the order of two to three person
weeks of effort per basin. This estimate assumes an analyst familar with the
procedure and with the general subject of rural nonpoint source loads. It also
assumes familiarity with use of the nonpoint calculator program. The avail-
ability and use of more extensive data than considered in this demonstration
would increase the time required. Report preparation is not included in the
time estimate.
4.5 VERIFICATION OF THE LOAD.ESTIMATION PROCEDURES
Considering the lack of measured nonpoint loads (both rural and urban)
available for comparison and the long-term average nature of the estimates
which have been made, verification of the procedure by direct comparison with
measured loads is quite difficult. Comparison with measured instream concen-
trations is a more promising approach. The results presented in Volume II in-
dicate the level of verification that can be expected for the approach used,
particularly in the case of the Sandusky Basin.
4.6 FUTURE APPLICATIONS
In the present study, considerable effort was expended in selecting a se-
ries of events for each basin so that consistent flow data were available for
use in the instream assessment. This was necessary to assure compatibility
and to allow an attempt at verification of the results. In actual applica-
tions, such a selection of actual events may be unnecessary. A possible ap-
proach would be to define typical average events for various stages in cover
occurring throughtout the year. These "typical" events could be equivalent to
121
-------
events that produce some fraction of the total soil loss which occurs during
some fraction of the year. The information needed to define such an event is
available in terms of the annual distribution of the R factor. Such distribu-
tions are tabulated in Wischmeier and Smith (1978) or may be constructed.
Wischmeier and Smith also provide tables on the magnitude of R for single
storms having various recurrence intervals at different locations. Therefore,
in an application it is possible to consider design storms with characteris-
tics that can be defined independently of an actual watershed.
4.7 USE OF THE LOAD ESTIMATION METHODOLOGY IN SPECIALIZED APPLICATIONS
Chapter 3 provides an example of the application of much of the basic
rural nonpoint source methodology to the problem of estimating long-term nutri-
ent fluxes in streams. This application shows that the procedures can be ap-
plied in ways which overcome some of their fundamental weaknesses (e.g., the
need for a delivery ratio),.while providing useful results. . It is likely that
other specialized applications can be developed also.
4.8 ATTAINMENT OF THE GOALS OF THE STUDY'
The primary goal of the study was to demonstrate the methodology under ac-
tual field condition. This goal has been accomplished, and most of the issued
discussed in this chapter have related to this demonstration. The nonpoint
loading procedures and the 208 screening methodology have also been shown to
be compatible which was one of the subgoals of the program.
The application points out the primary strengths of the methodology, its
relative simplicity and the ease with which basic calculations can be done and
its weaknesses—dependence on a delivery ratio, a higher level of spatial aggre-
gation in the case of practical applications in large basins, and the need for
large amounts of data. These characteristics are well demonstrated in the
studies of the various basins, which illustrate the degrees of data availabil-
ity likely in practice. As indicated above, these applications indicate that
major parameter refinements tend to be time consuming and, in many cases, of
122
-------
limited value. They also indicate the difficulty of determining or assigning
sediment delivery ratios in most cases.
4.9 IMPACT OF METHODOLOGICAL SHORTCOMINGS ON AN ASSESSMENT
There are several important features in the rural nonpoint methodology
which limit the accuracy that can be expected from the results of an assess-
ment. These features include: (a) a high level of spatial aggregation in
the analysis—an important fact since the USLE is intended for rather small,
homogeneous areas; (b) the use of a delivery ratio to account for sediment
transport; (c) the assumption that pollutants such as phosphorus are associ-
ated with sediment; and (d) the long-term average, nonhydrologic nature of
the USLE.
An attempt was made to overcome the lack of suitability of the USLE for
analyzing actual events by averaging over many events. Dealing with an aver-
age event in this manner is acceptable; however, proper averaging requires
many events occurring over a long period of time. Data are not always avail-
able to carryout such averaging.
Additional shortcomings occur in the urban methodology used, which deals
with annual loads and which depends upon street loading rates that are not
well established.
A screening methodology such as was applied here is intended for rela-
tively easy application using existing data. Overcoming some of the limita-
tions listed above would require greatly increased amounts of data to reduce
spatial resolution problems, to provide increased information on sediment
transport, and to provide data on runoff needed to allow soluble forms of con-
stituents to be included and to allow a more hydrologically based approach.
Since this demonstration illustrates the fact that needed data may not be
available even for the screening approach used, it seems reasonable to conclude
that more rigorous approaches can result in even more obstacles due to data
limitations, especially when large areas must be considered.
123
-------
The users of the nonpoint methodology should be well aware of its limita-
tions. However, these limitations should not prevent the use of the approach.
As the present study shows, applications can be made which result in useful
inputs to water quality assessments in spite of certain methodological short-
comings of the procedures used. The user should always recall that the meth-
odology is intended for screening purposes.
124
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REFERENCES
Austin, M. E. , Land Resource Regions and Major Land Resource Areas of the
United States (exclusive of Alaska and Hawaii). Soil Conservation
Service, USDA, Agriculture Handbook 296, March 1972.
Burgess and Niple, Ltd., Stream Pollution and Abatement from Combined Sewers
Overflows, Bucyrus, Ohio. FWQA, 1969.
Davis, M. J., et al., Estimation of Pollutant Loads from Nonpoint Sources Us-
ing the Nonpoint Caclulator. MRI Report for EPA, 1979.
