Sediment Production from Unpaved Oil Well Access 

Roads in the Allegheny National Forest

Prepared for:

United States Department of Energy

National Energy Technology Laboratory

Through

URS Corporation

Joel Siegel; URS Corporation

626 Cochrans Mill Road

Pittsburgh, Pa, 15236

and:

USDA Northern Research Station

Susan Stout; Northern Research Station

335 National Forge Road, P.O. Box 267

Irvine, PA 16329

By

Steven M. Bloser

Dr. Barry E. Scheetz

Center for Dirt and Gravel Road Studies

The Pennsylvania State University

University Park, PA 16802

FINAL REPORT

March 26, 2012
ii

STUDY NOTE

This study was funded and completed in two phases:

Phase one was completed in 2010 and consisted of 14 test conducted on “as is” roads in the 

Allegheny National Forest to provide a baseline and range of sediment productions. This was 

funded through the US Department of Energy’s National Energy Technology Laboratory.

(Agreement 2010-SC-RES-30033026, 400.4.650.920.003)

Phase two was completed in 2011 and consisted on 4 tests conducted on newly placed aggregate 

on roads in the Allegheny National Forest to provide a comparison of sediment production 

compared to the findings in phase one. This was funded through the USDA Northern Research 

Station. (Agreement 11-CS-11242302-066)

Description and results of both phases are included in this paper.
iii

ACKNOWLEDGEMENTS

This study was made possible by the United States Department of Energy through the National 

Energy Technology Laboratory (Agreement 2010-SC-RES-30033026, 400.4.650.920.003) and 

through the United States Department of Agriculture, Northern Research Station (Agreement 11-

CS-11242302-066)

The authors would like to extend their thanks to several people and organizations that have 

contributed to this study: Thanks to Tim Ziegler, Field Operations Specialist at the Center for 

Dirt and Gravel Road Studies at Penn State, for help with RainMaker design, construction, and 

field testing. Other support staff of the Center at Penn State including: Dave Shearer, Kathy 

Moir, Dave Creamer, Ken Corradini, Danielle Lombardi, and Adam Rado have played varying

roles in the development and implementation of this study. Two former Center employees, 

Megan Walsh and Jennifer Eismeier, deserve recognition for their ideas and efforts in the 

development of the original RainMaker apparatus and sampling procedure. Special thanks to 

everyone at the Allegheny National Forest, especially Charles Keeports and Dan Salm, for their 

cooperative efforts in planning the study, and for providing field assistance with road testing. 

Thanks also to Jim Letko, of Letko’s Dam Water Hauling, and Tom Tobin of Aquarius Water 

Hauling, for their patience and willingness to deliver water on short notice to some very remote 

locations.

Steven Bloser

216 Larson Transportation Institute

University Park, PA 16802

814-865-6967

smb201@psu.edu

Dr. Barry E Scheetz

214 Larson Transportation Institute

University Park, PA 16802

814-863-5956

se6@psu.edu
iv

Abstract

The Center for Dirt and Gravel Road Studies at Penn State University has been working 

to reduce sediment pollution generated by unpaved roads in Pennsylvania for over a decade. 

Runoff from unpaved roads is a large source of sediment pollution in many forested watersheds. 

The Allegheny Nation Forest, located in Northwestern Pennsylvania, has over 1,695 miles of 

unpaved access roads that serve the shallow oil wells in the Forest (USDA-FS unpublished, 

2010a). The purpose of this study was to quantify sediment generation rates from these oil 

access roads in the Allegheny National Forest. In addition, the objective of phase II of the study 

was to determine differences in sediment production after new aggregate had been placed on 4 

of the sites.

The experimental approach taken in this study was to use a rainfall simulation device to 

create a repeatable rainfall event and collect sediment load data. The rainfall simulator was 

used to collect sediment in road runoff on 14 sites, each of which was 100’ in length. The 

simulated rainfall event was 0.61” in 30 minutes which has a return interval of slightly less than 

2 months.

The 14 sites tested showed sediment productions ranging from 3.2 to 60 pounds of 

sediment for each 30 minute simulated rain event. The average sediment runoff from the sites 

was 24.7 pounds, which equates to a sediment production rate of 1,300 pounds per mile for 

each 30 minute simulated rain event. Extrapolation of these results indicates that a single storm 

of similar intensity and duration to the design storm could be expected to produce over 1,100

tons of sediment from the oil access roads in the Allegheny National Forest. An estimated 385 

tons of that sediment can be expected to enter directly into nearby streams during each storm.

The study also identified a significant ‘first flush’ effect on the road segments studied. 

Additionally, road segments that received more traffic have been more compacted and exhibit 

higher structural strengths. Without traffic stress, the best indicators of sediment production 

from the roads tested were road slope combined with road width. If the road is stressed by 

traffic, then sediment production becomes less dependent on road width and slope, and more 

dependent on road strength as measured by the California Bearing Ratio. Finally it was 

observed that sediment generation would be greatly reduced from roads with very low usage by 

establishing vegetative cover on the road surface.

After initial testing, 4 of the 14 sites were surface with new aggregate material. Two 

sites were surface with local “pit run” material as is standard procedure. Two of the sites were 

surfaced with Driving Surface Aggregate. The 4 sites were then tested a year later to determine 

sediment production. All four sites showd reductions in sediment production (39% and 65% for 

pit-run, 67% and 65% for DSA). The two pit-run sits averaged ten times as much sediment 

production as the DSA sites (26.1 lbs versus 2.5 pounds).
v

TABLE OF CONTENTS

List of Figures............................................................................................. vi

List of Tables.............................................................................................. vii

CHAPTER 1: Introduction............................................................................. 1

1.1 Background................................................................................. 1

1.2 Site Selection........................................................................... 2

CHAPTER 2: Rainfall Simulator..................................................................... 5

2.1 RainMaker Design Specifications..................................................... 5

2.2 RainMaker Calibration.................................................................... 6

2.3 RainMaker ‘Return Period’ Equivalence............................................. 8

CHAPTER 3: Methodology........................................................................... 9

3.1 Study Setup and Timeline ........................................................... 9

3.2 RainMaker Testing Procedure......................................................... 9

3.3 RainMaker Summary.................................................................... 11

CHAPTER 4: Results.................................................................................. 12

