MR
&EPA
United States Office of Environmental Processes
Environmental Protection and Effects Research
Agency Washington DC 20460
EPA/600/7-86/020
June 1986
Research and Development
Predicting Minesoil
Erosion Potential
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PREDICTING MINESOIL EROSION POTENTIAL
by
D. L. Jones, R. M. Khanbilvardi, and A. S. Rogowski
U.S. Department of Agriculture, ARS
Northeast Watershed Research Center
University Park, Pennsylvania 16802
EPA-IAG-D5-E763
Project Officer
Clinton W. Hall
Office of Energy, Minerals and Industry
Washington, D. C. 20250
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20250
U.S. Environmental Protection Agency
Itegion 5, Library (5PL-16)
230 S. Dearborn Street, Room 1670
Chicago, -IL 60604
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DISCLAIMER
This report has been reviewed by the Office of Energy, Minerals and
Industry, U.S. Environmental Protection Agency, and approved for
publication. Approval does not signify that the contents necessarily
reflect the views and policies of the U.S. Environmental Protection
Agency, nor does mention of trade names or commercial products constitute
endorsement or recommendation for use.
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FOREWORD
The Federal Water Pollution Control Act Amendments of 1972, in part,
stress the control of nonpoint source pollution. Sections 102 (C-l), 208
(b-2,F) and 304(e) authorize basin scale development of water quality
control plans and provide for area-wide waste treatment management. The
act and the amendments include, when warranted, waters from agriculturally
and silviculturally related nonpoint sources, and requires the issuance of
guidelines for both identifying and evaluating the nature and extent of
nonpoint source pollutants and the methods to control these sources.
Research program at the Northeast Watershed Research Center contributes to
the aforementioned goals. The major objectives of the Center are to:
. study the major hydrologic and water-quality associated
problems of the Northeastern U.S. and
. develop hydrologic and water quality simulation capability
useful for land-use planning. Initial emphasis is on the
hydrologically most severe land uses of the Northeast.
Within the context of the Center's objectives, stripmining for coal
ranks as a major and hydrologically severe land use. In addition, once
the site is reclaimed and the conditions of the mining permit are met,
stripmined areas revert legally from point to nonpoint sources. As a
result, the hydrologic, physical, and chemical behavior of the reclaimed
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land needs to be understood directly and in terms of control practices
before the goals of Sections 102, 208 and 304 can be fully met.
Signed:
t
V
H. B. Pionke
Director
Northeast Watershed
Research Center
IV
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ABSTRACT
Two experimental plots were instrumented with erosion pins to study the
correspondence between point erosion and erosion over an area on strip mine
soil. Using a rotating boom rainfall simulator, data were collected by
sampling the runoff every five minutes for the duration of the rainfall.
The amount of sediment eroded or deposited was measured after each simulated
rainfall using erosion pins. These results were compared to the sediment
load measured by runoff sampling, as well as to the predicted erosion using
two analytical models, the Universal Soil Loss Equation (USLE) and an
Erosion/Deposition (E/D) model. The E/D model was developed to be a more
comprehensive model than the USLE, by including partial area concepts of
hydrology and sediment transport equations. Erosion was predicted at
specific points on each plot, then an overall value for erosion was estimated.
Comparisons were then made between amounts of soil eroded or deposited
at a point using experimental techniques and numerical model predictions.
Spatial structure of soil loss distribution is evaluated. Discrepancies
between values observed at the pins and values expected based on model
results and sediment yield sampling are explained by increases in turbulence
and the amount of rain near the pins. Implications with regard to vegeta-
tion in the form of stalks are suggested.
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CONCLUSIONS
Experimental plot studies were conducted to evaluate the applicability
of erosion pins as point estimates of erosion. The data were compared with
results predicted by USLE and EDM models and with the sediment load measured
experimentally. For both plots, the erosion measured by the erosion pins
was 90-95% higher than that measured by sampling runoff, or predicted by the
EDM model. The causes for this difference were assumed to be the increase
in runoff turbulence and wind velocity around the pins. EDM model produced
comparable results. Protrusions are believed to increase the turbulence of
runoff resulting in local scour around the pins. The hypothesis has
implications with regard to the role of vegetation in erosion and potential
effects on washoff from vegetation. In addition to the above measurements,
soil erodibility factor (K) was calculated using the soil loss values
measured from runoff sampling and from erosion pin data based either on the
whole plot or 0.6 x 0.6 subarea length of slope. The results suggested
that there is change in soil erodibility after disruption of soil and
runoff erosivity component may vary as a function of surface roughness as
well as obstacles such as plant stalks. Thus in general it can be
concluded that:
the assumption of erodibility value being the same for the
disturbed material as for the undisturbed soil is not valid,
erosion measured at specific points with erosion pins over-
predicts areal erosion computed from runoff sampling,
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although USLE tends to overpredict erosion on plots as a whole
because it does not account for deposition, results computed
on the basis of individual subareas appear comparable,
erosion deposition model predictions are close to values
measured in runoff.
vii
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CONTENTS
Foreword iii
Abstract v
Conclusions vi
Figures ix
Tables x
1. Introduction 1
Need for Study 1
Objectives 3
2. Literature Review 4
Erosion Processes 4
Rainfall Simulation 7
Erosion Pins .<> 8
Predictive Models 10
The Universal Soil Loss Equation. 11
Erosion/Deposition Model 13
3. Materials and Methods ........... 15
Materials 15
Runoff Analysis 16
Erosion Pins 21
Evaluation of Erodibility 24
OSLE Application 27
Erosion/Deposition 29
4. Results and Discussion, 32
Runoff Analysis. 32
Erosion Pins 35
Prediction Results 40
DSLE Predictions 40
Erosion/Deposition Model. .... 43
Erodibility Evaluation . 51
References 53
Appendices
A. Rainfall simulator runoff sampling data 57
B. Calculations and calibrations to determine runoff 63
C. Elevation estimates for plots 3 and 4 (ft) 66
D. Runoff analysis 68
E. Erosion pin measurements 75
F. Cross section measurements. 84
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FIGURES
Number Page
3.1 VeeJet nozzle patterns 17
3.2 Nozzle attachment patterns 17
3.3 Compartmentalized catchment cans used with rainfall
simulator 18
3.4 Rainfall simulator V-notch weir calibration 20
3.5 Plot dimensions (m) and location of erosion pins 22
3.6 Diagram of erosion pin and measuring technique 23
3.7 Diagram of cross section measuring device 25
3.8 Location of erosion pins and subwatersheds for use in USLE
and erosion/deposition model 28
3.9 Flow chart of the erosion/deposition model 30
4.1 Runoff rates versus time, and erosion measured by runoff
sampling versus time; plot 3 (a) and plot 4 (b), runs
1-4 35
4.2 Erosion pin contour maps for plot 3 (a) and plot 4 (b) - run
#1; erosion contours (mm); plot dimensions (m) 36
4.3 Mosaics of kriged values of erosion pin (mm) for plot 3 (a)
and plot 4 (b) 37
4.4 Contour maps of predicted erosion (mm) using the USLE, plot
3 (a) and plot 4 (b) 41
4.5 Mosaics of predicted soil loss (mm) by USLE for plot 3 (a)
and plot 4 (b) 42
4.6 Mosaics of erosion (- ve) and deposition (+ ve) by EDM model
for plot 3 (a) and plot 4 (b) 44
4.7 Wind effect on the rainfall ratio at catchment cans 47
4.8 Mosaics of erosion (-) and deposition (+) calculated by EDM
model adjusted for scour around pins for plot 3 (a) and
plot 4 (b) 48
4.9 Topography (mm) of the 3 x 9 m plots after four rainfalls
for plot 3 (a) and plot 4 (b) 50
4.10 Predicted flow pathways (rills) for plot 3 (a) and
plot 4 (b) 51
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TABLES
Number Page
4.1 Sediment load measured from runoff sampling (mm) 32
4.2 Average soil loss (mm) on the two erosion plots as
determined by runoff sampling (1), pin measurements
(2), the USLE (3), and the erosion/deposition model
(EDM) unadjusted (4), and adjusted (5) for scour .... 33
4.3 Average erosion pin measurements for plots 3 and 4 .... 38
4.4 Analysis of rainfall in compartmentalized catchment cans
to determine the effect of the pin on rain falling per
unit area 40
Bl Calculations and calibrations for determining flow .... 64
B2 Calibration of weir to determine runoff 65
Dl Runoff analysis - sediment measured by runoff sampling . . 69
El Initial erosion pin measurements and erosion/deposition
measured in runs 1-4 76
E2 Erosion/deppsition measured by erosion pins (mm) 77
Fl Cross section data - center section - initial
measurements 85
F2 Cross section data from runs 1-4, center section 87
F3 Cross section data - right section - initial
measurements (in)0 93
F4 Cross section data from runs 1-4, right section 95
F5 Cross section data - left section - initial measurements
(in) 102
F6 Cross section data from runs 1-4, left section 104
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SECTION 1
INTRODUCTION
1.1. NEED FOR STUDY
Strip mining for coal is a common activity in Pennsylvania's Appalachian
region. Before reclamation of strip mined land, bare soil is exposed to wind
and rain, and the erosion process commences. Erosion degrades the land by
causing displacement of valuable topsoil, and can pollute streams by clogging
them with sediment and reducing the stream's capacity to carry flood flows.
As long as coal continues to be a valuable resource and strip mining
continues, these environmental problems will exist. The severity of the
problems can be diminished by proper planning of areas to be mined and by
accurate prediction of soil erosion and sediment yield. A reclamation
specialist utilizing this information can improve the planning and scheduling
of reclamation operations to minimize the controls needed to prevent soil
loss.
Several experimental methods and mathematical models have been developed
to estimate strip mine erosion. Erosion pins were used by Sams (1982) to
measure soil loss from strip mined land. The erosion pins, arranged on the
study area, serve as fixed reference points in the landscape, from which soil
loss or deposition can be measured. To supplement natural rainfall studies,
or to obtain erosional information for a storm of desired energy and
intensity, rainfall simulators have been used on small plots. To measure the
areal erosion from the rainfall simulator plots, runoff samples can be taken
and analyzed for sediment content.
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In general, as required by law, PL 95-37, slope percentages and slope
lengths of reclaimed areas are returned to the original, undisturbed
condition. However, it is not known whether the soil erodibility following
reclamation will be the same as before mining. The degree of stability
associated with erodibility is an important question to be resolved when
using predictive models to obtain accurate soil loss estimates for
minesoils.
The most common model used to predict erosion is the Universal Soil
Loss Equation (USLE) (Wischmeier and Smith, 1978). However, some adapta-
tions must be made to the USLE to apply it to strip mine sites, since it
was originally developed for use on agricultural lands. The equation is
used here to predict erosion at several points on the plots, then the
values are averaged to obtain one areal soil loss value per plot.
An Erosion/Deposition (E/D) model that was developed by Khanbilvardi
et al. (1983) incorporates more complex erosion mechanisms than the USLE
to predict erosion. The model was developed to predict soil erosion from
upland areas using the partial area concept, that is, the model does not
consider the entire drainage area as contributing to erosion as most
models do, but only certain portions of a watershed. The inputs to this
model are from parameters measured at points throughout the study area, as
well as areal values from the plot.
Accurate soil loss predictions can be extremely useful by enabling us
to assess the impacts of mining and to develop effective plans to control
soil loss and damage due to erosion. The hypothesis for this study is
two-fold: 1) soil loss over an area can be predicted from point loss
values and, 2) the established value for erodibility of undisturbed soil
is the same or very similar to that of disturbed soil after mining.
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1.2. Objectives
The specific objectives of this study include:
1) evaluate the erodibility of a minesoil using rainfall simulator
plots,
2) measure erosion at specific points on rainfall simulator plots
and compare the results to areal erosion values observed on
the plots from runoff sampling,
3) predict the potential erosion on the plots using the USLE and
compare the results to the erosion measured by runoff
sampling, and
4) predict the potential erosion on the plots using the
Erosion/Deposition model and compare the results to measured
erosion on the plots..
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SECTION 2
LITERATURE REVIEW
Sediment from stripmined areas is among sources of pollution and its
prediction is highly beneficial as a management tool in developing plans
to control sediment loss and erosion damage. This review of literature
assembles information concerning soil erosion process, the use of rainfall
simulator and erosion pins in erosion study, and soil loss determinations.
For clarity, this chapter is divided into four sections: erosion processes,
rainfall simulation, erosion pins, and predictive models.
2.1. EROSION PROCESSES
Erosion by rainfall and runoff is one of the most critical factors in
the process of land form evolution. Erosion is a two step process: the
detachment and the subsequent transport of detached soil. Detachment of
soil particles can occur in two ways. Each raindrop can cause detachment,
the erosive capacity of which depends on the kinetic energy per unit area
of the individual drop (Ekern, 1951). Soil detachment can also occur by
the shearing action of runoff water. The amount of soil that a given rain-
fall can detach depends on the rainfall intensity, the raindrop size
distribution, the raindrop fall velocity, and other characteristics such as
drop shape, impact angle, and the effect of wind (Meyer, 1963). The trans-
port of soil can occur by raindrop splash or by overland flow of the runoff.
Mutchler and Larson (1971) found that splash transport is absent when there
is no water depth on the soil surface, but increases for shallow depths of
water. However, splash transport probably decreases as the water depth
increases to three waterdrop diameters.
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The types of erosion associated with overland flow are sheet, rill and
gully erosion. Sheet erosion removes soil rather uniformly from every part
of the slope. When the flow begins to concentrate in small channels, rill
erosion begins. If the volume of water is large, gullies may form from the
downward cutting force of the water. This is called gully erosion.
Some basic factors which influence erosion include rainfall properties,
soil properties, topography, land management and flow properties. Rainfall
properties may be the most important of these because they include rainfall
intensity, duration and frequency. Wischmeier and Smith (1978) used the
rainfall intensity to determine the kinetic energy of the storm, then comput-
ed the product of the kinetic energy and the maximum thirty minute intensity
to obtain the rainfall erosion index, which is used in predicting erosion.
The rainfall erosion index values, or El values, were based on 22 year
station rainfall records. The sum of the storm El values for a given time
period is a measure of the erosive potential of the rainfall in that period.
The average annual total of the storm El values in a given area is the rain-
fall erosion index.
Soil properties that affect erosion include soil structure, texture, in-
filtration capacity, permeability, and erodibility. Erodibility is an
inherent property of the soil that allows some soils to erode more easily
than others when all other factors remain constant. Erodibility can be
affected fay particle size distribution, soil structure, organic matter and
moisture content, and wetting and drying conditions (Foster and Meyer, 1977).
Soil erosion is strongly related to topography in terms of slope length
and steepness. The elevation of the land determines the direction of the
water flow and the rill patterns that will develop. Zingg (1940) found that
on slopes of less than ten percent, erosion approximately doubled as slope
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doubled. Zingg (1940) also developed the equation to model the effect of
slope length on erosion per unit area for a given slope,
A = Ln (2.1)
where
A * average erosion per unit area
L = slope length
n = a coefficient
Foster and Meyer (1972) showed that the value of n depends on the relative
susceptibility of different soils to rilling and the resulting ratio of rill
erosion to interrill erosion. They indicated that where soil loss is
primarily from rills, n will approach one, but if the interrill erosion is
dominent it will approach zero. Young and Mutchler (1969) also indicated
that n increases with increasing slope length because rill erosion increases
faster than interrill erosion. Smith and Zingg (1945) measured soil loss on
several plots at intervals of ten feet and described the results with the
equation,
Y = 0.016L0'057 (2.2)
where
Y = average depth of soil loss (ft)
L = slope length (ft)
Land management refers to things such as land use, residual land use
effects, and vegetative cover and structures used to control erosion. It
is important to know how the land use will change the soil conditions.
Activities such as strip mining disrupt existing land use and drastically
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alter the soil in. several ways; the soil layers below the topsoil become
intermixed as the coal is mined, thus disturbing the natural soil layering.
The natural structure is altered and after the soil is replaced, it has a
higher bulk density because of compaction, and is also less porous.
Therefore, post reclamation properties of the soil may cause different
reactions to the erosive forces of rainfall than they did prior to mining.
Flow properties, particularly volume and velocity of overland flow,
will determine the carrying capacity of the water. Increased velocity
will increase the ability of the flow to transport soil.