Environmental Protection Agency (EPA), Areawide Assessment Procedures Manual,
EPA-600/9-76-014, pp. G-15 to G-16, 1976.
Heaney, J. P., W. C. Huber and S. J. Nix, Storm Water Management Model: Level
I - Preliminary Screening Procedures, EPA-600/2-76-275, 1976. p. 16.
Jaworski, N. A. Nutrients in the Upper Potomac River Basin. Technical Report
No. 15. Chesapeake Technical Support Laboratory, Federal Water Pollution
Control Administration, August 1969.
Jenny, H. "A Study on the Influence of Climate Upon the Nitrogen and Organic
Matter Content of the Soil," Missouri Agr. Exp. Sta. Res. Bulletin, 152,
1930.
Livestock Waste Facilities Handbook, MPWS 18, Midwest Plan Service, Iowa State
University, Ames, Iowa 50011, July 1975.
Logan, Terry J. , "Levels of Plant Available Phosphorus in Agricultural Soils
in the Lake Erie Drainage Basin," Army Corps of Engineers, Lake Erie Man-
agement Study, December 1977.
125
-------
Logan, Terry J., "Maumee River Basin Pilot Watershed Study, Summary Pilot Water-
shed Report," International Joint Commission Report, 1978.
McElroy, A. D. , et al., "Loading Functions for Assessment of Water Pollution
from Nonpoint Sources," EPA-600/2-76-151, 1976.
Menzel, R. G., "Enrichment Ratios for Water Quality Modeling," in Knisel, W. G.,
editor, CREAMS: A Field-Scale Model for Chemicals. Runoff, and Erosion
from Agricultural Management Systems, U.S. Dept. of Agriculture, Conser-
vation Research Report No. 26, 1980.
Mildner, William F. , "Streambank Erosion in the U..S. Portion of the Great Lakes
Basin," International Joint Commission Report, 1978.
Ohio Department of Natural Resources, Division of Lands and Soil, General Soils
Map of Crawford (1975), Hardin (1961), Marion (1972), Sandusky (1974),
Seneca (1968), and Wyandot (1973) Counties.
Ohio Department of Natural Resources, Division of Lands and Soil, Composition
of the Soil Associations of the General Soils Map of Crawford (1975),
Hardin (1961), Marion (1972), Sandusky (1974), Seneca (1968), and Wyandot
(1973) Counties.
Sartor, J. D., and G. B. Boyd, "Water Pollution Aspects of Street Surface Con-
taminants," EPA-R2-72-081, November 1972.
Schubel, J. R. , and H. H. Carter. "Suspended Sediment Budget for Chesapeake
Bay". In: Estuarine Processes. Volume II. Martin Wiley, ed., Academic,
New York. pp. 48-62, 1976.
Soil Conservation Service, "Erosion and Sediment Survey of Baltimore Regional
Planning Council Area." For Baltimore Regional Planning Council,
College Park, Maryland, December 1977.
126
-------
Strand, R. I., "Bureau of Reclamation Procedures for Predicting Sediment
Yields," in: Present and Prospective Technology for Predicting Sediment
Yields and Sources. Proceedings of Sediment Yield Workshop, November
1972. ARS-S-40. Agricultural Research Service, USDA. pp. 10-15. 1975.
U.S. Department of Agriculture, "Soil Conservation Service Technical Guide,"
Columbus, Ohio, Revised, February 1979.
Williams, K. F. , and L. A. Reed, "Appraisal of Stream Sedimentation in the
Susquehanna River Basin," U.S. Geological Survey Water Supply Paper
1532-F, 1972.
Wischmeier, W. H. and D. D. Smith, Predicting Rainfall Erosion Losses - A
Guide to Conservation Planning, USDA, Agriculture Handbook No. 537, 1978.
127
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APPENDIX I - P FACTORS, SLOPES, AND SLOPE LENGTHS USED IN CHAPTER 2
In the tables which follow, practice factors (P) and slopes and slope
lengths are tabulated for the regions considered in the demonstrations in
Chapter 2.
The practice factors were developed based on information in State Conser-
vation Needs Inventories. Practice factors are given as a function of land
use and capability class for each county. See Table 2-11 for definitions of
these uses and classes.
Slopes and slope lengths were provided to MRI by the Soil Conservation
Service. Slopes are given in percent and slope lengths in feet. These quan-
tities are tabulated by Land Resource Areas (LRAs) in the following displays.
Within each LRA, they are given as a function of land capability class.
The .slope and slope length factors (S and L) can be determined for the
-slopes and lengths as follows (Wischmeier and Smith, 1978):
S = (0.056 + 4.56s + 65.41s2)
where s
and
field slope in percent
72.6
where A. = slope length in feet
and m = 0.2 for gradients < 1%
m = 0.3 for 1 to 3% slopes
m = 0.4 for 3.5 to 4.5% slopes
m = 0.5 for 5% slopes and steeper
Reference: Wischmeier, W. H., and D. D. Smith, Predicting Rainfall Erosion
Losses - A Guide to Conservation Planning, U.S. Department of
Agriculture, Agriculture Handbook No. 537, 1978.