4.1 Results of “existing condition” Road Testing on 14 sites....................... 12

4.2 Results of “after aggregate” Road Testing on 4 sites ........................... 16

CHAPTER 5: Discussion ............................................................................. 19

5.1 Effect of Traffic During Testing........................................................ 20

5.2 Influencing Factors........................................................................ 20

5.2.1 Road Slope and Sediment.................................................. 21

5.2.2 Road Width and Sediment................................................. 21

5.2.3 Road Use and Sediment................................................... 22

5.2.4 Road Strength and Sediment ............................................. 23

5.2.5 Road Composition and Sediment........................................ 24

5.3 Multi-variable Analysis................................................................... 25

5.4 Sediment Production After Aggregate Placement ............................... 26

CHAPTER 6: Conclusions............................................................................ 29

Future Research................................................................................ 33

REFERENCES............................................................................................ 35

APPENDECES:

Appendix A: RainMaker flow and sediment data for all sites....................... 36
vi

LIST OF FIGURES

Figure 1.1: Example of the effect of road runoff on aquatic ecosystems. This image, taken in 

the Allegheny National Forest adjacent to site “C” in this study, shows road runoff 

entering Grunder Run at a crossing. (photo courtesy ANF)

1

Figure 1.2: Aerial view of Grunder (right) and Hedgehog (left) watersheds. Notice amount of oil 

wells and access roads in Grunder watershed. (map courtesy ANF)

2

Figure 1.3: Locations of the 14 road sites used in this study (green stars, labeled with letters). 

The town of Warren and the Allegheny River can be seen in the upper right. (map 

courtesy ANF)

4

Figure 2.1: RainMaker being run on road Segment “R” as part of this study. 5

Figure 2.2: A water truck fills two1,250 gallon collapsible bladders with water for the RainMaker. 5

Figure 2.3: Rainfall collection jars were used to measure and calibrate the rainfall simulator. 7

Figure 2.4: One of the petri dishes used to capture and suspend raindrops in order to measure 

their size.

7

Figure 2.5: Eight individual raindrops are being tracked in this still frame from the high speed 

video used to determine raindrop velocity.

7

Figure 2.6: ‘Return Period’ estimate for a 0.64 inch 30-minute rainfall is slightly under two 

months (0.16 years). (Aron et al. 1986)

8

Figure 3.1: An example of a RainMaker sampling point with the RainMaker in the background. 9

Figure 3.2: A sediment sample is taken at a sample point on site “N”. 10

Figure 3.3: An oil well sits idle as the RainMaker runs on site “R” in the background. 11

Figure 5.1: Average sediment concentration in samples over time for all 14 sites. T=0 when 

runoff reaches the sample point.

19

Figure 5.2: Runoff samples from site A are arranged by time, with T=1 on the right, through 

T=30 on the left. 

19

Figure 5.3: Sediment results from individual runs of the rainfall simulator. All roads were 

subject to 20 vehicle passes before run 2 and again before run 3.

20

Figure 5.4: Road slope in relation to average sediment production. (Site A omitted because of 

grass cover)

21

Figure 5.5: Road slope in relation to sediment production from RUN 1 only. (Site A omitted 

because of grass cover)

18

Figure 5.6: Road width, with and without berm, in relation to average sediment production. 

(Site A omitted because of grass cover and indeterminate width)

22

Figure 5.7: Road width, with and without berm, in relation to RUN 1 sediment production. (Site 

A omitted because of grass cover and indeterminate width)

22

Figure 5.8: Road use, or traffic volume, in relation to average sediment production. 22

Figure 5.9: The dynamic cone penetrometer pictured here is used to measure road surface 

strength. 

23

Figure 5.10: Road strength, expressed as California Bearing Ratio, in relation to average 

sediment production.(Site A omitted because of grass cover. No data for Site B & C)

23

Figure 5.11: The relationship of strength, expressed as California Bearing Ratio, to the percent 

of sediment increase from Run 1 to Run 3. (No data for Site B & C) 

24

Figure 5.12: The relationship of strength, expressed as California Bearing Ratio, to the traffic 

volume level. (No data for Site B & C) 

24

Figure 5.13: The relationship of the percent sand, silt, and clay in road surface samples to 

average sediment production. (Site A omitted because of grass cover)

24

Figure 5.14: Road “slope+width” factor, in relation to average sediment production for each run. 

(Site A omitted because of grass cover)

Figure 5.15: Summary of sediment reductions found between existing road surfaces and new 

aggregate

Figure 5.16: Visual comparison of the four sites in exiting condition during testing in 2010 and 

with new aggregate during testing in 2011. Average sediment production for three 

runs is included on each photo.

Figure 5.17: Summary of average sediment production by road type. All figures are in pounds 

of sediment produced per mile of road. Note the differing number of each type of 

sites that was available for use in obtaining average.

25

26

27

28
vii

Figure 6.1: Testing on site “E”. 29

Figure 6.2: This image, taken in the Grunder watershed in late March after a natural rain event, 

illustrates the effect of poor road material, excessive moisture, and vehicle traffic on 

sediment generation. (photo courtesy ANF)

30
viii

LIST OF TABLES

Table 2.1: Results of repeatability testing for the RainMaker. 22 rainfall collection jars 

were randomly placed on the roadway and subjected to three runs of the 

RainMaker.

6

Table 4.1: Summary of sediment results, in increasing order, along with site 

characteristics.

Table 4.2: Summary of sediment results from existing roads and after placement of new 

aggregate.

16

18

Table 5.1: Composition of road surface samples. Samples are in order of increasing sediment 

production. 

24
CHAPTER 1: INTROUDCTION

1.1 Background

The Commonwealth of Pennsylvania has recognized that a significant contributor of 

sediment pollution to Pennsylvania streams is runoff from publicly maintained dirt and gravel 

roads (Figure 1.1). In response to a statewide assessment of pollution sites on public unpaved 

roads, the Commonwealth established the Dirt and Gravel Road Maintenance Program 

[Program] in 1997 within the State Conservation Commission [SCC]. The Program provides a 

non-lapsing funding source with an objective of identifying the polluting sources and 

implementing solutions. The Center for Dirt and Gravel Road Studies [Center] was established 

within the Larson Transportation Institute on the University Park Campus of The Pennsylvania 

State University in 2000 to support the Conservation Commission’s Program. The Center 

identifies and refines Environmentally Sensitive road Maintenance Practices [ESMPs] to reduce 

sediment pollution, teaches these practices to municipal road crews, and coordinates a 

technical outreach and assistance program for townships in the Commonwealth.