2.2. RAINFALL SIMULATION
The factors mentioned are important when attempting to measure or quan-
tify erosion. Field studies of natural rainfall and erosion often require
long time periods of study to draw valid conclusions (Meyer, 1960). In
order to speed data collection and supplement natural rainfall studies,
rainfall simulators have been widely accepted as a useful tool for infil-
tration and erosion research (Bubenzer, 1980). Rainfall simulation is more
efficient, more controlled, and more adaptable to lab or field research than
natural rainfall studies.
Most simulators are developed Co simulate natural rainfall. Several
specific advantages of rainfall simulators include 1) storm intensity and
duration control, 2) storm repeatability, 3) control of plot condition at
the time of the storm event, and 4) speed of data collection (Bubenzer,
1980). The two main parts of a rainfall simulator are the nozzle or drop
former, and the mechanism that applies the spray. Some researchers have
stated that "the heart of any simulator is the drop forming method"
(Mutchler and Hermsmeier, 1963). The drop former can determine the extent
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to which rainfall can be duplicated and uniformly applied, because it controls
the intensity, flow rate and spray pattern.
Meyer and McCune (1958) analyzed the design of rainfall simulators in
their study. They found that the flat pattern VeeJet commercial spray
nozzles best satisfied the needs of researchers by supplying reasonable
intensity, drop size, drop velocity and distribution characteristics. The
VeeJet nozzles have a high flow rate and produce a long, narrow spray that
decreases in intensity with distance from the center. More precise rain-
fall simulation requires nozzles with lower flow rates and formation of
larger drops at higher velocities (Mutchler and Hermsmeier, 1963).
2.3. EROSION PINS
One method to quantify erosion is the use of erosion pins. An erosion
pin is a rod placed in the soil and is a fixed reference from which surface
advance and retreat can be measured. Measurement of the erosion pin from
the soil surface to the top of the pin may indicate whether soil loss or
deposition is occurring in the pin area. Changes in pin height may occur
due to natural processes such as freezing and thawing or wetting and
drying, when the ground surface expands or contracts and alters the
position of the pin.
Although the original erosion pins were wooden (Schumm, 1956a), most
studies recommended the use of longer lasting, slimmer metal stakes
(Colbert, 1956),, Many researchers also have advocated the use of a
permanent metal washer with the erosion pins. The washer aids in averag-
ing out the roughness of the soil surrounding the erosion pin, thus
allowing more accurate replication of pin measurements. However, a
permanent washer influences the surrounding soil erosion because it
prevents rain from striking the ground beneath the pin. After heavy
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rains, the washers may be elevated a few millimeters above the surrounding
soil. To avoid this problem, Schumm (1967) recommended use of a removable
washer that could be lowered over the pin before measuring, and removed
immediately afterward. The disadvantage of the removable washer is that
the readings are more susceptible to differences caused by variable
placement of the washer and compression of the washer into the soil.
The erosion pins can be used in one of two ways: they can be placed
flush with the ground surface and returned to that position after every
recording, or allowed to be exposed a few centimeters. If the pins are
placed flush with the surface, they should be in an area where no
deposition is expected. However, almost all slopes, even the shortest
and most level, have some deposition.
Erosion pins can be placed in several patterns, such as a transect
line, a grid pattern, or groups of pins at critical sites. Repeated
measurements at each pin are desirable to maintain an accurate account
of the erosional activity. The time intervals used by past researchers
on natural rainfall erosion pin measurements range from seven days
(Bridges, 1969) to more than one year (Schumm, 1956b).
Over the years, erosion pins have been shown to have several sources
of error. These include 1) disturbance of the soil during pin placement,
2) disturbance of the natural soil erosion and water flow patterns by the
erosion pin, 3) the effects of variations in the erosion pin's
environment, 4) disturbance of the pin by the recorder, and 5) errors in
recording (Haigh, 1978).
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2.4. PREDICTIVE MODELS
The efforts of many researchers in predicting soil losses from farm-
land and agricultural watersheds have been reported since the 1930fs.
Rainfall simulation and erosion pin studies provide researchers with
experimental methods of measuring soil erosion. Many studies have been
done to determine accurate soil loss prediction equations to take the
place of extensive field or laboratory work (Levesey, 1972; Holemam, 1972;
Kimberlin and Moldenhauer, 1977). However, not all models are suitable
for predicting strip mine erosion because of the unique conditions present
at the mined site. In selecting an appropriate predictive model,
important elements to be considered are time, watershed size, and sediment
source (Onstad et al., 1977). Some models predict sediment yield on an
annual basis. Others are designed to predict on a storm by storm basis at
regular time intervals. Watershed size is important in selecting a model,
because a model designed for a large watershed in which estimation errors
and localized differences are evened out may not be accurate for a small
watershed. Sediment source is an important factor because surface mined
areas are more prone to sheet and rill flow than channel or streamflow.
The model which has been the most readily usable to predict soil loss on
mined and reclaimed land is the Universal Soil Loss Equation (USLE)
developed by Wischmeier and Smith (1978) in cooperation with many other
researchers.
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2.4.1. The Universal Soil Loss Equation
The USLE is
A = RKLSCP (2.3)
where
A = average annual soil loss (Tons/acre)
R = rainfall factor (the number of rainfall erosion index
units)
K = soil credibility factor (the soil loss rate per erosion
index unit for a specified soil as measured on a plot
in continuous fallow, 72.6 feet long and with a 9%
slope)
L = the slope length factor (the ratio of soil loss from the
field slope length to that from a 72.6 foot length under
the same conditions)
S = slope steepness factor (the ratio of soil loss from the
field slope gradient to that from a 9% slope)
C = cover - management factor (the ratio of soil loss from an
area with specific cover and management, to that from an
area in continuous falloxtf)
P = support practice factor (the ratio of soil loss with a
conservation practice to that with straight row farming
up and down the slope).
The equation was developed based on 10,000 plot-years of soil loss data from
49 locations.
Although this equation is widely used and accepted, the USLE was origi-
nally intended for use on cultivated agricultural areas over a long period
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of time, and is often misused by people who are only superficially familiar
with the equation (Wischmeier, 1976). For large watersheds, where ample
opportunity exists for eroded sediment to be deposited between the point of
origin and the location where sediment yield predictions are desired, a
delivery ratio must be used. However, the USLE and a delivery ratio "may
not (be) designed for application to mined watersheds on a per storm basis"
(Pionke and Rogowski, 1981). An appropriate procedure may be to recalibrate
the USLE by use of standard plot studies as were used to calibrate the USLE
to farmland. However, this involves many unanswered questions, such as the
validity of extending small plot results to large watersheds, and the
effects of variable erodibility and increased slope lengths and percentages
found on reclaimed strip mines (Pionke and Rogowski, 1981). Also, there is
little information on spoil settling and the unfavorable effects settling
has on conservation practices.
Williams (1975) proposed a Modified Universal Soil Loss Equation
(MUSLE), in which a runoff factor replaced the rainfall factor, R. This
factor eliminates the need for a delivery ratio, but reflected the same
watershed characteristics such as drainage area, stream slope and water-
shed shape. The MUSLE is
Y = 11.8(Q x q )°'56 xKxLSxCxP (2.4)
P
where
Y = the sediment yield from an individual storm (T )
m
3
Q = the storm runoff volume (m )
q = the peak runoff rate (m /sec)
All other factors are the same as in the USLE.
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Chukwuma et al. (1979) also developed a modified form of the USLE,
which incorporated a two part runoff factor, one for sheet erosion and one
for rill erosion. This model, used to predict sediment yield for single
storm events, showed some improvement over the USLE predictions.
2.42. Erosion/Deposition Model
Another method of predicting erosion which incorporates the USLE and
several other water flow and soil loss prediction equations is the
Erosion/Deposition (E/D) model recently developed by Khanbilvardi et al.
(1983). This model attempts to accurately describe the erosion-sedimenta-
tion process on upland areas, using the partial area concept. That is,
the model does not consider the entire drainage area to contribute to
erosion but rather only certain portions of the watershed. The computer
model is applied to areas that have been divided into a grid, in which rill
and interrill zones have been delineated. The USLE is used to predict the
amount of sediment contributed from the interrill areas. The interrill
erosion is carried with the rill flow. The actual amount of soil that will
be transported off the site is dependent on the rill transport capacity.
If the capacity of the rill flow is great enough to carry all the eroded
soil, then the soil will be transported downslope and out of the area.
Otherwise, the carrying capacity of the flow will limit the amount of soil
transported, and the excess sediment will be deposited in the watershed.
The actual approach to the E/D model is a series of steps, starting
with the simulation of rill development by the computer. After the rills
have been defined, the contributing interrill areas are delineated. The
amount of eroded soil from these areas is calculated and routed along the
rill patterns. The rill transport capacity then determines the amount of
soil carried out of the area or deposited in the watershed.
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The experimental methods and computer models described in this section
are useful in predicting strip mine soil erosion. Much work has been done
in the past to discover better ways to predict, as precisely as possible,
sediment yield from a mined watershed, and work is still continuing in this
area. This study involves the use of several models and methods on a plot
scale, and includes comparisons among all methods.
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SECTION 3
MATERIALS AND METHODS
This chapter presents instrumentations and the methodology which were
used for this study and is divided into six sections: materials, runoff
analysis, erosion pins, evaluation of erodibility, USLE application, and
Erosion/Deposition Model.
3.1. MATERIALS
The primary objective of this study was to determine the correspond-
ence between point erosion and erosion over an area. To accomplish this
objective, a rotating-boom rainfall simulator was set up and used on two
experimental plots located at the field office of the Northeast Watershed
Research Center (NWRC), Klingerstown, PA. The plots were 3 m by 9 m,
had a slope of 6.5%, and were filled to the depth of 23 cm with Wharton
silty clay loam. Plots borders-were constructed from two 25 cm wide wooden
planks buried to the depth of 12 cm below the soil surface. In determining
particle size distribution, a soil sample was wet sieved through a 105
micron sieve. The particle size distribution of the sediment passing through
the sieve was determined using a Sedigraph 5000 particle size analyzer. The
soil was obtained from a topsoil pile at a strip mine site in Karthaus, PA.
The topsoil, which is routinely piled separately from the lower soil hori-
zons as part of the mining operation, was loaded into a truck and transport-
ed to Klingerstown. Bulk density measurements were taken at 33 locations at
the strip mine using a gamma probe to obtain an estimate of the density of
the soil after mining and reclamation. The topsoil was spread in the
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experimental plots and compacted as closely as possible to the average bulk
density of 1.40 g/cm measured on the reclaimed strip mined area.
The rainfall simulator has ten booms, each 6 m long, with 30 VeeJet
stainless steel nozzles located in the three patterns shown in Figure 3.1,
which are attached as shown in Figure 3.2. When operating at maximum
2
capacity, each spray nozzle covers an area of 5 m . The simulator was
centered between the two plots, and then used to "rain" on the plots for two
runs of 40 minutes each. The first run was made on dry soil, and the second
on wet soil approximately ten minutes after the first run. This series of
two runs was repeated three more times under the same sets of conditions.
The average intensity of the artificial rain, 7.76 cm/hr, was determined by
measuring the volume of water collected over the 80 minute time period, in
catchment cans placed under the simulator at evenly spaced intervals.
Special catchment cans were designed as shown in Figure 3.3, to determine if
the pin had any effect on the amount of rain that fell directly in the pin
area. The amount of water that fell into each compartment was measured,
then the amount per unit area for both sections was determined and compared.
The four runs were spaced over a period of one month during which the plots
were covered with plastic sheets to prevent exposure to natural rainfall.
The plots were kept free of vegetation for the course of the study.
3.2. RUNOFF ANALYSIS
As the artificial rainfall exceeded the infiltration into the plot, the
runoff began. From the time the rainfall started, the runoff was sampled
every five minutes to determine the concentration of soil coming off each
plot. The five minute interval was found to be most practical experimentally,
and the concentration of sediment during that interval was assumed to be
fairly constant. The samples were then taken to the laboratory for analysis.
16
-------
Figure 3.1. VeeJet nozzle patterns.
10
Figure 3.2. Nozzle attachment patterns.
17
-------
a. Top view
1/2 in. DIA.
Wooden Pin
Inner Catchment
yOuter Catchment
/ with
Overflow
Figure 3.3. Compartmentalized catchment cans
used with rainfall simulator.
They were allowed to settle for three days so the sediment could be separat-
ed from the runoff water. The water was decanted into a graduated cylinder,
measured and recorded. The sediment left in the collection bottles was
transferred to pre-weighed drying cans and dried in an oven at 105°C. After
three days in the oven, the weight of the soil was determined and recorded.
The data is contained in Appendix A.
18
-------
The runoff rate was determined by using a 22.4 degree V-notch weir with
a six hour water level recording chart. The runoff came off the plot into a
trough, then into a barrel with a flotation device attached to the recorder,
and through a weir. The weir was calibrated to determine the relationship
between the chart height and the flow through height (head) by measuring the
depth of water flowing over the weir, from the point of the V to the water
level, then noting the corresponding chart reading, Measurements were taken
several times, and the average from both plots was used as a conversion
factor in determining flow rate (see Appendix B). The equation used to
estimate flow rate was one developed by Lenz (1941):
V = 1.322 + °'522 N tan(6/2) H 2t5 (3.1)
3.281^ e m
m
where
3
V = volume (cm /sec)
H = head in meters
m
N = 0.035 + 0.033(tan(6/2))~°'8
6 = total angle of V-notch
e = 0.2475(tan(6/2))°*°9 + 0.34(tan(9/2))°'035
which was recommended for weirs between 28 and 90 degrees. The weir used in
the study was slightly smaller than the range given, so to check the accuracy
of the prediction, the relationship between chart height and flow rate was
determined by calibration of the weir. Water was allowed to flow through the
weir and maintained at a given notch height. The flow rate for each notch
height was computed by recording the time required to fill a given volume,
then dividing the volume by the time. This procedure was repeated for six
different notch heights, and the chart reading that corresponded to each
19
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notch height and flow rate was noted and graphed in Figure 3.4. The calibra-
tion of the weir showed Lenz's equation to underpredict the flow rate by a
factor of 1.8 (see Appendix B), so the calculations were done again, insert-
ing this conversion factor into Lenz's equation. In this way, the rate and
amount of runoff were determined.
The final step in analyzing the runoff was to determine the depth of
soil that had eroded from each plot, in order to compare this result to
other methods of predicting erosion. For each sample collected, the eroded
soil was computed by the equation :
Simulator V-Notch
Calibration
20 30 40 50
Notch Height (mm)
Figure 3.4. Rainfall simulator V-notch weir
calibration.
20
-------
SE = soil erosion (mm)
At = sample interval (sec)
3
C = soil concentration (g/cm )
s
3
Q = flow rate (cm /sec)
3
e = bulk density g/cm )
A = plot area (3.2)
A very small amount of soil was transported off the plot but was deposit-
ed on the lip of the trough rather than into the runoff. This soil was
collected, dried, weighed and added to the sum of the samples for each plot
and each simulated rainfall, to give the net erosion per plot. The experi-
mental work resulted in four values per plot of measured soil loss from runoff
sampling. These four values were added and compared to the soil loss
predicted and measured by the other methods used in this study.
3.3. EROSION PINS
One of the experimental methods used in this study of predicting erosion
over the area by point sampling is the technique of measuring erosion pins.
The erosion pins are one meter long reinforcing rods that were pounded firmly
into the soil, to avoid surface creep, leaving between five and ten centi-
meters of the pin exposed above the surface. Forty pins were placed on each
plot, in ten rows of four each (Figure 3.5). Base measurements were taken
for each erosion pin before any artificial rainfall was applied to the plot
using a pin measuring device, a micrometer and a washer that was slipped
over the pin, as shown in Figure 3.6. Following each simulated rainfall, the
micrometer measured the changes in soil elevation at the pin.
To evaluate the areal soil loss from the pin measurements, the changes
between readings from one run to the next were determined for each pin. The
21
-------
PLOT 3
PLOT 4
0.33 a
0.61
Figure 3.5. Plot dimensions (m) and location of erosion pins.