128
-------
TABLE 1-1. PRACTICE FACTORS BY LAND USE AND LAND CAPABILITY
CLASS FOR EACH COUNTY
Kent County DE
Practice Factor (xlOO)
LCC
1
2
3
4
6
7
8
10
20
28
LU= 1
78
89
87
85
86
89
95
100
100
0
2
78
89
87
85
86
89
95
0
0
0
3
78
89
87
85
86
0
95
0
0
0
4
0
89
0
85
0
0
0
0
0
0
5
78
89
87
85
0
0
95
0
0
0
6
78
89
87
85
0
0
95
0
0
0
7
78
89
87
85
0
89
95
0
0
0
8
0
0
0
0
0
0
0
0
0
0
9
50
56
0
0
0
0
0
0
0
0
10
50
63
0
50
0
0
53
0
0
0
11
100
75
0
100
0
0
100
0
0
0
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
60
60
60
60
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
51
89
89
89
89
89
89
89
89
89
0
0
0
0
0
0
0
0
0
0
0
New Castle County DE
LCC
1
2
3
4
6
8
10
16
18
20
23
27
28
LU= 1
70
89
0
69
86
85
66
0
79
100
0
0
70
89
0
69
0
85
66
0
0
0
0
0
0 100
70
89
75
0
86
85
66
0
79
0
0
0
0
Practice Factor (xlOO)
4
0
0
0
0
0
0
0
0
0
0
0
0
0
5
70
89
0
69
86
85
0
0
79
0
0
0
0
6
0
89
0
69
86
85
66
0
79
0
0
0
0
7
0
89
0
0
0
85
0
0
0
0
0
0
0
8
70
89
0
0
0
0
0
50
0
0
0
0
0
9
0
75
0
0
0
50
50
0
0
0
0
0
0
10 11 12 13 14 15 16
50 75
67 77
0 0
50 100
50 77
50 100
77 0
75 0
83 75
0 100
50 0
70 0
50 0
0
0
0
0
0
0
64
0
0
0
0
0
0
64
64
0
64
64
64
75
0
64
64
0
0
64
0
75
0
0
75
75
93
0
0
75
0
75
75
93
93
0
93
93
93
50
93
93
93
93
0
93
0
50
0
0
0
50
50
0
50
0
0
0
50
(continued)
129
-------
TABLE 1-1. (continued)
Anne Arundel County MD
Practice Factor (xlOQ)
LCC
1
2
3
4
6
7
8
10
11
12
16
18
20
22
23
24
27
28
LU= 1
70
86
92
91
83
100
100
83
0
0
0
84
0
0
0
0
0
0
2
70
86
92
91
83
0
100
83
66
0
0
84
0
75
0
0
0
0
3
0
86
92
0
83
0
100
83
0
0
0
84
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
70
86
92
0
0
0
0
83
0
0
0
84
0
0
0
0
0
0
6
0
86
0
0
83
0
0
83
0
0
84
0
75
0'
0
0
0
7
70
86
92
91
0
0
100
83
66
0
0
84
50
0
100
0
0
0
8
0
70
92
91
83
0
0
83
66
0
0
84
0
75
0
0
0
0
9
0
0
0
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
56
0
0
0
0
0
0
63
0
0
61
0
0
65
75
0
0
11
0
90
0
92
90
0
92
90
100
0
0
100
0 .
100
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
67
0
67
0
0
67
67
67
0
0
0
0
0
0
0
0
0
14
57
57
57
57
57
57
57
57
57
0
0
57
0
57
57
0
57
57
15
93
93
93
93
93
93
93
93
93
93
93
93
93
93
93
0
93
93
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Calvert County MD
Practice Factor (xlOO)
LCC
1
2
3
4
6
7
8
10
11
18
19
20
22
23
28
LU= 1
58
78
0
50
97
0
0
0
0
58
78
92
0
97
0 100
50 0
91 91
0 89
98 98
0 100
0
94
0
0
3
58
78
0
50
97
0
0
0
0
98
0
0
94
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
78
0
0
97
0
50
91
0
98
100
0
0
0
0
6
58
78
0
0
97
0
0
91
0
98
0
0
94
0
0
7
0
78
0
0
97
0
0
91
0
0
0
0
94
0
0
8
0
9
0
0 60
0
0
97
0
0
91
89
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
(continued)
130
10 11 12 13 14 15 16
77
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
85
0
0
85
0
0
85
0
89
0
0
89
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
72
0
0
0
0
0
0
72
72
0
0
72
0
0
72
0
71
0
0
0
0
0
71
0
0
0
0
0
0
0
0
94
94
0
94
0
94
94
94
94
94
94
94
94
0
0
0
0
0
0
0
0
79
0
0
0
0
0
0
0
-------
TABLE 1-1. (continued)
Charles County MD
LU= 1
LCC
1
2
3
4
6
8
10
11
12
16
18
20
22
23
24
28
96
94
0
92
100
93
79
100
96
0
89
0
80
100
0
0
96
94
58
92
100
93
79
100
96
0
89
0
0
100
o.