The Center has conducted more than 140 two-day training sessions which have been 

attended by over 5,000 state and township personnel. In the thirteen years that the DGRP has 

been in operation, over 2,000 individual road projects have been completed which mitigate

sediment pollution to streams of the Commonwealth. A 

more detailed description of the Program and its 

accomplishments to date can be found at 

www.dirtandgravelroads.org. 

In 2008, the Center completed a study funded by 

the Chesapeake Bay Commission [CBC] with the 

objective of quantifying sediment production values from 

unpaved roads. The CBC study implemented several 

ESMPs in order to quantify sediment after sediment 

reduction practices had been implemented on the roads. 

The CBC study employed an artificial rainfall simulator 

to provide a consistent and repeatable rain event on 

different sections of roadway. The procedures and 

methodologies developed in the 2008 CBC study were 

the basis for the current NETL study described in this 

report. A summary and full report on the Center’s 

previous CBC study is available on the Center’s website 

Figure 1.1: Example of the effect of road 

runoff on aquatic ecosystems. This image, 

taken in the Allegheny National Forest 

adjacent to site “C” in this study, shows road 

runoff entering Grunder Run at a crossing. 

(photo courtesy ANF)
2

at www.dirtandgravelroads.org under “research”.

The objective of Phase I of this study was to quantify sediment production rates for 

various unpaved roads created and maintained by the oil industry within the Allegheny National 

Forest. Another component of the study was to quantify sediment reductions after the 

placement of road aggregates, however, this component of the study has yet to be completed. 

The objective of Phase II of the study was to compare sediment productions after new 

aggregates had been placed on 4 of the sites.

1.2 Site Selection

The Center worked in cooperation with representatives from the Allegheny National 

Forest [ANF] to determine site selection criteria. ANF representatives also assisted in finding 

and evaluating many of the field sites that were used in this study. The Center worked 

cooperatively with ANF personnel throughout the study to make joint decisions on which sites to 

include.

Because of other sediment-related studies that were underway with Clarion University

and the US Geological Survey (USGS), it was determined that the watersheds of Grunder Run 

and Hedgehog Run, located just south 

of Warren, PA, would be the ideal 

location for this study. The two 

adjacent watersheds are very similar in 

size, slope, and cover. The major 

difference in the two watersheds was 

the amount of oil well development, 

with Grunder being heavily developed, 

and Hedgehog being relatively 

undisturbed (Figure 1.2). The 

comparative study between these two 

watersheds that is being conducted by 

Clarion University may provide further 

insights to sediment effects to aquatic 

habitat on a watershed scale, but are 

not part of this study or report. In 

2010, the USGS began sampling 

suspended sediment and streamflow to 

analyze sediment concentrations for 

the two basins during storm events. 

Figure 1.2: Aerial view of Grunder (right) and Hedgehog (left) 

watersheds. Notice amount of oil wells and access roads in 

Grunder watershed. (map courtesy ANF)
3

The major phase of oil and gas development began in Grunder Run watershed in the 

early 1980’s. By 1990, most of the 455 wells in this watershed were completed (USDA-FS

unpublished, 2010). In the last few years, some new wells and roads have been developed. In 

Hedgehog watershed, there are 27 recorded oil and gas wells, but only 7 are known to be active 

(USDA-FS unpublished, 2010). All of the 14 sites in Grunder and Hedgehog watersheds are on 

roads that have existed for at least 20 years. The roads in Grunder Run development receive 

use mainly from light trucks and all-terrain vehicles to operate and maintain the oil and gas 

wells. Some roads serve as main access routes to the majority of the oil and gas wells and tank

batteries.

The 14 sites tested in this study were chosen to cover a wide variety of road slopes, 

widths, surface materials, and use levels. Two of this Study’s 14 road sites were located in the 

Hedgehog Watershed and 11 sites were in the Grunder watershed. One site is located in the 

adjoining Sill Run watershed, on Forest Road 362. It was the intent of this study not only to give 

an overall sediment production average for the 14 roads, but to begin to determine which 

physical factors of the roads may be responsible for the highest sediment loads. Physical 

description and details of each site are included in the results section.

The second part of this study looked at sediment production after the placement of pitrun aggregate and Driving Surface Aggregate. Pit-run is a term used to describe a locally 

derived material that is mined from “unofficial borrow pits”. Historically, tt has been widely used 

on most of the access roads in this study in the past. Compared with commercially supplied 

aggregate, locally excavated pit-run is less expensive. Since pit-run is taken directly from the 

ground at various locations without processing, it is also highly variable in its composition and 

quality. Driving Surface Aggregate is a specially designed aggregate specification designed to 

achieve maximum compacted density to resist traffic and erosion. The DSA used in this study 

was limestone based, although limestone is not required.

The experimental approach taken in this study was to collect sediment loads on a 

section of road long enough to be representative of the roadway during a simulated rain even. 

To ensure an accurate comparison of each road segment, a device was constructed that would 

deliver water to the test site in a uniform manner. The details of the “RainMaker”, its 

performance characteristics, and testing protocol follow.
4

Figure 1.3: Locations of the 14 road sites used in this study (green stars, labeled with letters). The 

town of Warren and the Allegheny River can be seen in the upper right. (map courtesy ANF)
5

CHAPTER 2: Rainfall Simulator

The Center originally designed the first-generation rainfall simulator, or “RainMaker”, for 

use under a previous study funded by the Chesapeake Bay Commission, and described earlier 

in this report. The RainMaker is ideally suited to sediment monitoring because of the 

convenience and repeatability that cannot be achieved by sampling natural events. As part of 

this NETL study, the Center redesigned the RainMaker in order to improve road coverage, 

repeatability, drop size, and drop velocity to produce a more repeatable rainfall event that is 

more representative of natural rainfall. The “second-generation” RainMaker used in this study is 

described below. The RainMaker is designed to simulate a rain event on a 100’ length of road. 