22
-------
MICROMETER
PIN
MEASURING
DEVICE
Figure 3.6. Diagram of erosion pin and measuring technique.
resultant changes in soil elevation at each pin (positive or negative) were
then used in a contouring program SURFACE2, to contour erosion and
deposition data (Sampson, 1978).
To compute the gross areal erosion from each plot for each rainfall, the
generated grid matrices of 1800 values (30 x 60) were read using a computer
program. Each estimated soil loss value was multiplied by the associated
2
area of 23.5 in . The results were averaged to obtain one value for overall
erosion for the four runs. These values can then be compared to the sediment
measured by runoff sampling, and to soil loss predicted by the computer model
and the USLE.
23
-------
To study rill development on the rainfall simulator plots, cross-sectional
measurements were taken before any artificial rainfall occurred, and none after
the sequence of four, 80 minute rainfalls. One meter long reinforcing rods,
identical to the erosion pins, were driven into the soil, leaving approximately
ten centimeters exposed above the surface. The rods were placed in nine rows
of two each, with 0.88 m between rows, and 1.83 m between the pins of each row.
A horizontal bar was designed with silt openings to fit over the two pins in
each row and was checked to be sure it was level before taking a reading
(Figure 3.7). In this way, the measurement position was duplicated as closely
as possible for the second set of readings. The bar was designed with
openings 2.54 cm apart to enable passage of a metal ruler. The ruler was
allowed to slide through each opening until it reached the soil surface, as
shown in Figure 3.7, then a reading was taken of the distance from the soil
surface to the cross section rod. The readings were repeated across the plot
every 13 cm for all nine rows. Extensions were also made to measure from the
right pin to the edge of the plot, and also for the left pin. These readings
and the final readings were recorded, and the change in surface elevation was
determined. Using the SURFACE2 graphics system, a surface topography map was
created which shows the approximate location of rills. These rill locations
will be compared to the location of rills predicted by a E/D computer model
to be used in this study (Khanbilvardi et al., 1983).
3.4. EVALUATION OF ERODIBILITY
To obtain accurate soil loss estimates for minesoils, the soil credi-
bility must be known. The credibility, which is an inherent property of
the soil that allows some soils to erode more easily than others, when all
other factors remain constant, was assumed to be maintained both before
and after disruption of the soil. To test this assumption, the Universal
24
-------
,Plol Border
Metal Ruler
Cross Section Bar
Cross Section Pin
Figure 3.7. Diagram of cross section measuring device.
Soil Loss Equation or USLE (Wischmeier and Smith, 1978) was used. The USLE
equation (2.3) has been the most readily usable model to predict soil loss
on mined and reclaimed land. To solve for K, the soil erodibility, the
equation was rewritten as K = A/RLSCP. The soil loss value, A, was obtained
by measuring the sediment in the runoff from the rainfall simulator plots as
described above, then converted to tons/acre. The rainfall erosivity value,
R, was computed to reflect the intensity and energy of the rain storm
supplied by the rainfall simulator.
The erosivity or erosion index (R in the USLE) is defined as the product
of the energy and intensity. To use the USLE for predicting soil loss for an
individual storm, as it is the case for this study, the R factor can be
replaced by an energy-intensity (El. ) interaction for that storm (Foster
et al., 1982). The rainfall erosivity is then calculated as,
25
-------
R = EI3Q/100 (3.3)
where
E = storm kinetic energy in metric ton-m
per hectare per cm of rain =
210 + 89 log I,
I = rainfall intensity (cm/hr), and
I_0 = maximum 30 minutes intensity (cm/hr).
The LS (slope-slope length) factor was determined by computing one value
for the entire plot, using the equation derived by Wischmeier and Smith
(1978);
rt
LS = (L/22.13)m(65.4l sin 9 + 4.56 sin 6 + 0.065) (3.4)
where
L = total slope length in feet,
9 = angle of the slope,
m = 0.5 for slopes of 5% or more.
The cover factor (C) was set equal to 1.0 since there was no protective
vegetative cover, and the P factor was also set to 1.0 since there was no
conservation practice. Thus, we can solve for the K value since all other
values are known. This K value can then be compared to the K value
previously determined for Wharton silty clay loam by the Soil Conservation
Service.
26
-------
3.5. USLE APPLICATION
The USLE was also used in this study to predict erosion on the plots.
Although the USLE has been widely used to predict strip mine erosion, it
was originally intended for use on cultivated agricultural areas over a long
period of time. Adaptations can be made to the equation so it can be
applied to mining, but the factor values must be chosen carefully. For this
study, values were obtained for all factors either experimentally or from
established sources.
The USLE was used to predict erosion based on the characteristics of
many small subwatersheds on the plot to obtain a prediction based on the
plot as a whole.
Each plot was divided into a grid of 15 by 5 blocks, each block 0.61 x
0.61 meter square (Figure 3.8). The soil loss was predicted at the center
of each of these squares using the USLE, resulting in 75 values in all.
The R value was determined for the specific storm type supplied according
to the method described previously. The cover and conservation factors
were both equal to 1.0. The K value used in the prediction was the value
established for that soil type by the Soil Conservation Service. The
elevations were measured at each erosion pin, then elevations were estimated
at the center of each square of the grid by linear interpolation (Appendix
C). The distance between each elevation estimate was 0.61 meter, so slope
percentages could be inserted into equation (3.4) to determine the LS value
for each subarea. All the factor values were inserted into the USLE and 75
soil loss predictions were obtained. To obtain one erosion value per plot
using the USLE, the 75 values were averaged. The SURFACE2 contouring program
was used to create one contour map per plot of predicted soil loss values.
27
-------
PLOT 3
PLOT 4
0.61 m
0.61 to
Figure 3.8. Location of erosion pins and subwatersheds for use
in USLE and Erosion/Deposition Model.
28
-------
The prediction from the "entire plot" values was a simple matter of
multiplying the calculated R value, the LS value as described in the
Evaluation of Erodibility section, the K value established by the Soil
Conservation Service, and 1.0 for C and P. After conversions, a soil loss
prediction in millimeters was obtained.
3.6. EROSION/DEPOSITION MODEL
The second computational method used to predict erosion is the Erosion/
Deposition (E/D) Model developed by Khanbilvardi et al. (1983). To proper-
ly use this model, the study area was divided into homogeneous, equal size,
square subwatersheds. Each subarea was 0.61 m by 0.61 m, and represented by
a node point in the center. The input parameters were assumed to be constant
for a given subarea. From the given information, rill sources and patterns
were generated. The elevations, which had been estimated at the center of
each subarea, are some of the key inputs to the model, since they determine
the most likely direction of water flow in projecting rill pathways. The
contributing areas to the rills are assumed to have sufficient runoff to
transport the eroded soil to the rills. Therefore, the rills are assumed to
be the only flow system responsible for transporting the detached soil down
and off the plot. Erosion on the contributing areas was calculated using
the USLE. A flow chart of the model is shown in Figure 3.9.
To route the eroded sediment, the sediment supply rate was balanced
against the transport capacity of the rill flow. If the flow transport
capacity exceeded the amount of sediment supplied, all the sediment would
be carried by rill flow. Otherwise, the transport capacity of the rill
flow would limit the amount of soil that would move downslope, and the
excess sediment would be deposited on the plot.
29
-------
DELINEATE SILL AND INTE-WILL AREAS
COKPUTE 1NTE.WILL EROSION
COM»UTE SILL SCOUR
COflPARE:
RILL TRANSPORT CAPACITY
VS
AH3UNT OF DETACHED WTERIAL
IF CAPACITY > AMOUNT
[ IF CAPACITY < AH3UHT
EROSION A.-OUNT
EROSION CAPACITY
DEPOSITION AMOUNT - CAPACITY
CHECK IF ALL SILLS
INCLUDED IN ROUTING
Figure 3.9. Flow chart of the Erosion/Deposition Model.
To compute infiltration and rainfall excess, the water content of the
soil must be known. The initial moisture content was estimated in the
model from the available moisture characteristic and one-third and fifteen
bar water contents for the soil, which were determined experimentally to
be 0.195 and 0.094 g H-0/g of soil, respectively. The average particle
diameter of 0.27 cm, which is needed in determining the amount of sediment
that will be carried by rill flow, was also determined experimentally by
dispersing, then wet sieving an 800 g sample of soil. The soil
30
-------
permeability of 7 x 10~ cm/sec was obtained from research done by Cunningham
et al. (1977) on Pennsylvania soils developed from acid shale. The bulk
3
density used, 1.40 g/cm , was measured on the plots. The rainfall erosivity
and LS factor to be used in the USLE were determined by the procedure
described in the previous section.
Each model component was computed, then the routing procedure (Foster
and Meyer, 1972) of balancing the available sediment against the flow
transport capacity was carried out for each plot. The model predicted the
location and amount of erosion and deposition for the specified storm for
both plots, and gave the amount of soil that would come off the plots from
each rill outlet.
The final scour or deposition was printed in grid matrix form for all
75 points. The predicted amount of soil from each rill outlet was added
to get the cumulative amount of soil to be eroded from the entire plot,
then the average amount of soil over the plot was determined. These
results were compared to the soil loss results from the USLE, erosion pin
measurements, and runoff sampling. These comparisons are discussed in the
next section.
31
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SECTION 4
RESULTS AND DISCUSSION
4.1. RUNOFF ANALYSIS
The runoff rates, determined by the modified Lenz equation, sediment
concentrations and depth of soil eroded from the plot as measured by runoff
sampling, are shown in Appendix D. Each run consisted of parts A and B,
each 40 minutes. The cumulative depths of soil for each run were averaged
for each plot, shown in Table 4.1. The values for the four runs were
averaged for each plot and displayed in Table 4.2 opposite sediment load,
along with a summary of the other results.
TABLE 4.1. SEDIMENT LOAD MEASURED FROM RUNOFF SAMPLING (MM)
Plot Number Run Number
1A + IB = (0.1391 + 0.1076) = 0.25
2A + 2B = (0.1787 + 0.1562) = 0.33
3A + 3B = (0.0788 + 0.0848) = 0.16
4A -I- 4B = (0.0688 + 0.0819) = 0.15
X = 0.22 mm
1A + IB = (0.0711 + 0.0299) = 0.10
2A -I- 2B = (0.0734 + 0.0801) = 0.15
3A + 3B = (0.0391 + 0.0276) = 0.07
4A + 4B = (0.0362 + 0.0445) = 0.08
X = 0.10 mm
Ail error analysis was performed on the final equation used in the calcu-
lation of soil depth eroded as measured by runoff sampling. Since total depth
32
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TABLE 4.2. AVERAGE SOIL LOSS (MM) ON THE TWO EROSION PLOTS AS DETERMINED
BY RUNOFF SAMPLING (1), PIN MEASUREMENTS (2), THE USLE (3),
AND THE EROSION/DEPOSITION MODEL (EDM) UNADJUSTED (4),
AND ADJUSTED (5) FOR SCOUR
Source Plot 1 Plot 2
1. Sediment load 0.22 0.10
(runoff sampling)
2. Erosion pins 2.23 1.36
3. USLE - based on
4.
5.
i - plot
ii - subarea
EDM
EDM adjusted
0.75
0.15
0.24
1.15
0.75
0.13
0.37
1.66
(0.09)
equals the sum of sample interval depths, the total absolute error would be the
sum of the absolute error in depth for each interval. The absolute error in
sample interval depth can be found by determining the errors in each component
of the equation, which are sample interval, concentration, flow, bulk density,
and plot area. Conversion is a constant with no error. The following errors
were determined:
sample interval 5 sec
bulk density 0.04 g/cm
2
plot area 64 cm
3
concentration 0.01 g/cm
flow 0.66 cm /sec
To determine the error in the flow measurements, the following steps were
taken: assumed 0.10" error in reading chart and in measuring water level
on the chart, head = (0.10)(0.0374) = 0.00374 m (see Appendix B), then flow
33
-------
error was determined by inserting the value for head in the flow equation,
3
equation (3.1), to give 0.66 cm /sec. Therefore, the sample interval depth
error is
(5 sec)(0.01 g/cm3)(0.66 cm3/sec)
(0.04 g/cm3)(64 cm2)
= 1.29 x 10~2 cm
= 1.29 x 10~3 mm
Therefore, the total error in depth = (1.29 x 10~ )(8 intervals) = 0.01 mm.
Runoff versus time was plotted for each run and was found to be quite
variable (Figure 4.1). For the most part, the runoff rate peaked between
five and ten minutes of the first run, and remained at that level for the
second forty minute run. The average runoff to rainfall coefficient for
both plots was determined to be 0.34, or approximately 34% of the rainfall
was transported off the plot, and 66% infiltrated. The sediment concentra-
tions were higher for the first two runs than the last two, which may
indicate a large amount of fine particles were present initially, which
were easily eroded. As erosion continued, the majority of the remaining
particles were coarser, and were not detached as easily as the fines, or were
more likely to be deposited on the plot rather than be transported off the
plot. Another reason for variable sediment concentration may be that the soil
was compacted more in some areas than others on the plot to start, then at a
certain point during the rainfall, large clumps of soil came loose and were
transported off the plot in the runoff. Large depressions were observed on
the plots after certain runs, which may substantiate this hypothesis.
34
-------
550
J?200
J 150
= 100
e 50
E 0
I; ^ .--''
- \
-
; 2.
.
__ ^j <-
; 3.
- » "
4.
Dry -
/ \ W«t .
i t i i i » f i
JJ3J
J1J
o Z
1S 25 IS
If 15 S IS 25 35 S IS 2S 35
Tlm« (mini
b.
'300
^250
g200
_ 150
1 M
0
.
.
^^^£Z~.
1
«»^
.
^\
^/ \_^x
I
m
^ -'*
, : , ,'~~~, T"
1 1 1 1 1 1 1T
-
--------^--- -
a
*
, , . : T r^7~
-
_^ =^r^_ '
4.
Ory .
.«-«-: r~r"^-^_
.02
Jft
0
1 1S JS M
Tim* (min)
Figure 4.1. Runoff rates versus time, and erosion measured
by runoff sampling versus time; plot 3 (a) and
plot 4 (b), runs 1-4.
4.2. EROSION PINS
Creation of contour maps from the forty erosion pin measurements gave the
areal distribution of the erosion, as shown in Figure 4.2 for run 1. The
amount of surface decline for the four runs, obtained from pin measurements,
were averaged for each plot. Erosion pin measurements were then used as point
samples to create (Sampson, 1978) a mosaic map (Figure 4.3) of erosion
(negative) and deposition (positive) on both plots. The pins measured both
erosion and deposition, but the overall results for both plots indicated soil
35
-------
O/
a.
Figure 4.2. Erosion pin contour maps for plot 3 (a) and plot 4 (b) - run //I;
erosion contours (mm); plot dimensions (m).
-------
a.
b.
11 to -0.9
0.3 to 0
0 to + 2.0
-0.9 to -0.3
i i 0
ESZS3+2.0 to +22.0
Average Soil Losses (mm)
Figure 4.3. Mosaics of kriged values of erosion pin (mm)
for plot 3 (a) and plot 4 (b).
37
-------
loss for each run, shown in Table 4.3. The averages used in the final analysis
can be seen in Table 4.2. The individual pin measurements can be seen in
Appendix E. The error associated with the pin measurements was found to be
0.74 mm through repeated measurements of several pins. In addition to the error
in the measurements, there was a great deal of variability in the experimental
technique. The rainfall simulator had some operational problems which may have
caused the differences in soil loss from one run to the next. A problem with
the simulator was that rust formed on the inside of many of the pipes and nozzless
and caused improper spray application. The water flowing through the rusted pipes
and nozzles trickled straight down rather than fan out, so distinct gullies were
formed under the paths of the malfunctioning nozzles, which created more erosion
than under the proper operating conditions. The nozzles were cleaned as
thoroughly as possible in between runs, but it was impossible to remove the rust
from the pipes. The nozzles became clogged during some runs more than others,
resulting in high variability in the pin measurements.
TABLE 4.3. AVERAGE EROSION PIN MEASUREMENTS
FOR PLOTS 3 AND 4
Plot No. Run No.