0
0
94
0
92
100
93
0
0
0
0
89
0
0
100
0
0
Practice Factor (xlOO)
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
94
0
0
0
0
79
0
96
0
89
0
0
0
0
0
6
0
94
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
96
94
58
92
0
93
79
00
0
0
89
75
0
100
- o
0
8
0
94
0
92
100
93
79
100
96
0
89
75
0
100
0
0
9
0
0
0
73
0
0
0
0
0
0
0
0
0
0
0
0
10
0
100
50
0
100
0
83
100
100
0
0
0
0
0
0
0
11
0
100
0
86
100
86
100
100
0
0
0
0
100
100
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
• o
0
13
0
60
60
0
0
60
0
0
60
0
60
0
60
0
0
60
14
57
57
57
57
57
57
0
57
57
0
0
0
57
0
0
57
15
93
93
93
93
93
93
93
93
93
93
93
93
93
93
93
93
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Howard County MD
Practice Factor (xlOO)
LU=
LCC
1
2
3
4
6
7
8
10
11
12
16
18
19
20
22
23
27
1
50
79
0
79
77
0
63
83
0
0
0
68
0
0
59
0
0
2
50
79
67
79
77
86
63
83
92
0
0
68
79
0
59
91
0
3
50
79
0
79
77
0
0
83
0
0
0
68
0
0
59
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
79
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
50
79
0
79
77
0
0
83
0
0
50
68
0
0
59
0
0
7
0
79
0
0
77
0
0
83
0
0
0
0
0
0
59
0
0
8
0
0
0
0
0
0
0
83
0
0
0
0
0
0
0
0
0
9
0
0
0
57
0
0
0
0
0
57
0
0
0
0
0
0
0
10
0
100
0
0
50
0
50
100
0
0
0
83
0
0
0
0
0
11
83
80
100
69
80
0
69
80
0
69
78
86
0
78
86
0
0
(continued)
131
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
59
59
59
59
59
0
0
59
0
0
0
0
0
0
59
0
59
0
55
0
0
55
0
0
55
0
0
0
55
0
0
0
0
55
86
86
86
86
86
0
86
86
0
86
86
86
86
86
86
86
0
0
55
0
55
55
0
55
55
0
0
55
55
0
0
55
55
0
-------
TABLE 1-1. (continued)
Kent County MD
Practice Factor (xlOO)
LU=
LCC
1
2
4
6
7
8
10
18
20
23
28
1
51
92
95
94
92
97
88
93
0
0
0
2
51
92
95
94
92
97
88
0
0
0
0
3
51
92
0
94
0
97
88
0
100
0
0
4
0
0
95
0
0
0
0
0
0
0
0
5
51
92
0
94
0
97
88
93
0
0
0
6
51
92
95
94
92
0
88
93
0
0
0
7
0
92
95
0
0
0
0
0
0
0
0
8
51
0
0
94
0
0
0
0
0
0
0
9
0
0
93
0
0
50
0
0
0
0
0
10
0
68
81
0
0
0
0
0
0
0
0
11
0
81
0
81
100
81
0
100
0
0
0
12
0
0
58
0
0
0
0
0
0
0
0
13
58
58
62
58
0
58
58
58
58
58
58
14
0
62
94
62
0
62
0
0
0
0
0
15
94
94
57
94
0
94
94
94
94
0
0
16
0
57
57
57
0
0
0
0
0
0
0
Montgomery County MD
LU= 1
Practice Factor (xlOO)
8
10 11 12 13 14 15 16
LCC
1
2
3
4
6
8
10
12
16
18
19
20
22
27
55
79
0
73
79
100
81
0
0
98
0
0
88
0
0
0
0
0
79
0
0
0
0
0
0
0
0
0
55
79
78
73
79
0
81
0
100
98
0
0
88
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
79
78
73
79
0
81
0
0
98
0
0
88
0
0
79
0
73
79
0
81
50
0
0
0
0
88
0
0
79
0
0
79
0
81
0
0
0
0
0
0
0
0
55
78
73
79
0
81
50
0
0
0
0
88
0
0
50
0
0
0
0
0
0
0
0
0
0
0
0
0
81
50
50
54
0
61
50
0
78
0
0
61
0
0
0
0
89
81
89
81
89
88
89
0
0
89
0
0
55
0
0
0
0
0
0
0
0
0
0
0
0
0
75
0
55
55
0
55
55
0
0
0
0
55
0
75
93
0
75
75
0
75
75
75
75
93
75
75
0
93
56
93
93
93
93
93
93
93
93
56
93
93
93
0
56
56
56
56
0
56
56
56
56
56
0
56
56
(continued)
132
-------
TABLE 1-1. (continued)
Prince Georges County MD
LCC
1
2
3
4
6
7
8
10
11
12
18
19
20
22
23
24
27
28
LU= 1
57
85
93
85
92
90
90
93
87
100
96
93
0
93
57
85
93
85
92
90
90
93
87
0
96
93
0
93
0 100
0 0
0 0
0 0
0
85
93
0
92
0
0
93
0
0
96
93
0
0
0
0
0
0
Practice Factor (xlOO)
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
85
0
0
92
0
90
93
0
0
96
93
0
0
0
0
0
0
6
0
85
93
85
92
0
0
93
0
0
96
0
0
93
o-
0
0
0
7
0
85
0
0
92
0
0
93
0
0
0
0
0
93
0
0
0
0
8
0