It delivers approximately 1.2 inches of rainfall per hour in a highly controlled and repeatable 

event (Figure 2.1). 

2.1 RainMaker Design Specifications

-water source: delivered to site and stored for use in collapsable bladders (figure 2.2).

-pump: 3”, 5hp Honda water pump.

-body: 100’ x 11⁄2” PVC pipe (in 10’ sections).

-risers: 11 PVC risers at 10’ intervals, each 1⁄2” in diameter and 10’ tall. A “T” extends 2.5 

feet in both directions laterally towards the adjacent risers in order to position a 

nozzle every 5 feet along the sample road section.

-nozzles: One 180onozzle every 5 feet along road (Rainbird® MPR Rotary Nozzles).

Figure 2.1: RainMaker being run on road Segment “R” 

as part of this study.

Figure 2.2: A water truck fills two 1,250 gallon 

collapsible bladders with water on site “T”.
6

Table 2.1: Results of repeatability testing for the RainMaker. 22 rainfall collection jars were 

randomly placed on the roadway and subjected to three runs of the RainMaker.

Sample Std Dev

Container Run 1 Run 2 Run 3 Average Between Runs

1 0.79 0.81 0.44 0.68 0.21

2 0.80 0.76 0.51 0.69 0.16

3 0.77 0.77 0.74 0.76 0.02

4 1.29 0.24 0.89 0.81 0.53

5 1.50 1.51 1.48 1.50 0.01

6 1.84 1.69 1.83 1.78 0.08

7 1.24 1.27 1.37 1.29 0.07

8 1.50 1.56 1.19 1.42 0.20

9 1.21 1.14 1.26 1.20 0.06

10 1.95 1.80 1.73 1.83 0.12

11 1.26 1.23 1.27 1.25 0.02

12 0.69 0.63 0.84 0.72 0.11

13 1.14 1.13 1.14 1.14 0.01

14 1.43 1.48 1.33 1.42 0.08

15 1.93 1.86 1.78 1.86 0.08

16 1.67 1.59 1.48 1.58 0.10

17 1.05 1.08 1.22 1.12 0.09

18 1.46 1.48 1.48 1.48 0.01

19 1.29 1.29 1.28 1.29 0.01

20 1.37 1.37 1.23 1.32 0.08

21 0.72 0.82 0.90 0.82 0.09

22 1.12 0.99 1.50 1.20 0.26

Average 1.27 1.21 1.22 1.23 0.11

St Dev within run 0.38 0.41 0.38 0.37

Std Dev between colleciton points in one run (evenness) = 0.37 or 30% of mean

Stand Dev between three sample runs (consistency) = 0.11 or 8.9% of mean

Average rainfall rate is 1.23 inches per hour (0.62 inches per 30 minute test event)

Rainfall Intensities (inches per hour)

-pressure: 30 psi measured on gauge at the terminal end of RainMaker.

-rainfall rate: measured average of 0.62 inches in 30 minutes (1.23 inches per hour).

2.2 RainMaker Calibration

The primary purpose of the RainMaker is to create a highly repeatable rainfall event. 

The repeatability of the setup was verified by collecting and measuring rainfall for three separate 

events on a flat paved surface (Figure 2.3). Rainfall intensities from the repeatability testing can 

be found in Table 2.1. The average rainfall intensity over the entire road was 1.23 inches per 

hour. The variability between rainfall collection jars within a single run of the RainMaker

approximates the “evenness” of precipitation over the road. The standard deviation between 

collection jars was 0.37, or 30% of the mean intensity. This indicates that although the average 

intensity of rainfall is 1.23 in/hr, most rainfall rates can be expected to vary between 0.86 and 

1.60 in/hr for any point on the road. 
7

Consistency between separate 

RainMaker runs is of a greater 

importance to this study than evenness 

of coverage over the road. The 

primary advantage of the rainfall 

simulator is that it provides the same 

storm event every time it is run. 

Analysis of the data presented in Table 

2.1 indicates that the standard 

deviation between runs of the 

RainMaker is 0.11 or 9% of the mean 

intensity. This means that most points

on the road can be expected to vary by 

less than 9% between separate runs of 

the RainMaker. A paired-t test was also run on the data presented in Table 2.1 to test the

statistical significance of the repeatability of the separate runs. The results of the paired-t test 

indicated with a 95% confidence that there were no significant differences in rainfall intensity 

between the three runs. In summary, the RainMaker does a satisfactory job of providing an 

evenly distributed rain event (SD of 30% of mean within run), and an excellent job of providing 

repeatability by providing the same rainfall intensity at the same points on successive runs (SD 

of 9% of mean between runs).

In order to better characterize the simulated storm event, testing was done to determine 

the size of the raindrops produced by the RainMaker. Fifteen covered petri dishes with a 

mixture of STP motor oil treatment and mineral oil [REF 2001] were placed randomly through 

the rainfall area (Figure 2.4). Each dish was individually uncovered for approximately one 

Figure 2.3: Rainfall collection jars were used to measure and 

calibrate the rainfall simulator. 

Figure 2.5: Eight individual raindrops are being 

tracked in this still frame from the high speed 

video used to determine raindrop velocity. 

Figure 2.4: One of the petri dishes used to 

capture and suspend raindrops in order to 

measure their size. 
8

second; enough to allow rain drops to impact the oil mixture. The hydrophobic characteristics of 

the fluid mixture suspended the individual rain drops in the mixture from which they could be 

photographed. The numbers and size of the individual rain drops in each of the fifteen petri 

dishes was measured with a custom written program in MatLab. The average size of the rain 

drops measured was 0.84mm. Although it is on the smaller end, 0.84mm is within the normal 

size distribution of what most sources consider a moderate rainfall (Rogers, 1979).