3 1
2
3
4
4 1
2
3
4
Soil Loss
(mm)
-3.52
-1.38
-2.97
-1.04
X = -2.23
-1.85
-1.78
-0.41
-1.39
X = -1.36
38
-------
A second problem occurred at the end of each forty minute run when the
simulator was turned off. Although the simulator was turned off firmly,
the nozzles dripped for a few minutes, and caused increased soil detachment
directly beneath the nozzle. Not all nozzles dripped, and the position in
which the rotating booms came to a stop varied, so different areas received
the additional impact each time. Occasionally, the dripping occurred
directly on or around an erosion pin, which gave deceptively high values for
erosion when the pin was measured. To circumvent this problem in successive
runs, the water supply was shut down at the end of the forty minute run, but
the booms continued to spin for a few minutes. In this way, the excess water
was spread out over the plots with a low intensity rather than drip on
specific areas and cause increased erosion.
The mean measured values from the erosion pins were approximately ten
times greater than the mean values of sediment load measured by runoff
sampling. Because of the somewhat large error associated with the erosion
pin measurements, the runoff sampling results may be a bit closer than
first appears. However, it would be inaccurate to say that the two
measurements are comparable because of the considerable degree of
variability.
A reason for the high soil loss values measured by the erosion pins,
beyond those already mentioned, may be that the physical presence of the
pin caused changes in water flow and soil loss patterns, and may even have
increased erosion at the pin. Analysis of the compartmentalized catchment
cans indicated that, in 83% of the measurements, more rain fell around the
pin per unit area than outside the pin area (Table 4.4). This may have
led to increased erosion at the pin simply because of the pin's presence.
39
-------
A pin with a smaller circumference may cause less localized soil loss. These
factors will be accounted for when using Erosion/Deposition Model to predict
soil loss factor in this chapter.
TABLE 4.4. ANALYSIS OF RAINFALL IN COMPARTMENTALIZED
CATCHMENT CANS TO DETERMINE THE EFFECT OF THE
PIN ON RAIN FALLING PER UNIT AREA
Sample No.
1
2
3
4
5
6
Rain Around Pin
(cm)
13.7
11.5
17.1
13.9
12.9
13.2
Rain Outside Pin
(cm)
13.7
11.6
16.1
11.2
8.3
11.8
Concerning operator error, it is difficult to gather readings that are
accurate to the nearest millimeter. Some recorded results were smaller than
the experimental error in measuring the pins, which indicates that some
apparent erosional values may actually have been only a result of operator
error.
4.3. PREDICTION RESULTS
4.3.1. USLE Predictions
To determine one erosion value per plot using the USLE, contour maps
(Figure 4.4) and mosaic map (Figure 4.5) were generated. These maps gave
some idea of the areal distribution of the predicted soil loss. The
agreement between the soil loss predicted by the USLE and the sediment load
measured in runoff sampling was more satisfactory than between the pins and
runoff sampling (Table 4.2). The USLE prediction for the entire plot values
was much higher than the runoff sampling values, and fell in between the
40
-------
a.
Figure 4.4. Contour maps of predicted erosion (mm) using the USLE,
plot 3 (a) and plot 4 (b).
-------
a.
b.
1-0.9 to -0.3
Average Soil
Y///////A - Q.3 to 0
Losses (mm)
Figure 4.5. Mosaics of predicted soil loss (mm) by USLE
for ploc 3 (a) and plot 4 (b).
42
-------
values for the erosion pins. In general, the USLE is expected to overpredict
soil loss because the equation does not account for soil deposition on the
slope or in depressions in the field. However, for the small plot scale
used in this study, where very little deposition occurs, the equation should
give a fairly good soil loss estimate. For the subareas, the equation
underpredicted soil loss for plot 3 by a factor of 1.5, and overpredicted
soil loss for plot 4 only 0.7 times. The plot value was about 7.7 and 17
times larger for plots 3 and 4, respectively. Therefore, the overprediction
by USLE can be improved by using USLE separately for different subareas of
the plot. The equation was designed to estimate the long term average annual
soil loss, so when applying it to specific storms, the results must be
interpreted cautiously. The predictions for plots 3 and 4 are the average
soil loss values for numerous recurrences of the specific storm energy and
intensity used in the study. As with any average, the soil loss from any one
of these events may vary considerably.
4.3.2. Erosion/Deposition Model
When analyzing the predicted erosion from the E/D model and the measured
erosion, a fairly good correspondence between the two was found. Mosaics
were created (Figure 4.6) to display the predicted values. The average soil
loss from each plot is given in Table 4.2. Although the plot 4 predicted
value is over three times larger than the measured value, they become much
closer when one considers a possible sink on the plot, such as a leak between
the plot and runoff trough, or from the side boards of the plot. During run
2, a slight leak was observed on plot 3 between two side boards. It was
quickly repaired and was not observed again, but may have had some impact on
the measured sediment load. When first applying the model, the experimental
value for average particle size diameter of 0.27 cm was used. This gave a
43
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a.
b.
1-0.9 to -0.3
0
GXXS2+2.0 to +22.0
-0.3 to 0
0 to +2.0
Average Soil Losses (mm)
Figure 4.6. Mosaics of erosion (- ve) and-'deposition (+ ve) by
EDM model for plot 3 (a) and plot 4 (b),.
44
-------
very large result for final scour and deposition off the plots. The
computer model then used the D__ (90% finer by weight) particle size
which was about 3.0 cm. Inserting this value into the computer model
gave much more reasonable results, which were used in the final analysis.
The computed value was used rather than the experimental value because it
is likely that the smaller particles clumped together to form larger soil
aggregates in the runoff, and resulted in a larger average particle
diameter.
The results show that the mean measured values from erosion pins were
approximately ten times greater than the mean values expected on the basis
of sediment load measured in runoff. Strictly speaking, the two measure-
ments are not comparable because of the considerable variability associated
with pin data as contrasted with the averaging effects of runoff. Although
in here the discussion is directed towards possible effects associated with
man installed metal erosion pins, similar rationale may apply to growing
plant stalks, stones and other surface protrusions. The presence of erosion
pins could by "itself be the reason for the higher soil loss values measured
at the pins. Pins will cause flow disturbance and possibly create local
"scour." Local scour would occur adjacent to the pin, and its magnitude
would vary according to flow, pin condition, and sediment concentration in
runoff. Pins will increase the flow velocity in the down-pin section and,
therefore, increase the capacity of flow to carry more sediment than in the
up-pin section of the flow path. Flow around a pin, however small in scale,
creates eddy structure or a system of vortices. This behavior is the basic
mechanism of local scour. The scour around a bridge pier is a large scale
manifestation of local scour and has long been recognized by many researchers
(Posey, 1949; Laursen and Toch, 1956). Carstens (1966) attempted to quantify
45
-------
the local scour around a cylinder. He defined a sediment number (N) as,
N
Yf
where
V * mean velocity of the undisturbed
flow (cm/sec)
2
g » acceleration of gravity (cm/sec )
d = mean sediment size (cm), and
Y and y_ = specific weights of sediment and
fluid (g/cm3)
His results indicated that sediment number N for undisturbed flow is one-half
of N for flow disturbed by a vertical bar. This suggests that maximum
velocity around a cylinder in a two-dimensional flow may be approximately
twice the undisturbed condition. Such increase in velocity which is an
integral part of the flow system would be influenced by the pin physical
characteristics. In general the depth of scour around a cylinder (d ) can
be related to the cylinder Reynolds Number (R ) (Shen, 1971),
(4.2)
where
Vd
R = "
e v
d = pin diameter (cm.)
46
-------
V = flow kinematic viscosity
(cm /sec)
ct and 3 = constants
In addition to the eddy system, the wind velocity and direction could increase
the flow around the pins. Analysis of the data from compartmentalized catch-
ment cans (Figure 3.3) showed that more rain fell immediately around the pins
than outside the pin area and that the amount increased with wind speed
(Figure 4.7). We would therefore expect erosion to increase around the pin
due to a larger volume of flow.
145
Wind Sp««d (km/hr)
Figure 4.7. Wind effect on the rainfall ratio at
catchment cans.
When the above factors were incorporated into the EDM model the adjusted
output (Table 4.2) indicated a much closer agreement between the EDM predicted
values (Figure 4.8) and measured erosion pin data. It appears, should
appropriate parameters be available, a mathematical model such as an
47
-------
a.
b.
mil -0.9 to-0.3
0
^2.0 to+22.0
r////////i -0.3 to 0
0 to +2.0
+22.0 to +99.0
Average Soil Losses (mm)
Figure 4.8. Mosaics of erosion (-) and deposition (+) calculated
by EDM model adjusted for scour around pins for plot
3 (a) and plot 4 (b).
48
-------
EDM model could be used to predict the local scour around the pins. The
mechanism could be responsible for washoff and enhanced runoff concentration
of topically applied sprays from growing vegetation. It may also point to a
possible mechanism and location of erosion on vegetated areas with a high C
factor.
There are several possible reasons for discrepancies between the two
measurements. A major point about the model in general is there is less
room for error when using the model on such small subwatersheds. Another
reason for the discrepancy may be that the differences in soil compaction
cause irregular soil loss patterns. The soil may have been compacted
more in some areas than others, either due to the compaction done by hand
in the beginning of the study, or due to natural settling. Thus, rills
would be formed more easily in the areas of lesser compaction, although
the computer model has no way of accounting for a variance such as this.
Another irregularity that may occur that cannot be predicted by the model
is the effect of raindrop splash on contributing areas. In a storm of
fairly high intensity such as the one utilized in this study, the water-
drop splash may cause soil to enter the rill channels from areas that are
not adjacent to the rills. This situation would cause an underprediction,
because the model only considers the amount of eroded soil in each rill to
originate from either the rill itself, or interrill areas directly adjacent
to the rills.
The cross section measurements gave some idea of where the rills
develop by plotting the change in land elevation before and after the
four rainfalls. Figure 4.9 shows deformation of the soil surface in
plot 3 and 4 after the four simulated rainfalls. The rill (flow pathways)
location predicted by the EDM model (Figure 4.10) were, then, compared to
49
-------
the rills indicated by the cross section measurements taken on the plots
(Figure 4.9). In general, the agreement between location of rills predict-
ed by E/D model and observed at the site was satisfactory. However, some
discrepancy existed, especially near the edge of the plots. It was
observed during the experiment that some runoff did follow a path along the
right edge of the plot, although the plot borders caused a channel to form
directly adjacent to them that would not have formed naturally. This
NS
Figure 4.9. Topography (mm) of the 3 x 9 m plots after four
rainfalls for plot 3 (a) and plot 4 (b).
50
-------
1 '
\
\
/
>
\
\
X Plot -
Boundaries
Interrill
Areas
-Rill
Patterns
- Flow
Outlets
Figure 4.10.
Predicted flow pathways (rills) for
plot 3 (a) and plot 4 (b).
phenomena was also recognized by Hudson (1957). He stated that plot boundaries
can actually activate erosion, then leave a channel which concentrates runoff
and tends to scour.
4.4. ERODIBILITY EVALUATION
A major concern in correctly using the USLE is to quantify the relation-
ship of soil erosion to erosivity of the eroding agents and to the erodibility
of soil. These properties vary in space and time and they are affected by
climate, soil, topography, cover, and management. For example, a soil highly
susceptible to erosion by raindrops may not be susceptible to erosion by
surface runoff. However, present descriptions of erosivity of raindrop impact
51
-------
and surface runoff is gross and generally incomplete to use fundamental mea-
sures of soil erodibility. Most of the knowledge on soil erodibility is
empirical, which makes it difficult to use in erosion prediction equation.
For example, soil erodibility factor (K) used in this study was 0.32 which is
an established value for the soil type by Soil Conservation Service. This
value is normally assumed to be constant before and after soil disruption.
To evaluate this assumption, the K value was determined by using the measured
soil loss to solve the USLE.
The K value determined by runoff sampling was very low (Table 4.2). When
the average Lg value S on each 0.6 x 0.6 m subarea (rather than L,, value for
the plot) was used in calculations a more realistic value of K was obtained.
However, when erosion pin data were used to calculate K value, a significant
overprediction was observed (Table 4.2). This result indicates that the
assumption of the erodibility of the disturbed soil being the same as the
established value for undisturbed soil erodibility is not valid. Table 4.2
shows that K values obtained from erosion pins measurements were very large.
This overprediction could be explained in part by the fact that pins presence
greatly increase the runoff erosivity. Therefore, the value in Table 4.2 for
erosion pins might have to be considered as a composite factor for soil
erodibility and increase in runoff erosivity.
52
-------
REFERENCES
1. Bridges, E. M. Eroded Soils of the Lower Swansea Valley. Soil Sci.,
20(2):236-245, 1969.
2. Bubenzer, G. D. An Overview of Rainfall Simulators. Paper presented at
1980 meeting of the Am. Soc. of Agric. Engrs., San Antonio, Texas.
Paper No. 80-2033, 1980.
3. Carstens, M. R. Similarity Laws for Localized Scour. Proc. Paper 4818,
J. Hydraulic Division, ASCE 92(HY3):13-36, 1966.
4. Chukwuma, G. 0., W. H. Edwards, and G. D. Schwab. Rainfall-Runoff Factor
for Soil Loss from Small Watersheds. Paper presented at the 1979 meeting
of the Am. Soc. of Agric. Engrs. and CSAE, June 24-27, University of
Manitoba, Winnipeg, Canada, 1979.
5. Colbert, E. C. Rates of Erosion on the Chinle Formation. Plateau.,
28(4):73-76, 1956.
6. Cunningham, R. L., E. J. Ciolkosz, R. P. Matelski, G. W. Petersen, and
R. Pennock, Jr. Characteristics, Interpretations, and Uses of Pennsylvania
Soils Developed from Acid Shale. Agricultural Experiment Station Report
362, 1977.
7. Ekern, P. C. Raindrop Impact as the Force Initiating Soil Erosion. Soil
Sci. Soc. Am. Proc., 15:7-10, 1951.
8. Foster, G. R., and L. D. Meyer. A Closed-Form Soil Erosion Equation for
Upland Areas. In: Sedimentation (Einstein), H. W. Shen (ed.), 1972.
pp. 1-19.
9. Foster, G. R., and L. D. Meyer. Soil Erosion and Sedimentation by
Water - An Overview. In: Proceedings National Symposium on Soil
Erosion and Sedimentation by Water, Dec. 12-13, Chicago, Illinois, 1977.
53
-------
10. Foster, G. R. , F. Lombard!, and W. C. Moldenhauer. Evaluation of
Rainfall-Runoff Erosivity Factors for Individual Storms. Trans.
ASAE, 25(1):124-129, 1982.
11. Haigh, M. J. Evolution of Slopes on Artificial Landforms - Blaenavon,
U.K. Research Paper No. 183, The University of Chicago, Department of
Geography, Chicago, Illinois, 1978.
12. Holeman, J. N. Procedures Used in the SCS to Estimate Sediment Yield.
Proceedings Sediment Yield Workshop, USDA Sedimentation Laboratory,
Nov. 28-30, Oxford, Mississippi, 1972.
13. Hudson, N. W. The Design of Field Experiments on Soil Erosion. J.
Agric. Eng. Res., 2(l):56-65, 1957.
14. Khanbilvardi, R. M., A. S. Rogowski, and A. C. Miller. Modeling Upland
Erosion. Water Resour. Bull., 19(l):29-35, 1983.
15. Kimberlin, L. W., and W. C. Moldenhauer. Predicting Soil Erosion.
Proceedings National Symposium on Soil Erosion and Sedimentation
by Water, Dec. 12-13, Chicago, Illinois, 1977.
16. Laursen, E. M., and A. Toch. Bulletin No. 4. Iowa Highway Research
Board, May 1956.
17. Lenz, A. T. Viscosity and Surface Tension Effects on V-Notch Weir
Coefficients. In: Agriculture Handbook No. 224, Field Manual for
Research in Agricultural Hydrology, Trans. ASCE, 108:759-802, 1941.
18. Levesey, R. H. Corps of Engineers Methods for Predicting Sediment Yield.
Proceedings Sediment Yield Workshop, USDA Sedimentation Laboratory,
Nov. 28-30, Oxford, Mississippi, 1972.
19. Meyer, L. Donald, and Donald L. McCune. Rainfall Simulator for Runoff
Plots. Agr. Eng., 39(10):644-648, 1958.
54
-------
20. Meyer, L. D. Use of the Rainulator for Runoff Plot Research. Soil Sci.
Soc. Am. Proc., 24(4):319-322, 1960.