85
0
0
0
0
0
0
0
0
96
0
0
0
0
0
0
0
9
0
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
100
83
60
83
90
0
100
82
0
0
80
100
0
100
0
0
0
0
11
100
67
0
0
67
100
100
67
0
0
86
100
0
86
0
0
0
0
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
87
87
87
87
87
87
87
87
87
87
87
87
0
0
0
87
0
14
0
86
86
86
0
86
0
86
0
0
0
86
0
86
86
0
86
0
15
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
16
0
80
80
80
80
0
80
80
80
80
80
80
80
80
80
80
0
0
Queen Annes County MD
LCC
1
2
3
4
6
8
10
18
20
22
28
LU= 1
63
69
78
85
72
97
69
0
83
68
0
63
69
0
85
72
97
0
0
83
0
0
63
69
78
85
72
97
69
0
0
0
0
Practice Factor (xlOO)
10 11
4
0
0
0
0
0
0
0
0
0
0
0
5
63
69
0
85
72
97
69
50
0
68
0
6
.0
69
0
0
72
0
0
0
0
0
0
7
63
69
0
85
0
97
0
0
0
68
0
8
63
69
0
0
0
0
0
0
0
0
0
9
0
50
0
0
94
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
84
0
0
0
0
0
0
0
0
0
12 13 14 15 16
0
0
0
0
52
0
0
0
0
0
0
52
52
52
52
0
52
52
52
52
52
52
0
61
0
61
94
0
0
0
0
0
0
94
94
0
94
0
94
0
94
94
94
94
0
0
0
0
0
0
0
0
0
0
0
(continued)
133
-------
TABLE 1-1. (continued)
St Marys County MD
Practice Factor (xlQQ)
10 11
0 50
71 66
0 100
50 84
LU=
LCC
1
2
3
4
6
7
8
10
12
18
22
23
24
27
28
1
62
81
84
89
92
0
89
89
0
91
0
0
0
0
0
2
62
81
84
89
92
87
89
89
0
91
0
0
0
0
0-
3
0
81
84
89
0
0
89
89
0
91
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
91
0
0
0
0
0
5
0
81
0
0
0
0
0
89
0
0
0
0
0
0
0
6
0
81
84
0
0
0
0
89
0
91
0
0
0
0
-Q-
7
0
81
0
89
0
0
0
0
0
0
0
0
0
0
0
8
0
81
84
89
92
87
89
0
0
91
0
93
0
0
0
9
0
0
0
0
0
0
53
0
0
0
0
0
0
0
0
0
0
0
75
0
66
0
84
66
0
50 100
0 0
0 0
0 0
0 0
0 0
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55
55
55
0
0
55
0
0
55
55
0
0
0
55
56
56
0
56
0
0
56
56
0
56
56
56
0
56
0
96
96
96
96
96
0
96
96
96
96
96
96
96
0
0
0
95
0
0
0
0
95
95
0
95
0
0
0
0
0
Crawford County OH
LU= 1 2 3
LCC
1 69 69 69
2 63 63 63
4 91 91 91
6 98 98 98
8 100 100 100
10 100 100 100
12 0 0 0
18 0 100 0
Practice Factor (xlOO)
10
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
5
69
63
91
98
100
100
0
100
6
0
0
91
0
0
0
0
0
7
69
63
91
98
100
100
0
0
8
0
0
0
0
0
0
0
0
9
50
0
0
0
0
50
0
0
11 12 13 14 15 16
79
77
74
77
74
0
0
70
0
0
0
0
0
0
0
0
60
60
60
60
60
60
0
60
0
60
60
60
60
0
0
0
90
90
90
90
90
0
90
90
0
0
0
0
0
0
0
0
(continued)
134
-------
TABLE 1-1.. (continued)
Hardin County OH
LCC
1
2
3
4
6
8
10
LU= 1
0
60
0
85
79
76
0
60
0
85
79
76
100 100
0
60
0
85
79
76
0
Practice Factor (xlOO)
10
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
5
0
60
0
85
79
76
0
6
0
0
0
0
0
0
0
7
0
60
0
85
79
76
0
8
0
0
0
85
79
0
0
9
0
0
0
0
0
0
0
11 12 13 14 15 16
50
59
0
56
59
56
59
0
0
0
0
0
0
0
0
58
58
58
58
58
0
0
0
0
54
0
54
0
90
90
0
90
90
90
0
0
0
0
0
0
0
0
Huron County OH
LCC
1
2
3
4
6
8
10
11
12
18
22
LU= 1
50
56
91
100
69
94
100
100
94
100
0
50 50
56 56
91 91
100 100
69 69
94 94
100 100
0 100
94 94
0 0
0 0
Practice Factor (xlOO)
4
0
0
0
0
0
0
0
0
0
0
0
5
0
56
91
100
69
94
100
0
94
0
0
6
0
0
0
0
0
0
0
0
0
0
0
7
50
56
0
100
69
94
0
0
94
0
0
8
50
0
0
100
0
94
0
0
94
0
0
9
0
' 0
0
55
0
0
0
0
0
0
0
10
50
0
0
100
100
0
0
0
0
0
0
11
100
80
.0
83
80
83
80
0
83
100
0
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
57
57
0
57
57
57
0
57
57
57
0
0
53
0
53
0
53
0
0
0
0
0
90
90
90
90
90
90
90
0
90
90
90
0
0
0
50
0
0
0
0
0
0
0