In further efforts to better characterize the simulated rainfall, the velocity of individual 

raindrops was measured as well. Individual rain drops were videotaped with a Fastcam Ultima 

1024 high speed video camera at 5000 frames per second to provide slow-motion images from 

which the speed of the rain drops could be measured. “Photron Motion Tools Analysis 

Software” was then used to determine the velocities of the individual drops. The drops 

averaged 11.5 miles per hour. Figure 2.5 is one of the photographic images of the drop 

movement that was used in the calculations. This velocity matches expected raindrop velocities 

for a moderate rainstorm of 9 to 16 miles per hour (Rogers, 1979).

2.3 RainMaker ‘Return Period’ Equivalence 

The “RainMaker” simulates a 1.23 inches per hour rainfall on a 100’ length of road. The 

magnitude of the simulated rainfall was chosen to represent a ‘modest’ but not an extraordinary 

event for the region. The applied rate of 0.62 inches per one half-hour for this region of 

Northwestern Pennsylvania is equivalent to approximately a 1.9 month rain event (return period 

= 0.16 years). The return period for 0.62 inches of rainfall in 30 minutes was extracted from 

Aron et al. [1986] and extrapolated from the data presented in Figure 2.6. 

Figure 2.6: ‘Return Period’ estimate for a 0.62 inch 30-

minute rainfall is slightly under two months (0.16 years). 

(Aron et al. 1986)

0.16 yrs
9

CHAPTER 3: Methodology

3.1 Study Setup and Timeline

Working with the Allegheny National Forest (ANF) in 

the Spring of 2010, the Center identified approximately 25 

potential test sections of roadway 100 feet in length where the 

rainfall simulator could be applied. The initial study planned to 

incorporate 18 sections of roadway. In cooperation with the 

ANF, the Center narrowed down the site list to 14 sites that 

would be tested “as is” to quantify sediment production from 

“typical” oil well access roads. Four of these sites had been 

previously identified by the ANF as locations where new 

surface aggregates would be placed. For the remaining four 

rainfall simulator tests, it was decided that “after” tests should 

be run on the four sections of roadway that would receive new 

aggregate (two with pit-run and two with Driving Surface 

Aggregate). 

The Center completed construction and calibration of 

two RainMaker devices in the Spring and early Summer of 2010. The first road site to be 

tested, site K, was completed on June 18th, 2010. Working around summer rainstorms, the 

Center completed the remaining 13 “as is” test sections with the help of ANF personnel 

throughout the summer. All testing was done in accordance with the RainMaker testing 

procedure described below. The last road segment, site T, was completed on August 19th, 

2010. The Center took all sediment samples back to the Water Lab at Penn State Institute for 

Energy and the Environment for analysis of total suspended solids. Photographs, detailed 

measurements, and road surface samples were also taken from each site to help in later 

analysis of sediment production.

In the Spring of 2011, four of the sites received placement of a new aggregate surface 

(sites B & F with “pit-run aggregate,” and sites G & C with Driving Surface Aggregate). The 

RainMaker was run on these sites in the fall of 2011 in order to obtain sediment production 

figures for the new aggregates.

3.2 RainMaker Testing Procedure

 General Considerations

o RainMaker was run after at least 2 days of dry weather to avoid saturated conditions.

o A 100-ft stretch of road was evaluated for each test site.

Figure 3.1: An example of a 

RainMaker sampling point with the 

RainMaker in the background.
10

o Each test section of road was subjected to three 30-minute runs of the RainMaker.

o Nozzles were checked before each run, and any clogged or malfunctioning nozzles 

were replaced.

 Step-by-step procedure

o Set up RainMaker on test section. Insure the collapsible water bladders have a flat 

place to lay uphill of the test section.

o Insure sample points are ready for collection. This procedure was site-specific, but 

included activates such as digging sampling points (see Figure 3.1), installing 

sheeting to make sampling easier, and insuring no runoff bypasses sampling point.

o Flush any trenches or channels that were dug for the collection system, and test the 

sampling setup using a garden hose from the terminal end of the RainMaker.

 RainMaker Run 1

o Pump was turned on and adjusted to 30psi at the gauge on the terminal end of the 

RainMaker. The pump ran for a total of 30 minutes in each run.

o Sampling:

 During each run of the RainMaker runoff flow rate and sediment samples were 

captured at regular intervals to determine the amount of sediment leaving each 

site (Figure 3.2).

 Sediment: Runoff samples were taken at regular intervals and returned to the 

Water Lab at Penn State for Total Suspended Solids analysis. A total of six 

samples were collected at each sample point for each run of the RainMaker. 

With Time=0 (T=0) set when the wetting front reached the sample point, 

sediment samples were collected at one (T=1), five (5), ten (10), fifteen (15), 

twenty (20), and thirty (30) minutes after runoff reached the sample point.

 Flow: Flow rate was determined by 

timing the amount of time it took to fill a 

container of known volume. The runoff 

flow was measured at T=1, T=5 and at 

five minute intervals until T= 60 or until 

runoff stopped. 

o After running for 30 minutes, the pump was

turned off. Because of the delay in water 

reaching the sample point, the pump was 

typically shut off around T=25 in the sample 

timeline.

Figure 3.2: A sediment sample is taken at 

a sample point on site “N”.
11

 Drying Time 1

o After the pump was turned off, the road was allowed to dry for a period of one hour.

o Approximately 30 minutes into the 60 minute drying cycle, a light truck was driven a 

total of 20 passes over the entire test section. This was done to simulate traffic and 

further stress the road surface before the next RainMaker run.

 RainMaker Run 2

o After the 60 minute drying time, the pump was turned back on for 30 minutes at 

30psi. The sampling procedure outlined in “RainMaker Run 1” above was repeated.

 Drying Time 2

o Another 60 minute drying cycle with 20 light truck passes was completed.

 RainMaker Run 3

o After the 60 minute drying time, the pump was turned back on for 30 minutes at 

30psi. The sampling procedure outlined in “RainMaker Run 1” above was repeated.

3.3 RainMaker Summary

By collecting sediment samples and measuring flow volumes at each sample point at 

regular time intervals, total sediment loading can be calculated for each site. Each time the 

RainMaker is run, it is run for three 30-minute sample periods as described in the “RainMaker

procedure” above. The flow rates and sediment concentrations for these three runs are then 

combined to obtain the average sediment production for each section of road. 