21. Meyer, L. D. Philosophy and Development of Simulated Rainfall for
Erosion Control Research. Paper presented at the 1963 Winter
Meeting of the Am. Soc. of Agric. Engrs., Dec. 10-13, Chicago,
Illinois, 1963.
22. Mutchler, C. K., and L. K. Hermsmeier. Review of Rainfall Simulators.
Paper presented at the 1963 Winter Meeting of the Am. Soc. of Agric.
Engrs., Dec. 10-13, Chicago, Illinois, 1963.
23. Mutchler, C. K., and C. L. Larson. Splash Amounts from Waterdrop Impact
on a Smooth Surface. Water Resour. Res., 7(1):195-200, 1971.
24. Onstad, C. A., C. K. Mutchler, and A. J. Bowie. Predicting Sediment
Yield. Proceedings National Symposium on Soil Erosion and Sedimenta-
tion by Water, Dec. 12-13, Chicago, Illinois, 1977.
25. Pionke, H. B., and A. S. Rogowski. Implications of Water Quality on
Reclaimed Lands. In: Economics, Ethics, Ecology: Roots of
Productive Conservation, Walter E. Jeske (ed.), 35th Annual Meeting
Soil Conservation Society of America, Aug. 3-6, Dearborn, Michigan,
Soil Conservation Society of America, Ankeny, Iowa 50021, 1981.
pp. 426-440.
26. Posey, C. J. Why Bridges Fail in Floods. Civil Engr., 19:42-90, 1949.
27. Sampson, R. J. Surface II Graphics System. Kansas Geological Survey,
Lawrence, Kansas, 1978.
28. Sams, J. I. Erosion of Strip Mine Lands. Paper in Environmental
Pollution Control, The Pennsylvania State University, University
Park, Pennsylvania, 1982. 78 pp.
55
-------
29. Schumm, S. A. Evolution of Drainage Systems and Slopes in Badlands of
Perth Amboy, New Jersey. Geol. Soc. of Am., Bull. 67, 597-646, 1956a.
30. Schumm, S. A. The Role of Creep and Rainwash on the Retreat of Badland
Slopes. Am. J. Sci., 254:693-706, 1956b.
31. Schumm, S. A. Erosion Measured by Stakes. Revue de Geomorphologie
Dynamique, 17:161-162, 1967.
32. Shen, H. W. (ed.). River Mechanics. Colorado State University, Fort
Collins, Colorado, 1971.
33. Smith, D. D., and A. W. Zingg. Investigation in the Erosion Control
and Reclamation of Eroded Shelby and Related Soils at the Conservation
Experiment Station, Bethany, MD. 1930-1942. USDA Tech. Bull. No. 833,
1945. 175 pp.
34. Williams, J. R. Sediment Routing for Agricultural Watersheds. Water
Resour. Bull., 11(5)-.965-974, 1975.
35. Wischmeier, W. H. Use and Misuse of the Universal Soil Loss Equation.
J. Soil Water Conserv., Jan./Feb., p. 5, 1976.
36. Wischmeier", W. H., and D. D. Smith. Predicting Rainfall Erosion Losses.
U.S. Department of Agriculture, Science and Education Administration,
Washington, D. C. Agriculture Handbook 537, 1978.
37. Young, R. A., and C. K. Mutchler. Soil Movement on Irregular Slopes.
Water Resour. Res., 5(5):1084-1089, 1969.
38. Zingg, A. W. Degree and Length of Land Slope as it Affects Soil Loss
in Runoff. Agr. Eng., 21(2):59-64, 1940.
56
-------
APPENDIX A
RAINFALL SIMULATOR RUNOFF SAMPLING DATA
57
-------
(Jl
CO
-»: ;*; T x x x ~x X
r r r r r r f r
O CJ C) Cl Ci O Cl Ci
ci o o c? Ci Ci o a
iji Ui l|*' UJ U*i ifi 'f' M'
m ru ju ru m ru ru ru
M u'i i»'j u:i in ui ui n'i
M DI to r.) r 4> 4.- 4.- 4> 4> i;- $
in ui in in in in in ui
4- iii M ru ru »- »-
Ci in o in O ui o ui
- 1 - 1 -J -1 - 1 Crt uj iT>
- 1 m f - 1 - 1 i-* »- 4i
Ul Ci k> O lil O O U
iji m (ii ui O'l tfi ui ui
.11 in ru in ui -I u"' o~'
r
Cl
o
Cl
H
f'l
Z l_ ^n
H o r -o
nm
UJ
13 }=
X
3d -<
*~i n
Z 3
- m
^
^ <:
fn
Oi~~i
LJ
- n
r
;c a- x x x ?: I; x
i" r r r r r r r
O C i C ' C) O Ci C) Ci
CJ O O Ci Ci O C) O
'M II' Ul Ui if' III Mi Ul
m in ru ru ru m m ru
ui ti) ui ui in 01 in (ii
i/i ni rn M r»i m m co
ru ru M ru ru ru ru ru
»-»»-»»-»»-»»-»»>-*»-*
3' 3- 1> 3> 3» 3;- 3- 3»
l> l> P 4> ! »> * l>
in in ui m in in ui ui
t ui in ru ru - «-
O ui Ci in ».*) ui Ci in
-4 iii fi"i ijn lit iii lit in
o -i fu w in in >- in
o ui a ui o ui o ci
»-
ui ui ui in in -J c>i i
in oj - 1 u'< ui ! t ui
r
n
fi
o
3>
ni
Z C 3)
nor -o
OfTI
UJ
Pj ^-
X
.*«
If H
i* o-*
^* !H
-: m
.-,
a <:
rp
-' r
- ul
»\
III 1.1
r
X X X X. X X X X
rrrrrrrr
Ci O C> Cl Ci O Cl O
Q 0 Q 0 0 Q 0 0
IU Iji Ul U> U' Ul IJI IJI
ru ru ni m ru ru ru ru
ca D'j a) i/i ui ui pi ul
(xl D) (XI CM Dl 1X1 Cil 0}
ru ru ru fu ru ru ru ru
Ul UJ UJ Ul UJ UJ UJ UJ
|~A ^.* ^«* |.* ^~* ^~A ^.A ^*
4.- |> 4.- 4.- 4;- 4> 4.- .|>
in in m in in in ui in
*- ui uj ru ru »-* »-*
o in o in O in o in
to in i*n -.| -4 (Ti -4 in
uj i'o ru in ui ni o ui
O O O O Ci O Ci Ci
I-*
(!» in (ft -i co cd «-* ijj
ti'J O OJ U1 0» - 1 4" Ul
r
8
o
3>
m
zc »
nor T»
Oni
LU
Dl ^~3
X
3 -i
|-~H ^*^
ii !:f
**-x fTl
^
C r
~ ul
Ci Cl
*-- 1-1
r
Cl
-i
c
-i
b
IT
l-<
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?
iii
t-^
^?
r
-i
CJ
11
C
n
~«
Cr'
u
HI
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r
T-
-n
m
6
"
* «
:u
3'
i- 1
- r
^;
1 "
Ul
i
"»
r.
i*
ri
ij
n
fn
j-
TI
f
-?
i':>
ri
V
1
1
!-
l"J
I-*
Cl
l-» t I
O O O
i-i
3-
r i
o o o o o o o o
ill Hi i|l U'I if' ifi Ui !
C"i Ci O in O Ci <"' O
5SJi
- -1
-* O
O O
r-» r-* IJI 11*1 ill Ij'l Ci l"1
O O in O Ci Ul O O
"Si Ci
~- PI J!
ii J- J.
*;' ci 11
-------
LGC DATE R F
U L
N C
T
KLG032332 13 4
KLG092232 IB 4
KLG032SS2 IB 4
KLG092222 IB 4
KLG092S22 13 4
KLGOS2222 IS 4
KLG092S32 IB 4
K.LG0323S2 13 4
LOG DATE R P
U L
N Q
T
KLG 100532 2A 3
KLG 100522 2A 3
KLG 100532 2A 3
KLG 100522 2A 3
KLG 100522 2A 3
KLG 100522 2A 3
KLG 100522 2A 3
KLG 100522 2A 3
LOG DATE R P
Ll L
N Q
T
KLG 100 532 2A 4
KLG 100532 2A 4
KLG 100532 2A 4
KLG 100332 2A 4
K'LG 100532 2A 4
K'LG 100532 2A 4
KLG1CGS32 HA 4
KLG 100532 2A 4
LCC DATE R P
U L
N Q
T
KLG 100532 2E 3
KLG 1005 =2 £B 3
KLG 100522 2B 3
KLG 100532 25 3
KLG 100 532 23 3
KLG 100532 £3 3
KLG1CC-532 £3 3
KLG 100532 £3 3
SULK
DEN
1.45
1.45
1.45
1.45
1.45
1.45
1.45
1.45
BLLK
DEN
1.45
1.45
1 .^5
1.45
1.45
1.45
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1.45
1.45
1.45
1.45
1.43
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625
625
710
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650
535
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5
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800,
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310
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310
770
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SOIL
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1.15
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1 . 20
1.20
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CHAr T
READING
10
15
20
25
30
35
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TIME
(MIM)
5
10
15
2C
25
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35
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730
V'-JU.
(ML)
300
6.3
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(-3)
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1.25
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LGC D*7E
KLG 100552
KLG10O522
KLG 100522
KLG1005S2
KLGi005S2
KLG 100532
KLG1005S2
KLG1005S2
LDC DATE
KLG 10 1922
KLG 10 1382
KLG 10 1382
KLG 10 1382
KLG 10 1382
KLG 1O 1382
KLG1013S2
KLG 10 1382
LCC DATE
KLG 10 1382
KLG 10 1382
KLG 10 1382
KLG1013S2
KLG 10 1382
KLG 10 1382
KLG 10 1332
KLG 10 1382
LCC DATE
KLG 10 1382
KLG101382
KLG 10 1382
KLG 10 1322
KLG 10 1382
KLG1013S2
KLG 10 1332
KLG 10 1352
m
,-t
U
N
23
23
23
2S
23
23
2S
2B
R
U
N
3A
3A
3A
3A
3A
3A
3A
3A
R
U
N
3A
3A
3A
3A
3A
3A
3A
3A
R
U
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3B
3B
38
3E
35
32
3B
3B
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1
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1.45
1.45
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1.45
1.45
1.45
1.45
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1.45
1.45
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BULK
DEN
1.45
1.45
1.45
1.45
1.45
1.45
1.45
1.45
SULK
DEN
1.45
1.45
1.45
1.45
1.45
1.45
1.45
1.45
VCL
'ML)
10
15
20
25
30
35
40
TIME
(WIN)
5
10
15
20
25
30
3S
40
TIME
(WIN)
10
15
£0
25
30
35
40
TIME
(WIN)
10
15
20
35
TSO
7SC
780
710
715
750
74O
770
VOL
(ML)
4.5
4. 0
3.3
3.4
3.5
3.6
3.3
6.3
SCIL
(G)
810
800
73O
760
800
750
780
800
VCL
(ML)
SCO
230
800
SCO
£40
770
ScO
7SO
3.3
3.3
3.7
3.7
4.4
SulL.
730
740
SCO
ScO
730
730
730
760
VCL
(ML)
3.3
3.4
3.3
3.1
2.3
3.1
3. 1
3. 1
SCIL
(G)
4.1
4.2
4. 3
3.3
4
-3- A.
3.S
*. o
4. c-
1.05
1. 10
1.20
1.25
1. 30
1.3O
1.20
CHAR'
(IN)
1.15
1.15
1.20
1.20
1.20
1.20
1.20
1.20
CHART
READING
(IN)
0.35
0.35
0. 35
0.35
0.3S
0.35
0.35
0.33
CHART
READING
(IN)
1.20
1.20
1.20
1.20
1.20
1.20
i. 20
1. IS
60
-------
LLA< DA i c.
KLG 10 1932
KLG 10 1932
KLG 10 1932
KLG 10 1982
KLG101932
KLG 10 1932
KLG1019S2
KLG 10 1932
LOC DATE
KLG 102632
KLG 102632
KLG 102632
KLG 102632
KLG 102632
KLG 102632
KLG 102632
KLG 102632
LCC DATE
KLG 102632
KLG 102632
KLG 102632
KLG1C2632
KLG 102632
KLGicasaa
KLG 10£SS2
KLG 102632
LOC DATE
KLG 102632
KLG 102632
KLG 102632
KLG 102632
KLG1C£.3£
KLG 102632
KLG 102632
KLG 102632
P
U
N
3B
3B
33
3B
3B
33
3B
3B
R
U
N
4A
4A
4A
4A
4A
4A
4A
4A
R
U
N
4A
4A
4A
4A
4A
4A
4A
4A
R
U
N
4S
43
48
4B
43
43
*-3
43
P
L
0
T
4
4
4
4
4
4
4
4
P
L
C
T
3
3
3
3
3
3
3
3
P
L
0
T
4
4
4
4
4
4
4
4
P
L
0
T
3
3
3
i
3
3
2
3
BULK
DEN
1.4=
1.45
1.45
1.45
1.45
1.45
1.45
1.45
BULK
DEN
1.44
1.44
1.44
1.44
1.44
1.44
1.44
1.44
BULK
DEN
1.42
1 . 4£
1.42
1.42
1.42
1.43
1.42
1.42
BULK
DEN
1.44
1.44
1.44
1.44
1.44
1.44
1.44
1.44
TIME
(MIN)'
f » t
jU -Li-
10
IS
20
25
30.
35
40
TIME
-------
LGC DATE
U
N
P BULK
L DEN
0
T
KLG102632 46 4 1
KLG102632 48 4 1
KLG102632 48 4 1
KLG102632 48 4 1
KLG102632 48 4 1
KLG102632 48 4 1
KLG102632 48 4 1
KLG102632 48 4
,42
,42
,42
,42
42
..42
5
10
15
20
25
30
35
4O
VCL
(ML)
320
73G
SCO
730
310
300
730
SCI:
(G t
2.7
2. 4
2.4
£-3
2.6
2.5
2. 7
2.S
i. G5
i. OS
1. 1C
1. 10
1. 10
1,10
« ^.«*
i . C'w*
1.20
62
-------
APPENDIX B
CALCULATIONS AND CALIBRATIONS TO
DETERMINE RUNOFF
63
-------
TABLE Bl: Calculations and calibrations for determining flow
1) Conversion between chart height and notch height
Run #1
Chart ht. (in.)
1.55
1.80
1.85
1.90
Notch ht.(in.)
2.00
2.50
2.60
2.70
Run #2
Conversion factor
(m HjO/in. chart)
0.0328
0.0353
0.0357
0.0361
1.95 2.60 0.0339
Average conversion factor (from chart to notch) -6.84
2) Sample calculation for conversion between notch reading and
head (m) for Lenz's equation -
Notch 2.00 in _ 1.29 x 2.54 cm x 1 m m 0.0328 m H20/in. chart
Chart 1.55 in in 100 cm
Averaged with measurements taken previous to study;
Conversion « 0.0374
Head (m) - (Chart meas. in inches)(0.0374)
64
-------
TABLE B2: Calibration of weir to determine runoff
3
Sample comparison between predicted and measured runoff (cm /sec)
Date: 9-28-82 Run #1 Plot #3
Chart
Reading
(in)
1".
1.00
1.00
0.95
0.90
0.90
0.95
1.10
1.10
Chart
Reading
(ft)
0.20 ft
0.20
0.20
0.19
0.18
0.18
0.19
0.22
0.22
Notch
Height
(in)
1.38
1.38
1.30
1.23
1.23
1.30
1.50
1.50
Predicted
Runoff
(cm /sec)
Lenz's Eqn.
84.5
84.5
74.7
65.5
65.5
74.7
106.4
106.4
Measured
Runoff
(cm /sec)
see graph Fig. 4
155
155
130
110
110
130
205
205
Average difference between predicted and measured runoff for all
measurements - 1.79 or 1.8
65
-------
APPENDIX C
ELEVATION ESTIMATES FOR PLOTS 3 AND 4 (FT)
66
-------
Plot 3 Column no.