Marion County OH
Practice Factor (xlOO)
LU=
LCC
1
2
4
6
8
10
22
1
100
100
93
0
100
0
0
2
100
100
93
100
100
100
0
3
0
100
93
100
100
0
0
4
0
0
0
0
0
0
0
5
0
100
93
100
100
0
0
6
0
0
0
0
0
0
0
7
100
100
93
100
100
100
0
8
0
0
93
0
0
0
0
9
0
50
0
50
0
0
0
10
0
100
66
0
100
100
0
11
0
69
62
69
62
0
0
(continued)
.135
12 13 14 15 16
0
0
0
0
0
0
0
0
51
51
51
51
0
0
0
0
51
0
0
0
0
92
92
92
92
92
0
92
0
0
0
0
0
0
0
-------
TABLE 1-1. (continued)
Rich!and County OH
LCC
1
2
3
4
6
8
10
18
22
LU= 1
68
73
0
88
78
68
73
50
88
78
100 100
69 69
0 0
0 0
68
73
0
88
78
0
69
67
0
Practice Factor (xlOO)
10 11
4
0
0
0
0
0
0
0
0
0
5
68
73
0
88
78
100
69
0
0
6
68
73
0
88
78
0
69
67
50
7
68
73
0
88
78
0
69
67
50
8
68
73
0
88
0
0
69
0
0
9
0
0
0
0
50
0
50
50
0
12 13 14 15 16
0
0
50
50
0
50
0
0
0
100
95
0
84
95
84
95
100
0
0
0
0
0
0
0
0
0
0
60
60
0
60
60
0
60
0
0
0
60
60
60
60
60
60
0
0
90
90
0
90
90
90
90
90
90
0
0
0
0
0
0
0
0
0
Sandusky County OH
LCC
1
2
3
4
6
7
8
10
11
12
19
22
LU= 1
94
76
97
81
96
74
84
100
82
81
100
50
2
94
76
97
81
96
74
84
100
82
0
0
0
3
94
76
97
81
96
74
84
100
82
81
100
50
4
0
0
0
0
0
0
0
0
0
0
0
0
5
94
76
0
81
96
0
84
0
82
81
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
7
0
76
0
81
96
74
84
100
82
81
0
0
8
94
76
97
81
96
74
84
100
82
0
0
0
• 9
0
0
50
65
0
0
50
0
0
0
0
0
Practice Factor (xlOO)
10
0
0
0
0
0
0
0
0
0
0
0
0
11 12 13 14 15 16
0
50
0
71
50
50
71
0
50
0
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
53
0
53
53
0
53
0
53
0
0
51
51
51
0
51
51
0
51
0
0
51
90
90
0
90
90
90
90
0
0
90
90
90
0
0
0
0
0
0
0
0
0
0
0
0
(continued)
136
-------
TABLE 1-1. (continued)
Seneca County OH
LU= 1 2 3
LCC
1 84 84 84
2 53 53 53
3 84 84 84
4 90 90 90
6 100 100 100
7 64 64 64
8 82 82 82
10 0 100 100
11 0 0 0
18 0 0 0
20 0 0 0
22 0 0 0
Practice Factor (xlOO)
10
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
5
84
53
84
90
100
64
82
100
0
50
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
7
0
53
84
90
100
0
82
100
0
0
0
0
8
0
53
0
90
0
0
0
0
0
0
0
0
9
0
0
0
0
50
0
0
0
0
0
0
0
11 12 13 14 15 16
91
91
91
91
91
91
91
91
0
0
0
91
0
0
0
0
0
0
0
0
0
0
0
0
58
58
58
58
58
0
58
0
0
0
0
0
0
58
58
58
0
0
0
0
58
0
0
0
89
89
89
89
89
89
89
89
0
0
89
89
0
0
0
0
0
0
0
0
0
0
0
0
Wyandot County OH
LCC
1
2
3
4
6
8
10
11
18
22
LU= 1
60
52
0
90
69
90
83
60 60
52 52
0 100
90
90
69
90
83
0 100 0
0 100 100
0 100 0
69
90
83
Practice Factor (xlOO)
4
0
0
0
0
0
0
0
0
0
0
5
0
52
0
90
69
90
83
0
0
0
6
0
52
0
90
0
0
83
0
0
0
7
0
52
0
90
69
90
83
0
50
0
8
60
52
0
90
69
90
83
0
0
0
9
0
0
0
0
50
0
0
0
0
0
10
0
0
0
100
50
100
0
0
0
0
11
63
91
100
62
91
62
91
0
57
57
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
63
0
63
63
63
63
0
63
0
0
0
55
0
0
55
0
0
0
0
93
93
0
93
93
93
93
93
93
93
0
0
0
0
0
0
0
0
0
0
(continued)
137
-------
TABLE 1-1. (continued)
Fairfax County VA
LCC
1
2
4
6
7
8
10
11
12
16
18
19
20
22
23
27
LU= 1
0
0
60
87
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
75
94
60
87
0
0 100
0 0
0 0
0 0
0 0
0 58
0 0
0 0
0 83
0- 0
0 0
Practice Factor (xlOO)
10 11
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
75
94
60
87
100
100
0
0
100
0
0
100
0
83
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
•Q-
0
7
0
0
0
0
0
0
0
0
0
0
58
0
0
0
0
0
8
0
0
0
87
0
0
0
0
0
0
0
0
0
0
0
0
9
0