Figure 3.3: An oil well sits idle as the RainMaker runs on site “R” in the background. 
12

CHAPTER 4: Results

4.1 Results of “existing condition” Road Testing on 14 sites

Sediment loads from each site were determined by combining the Total Suspended 

Solids (TSS) concentration found in the water samples with the know flow volumes for each 

simulated rainfall event. The RainMaker was run three times on each site, with 20 light truck

passes before runs 2 and 3. All sediment figures are in pounds per 30 minute event unless 

otherwise noted. Sediment production rates (3 run average) ranged from 3.2 pounds to 60.1 

pounds for the segments in this study. Site A, with the least amount of sediment, was a grass 

covered road where only the wheel tracks contained exposed soil. Site B, with the most amount 

of sediment, was a high traffic USFS road that was steep with runoff flowing in channels on the 

road surface.

The sediment results for individual sites, along with pertinent site information and photos 

are listed below. A summary of the site data and results can be found in Table 3.1. Sites are 

described in order of increasing sediment production (3 run average):

Site A: 3.2 pounds of sediment

(1.0 lbs, 2.1 lbs, 6.4 lbs in successive runs)

Tested: 8/18/2010

Slope: 14.7% Width: 13’

Traffic: Very Low CBR(strength): 31

Material: Loam: 43% sand, 31% silt, 26% clay

Site Notes: Grass covered road. AASHTO #1 stone 

exposed in wheeltracks. Minimal use gated road. 

Ditches also heavily grassed. Running vehicle over 

road between tests made a lot of difference, both 

visually and in sediment data. Most runoff came 

down wheel tracks.

Site T: 7.4 pounds of sediment

(7.1 lbs, 7.0 lbs, 8.0 lbs in successive runs)

Tested: 8/19/2010

Slope: 3.3% Width: 12’ (16’ with berm)

Traffic: High CBR(strength): 141

Material: Sandy Loam: 75% sand, 14% silt, 11% 

clay

Site Notes: Flattest road tested. Surface largely 

sand. A few large stones on top. This is a USFS 

Road with a relatively higher traffic count. Road 

surface was hard-packed. Traffic passes between 

tests had little effect, visually or in sediment data. 

Runoff was on road and in ditches.
13

Site S: 7.9 pounds of sediment

(4.2 lbs, 10.1 lbs, 9.4 lbs in successive runs)

Tested: 7/27/2010

Slope: 7.6% Width: 11’ (11’ with berm)

Material: Sandy Clayey Loam: 52% sand, 24% 

silt, 24% clay

Traffic: Med CBR(strength): 91

Site Notes: Surface was a mixture of large stone 

with dirt and some exposed bedrock. Fabric was 

under surface and exposed in some locations. 

Ditches were non-existent and all runoff came 

down channels cut into road surface.

Site R: 11.1 pounds of sediment

(9.3 lbs, 9.8 lbs, 14.3 lbs in successive runs)

Tested: 7/27/2010

Slope: 9.0% Width: 11.5’ (11.5’ with berm)

Traffic: Low CBR(strength): 99

Material: Loam: 47% sand, 29% silt, 24% clay

Site Notes: Ditches were cutoff from surface, and 

all runoff came down wheel tracks. Surface was 

hard packed with large rocks on surface. Fabric 

was under surface and exposed in some locations. 

Traffic had little visual effect.

Site K: 12.7 pounds of sediment

(7.9 lbs, 15.4 lbs, 14.9 lbs in successive runs)

Tested: 6/18/2010

Slope: 4.4% Width: 12’ (13’ with berm)

Traffic: Med CBR(strength): 67

Material: Sandy Loam: 57% sand, 27% silt, 16% 

clay

Site Notes: Road had 10-20% ballast rock on 

surface. Ditches were cutoff from surface, and all 

runoff came down wheel tracks. Traffic passes 

made a difference, both visually and in sediment 

data. Runoff ran on road surface and in ditch.

Site G: 13.0 pounds of sediment

(15.1 lbs, 14.0 lbs, 10.9 lbs in successive runs)

Tested: 7/2/2010

Slope: 16.2% Width: 12.5’ (12.5’ with berm)

Traffic: High CBR(strength): 126

Material: Loam: 41% sand, 38% silt, 21% clay

Site Notes: Very hard packed surface with some 

rock. High traffic road. Ditches were non-existent 

and all runoff came down channels cut into road 

surface. Traffic passes made little difference 

visually. One of few sites to have less sediment 

in successive runs.
14

Site I: 14.7 pounds of sediment

(14 lbs, 16.5 lbs, 13.7 lbs in successive runs)

Tested: 8/17/2010

Slope: 20.5% Width: 13’ (17’ with berm)

Traffic: Low CBR(strength): 81

Material: Loam: 43% sand, 33% silt, 24% clay

Site Notes: Steepest road tested. A lot of rocks 

present on road surface. Most runoff ran down 

wheel tracks, with some running in ditch. Traffic 

passes made little difference visually or in 

sediment data.

Site C: 14.9 pounds of sediment

(11.9 lbs, 14.6 lbs, 18.0 lbs in successive runs)

Tested: 7/8/2010

Slope: 7.2% Width: 10’ (14’ with berm)

Traffic: Low CBR(strength): unknown

Material: Loam: 45% sand, 30% silt, 25% clay

Site Notes: Runoff ran in wheel track and ditch. 

Lower use road with little rock on surface. 

Traffic passes made a difference visually and in 

sediment data.

Site L: 28.4 pounds of sediment

(12 lbs, 35.7 lbs, 37.5 lbs in successive runs)

Tested: 6/31/2010

Slope: 13.8% Width: 12’ (15’ with berm)

Traffic: Low CBR(strength): 71

Material: Loam: 51% sand, 31% silt, 18% clay

Site Notes: In-sloped road with runoff flowing in 

ditch. Relatively low use road with soft surface. 

Water and traffic has large effect on surface, both 

visually and in sediment data. Surface became 

muddy and slippery, making walking or driving 

difficult.