12345
Row
1 95.25 95.26 95.32 95.39 95.38
2 95.08 95.10 95.12 95.12 95.10
3 94.98 94.95 94.91 94.91 94.88
4 94.88 94.83 94.82 94.80 94.76
5 94.69 94.67 94.66 94.63 94.59
6 94.51 94.52 94.52 94.48 94.43
7 94.34 94.36 94.33 94.34 94.34
8 94.17 94.18 94.16 94.13 94.14
9 94.10 94.06 94.02 94.00 93.95
10 93.95 93.94 93.91 93.90 93.80
11 93.81 93.79 93.78 93.77 93.76
12 93.64 93.62 93.63 93.63 93.62
13 93.47 93.49 93.53 93.52 93.54
14 93.19 93.24 93.35 93.40 93.25
15 92.26 93.03 93.00 93.00 92.98
Plot 4
1 95.54 95.61 95.68 95.75 95.81
2 95.45 95.54 95.62 95.70 95.78
3 95.37 95.45 95.53 95.58 95.63
4 95.24 95.33 95.41 95.46 95.49
5 95.07 95.20 95.29 95.33 95.35
6 94.91 95.02 95.10 95.15 95.23
7 94.84 94.89 94.96 95.02 95.06
8 94.71 94.75 94.82 94.87 94.91
9 94,51 94.60 94.68 94.73 94.78
10 94.29 94.42 94.51 94.59 94.64
11 94.18 94.25 94.34 94.42 94.50
12 94.01 94.06 94.14 94.21 94.25
13 93.84 93.91 94.00 94.07 94.13
14 93.54 93.55 93.82 93.89 93.94
15 93.33 93.36 93.57 93.65 93.72
67
-------
APPENDIX D
RUNOFF ANALYSIS
68
-------
TABLE Dl : RUNOFF ANALYSIS - SET) I MEN V MEASURED BY RUNOFF SAMPLING
ON
vO
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LQCAT ION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
92882
92882
92382
92882
92882
92882
928sa
92382
DATE
92882
92882
92882
92882
92882
92832
35882
92882
DATE
100583
100585
1O05S2
i OO582
ioo582
loosaa
toosaa
1005:32
RUN
1A
1A
1A
1A
1A
1A
1A
IA
RUN
IE)
IB
IB
IB
IB
IB
tu
IB
RUM
2A
2A
2A
2A
2A
2A
3A
2A
. PLOT
3
3
3
3
3
3
3
3
PLOT
3
3
3
3
3
3
3
3
PLOT
3
3
3
3
3
3
3
3
TIME
MIN
5
10
15
20
2B
30
35
40
TIME
MIN
5
10
15
2O
25
3O
35
4O
TIME
MIN
5
10
15
20
25
3G
35
40
RUNOFF
CM**3/SEC
152.15
152.15
134.44
117.9-3
117.39
134.44
191.57
191.57
RUNOFF
CM**3/SEC
191.57
191.57
191.57
191.57
191.57
191.57
191.57
191.57
RUNOFF
CM**3/SEC
213.32
236.48
236.48
236.48
a 36. 48
236.*ia
236.48
236.48
CONC
MG/L
3OOOO.
16286.
13182.
11467.
10OOO.
10OOO.
8621.
8193.
core
MG/L
8116.
7284.
8272.
8903.
7143.
8378.
7333.
8516.
CONC
MG/L
1O7BO.
1141O.
19012.
9459.
9367.
9136.
922 1 .
atas.
ERGS
riM
O. O400
O.O216
O.O155
O.O119
0.0103
O.O117
O.O144
O.O137
CUMA
0. 1391
EROS
MM
O.0137
0.0122
O.O139
O.O149
0.0121
O.O140
O.0124
O.0144
CUMA
O. 1O76
EROS
MM
o.oaoo
O.O23t.
0. 0392
0.0146
O.O194
O.0189
O.O191
O.OUi9
CUMA
0. 17Ji7
-------
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
ioosa2
100582
10O5S2
1O0582
100582
100582
loosad
100582
DATE
101982
101382
1O1382
101382
101382
1O1382
101382
101332
DATE
101382
101382
101382
101332
1O13S2
101382
101382
1O1332
RUN
2B
2B
2B
2B
2B
2B
2B
2B
RUN
3A
3A
3A
3A
3A
3A
3A
3A
RUN
3B
3B
3B
3EJ
3b
3B
3U
38
PLOT
3
3
3
3
3
3
3
3
PLOT
3
3
3
3
3
3
3
3
PLOT
3
3
3
3
3
3
3
3
TIME
MIN
5
10
15
ao
25
30
35
40
TIME
MIN
5
10
15
20
25
30
35
40
TIME
MIN
5
1O
15
20
25
30
35
4O
RUNOFF
CM**3/SEC
261. 06
261.06
261 . O6
261.O6
261.O6
236.48
236.48
236.43
RUNOFF
CM**3/SEC
213.33
213.33
236.48
236.48
236. 48
236.48
236.48
236. 48
RUNOFF
CM**3/SEC
236.48
236.48
236.48
236.48
236.48
236.48
236.48
213.33
CQNC
MG/L
75OO.
8554.
12785.
361O.
aooo.
7467.
8667.
8182.
core
MG/L
5185.
4625.
4177.
4342.
4625.
4333.
5641.
5875.
CQNC
MG/L
5125.
5O6O.
'5375.
475O.
4*344.
4335.
5854.
5337 .
EROSA
MM
O.O169
O.0133
0. O283
O.O216
O.O180
O.O152
0.0177
O.0167
CUMA
0.1541
EROSA
MM
0.0035
O.OO85
O. O085
O.OO33
0.0034
0.01O1
0.0115
o.oieo
CUMA
0.0784
EROSA
MM
0.0104
0.01O3
O.O1 ID
O. OO37
O. OO33
0.0101
O.O1 11)
o.oioa
CUMA
O.O34L
-------
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
1O2682
1O2682
102682
102682
1O2682
102682
1O2682
102632
DATE
1O2682
1O2682
102682
1O2682
102682
102682
102682
102682
RUN
4A
4A
4A
4A
4A
4A
4A
4A
RUN
4B
4B
4B
4B
4B
4B
4B
4B
PLOT
3
3
3
3
3
3
3
3
PLOT
3
3
3
3
3
3
3
3
TIME
MIN
5
10
15
20
25
30
35
4O
TIME
MIN
5
10
15
20
25
3O
35
40
RUNOFF
CM* * 3/SEC
213.33
213.33
236. 48
236.48
213.33
213.33
213.33
236.48
RUNOFF
CM** 3/SEC
236.48
236. 48
236.48
236. 48
236.48
236.48
236. 48
236.48
CONC
MG/L
4375.
3837.
4353.
5714.
3951 .
4444.
3875.
494O.
CQNC
MG/L
5357.
494O.
5233.
5250.
425O..
4342.
5O62.
5438.
ERO3A
MM
o.ooai
O.OO71
O.0089
0.0117
O.OO73
0.0032
O.OO72
0.01G1
CUMA
O. 0687
ER03A
MM
O.011O
O.OIO1
O.OlOtJ
o.oioa
O.0087
O.OO39
0.01G4
0.0113
CUMA
AT I ON
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
92882
92882
92882
92882
92882
92882
92882
92832
RUN
1A
1A
1A
1A
1A
1A
1A
1A
PLOT
4
4
4
4
4
4
4
4
TIME
MIN
5
1O
IS
20
25
30
35
4O
RUNOFF
CM** 3/SEC
64.46
83.35
iia.oo
11B.OO
102.81
11S.OO
iia.oo
118,00
CONC
MG/L.
27O91 .
13770..
11756.
E154.
8613.
9194.
56. -JO.
sono.
ERfKiA
MM
G.0151
O.0105
O.O12O
G.OOU3
O.OO76
G.OO'j-t
O.OG5f
O.OO5J
CUMA
O.OV 37
-------
N)
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
92882
92882
92882
92882
92885
92882
32882
92882
DATE
100582
100582
1O0582
1OO582
1O05S2
1OO582
1O05S2
100582
DATE
1OO582
1O0582
100582
1OO582
1O0532
1OO532
1OO582
1O0582
RUN
IB
IB
IB
IB
IB
IB
IB
IB
RUN
2A
2A
2A
2A
2A
2A
2A
2A
RUN
2B
2B
2B
2B
2B
2B
2B
2B
PLCJT
4
4
4
4
4
4
4
4
PLOT
4
4
4
4
4
4
4
4
PLOT
4
4
4
4
4
4
4
4
TIME
MIN
5
10
15
2O
25
3O
35
40
TIME
MIN
5
10
15
20
25
3O
35
4O
TIME
MIN
5
10
15
20
25
3O
35
40
RUNOFF
CM«*3/SO:
88.35
1O2.81
102.81
11S.OO
118.0O
134.44
152.16
152.16
RUNOFF
CM«*3/SEC
134.44
134.44
134.44
134.44
134.44
134.44
134.44
1 34 . 44
RUNOFF
CM«*3/SEC
171.19
191.57
236. 48
236.48
261.06
237. 10
237. 10
236.43
CONC
MG/L
416O.
334O.
3803.
3636.
3538.
3243.
32OS.
3099.
CONC
MG/L
96G5.
7975.
13291.
6962.
6667.
641O.
5714.
5897.
CONC
MG/L.
5769.
5128.
SOOO.
4789.
4395.
48OO.
4459.
81 B2.
ERO3A
MM
0. OG32
0. O034
O. OO34
O.O037
O.O036
0. 0033
O.O042
G.OO41
CUMA
O.0293
EROSA
MM
O.O111
O.O092
0.0154
o.ooai
O.OO77
0.0074
O. OO66
O. G06S
CUMA
O. O725
EROSA
MM
O.OO85
0. 0035
O.G1O2
0. OO3S
O.O11O
0.0119
O.G11O
O.Olbir
G IMA
O.OoYC
-------
U)
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
LOCATION
KLU
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATEI
IO13S2
101982
1O1382
1O13B2
1O1382
101382
101982
101982
DATE
101382
101382
101382
1O13S2
1O1382
101982
101982
101982
DATE
1O2682
102682
102632
1O2682
1O2682
1O2682
102682
102632
RUN
3A
3A
3A
3A
3A
3A
3A
3A
RUN
3B
3B
3B
3B
3B
3B
3B
3B
RUN
4A
4A
4A
4A
4A
4A
4A
4A
PLOT
4
4
4
4
4
4
4
4
PLOT
4
4
4
4
4
. 4
4
4
PLOT
4
4
4
4
4
4
4
4
TIME
MIN
5
10
15
20
25
3O
35
4O
TIME
MIN
5
1O
15
2O
25
3O
35
40
TIME
MIN
5
1O
15
20
25
30
35
4O
RUNOFF
CM**3/SEC
134.44
134.44
134.44
134.44
134.44
134.44
134.44
134.44
RUNOFF
CM**3/SEC
152. 16
152. 16
152.16
152.16
152. 16
152. 16
152. 16
152.16
RUNOFF
CM**3/SEC
152.16
152. 16
171. 19
171.19
171.19
171. 19
171. 13
171.19
CONG
MG/L
481O.
4595.
4125.
378O.
3718.
3974.
3374.
4O79.
CONC
MG/L
4342.
4250.
4286.
4507.
4783.
4868.
5135.
5256.
CONC
MG/L
3133.
2387.
3O12.
2973.
305t>.
3231.
31533.
325O.
ER03A
MM
0.0056
O.OO53
O.OO48
O.OO44
0.0043
O. 0046
O.0046
0.0047
CUMA
O.0383
EROSA
MM
O. 0057
O.OO56
O.0056
O.0053
0.0063
O.OO64
0.0067
O.OOG'J
CUMA
O.0491
EROSA
MM
0.004?
O.OO4O
O.O045
O.OO45
O.OO46
O.OGr;»0
O.004C
O.OOVJ
CIJ-Vi
O.O3C3
-------
AT I ON
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
DATE
1O2682
102682
102682
102682
1O2682
102682
1O2682
102682
RUN
4B
4B
4B
4B
4B
4B
4B
4B
PLOT
4
4
4
4
4
4
4
4
TIME
MIN
5
10
15
2O
25
3O
35
4O
RUNOFF
CM**3/SEC
171.13
171.13
191 . 57
131.57
191.57
131.57
236.48
236.48
CONG
MG/L
3233.
3O77.
30OO.
2-343.
321O.
3125.
3465.
3231.
F.ROSA
MM
O.OO50
O.OO46
O.OO51
O.OO5O
O.OO54
O.OOC3
O.0072
O.COG3
CUMA
O.O444
-------
APPENDIX E
EROSION PIN MEASUREMENTS
75
-------
1 ABLE El : INITIAL EROSION PIN MEASUREMENTS AND EROSION/DEPOSITION
MEASURED IN RUNS 1 - 4
EROSION PIN DATA FROM RAINFALL SIMULATOR - INITIAL MEASUREMENTS
LGC DATE
PLOf 1
KLGo-?>2ia2
KLGO'^aiaa
KLG032182
KLG032182
KLG032182
KLGO32182
KLGG'32ia2
PLOT 2
KLGO92182
KLGO32182
KLG 0-321,3^
KLG032182
KLGO9ai8c»
KLGO92182
KLGO'32182
R
u
N
o
0
0
o
0
0
0
o
o
o
o
o
o
o
PIN MEAS
ID (MM)
301112.
3O2316.
304118.
305316.
307121.
308313.
310117.
4O1116.
4O23 9.
404116.
4053 9.
4O71 8.
4OS3 3.
4101.17.
29
00
60
14
04
68
43
34
66
04
96
91
44
04
PIN MEAS
ID (MM)
3O1216.
3O2415.
304213.
305425.
3O7S21.
308422.
310217.
4012 3.
402413.
4042 8.
4O5414.
4072 3.
4O84 6.
410216.
19
7O
18
08
80
38
74
26
32
26
41
83
52
88
PIN MEAS
ID (MM)
3O13 9.
3O31 9.
30431 1 .
3O6121.
3O7317.
309119.
31O317.
401317.
4O311S.
4043 7.
4O61 9.
4O73 9.
4O91 9.
41O323.
16
58
02
42
01
59
52
12
95
93
55
22
52
34
P IN MEAS
ID (MM)
3O1417.
3O3216.
3O4414.
3O6218.
307414.
3O9218.
31O422.
401418.
4O3212.
4O4411.
406210.
4O74 6.
4O32 8.
41O41O.
20
28
O7
20
28
44
17
95
09
60
65
82
72
22
PIN MEAS
ID (MM)
3O21 13. 52
30331 4. O9
3051 1 1 . 6O
3O6318.87
308112.63
3O9317.66
402114.19
4O33 9.86
4O5114.95
4O63 8.16
4OB11O.3O
403310.28
PIN MEAS
ID (MM)
3O2223. 54
303418.56
305220. 30
3O642O. 74
308212.84
309414.68
4O22 1O.54
4O34 3.32
4O5211.72
406414.43
4O8211.SO
4034 5.50
KEL'r: PIN ID
3011
P RP
L OI
O UN
T
N
0
-------
TABLE E2: EROSION/DEPOSITION MEASURED 3V EROSION ?INS 'MM'.
LOCATION DATE RUN*N PIN READING(MM) EPuS/DE?(MM
PLOT ROW PIN
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
Ki /"
U.19
KLG
KLG
KLG
KLG
'32332
92832
'32222
32382
32282
92882
92232
92282
32282
32882
32882
32882
92882
32882
92882
32882
92882
92882
92282
92882
32282
32222
92282
92222
92282
32282
' '32832
32222
92222
92222
92232
92232
92SS2
92232
92322
92382
32332
92282
92222
92282
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1 .