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 100
0 84
93
84
90
93
84
0
93
0
0 100
0 100
0 100
0 100
0 0
0 0
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
60
60
0
0
60
60
0
0
60
0
0
0
60
0
65
65
65
65
65
65
65
0
0
0
65
0
65
65
0
65
0
64
64
64
64
64
64
64
64
64
64
64
64
64
64
0
50
50
50
50
0
50
50
0
50
0
50
0
50
50
0
50
Fauquier County VA
LU=
LCC
1
2
3
4
6
7
8
10
11
12
16
18
19
20
22
23
1
59
85
0
0
87
0
85
80
0
69
0
100
100
100
82
68
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
59
85
0
0
87
0
85
80
0
69
0
100
0
100
82
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
59
85
0
50
87
0
85
80
0
69
0
100
100
100
82
68
6
59
85
0
0
87
50
85
80
0
69
0
100
0
0
82
68
7
0
85
0
0
87
0
0
80
0
69
0
0
0
0
82
68
8
0
0
0
0
87
0
0
0
0
0
0
100
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 92
(continued)
138
Practice Factor (xlOO)
10
0
0
0
0
62
0
0
0
0
0
0
0
0
0
0
0
11 12 13 14 15 16
81
79
0
80
79
79
80
79
79
80
0
78
0
77
78
82
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
68
68
0
68
68
0
68
68
0
68
0
68
0
0
68
68
0
73
0
0
73
0
73
73
73
73
0
73
0
0
73
73
0
64
64
64
64
64
64
64
64
64
64
64
64
64
64
64
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-------
TABLE 1-1. (continued)
Gloucester County VA
LCC
1
2
3
4
6
7
8
10
18
20
22
24
28
LU= 1
0
72
80
90
0
80
88
0
0
0
0
0
0
70
72
80
90
84
80
88
0
0
0
0
0
0
0
72
80
90
0
80
88
0
0
0
0
0
0
Practice Factor (xlOO)
10 11
4
0
0
0
0
0
0
0
0
0
0
0
0
0
5
70
72
80
90
84
80
88
79
0
0
100
0
0
6
70
72
80
90
0
80
88
79
0
0
100
0
0
7
0
72
80
90
0
80
88
79
0
0
100
0
0
8
0
0
80
90
0
80
88
0
0
0
0
0
0
9
50
61
50
58
50
56
67
100
0
0
0
0
0
0
0
0
75
0
75
0
0
0
0
0
90
90
90
88
0
0
88
90
0
0
94
0 100
0 0
12 13 14 15 16
0
0
0
0
0
0
0
0
0
0
0
0
0
56
56
56
56
0
56
56
0
0
56
0
56
0
0
0
0
73
0
0
73
0
0
0
0
0
73
76
76
76
76
76
76
76
76
76
76
76
76
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Loudoun County VA
Practice Factor (xlOO)
LCC
1
2
3
4
6
7
8
10
11
12
16
18
19
22
23
LU= 1
78
79
0
86
89
72
85
88
64
64
74
83
0
0
78
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
78
79
0
86
89
72
85
88
64
64
74
83
67
83
78
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
78
79
0
86
89
72
85
88
64
64
74
83
67
83
78
6
0
79
0
0
0
72
85
88
64
0
0
0
0
0
0
7
78
79
0
0
89
72
85
88
64
64
74
83
67
0
78
8
78
79
0
0
89
72
85
88
0
0
0
0
0
0
0
9
0
50
0
0
50
0
0
0
0
0
0
100
73
0
0
10
50
50
0
50
50
0
50
95
50
50
0
0
0
0
100
11
79
83
0
94
83
83
94
83
83
94
91
91
88
91
88
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13
0
69
0
0
69
69
69
69
0
69
69
69
69
69
69
14
70
70
70
70
70
70
70
70
70
70
70
70
70
70
70
15
0
74
74
74
74
74
74
74
74
74
74
74
74
74
74
16
0
54
0
0
54
' 54
54
54
0
54
54
54
54
54
54
(continued)
139
-------
TABLE 1-1. (continued)
Prince William County VA
LU= 1
Practice Factor (xlOO)
8
10 11 12 13 14 15 16
LCC
1
2
4
6
7
8
10
11
12
16
18
19
22
23
24
27
0
71
0
85
0
0
66
0
0
0
0
0
100
50
0
0
0
0
97
0
0
0
0
0
0
0
0
0
0
0
0-
0
0
0
0
0
0
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
97
85
0
0
0
50
0
0
100
0
0
0
0
0
50
71
97
85
50
100
66
50
100
0
100
0
0
50
o-
0
0
71
97
85
0
0
66
50
100
0
0
50
0
50
100
0
0
71
97
85
0
0
66
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
83
50
88
0
0
0
0
50
0
0
0
0
0
0
0
83
86
84
86
50
84
86
0
84
93
75
0
0 .