Site N: 29.7 pounds of sediment

(14.3 lbs, 40.5 lbs, 34.4 lbs in successive runs)

Tested: 8/18/2010

Slope: 5.6% Width: 13’ (15’ with berm)

Traffic: High CBR(strength): 103

Material: Loam: 49% sand, 31% silt, 20% clay

Site Notes: High use “trunk” road. Very few 

stones on surface. Water and traffic has large 

effect on surface, both visually and in sediment 

data. Ditches were cut off and water ran down 

wheel tracks.
15

Site E: 35.9 pounds of sediment

(7.8 lbs, 36.9 lbs, 62.8 lbs in successive runs)

Tested: 6/30/2010

Slope: 9.2% Width: 13’ (13’ with berm)

Traffic: Low CBR(strength): 65

Material: Loam: 73% sand, 14% silt, 13% clay

Site Notes: Road was mostly sand with some 

small stones. Very deep ruts (6”) in road surface 

carried all runoff. Traffic had large effect on 

road, visually and in sediment data.

Site F: 42.7 pounds of sediment

(47.1 lbs, 42.2 lbs, 38.8 lbs in successive runs)

Tested: 7/2/2010

Slope: 13.3% Width: 15’ (15’ with berm)

Traffic: High CBR(strength): 133

Material: Loam: 46% sand, 33% silt, 21% clay

Site Notes: Higher traffic road with hard packed 

surface with few rocks. No ditches, water runs in 

wheel tracks. Traffic had very little visual effect. 

One of few sites to have less sediment in 

successive runs. This site had the highest “run 1” 

sediment before traffic.

Site M: 43.1 pounds of sediment

(23.6 lbs, 50.5 lbs, 55.2* lbs in successive runs)

Tested: 6/31/2010

Slope: 19.2% Width: 10’ (15’ with berm)

Traffic: Low CBR(strength): 76

Material: Loam: 47% sand, 33% silt, 20% clay

Site Notes: Very steep road. Low traffic volume 

and soft surface. All runoff ran down 

wheeltracks. Traffic had large effect, visually and 

in sediment data.

* Run 3 was not completed due to water truck 

problems. Data extrapolated based on other sites

average increase from run 2.

Site B: 60.1 pounds of sediment

(31.7 lbs, 67.0 lbs, 81.7 lbs in successive runs)

Tested: 7/8/2010

Slope: 12.7% Width: 11’ (16’ with berm)

Traffic: Med CBR(strength): unknown

Material: Loam: 48% sand, 32% silt, 20% clay

Site Notes: Surface was a mixture of soil and 

rock. Ditches were cut off. Water ran down 

channels in road surface. Traffic passes made 

noticeable difference, visually an in sediment 

data. This site had the highest total average 

sediment (60.1 lbs), and the highest single run 

sediment (81.7 lbs for run 3).
16

 The sediment results for the 14 sites that were tested in this study are shown in Table 

4.1, along with most of the physical information about each site. All sediment figures are in 

pounds per 30 minute event, and are based on the average of three RainMaker runs unless 

otherwise noted. When results from the 14 100’ test sections are averaged, it shows that typical 

100’ section of oil access roads in the Allegheny National Forest can be expected to lose almost 

25 pounds of sediment in a single 30 minute storm event with a ~2 month return cycle. 

It should be noted, and will be discussed in detail later, that the rainfall simulator results 

represent a conservative estimate of sediment loss. Sediment loss from a similar sized actual 

rain event would be higher because of compounding effects form upslope road drainage and off 

site drainage, instead of a limited 100’ rainfall on the road only. 

4.2 Results of “after aggregate” Road Testing on 4 sites (Phase II)

New aggregate was placed on 4 of the sites in early 2011 (sites B & F with “pit-run 

aggregate,” and sites G & C with Driving Surface Aggregate). RainMaker testing, using the 

same procedures as previously described, was completed on these four sites in the Fall of 

2011.

Table 4.1: Summary of sediment results, in increasing order, along with site characteristics. 

Site Slope Road rd + berm Strength traffic Site

ID % width (ft) width (ft) (CBR) level Run 1 Run 2 Run 3 Avg ID

A* 14.7 na 13 31 v. low 1.0 2.1 6.4 3.2 A*

T 3.3 12 16 141 high 7.1 7 8 7.4 T

S 7.6 11 11 91 med 4.2 10.1 9.4 7.9 S

R 9 11.5 11.5 99 med 9.3 9.8 14.3 11.1 R

K 4.4 12 13 67 med 7.9 15.4 14.9 12.7 K

G 16.2 12.5 12.5 126 high 15.1 14.0 10.0 13.0 G

I 20.5 13 17 81 low 14.0 16.5 13.7 14.7 I

C 7.2 10 14 na low 11.9 14.6 18.0 14.9 C

L 13.8 12 15 71 low 12.0 35.7 37.5 28.4 L

N 5.6 13 15 103 high 14.3 40.5 34.4 29.7 N

E 9.2 13 13 65 low 7.8 36.9 62.8 35.9 E

F 13.3 15 15 76 high 47.1 42.2 38.8 42.7 F

M 19.2 10 15 133 low 23.6 50.5 55.2** 43.1 M

B 12.7 11 16 na med 31.7 67.0 81.7 60.1 B

* Site A on grass road: not used in averages. Avg 15.8 27.7 28.6 24.7 pounds

* Site M, run 3 extrapolated Avg 836 1,463 1,511 1,306 lbs/mile

Run 1 Run 2 Run 3 Average

Lbs Sediment per 30 min. event
17

Site B: New Pit-Run

Existing Road: 60.1 lbs (31.7, 67.0, 81.7)

New Pit-Run Surface: 36.8 lbs (20.2, 41.4, 48.9)

Sediment Reduction: 39%

Slope: 12.7% Width: 11’ (16’ with berm)

Site Notes: This site had the highest overall sediment 

production in “existing road” testing. Although 

sediment production was less on the new pit-run 

surface, the 36.8 lbs of sediment found was still higher 

than 11 of the 14 original “existing condition” tests. 

Traffic created a significant increase in sediment 

production for both the existing road and new surface.

Site F: New Pit-Run

Existing Road: 42.7 lbs (47.1, 42.2, 38.8)

New Pit-Run Surface: 15.4 lbs (23.3, 13, 9.8)

Sediment Reduction: 64%

Slope: 13.3% Width: 15’ (15’ with berm)

Site Notes: This is one of the higher traffic roads in the 

study. It is also one of the few sites that showed a 

decrease in sediment production after traffic in 

successive runs, indicating that traffic did not have an 

effect on sediment production.