1
1
1
1
1
1
1
1
1
1
1
1
3 1
3 1
3 1
3 1
3 2
3 2
3 2
3 2
3 3
3 3
3 3
3 3
3 4
3 4
3 4
3 4
3 5
3 5
3 5
3 5
3 6
3 6
3 6
3 6
3 7
3 7
3 7
3 7
3 3
3 2
3 3
3 3
3 9
3 9
3 9
3 9
310
310
310
310
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
10.22
16.20
10.40
2S. 43
39. SE
£3.34
13.19
24.38
20.00
17.20
17.90
22. 14
22.16
16.08
11.60
19.32
9.72
23.52
14.06
22.50
22.52
14.53
20.44
23.92
32. AS
22.05
26.04
17.34
15.25
17.52
22.30
3O.34-
21.36
17.26
21.70
14.38
22.38
20.11
16. 57
22.34
2.07
-0.01
-1.24
- 1 1 . 22
-20. 30
-5.30
-2. 19
-9. 13
-10.42
-0.92
-3. SI
-9.52
-3.56
3.10
-0.53
-5.25
1.32
-3.23
2. OS
2.52
-1.10
3.67
-1.57
-3.24
-11.44
-0.25
-9.03
-3.56
-2.57
-4. £3
-3. 12
-3. **6
-1.77
1. IS
-4,04
-G.2O
-5.49
-2.37
0.95
-0. 17
KLG
KLG
KLG
32222
92332
92332
1
1
1
411
4 1 2
4 1 3
AVE = -3.32
16.49 0.45
16.62 -7.36
17.63 -0.56
77
-------
LOCATION DATE RUN*N FIN READING tMft; EPCS-'C:"
16.17 E.7S
19.91 -S.7E
11.74 -1.20
9.43 0.23
15.93 -£.01
19.46 -0.51
14.35 -2.76
13.91 -9.05
12. 53 -2.66
19.31 -3.27
10.02 -1.76
9.63 -1.75
14.54 -2.94
16.50 -1.55
14.61 -2.S9
11.42 -1.46
18.20 -3.79
9.96 -O.41
10. SO 0.15
9,34 -1.63
15.70 -1.21
10.44 -1.53
11.36 -1.53
4.50 4.72
9.5O -2. 63
13.35 -3.05
12.02 -0.22
10.17 -1.73
7.32 -1.3O
13.13 -3.61
13.50 -4,7H
11.93 -1.65
8.74 -3.24
17.29 -0.25
15.20 1.63
25.56 -2.22
13.27 -3.05
AVE = -1.33
KLG 10O5S2 2 311 5. SO 4.72
KLG 100582 2 312 14.94 1.26
KLG 1OOSS2 2 313 11.73 -1.33
KLG 100582 2 314 29.67 -1.19
KLG 1OO5S2 2 321 SO.05 -10.23
KLG 10O5S2 2 322 2S.43 0.36
KLG 10O532 2 323 20.36 -2.17
KLG JOOEL32 2 324 27.14 -2.26
KLG 10O532 2 331 25.31 -5.31
78
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
92SS2
92SS2
92S82
92382
92S82
92332
32882
'32832
92882
32882
32S82
32882
92882
32882
92382
92882
92882
92882
32882
32882
92882
92882
92882
32882
92882
92882
92882
92882
32882
92382
32832
32882
92S32
92332
92332
92382
92832
4
I
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4 1
4 2
4 2
4 2
4 2
4 3
4 3
4 3
4 3
4 4
4 4
4 4
4 4
4 5
4 5
4 5
4 5
4 6
4 6
4 6
4 6
4 7
4 7
4 7
4 7
4 3
4 3
4 3
4 3
4 9
4 9
4 9
4 9
410
410
410
410
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
-------
LOCATION DATE PUN*N PIN READING ('Sw> EPGS/CEP-T-
KLG 10O5S£ £ 3 3 £ 16.73 0.-*£
KLG 10C5S£ £ 333 16.02 1.33
KLG 1OOS8£ £ 334 £6.IS 1.96
KLG 1OO582 £ 341 ££.30 -0.7*
KLG 10O58£ £ 342 12.91 3.17
KLG 10O58£ £ 343 12.02 -0.42
KLG 10O5S£ £ 344 £0.3£ -1.00
KLG 1OO53£ £ 351 16.44 -6.72
KLG 10O582 2 3 5 £ 13.00 5.53
KLG 10058£ £ 353 10.50 3.56
KLG 10O5S£ £ 354 ££.36 0.14
KLG 10O5S£ £ 361 20.31 £.£1
KLG 10O5S2 £ 3 6 £ 11.£3 3.£5
KLG 100582 2 363 26.73 -6.34
KLG 10O582 £ 36* £4.04 -0.06
KLG 10OSS2 £ 371 36.04 -3.56
KLG 10O58£ £ 3 7 £ 26.2C -4.15
KLG 10OS3£ £ 373 £5.64 0.4O
KLG 10058£ £ 374 17.13 0.66
KLG 10O5S2 £ 331 16.'10 -0.35
KLG 100582 £ 382 17. £3 0.29
KLG 10O582 2 333 £0.34 1.96
KLG 10O582 £ 334 31. £3 -0.45
KLG 10Q5S£ £ 331 20.36 0. SO
KLG 10O532 £ 3 3 £ 11.67 5.53
KLG 10O58£ £ 393 20'. 60 1.10
KLG 10O58E £ 3 3 * 14.30 0.05
KLG 1005S£ £ 310 1 £5.6* -£.66
KLG 10053£ £ 310 £ £1.60 -1.43
KLG 10O58E £ 310 3 16.10 0.47
KLG 10053£ £ 310 4 43.34 -27.OC
AVE = -0.35
KLG 10O5S2 £ 411 13.36 £.63
KLG 1QQ5S2 £ 415 16.3O O. 3£
KLG 10O53£ £ 413 £3.14 -10.46
KLG 10Q53E £ * 1 4 13.03 -1.91
KLG 10O53£ £ 4 £ 1 £4.36 -*.95
KLG 10058£ £ 4 £ £ 12.93 -1.13
KLG 10O53£ £ 4 £ 3 11. £5 -1.32
KLG 1QOSS2 £ 4 £ 4 13.33 -4.0O
KLG 10OS3£ £ 431 £O.73 -1.32
KLG 10O58E £ 4 3 £ 11.79 3.06
KLG 1O053£ £ 433 ££.36 -3.95
KLG 10O53£ £ 434 16.00 -3.42
KLG 10O5S2 £ 441 £1.33 -£.07
KLG 10O5SE £ 4 4 £ 10. £7 -0.25
KLG 10058£ 2 443 9.54 0.1*
KLG 10053E £ 444 15.60 -1.06
KLG 100SSE £ 4 5 i 19.33 -2.33
79
-------
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
10O53£
1OO52£
100S3£
1QO58£
10O5S£
100532
100582
100582
100582
100582
100582
100532
100582
10O582
10O532
10O532
100532
10O582
100582
100532
100582
10O582
100532
£
£
£
£
£
£
£
£
£
£
£
£
2
£
2
2
£
£
£
£
2
£
£
4 5
4 5
4 5
4 6
4 6
4 6
4 6
4 7
4 7
4 7
4 7
EnCS/DEF
18. A2 -2. SI
10.32 0.6C
19.90 -1.70
9.SS O.OS
16.10 -5,60
7.71 £.13
££.86 -7.16
10.45 -0.01
12.96 -1.60
5.9C -1.4C
12.34 -£.34
15.70 -£.35
12.37 -0.35
11.08 -0.91
5.60 £.£Z
£0.££ -7.09
16.14 -£.64
11.69 0.24
12.2S -3.54
18.02 -0.73
14.16 1.04
£6.15 -0.59
12.67 0.6O
AV'E = -1.71
KLG 101982 3 311 4.£6 1.24
KLG 101982 3 3 1 £ 18.94 -4.00
KLG 101982 3 313 11.20 0.58
KLG 101982 3 314 £9.63 -0.01
KLG 101982 3 3 £ i 53.£6 -3.21
KLG 101932 3 3 £ £ 3C.37 -£.39
KLG 101982 3 323 27.28 -6.92
KLG 1O198S 3 3 £ 4 £7.7£ -C.53
KLG 101982 3 331 30.98 -5.67
KLG 101982 3 3 3 £ ££.53 -5.3O
KLG 101982 3 333 ££. 34 -6.32
KLG 101982 3 334 36.OO -9.32
KLG 1O1982 3 341 £7.0£ -4.12
KLG 1O19S2 3 342 16.££ -3.31
KLG 101982 3 343 19.73 -7.76
KLG 10198£ 3 344 £5.73 -5.46
KLG 1019S£ 3 351 20.5£ -4.03
KLG 101982 3 351 £0.5£ -4.03
KLG 101982 3 3 5 £ 17. OO 1. OO
KLG 101982 3 353 12.59 -£.09
KLG 10198£ 3 354 ££.06 0.3O
KLG 1019S£ 3 361 24.21 -3.9O
KLG 101982 3 3 6 £ 17.43 -6.20
KLG 101982 3 363 £7.43 -0.65
KLG 101982 3 364 £7.36 -3.32
KLG 101982 3 371 41.22 -5.13
KLG 101982 3 3 7 £ 29.27 -3.07
80
-------
T8
-
SE'T
3-* '0
6«7'0
TS'l
95 '1
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0*7*0
91'E
09 "T
t-0
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96 '0
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61
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93
61
EE
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TI
T
K
£
3
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«7
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3
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<7
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3
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-------
Z8
t.O'T- = 3AV
95 '+ *6'9£
E*'0- 6*r PiT
*=."*.- OE'ES
59 '0 &V95
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65'5- ST't72
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-------
APPENDIX F
CROSS SECTION MEASUREMENTS
84
-------
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LOC DATL
KLG1004Bd
KLG1OO432
KLG1GG432
IU.G1OO432
KL.G1OO432
KLG1OO482
KLG1GO482
KLG10O482
KLG 100432
KLG1OO482
KLG1OG432
KLGlOG4a2
KLG1OO432
KLGlOO I
49O V
4-:> IB
491':)
MEAS
(IN)
7. 2O
7.00
7.75
8.07
7.8O
1111
4.60
4.90
4.3O
5. 2O
5. MO
5. 50
5.70
5.25
5.3O
5.30
5.34
5.92
4.7O
5. 13
5.55
1 11 I
4.75
5.O4
5.45
4 , 4O
4.ao
5. Li)
5. 30
5 . Gf:j
5.OO
4.c:G
'+ . 2n
4.OO
llll
-------
r-ti_
ECTION DATA FROM RUNS 1 - 4, RIGH
uuCATIGN
DAT
RUN*N!
PIN
READING*TN>
PLOT ROW LOG
KLG
KLG
Kl_G
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
Ki_G
KLG
KLG
KLG
KLG
KLG
KLG
Kl_G
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
102732
102732
102732
102732
102732
102732
1027S2
1O27S2
1027S2
102732
102782
102732
102732
102732
102732
102732
102732
102732
102732
102732
102782
102732
102732
102732
102732
1O2732
102732
102732
102732
102732
1O£7S£
102732
102732
102732
102732
102732
102732
102732
1O2732
102732
102732
102732
102732
102732
105732
10E732
2
2
2
2
2
2
2
2
3.
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
£
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 i
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 1
3 2
3 2
3 2
3 2
3 2
3 2
3 2
3 2
3 2
3 2
3 2
3 S
3 2
3 2
3 a
3 2
3 2
3 5
3 2
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
1
2
3
4
5
6
-T
i
3
9
10
11
12
13
14
15
16
17
13
13
1
2
3
4
5
6
7
I
3
3
10
11
1£
13
14
15
16
17
13
13
1
2
3
4
5
6
-r
3
3.12
3. IS
8.13
S.13
s. as.
s. as
3.23
3.38
3.45
3.50
3.50
3. 66
3.76
9.05
3.35
3.13
3. 16
3.1O
11.11
7.15
7.32
7.26
7.50
7.62
7.31
S.OS
3.30
3.33
3.45
3.53
3.32
3.75
3.30
3.10
3. 10
3. 11
10
11
6.33
6.33
7.25
7.30
7.37
7.4O
7.53
7.3H
3
11
-0. 13
-O.OS
0.05
-0.3E
-0.4£
-0.62
-0.35
-l.OS
-1.05
-1.26
-1.31
-1.64
-1.45
-1.73
-1.76
-1.70
0.0
1.55
1.43
1.33
l.OS
0.33
-0.01
0.25
-0.05
-O.OS
-0.75
-1.03
-i.57
-1.57
-1.75
-5.00
-2. 10
-5. 16
-2.20
0.0
-0.33
0.47
0.30
0.35
0. 13
0.20
-0.03
-C. 33
95
-------
L.GCATIGN D~7E RUN*N PIN READING! IN; Er-05, DEF
KLG 1GE7SE £ 339 7.7S -0.£6
KLG 102732. £ 3 3 10 3.02 -0,70
Kl_G 1O2732 £ 3 3 11 7.90 -0,60
KLG 10E7S2 2 3 3 12 7.92 -O.6E
K!_G 102732 2 3 3 13 7.'34 -0.39
KLG 1027S2 2 3 3 14 3.00 -1.30
KLG 102732 2 3 3 15 8.GO -1.35
KLG 102732 2 3 3 16 '.95 -1.45
KLG 102782 2 3 3 17 7.93 -1.53
KLG 102732 2 3 3 13 7.75 -1.35
KLG 102732 2 3 3 19 11.11 0.0
KLG 102732 2 3 3 20 11.11 0.0
KLG 1027S2 2 341 6.32 -0.3S
KLG 102732 2 342 6.75 -0.55
KLG 102732 2 343 5.73 -0.43
KLG 102732 2 344 6.63 -0.43
KLG 102732 2 345 6.65 -0.45
KLG 102732 2 346 6.65 -0.4O
KLG 102732 2 347 6.65 -0.45
KLG 102732 2 343 6.75 -0.55
KLG 102782 2 349 6.94 -O.79
KLG 102732 2 3 4 10 6.94 -0.79
KLG 102732 2 3 4 11 £..71 -0.51
KLG 102782 2 3 4 12 7.17 -0.94
KLG 102732 2 3 4 13 7.2O -0.9O
KLG 102782 2 3 4 14 7.24 -0.94
KLG 1OE7S2 £ 3 4 15 7.£5 -O.93
KLG 102732 £ 34 16 7.21 -0.31
Kl_G 10E7S£ £ 3 4 17 7. EC -0.30
KLG 10E7S2 2 3 4 IS 7.15 -0.70
KLG 10E732 2 3 4 19 7.0O -0.SO
KLG 102732 2 351 7.80 0.45
KLG 102782 £ 3 5 £ 7.7S O. 53
KLG 102782 2 353 7.70 0.55
KLG 102732 2 354 7.63 0.57
KLG 102782 2 355 7.74 0.51
KLG 1O278E 2 356 7.7E 0.31
KLG 102732 £ 357 7.67 C.3S
KLG 1OE73E 2 353 7.73 0.15
KLG 10E732 2 359 7.76 0. IE
KLG 10£7S£ £ 3 5 10 7.31 -0.01
KLG 10E7SE £ 3 5 11 7.90 -0.10
KLG 10£7S2 2 3 5 12 7.37 -O.O7
KLG 102732 2 3 5 13 7.9O -0.15
KLG 102732 2 3 5 14 7.93 -0.2O
KLG 102732 2 3 5 15 8.00 -O.E7
KLG 102732 2 3 5 16 3.0O -0.4O
KLG 1O£78£ £ 3 5 17 3.0O -0.40
KLG 10E73E £ 3 5 13 8.03 -0.53
KLG 10E7SE £ 3 5 13 8.0E -0.52
KLG 10E73S £ 361 7.53 -O.E3
KLG 10E73E 2 3 6 £ 7.53 -0.IS
KLG 1027S2 £ 363 7.55 -G.2O
96
-------
LGC AT I uN DATE P. LN*N PIN R EADING 11 r> E? G3.C E?