100
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
74
74
0
74
0
0
74
0
0
0
0
0
0
0
0
0
72
72
72
72
72
72
72
72
72
72
0
0
74
0
0
72
0
74
74
74
74
74
74
74
74
74
74
74
50
74
74
0
0
50
50
50
50
0
50
0
0
0
50
50
50
50
0
0
140-
-------
TABLE 1-2. SLOPE LENGTH IN LAND RESOURCE AREA
BY LAND CAPABILITY CLASS
Slope length (ft)
LCC
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
LRA= 99
200
200
300
200
300
.175
300
400
300
150
250
250
250
145
250
250
250
100
250
225
225
100
225
200
225
145
225
225
225
111
400
150
300
400
333
200
333
300
333
200
200
' 200
200
191
200
200
200
175
150
125
125
230
125
100
125
191
125
125
125
148
586
250
518
518
518
250 •
518
518
518
200
150
150
150
230
150
150
150
150
200
275
275
300
275
350
275
230
275
275
275
149
586
600
400
700
633
300
633
800
633
. 500
600
800
700
380
700
700
700
300
800
800
800
200
800
800
800
380
800
800
800
153
650
300
700
600
633
200
633
600
633
100
600
600
600
200
600
600
600
200
150
150
150
200
150
200
150
200
150
100
150
141
-------
TABLE 1-3. SLOPE IN LAND RESOURCE AREA BY
LAND CAPABILITY CLASS
Slope (%)
LCC
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
LRA= 99
1.0
4.0
1.0
1.0
1.0
9.0
1.0
1.0
1.0
15.0
4.0
4.0
4.0
13.0
4.0
4.0
4.0
15.0
4.0
6.5
6.5
22.0
6.5
9.0
6.5
13.0
6.5
6.5
6.5
111
1.0
4.0
1.0
1.0
1.0
8.0
1.0
1.0
1.0
15.0
4.0
' ' 4.0
4.0
18.0
4.0
4.0
4.0
22.0
8.0
6.5
6.5
44.0
6.5
5.0
6.5
18.6
6.5
6.5
6.5
148
1.0
5.0
1.6
1.6
1.6
10.0 "
1.6
1.6
1.6
10.0
12.0
12.0
12.0
15.0
12.0
12.0
12.0
20.0
23.0
29.0
29.0
30.0
29.0
35.0
29.0
15.0
29.0
29.0
29.0
149
1.0
3.0
2.0
2.0
2.0
7.0
2.0
2.0
2.0
. 8.0
2.0
2.0
2.0
13.6
2.0
2.0
2.0
20.0
3.0
3.0
3.0
30.0
3.0
3.0
3.0
13.6
3.0
3.0
3.0
153
1.0
3.0
1.0
4.0
2.7
8.0
2.7
3.0
2.7
12.0
4.0
4.0
4.0
7.7
4.0
4.0
4.0
7.7
12.0
12.0
12.0
7.7
12.0
20.0
12.0
7.7
12.0
4.0
12.0
142
-------
APPENDIX II - SOIL AND NUTRIENT LOSS CALCULATIONS FOR CHAPTER 3
Soil loss was estimated using the Universal Soil Loss Equation (USLE)
(Wischmeier and Smith, 1978). The calculations were done using a computerized
procedure (the nonpoint calculator) described by Davis and Nebgen (1979). Cal-
culations were performed on a county basis. There are 43 counties* in the
Potomac Basin and 78 in the Susquehanna. (Several counties were not included
because only a very small fraction of their area was in one of the basins.)
The important factors involved in the analysis were the parameters in the
USLE (rainfall factor, soil credibility factor, slope-length and steepness fac-
tors, cover and management factors, and the support practice factor), land use
information, and soil phosphorus concentration and the enrichment ratio. To
the extent possible, these factors were obtained from the national data base
which accompanies the nonpoint calculator. That data base is intended for pre-
liminary screening calculations, so it should be emphasized that the calcula-
tions made were not highly refined ones.
Land, use information was obtained from the 1967 Conservation Needs Inven-
tory (CNI). That inventory provides nonurban land use in 16 categories and by
land capability class. The conservation needs specified by the 1967 CNI were
used to estimate the support practice factor, again by land use and Tand capa-
bility class. These data on land use and practice factor are contained in the
data base for each county in the United States. Rainfall factors were esti-
mated on a county-by-county basis for both watersheds.
Actually these are county portions of subbasins. There is some double
counting since some counties are in more than one subbasin.
143
-------
Soil erodibility factors, slope factors, and cover factors are provided
in the data base by land resource area (LRA) (see Austin (1972) for a discus-
sion of LRAs). There are 156 LRAs in the coterminous United States. Soil
phosphorus concentration was also estimated by LRA using results presented by
McElroy et al. (1976). The LRAs in which the two river basins are located and
soil phosphorus concentration in each LRA are given in Table II-l.
There is no easy way to estimate how the enrichment ratio for phosphorus
might vary throughout the basins. In the calculations, a uniform value of 2
was assumed for enrichment.
TABLE II-l. LAND RESOURCE AREAS INCLUDED IN STUDY
Average total soil
phosphorus concentration
LRA classification number and name (ppm)
101
126
127
128
140
147
148
149
Ontario-Mohawk Plains
Central Allegheny Plateau
East Allegheny Plateau and Mountains
South Appalachian Ridges and Valleys
Glaciated Allegheny Plateau and Catskill
Mountains
North Appalachian Ridges and Valleys
Northern Piedmont
Northern Coastal Plain
700
700
300
300
500
300
500
100
144
-------
APPENDIX III - ENGLISH TO METRIC CONVERSIONS FOR VOLUME I
TABLE III-l. ENGLISH TO METRIC CONVERSION FACTORS
To convert
Into
Multiply by
inches
feet
miles
square miles
acres
pounds
tons
gal Ions
106 gal /day
pounds/acre
tons/acre
pounds/mi le
tons/mile
°F
^
centimeters
meters
kilometers
square kilometers
hectares
kilograms
tons - metric
..liters
cubic meters/day
kilograms/hectare
tons/hectare
ki 1 ograms/ki 1 ometer
tons/kilometer
°C
El
Km
m
2.540
0.3048
1.609
2.590
0.4047
0.4536
- 0.9072
3.785
3.785
1.121
2.242
0.2819
0.5638
(°F-32)0.5556
1.735
1.292
Erosion index (El) and soil credibility factor (K) con-
version factors from Wischmeier and Smith (1978).
145
------- |