7,SO -G,£G
7. o£ -0. £7
7.SO -0.13
7.50 -0.15
7,43 -0,03
7.50 -O.OS
7.46 -0.06
7.43 0.02
7.47 -O.G7
7.43 0.07
7.50 O.G£
7.<*4 0.IS
7.33 0.17
7.4A 0.03
7.55 -0-02
7.47 -0.03
11.11 0.0
7.'30 -C.20
7.98 -0.33
7.35 -0.35
7.'30 -0.3O
7.93 -0.35
7.39 -O.34
7.35 -O.30
7.23 -0.£3
7.SS -0.3S
7.33 -0.37
7. S3 -0.33
7.3£ -0.27
7,33 -0.3S
7.7G -0.36
7.75 -0.40
7.7£ -0.32
7.74 -0.43
7.65 -0.40
11.11 O.O
11.11 0.0
6.62 1.31
S.7C 1.30
6.75 0.97
6.30 0.9C
6.36 0.74
6.97 0.43
6.38 0.35
7.0O O.£0
7.05 0.03
7.20 -0.23
7.25 -0.17
7.45 -0.47
7.-42 -0.34
7.51 -0.5.1
97
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
102732
1C£7S£
10£73£
1 02782
103732
102782
102782
102732
102782
102732
102732
102732
102732
102732
102782
102782
102732
102782
102782
102732
102732
1O2782
102732
102732
102732
102782
102732
102732
102732
102732
102732
102732
102732
102782
102782
102782
10£78£
10£73£
10£73£
10£7S£
10£78£
102732
102732
102732
10£73£
10£732
10£73£
10£73£
102732
10£7S£
10E7S2
£
£
£
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£
2
£
£
£
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2
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2
£
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2
2
2
£
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2
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2
£
£
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£
£
£
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£
£
£
£
£
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£
£
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 6
3 S
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 7
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
3 3
4
5
6
7
3
3
10
11
12
13
14
15
16
17
18
13
20
1
2
3
4
5
6
7
8
3
10
11
12
13
14
15
16
17
13
13
£0
1
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3
4-
5
6
7
3
3
10
11
12
13
14
-------
86
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6.3 "C
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95 '0-
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LOCATION DATE RLJN*N PIN READ IMG ( IM
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
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KLG
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KLG
KLG
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102732
102732
102732
102732
102732
102732
102732
102782
102782
102782
102732
102732
102732
102732
102732
102732
102732
102732
102782
102732
102732
102732
102732
102782
102782
102782
102732
102732
102732
102732
102732
102732
102732
102782
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102782
102732
102732
102732
1027S2
102732
102732
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
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2
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2
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2
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2
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2
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4
4
4
4
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4
4
4
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4
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4
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4
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4
4
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4
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4
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4
4
5
5
5
5
5
5
5
5
5
5
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5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
7
7
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7
7
7
7
7
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17
13
19
1
2
3
4
5
6
7
3
9
10
11
12
13
14
15
16
17
IS
19
20
1
2
3
4
5
6
7
3
9
10
11
12
13
14
15
16
17
13
19
1
2
3
4
5
6
7
3
9
5.31 0.63
11.11 0,0
* i * i -*\ ^
i j.. j. I -
6.03 -1.32
6.07 -i.SS
6.02 -1.G2
5.36 -0,91
5.95 -C.32
5.33 -C,73
5.30 -0,57
5.63 -0.4S
5.60 -0.40
5.5C -0.1S
S.-tO O.C
5.42 0.03
5.44 0.11
5.30 0.25
5.23 0.35
5.35 0.25
5.20 0.15
5. SO 0.07
11.11 0.0
11.11 0.0
5.47 0.53
5.52 0.13
5.43 0.10
5.37 -0.27
5*35 -0.60
5.33 -0.73
5.27 -0.42
5.08 -0.23
5.10 -0.17
5.03 -0.15
5.OO 0.04
5.03 O.OE
5.02 0.16
5.17 0.13
5.20 0.13
5.13 0.12
5.13 0.27
5.2O 0.25
11.11 0.0
5.05 -0.55
5.0O -0.45
4.90 -0.35
4.90 -0.50
4.S7 -0.37
4.37 -0.32
4.80 -0.55
4.S3 -0.2S
4.35 -0.10
100
-------
ro
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I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I II I III II
O O O O O O O O O O O O O O O O O G O O O O O O O O O O G O O O O o O O Ci O O O O O O ') O I'.'' ' '' '"' i" ' . ';'<
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-------
VAEJLL Fb: CWCfciU btCTION DATA - LEFT SHOT ION - INITIAL MEASUREMENTS (IN)
o
NJ
LOG DATE R PIN MEAS PIN MEAS
U ID (IN) ID (IN)
N
KEY: FIN ID 3101
PIN MEAS PIN MEAS PIN MEAS PIN MEAS
ID (IN) ID (IN) ID (IN) ID (IN)
KLG10O482 1
KLG1OG482 1
KLG1OO4&2 1
KLGlOG^b'2 1
KLG1GG482
KLG1OO4S2 1
KLG1GG4&2
KLG1GG4J2 i
KLGlOO4b2
Kl_GlOGHb2
KLGlGO4b2
KLGIOG432
KLG1OO482
KLG1OOH82
KLG!OG4b2
KLGlOG4b2
KLGiOG4t»2 1
KLG1GG482 1
KLGlGG^bc; 1
KLG1GG4J2 1
KLG10OHU2
PR L
LQ Q
out c
T A
T
I
Q
1 3101
1 3107
1 3113
1 3119
1 3206
1 3212
i 32 1 a
i 33Gb
1 3311
1 3J1 f
1 34O3
1 3^+09
i 3415
1 3b02
1 3bOb
1 3Li 1 4
1 3601
1 3607
1 3613
1 3613
1 J/Ob
1 3/11
1 3/17
N
7.05
7.70
7.65
7.40
7. 15
7.25
7.60
t.. 15
6.3O
6.4O
s.ao
6.3O
6.30
6. 13
6.65
6.50
6. 85
6.9O
6.92
6. 7O
a. 25
a. 40
/. 48
31O2
31O8
3114
32O1
32O7
3213
3219
3306
3312
3318
34O4
34 1O
3416
3503
35O9
3bl5
3602
36OS
3614
3620
3706
3712
3718
7.OO
7.70
7.65
6.95
7.10
7.3O
7.55
6.20
6.30
6.40
6. OS
6.35
6.25
6.3O
6.65
6. SO
7.00
6.90
6.85
6. 8O
a. ss
8.20
7.43
31O3
31O9
3115
32O2
3208
3214
3301
33O7
3313
3319
3405
3411
3417
3504
35 1O
3S16
3603
36O9
3615
3701
3707
3713
3719
7.30
7.60
7.65
7. 2O
7.10
7.4O
5. SO
6. 15
6.3O
6. SO
6. OS
6. 2O
6. 30
6.45
6.63
6. 50
7. 15
6.9O
6.95
7.68
8. SO
8.05
7.45
31O4
31 1O
3116
32O3
3209
3215
3302
33O8
3314
3320
3406
3412
3418
3505
3511
3517
3604
3610
3616
37O2
3700
3714
3720
7.3O
7.70
7. SO
7.18
7.18
7.40
5.70
6.3O
6.30
6.50
6.15
6.25
6.25
6. 50
6.65
6.38
6.90
6.30
6.9O
8.O5
a. so
7.35
7.35
31OS
3111
3117
3204
3210
3216
33O3
3309
3315
3401
3407
3413
3413
35O6
3512
3518
3605
3611
3617
3703
3703
3? 15
3301
7.3O
7.7O
7.33
7.15
7.25
7.50
5.65
6.35
6.3O
5.SS
6.25
6.35
6.25
6.45
6.68
6. 45
6.90
6. .3 3
6.83
8. 05
8.55
7 . 70
6.93
31O6
3112
3113
3205
3211
3217
3304
3310
3316
34O2
34O8
3414
3501.
3SO7
351 3
3519
3606
3612
3618
37O4
37 tO
3716
3~d(jcl
7.55
7.65
7.4O
7.25
7.3O
7.6O
6.00
6. 3b
6.35
5.65
6. 25
6.35
6.OG
6 . 6O
6.64
mi
6. as
6.9O
fc>. as
a. to
a.4o
7.63
6 . 3 .1
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-------
TA3LE F6: CROSS SECTION DATA FROM RUNS 1 - 4, !_£FT SEC'
LOCATION DATE RUN*1M PIN READING * IN>
PLOT ROW LOG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
1027S2
1027S2
102732
1027S2
1027S2
102732
1027S2
10£782
1027S2
1027S2
1027S2
102732
1027S2
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102782
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3 1 1
312
313
314
315
316
317
312
3 1 3
3 1 10
3 1 11
3 1 12
3 1 13
3 1 14
3 1 15
3 1 16
3 1 17
3 1 13
3 1 13
321
322
323
324
325
326
327
323
323
3 2 10
3 2 11
3 2 12
3 2 13
3 2 14
3 2 15
3 2 16
3 2 17
3 2 IS
3 2 13
3 3 1
332
333
334
335
336
337
333
7.48 -O.H2
7.40 -O.tO
7.5. -0.28
7.64 -0.34
7.65 -0.35
7.43 0.07
7.54 0.16
7.50 0,£0
7.47 0.13
7.32 0.35
7.4O 0.3O
7.23 0.3T
7.27 0.3S
7.27 0.3S
7.25 0.4O
7.20 0.3C
7.15 0.23
7.16 0.24
7.23 0.17
2.10 -1.15
S.OO -O.SO
7.'30 -0.75
7.6O -0.-+5
7.7S -O.53
7.60 -0.45
7.54 -0.4-+
7.55 -0.45
7.53 -G.-+O
7.55 -0.30
7.36 -0.86
3.14 -0.39
3.2* -0.94
3.16 -0.76
3.15 -0.75
3. OS -0.53
3.10 -O.SO
7.65 -0.05
7.55 0.0
6.35 -1.35
6.7S -l.OS
6.76 -1.11
6.72 -0.73
6. 73 -0. 53
6.70 -0.50
6.76 -0.61
6.30 -0.5G
104
-------
SOT
50 '0- -05*1
05 -0- 02 '1
*r*r'
-------
C*7E RUN*N FIN READ ING 'IN' EPOS. C-E
6.73 0.12
7.IS -0.22
7.15 -0.30
7.10 -C.20
7.13 -0.23
7.13 -0.23
6.95 -0.05
7.O4 -O.li
6.35 -0.05
6.97 -0.05
6.98 -0.13
6.98 -0.03
6.35 -0.05
7.05 -0.22
6.92 -0.13
6.37 -0.27
6.75 0,05
7.2G 0.48
7.20 0.85
7.34- 0.71
7.3^9 0.71
7.48 0.77
7.59 0.36
7.65 0.35
7.75 0.75
7.S5 0.70
7.9O 0.50
7.38 0.42
7.93 0.21
7.35 0.10
7.89 -0.04
7.9C -0.2O
7.'30 -0.27
7.96 -O.43
7.94 -0.46
7,95 -0.5O
7.30 -0.45
6.20 0.73
6.31 0.62
6.30 0.65
6.34 0.54
6.43 0.37
6.52 0.33
6.55 0.3O
6.60 0.10
6.70 -0.15
6.74 -O.G3
6.70 -0.15
6.73 -0.23
6.82 -0.33
6.50 -0.42
106
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KL.G
102732
1027S2
1027S2
102732
102732
1027S2
102732
102732
102732
102732
102782
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
102732
1O2732
102732
1O2732
102732
102782
102732
102732
102732
102732
1O27S2
102732
102732
102732
1O2732
1O2732
102732
102732
102732
102732
102782
102732
102732
102732
102732
102782
102732
102782
1C27S2
1G2732
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
364
365
366
367
363
363
3 6 10
3 6 11
3 6 12
3 6 13
3 6 14
3 6 15
3 6 16
3 6 17
3 6 13
3 6 19
3 6 2O
371
372
373
374
375
376
377
3'73
379
3 7 10
3 7 11
3 7 12
3 7 13
3 7 14
3 7 15
3 7 16
3 7 17
3 7 13
3 7 13
3 7 20
3 3 1
382
383
384
335
386
387
333
3 8 3
3 a 10
3 3 11
3 8 12
3 3 13
3 3 14
-------
LOCATION DATE RUN*N PIN READING < IN;
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
102732
1O2782
1O2722
103722
102732
102782
102732
102732
102782
1027S2
102782
102782
102782
102782
1O2782
102782
102782
102782
102782
102782
102782
102782
102782
102782
102782
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3 3 15
3 3 16
3 8 17
3 3 13
3 8 19
391
392
393
394
395
3 9 6
397
393
3 9 9
3 9 1O
3 9 11
3 9 12
3 9 13
3 9 14
3 9 15
3 9 16
3 9 17
3 9 13
3 9 19
3 9 20
KLG
KLG
KLG
KLsj
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
KLG
102782
102782
102732
102732
102782
102782
102782
102782
1027S2
102782
102732
1027S2
102782
102782
102782
102782
102782
102732
102782
102732
102782
102732
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
4
4
4
4
4
x»
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
A
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
2
3
4
5
6
7
3
9
10
11
12
13
IA
15
16
17
18
19
1
2
3
6.36 -0.5':.
6.90 -0.72
6,90 -0.77
6. 90 -0,75
6.34 -0.3-
7.90 1.00
7.9O 1.00
7.90 1.05
3.02 0.96
3.22 0.73
3.27 0.3S
3.45 O.E3
S.50 O.C
8.50 O.C
3.47 -O.GA
3.65 -0.33
3.6S -0.<*S
3.73 -0,63
3.75 -0.65
3.63 -0.75
S.76 -0.96
3.90 -1,17
9.00 -1.35
3.33 -1.23
3.75 ~1>£f
= -0.2'd
3.53 0.97
3.44 1.06
3.56 0.99
3.60 0.95
3 . 55 0 . 30
3.75 0.65
3.30 Q.^S
3.90 0.10
S.S5 0.23
3.9S -0.25
9.05 -C.35
S.95 -0.45
3.90 -0.35
3.39 -0.54
3.35 -0.55
3.35 -0.55
3. 37 0. 72
9.03 -0.35
3.90 -0.37
3.23 1.3E
3.^2 1.61
3.50 1.35
107
-------
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TECHNICAL REPORT DATA
(f lease read {uuruceions on rite reverse before completing)
1. REPORT NO. 2.
4. TITUS AND SUBTITLE
Predicting Minesoil Erosion Potential
7. AUTHOH(S)
D. L. Jones, R. M. Khanbilvardi, and A. S. Rogowski
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Northeast Watershed Research Center
USDA-ARS, 110 Research Building A
University Park, Pennsylvania 16802
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Environmental Processes & Effects Research
Office df'-Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
3. RECIPIENT'S ACCESSION-NO.
S. REPORT DATE
a. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT NO.
7
10. PROGRAM SLSM6NT NO.
11. CONTRACT/GRANT NO.
EPA-IAG-D5-E763
13. TYPg OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
EPA/ 600/16
18. SUPPLEMENTARY NOTES
This project is part of the EPA-planned and coordinated Federal Interagency
Energy/Environment R&D Program.
IB. ABSTRACT
Two experimental plots were instrumented with erosion pins to study the cor-
respondence between point erosion and erosion over an area on strip mine soil. Using
a rotating boom rainfall simulator, data were collected by sampling the runoff every
five minutes for the duration of the rainfall. The amount of sediment eroded or
deposited was measured after each simulated rainfall using erosion pins. These
results were compared to the sediment load measured by runoff sampling, as well as to
the predicted erosion using two analytical models, the Universal Soil Loss Equation
(USLE) and an Erosion/Deposition (E/D) model. The E/D model was developed to be a
more comprehensive model than the USLE, by including partial area concepts of
hydrology and sediment transport equations. Erosion was predicted at specific points
on each plot, then an overall value for erosion was estimated.
Comparisons were then made between amounts of soil eroded or deposited at a point
using experimental techniques and numerical model predictions. Spatial structure of
soil loss distribution is evaluated. Discrepancies between values observed at the
pins and values expected based on model results and sediment yield sampling are
explained by increases in turbulence and the amount of rain near the pins. Implica-
tions with regard to vegetation in the form of stalks are sugsested.
17. (Circle One or More)
KEY WORDS AND DOCUMENT ANALYSIS
a- , DESCRIPTORS
f_5fivir3nin%ntl _/ 3^gc*Bfnii.rY
G»oqraeny R.tinuvj
Erosion
Energy Conversion
Physical Cheaucry
MatenaU Handling
[AOTQanic Chemistry
Organic Ch«mutrr
Chemical £nain««rin9
b.lOENT!FISRS/OP6N ENDED TERMS
'I-3t«i rMft^in^ rranufi | ^fff . it
s< *** f.»»- ..^ ^j^ -
19. SECURITY CLASS (Tha Report)
20. SECURITY CLASS , T/'iu page>
c. COSATI Field/Group
6F 8A 8F
8H 10A 10B
7B 7C 13B
21. NO. Of PAGES
22. PRICE
EPA Form 2220-1 (3-73)
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INSTRUCTIONS
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