Site G: New Driving Surface Aggregate

Existing Road: 13.0 (15.1 lbs, 14.0 lbs, 10.9)

New DSA: 4.2 lbs (6.3, 2.9, 3.5)

Sediment Reduction: 67%

Slope: 16.2% Width: 12.5’ (12.5’ with berm)

Site Notes: This site is located only ~300 feet from site 

F and also sees significant traffic. Also like site F, 

traffic did not increase sediment production. The 4.2 lbs 

found here was significantly lower than all of the 

“existing road” tests (other than grassed road).

Site C: New Driving Surface Aggregate

Existing Road: 14.9 (11.9 lbs, 14.6 lbs, 18.0)

New DSA: 0.8 lbs (1.5, 0.5, 0.4)

Sediment Reduction: 95%

Slope: 7.2% Width: 10’ (14’ with berm)

Site Notes: This site showed the lowest sediment 

production by far of any site in the study, including the 

grassed road. This site also had a decrease in sediment 

production after traffic between the runs.
18

Table 4.2 summarizes the 

sediment production from the existing 

roads and newly place aggregate for 

site B, F, G, and C. While all sites 

showed a reduction in sediment

compared to their “existing road” tests, 

the two pit-run sites produced ten times 

as much sediment as the two DSA sites 

(26.1 lbs and 2.5 lbs respectively)

Table 4.2: Summary of sediment results from existing roads 

and after placement of new aggregate.

Site % sediment

ID timeframe Run 1 Run 2 Run 3 Avg reduction

existing 31.7 67.0 81.7 60.1

new pit-run 20.2 41.4 48.9 36.8

existing 47.1 42.2 38.8 42.7

new pit-run 23.3 13.0 9.8 15.4

existing 15.1 14.0 10.0 13.0

new DSA 6.3 2.9 3.5 4.2

existing 11.9 14.6 18.0 14.9

new DSA 1.5 0.5 0.4 0.8

site B

site F

site G

site C

Lbs Sediment per 30 min. event

39%

64%

67%

95%
19

CHAPTER 5: Discussion

The average sediment production found in the 42 individual rainfall simulation tests on 

these 14 sites was 24.7 pounds per 30 minute event. This equates to a sediment production 

rate of 1,304 pounds per mile of road for a single 30 minute 0.62” rain event. Given that the 

Grunder Run watershed alone has approximately 52 miles of unpaved roads, it can be 

extrapolated that a single watershed-wide storm event similar to our design storm will cause a 

loss of nearly 34 tons of road sediment in the watershed (USDA-FS unpublished, 2010). 

It is important to note when extrapolating, that these figures represent sediment loss 

from the road, not necessarily into a stream. Some roads higher in the watershed may not be 

hydrologically connected directly to the stream system. Unfortunately, many of the roads and 

ditches in the watershed serve as direct extensions of the stream channel. Based upon an 

inventory and assessment of 16,500 miles of unpaved roads throughout Pennsylvania, 35% of 

unpaved roads statewide are typically directly connected to streams. Applying this ratio to the 

Grunder watershed would yield an estimate of 12 tons of road sediment entering directly into 

Grunder Run in a single storm of similar intensity and duration to the RainMaker.

In addition to the total sediment load described above, the rainfall simulator testing also 

showed a significant “first flush” of sediment on the tested roads. The “First Flush” concept 

states that the majority of sediment pollution is generated at the beginning of the precipitation 

event. As the event continues, the easily detached sediment has been washed away, and the 

remaining material will be more resistant to erosion. Figure 5.1 shows the decrease in sediment 

production over time when the data for all 14 sites are combined. Figure 5.2 visually illustrates 

the decrease in sediment over time for site A (grassed site with least total sediment). These 

results clearly show the effect of the “first flush” of sediment during the first 5 minutes of the 

rainfall event. 

Figure 5.1: Average sediment concentration in samples 

over time for all 14 sites. T=0 when runoff reaches the 

sample point. 

R2 = 0.9173

R2 = 0.9715

R2 = 0.9802

0

5,000

10,000

15,000

20,000

25,000

0 5 10 15 20 25 30 35

TSS (mg/L)

Sample Time (min)

Average Sediment Concentrations Over Time

Run 1

Run 2

Run 3

Figure 5.2: Runoff samples from site A are arranged by 

time, with T=1 on the right, through T=30 on the left. 
20

5.1 Effect of Traffic During Testing

Each site test consisted of three individual rainfall simulator runs. The initial run (Run 1) 

on each site was completed on the road “as is” with no pre-traffic stresses other than normal 

use. Between Run 1 and Run 2, twenty light truck passes were run over the road segment. 

Again between Run 2 and Run 3, an additional twenty light truck passes were done. These 

passes were done in an effort to accelerate the pressures and stresses that roads would see 

under normal use. Figure 5.3 illustrates the sediment results for each individual run of the 

rainfall simulator. Most sites, as expected, showed an increase in sediment production after 

traffic had been applied. Ten of the 14 sites experience sediment increases of 50% or greater 

between runs 1 and 3. Two of the 14 sites (sites T and I) showed very little difference in 

sediment production between runs 1, 2, and 3. Contrary to expected, two of the sites (sites G 

and F) showed significantly less sediment when tested after traffic. Sites G and F were located 

in succession, about 500 feet apart, on a main “trunk” line road. Their resistance to traffic may 

have been a function of their material and frequent use. 

5.2 Influencing Factors

As part of this study, measurements and notes were taken on as many site parameters 

as possible. It was hoped that the amount of sediment production from the road segments 

could be linked to one or more site variables such as road slope, road width, or road 

composition. Unfortunately, there was no strong correlation of sediment production to any one 

factor or combination of factors. The factors are discussed separately below.

Figure 5.3 Sediment results from individual runs of the rainfall simulator. All roads were 

subject to 20 vehicle passes before run 2 and again before run 3.

0

10

20

30

40

50

60

70

80

90

A T S R K G I C L N E M F B Pounds of Sediment

Site ID

Pounds of Sediment Measured in Individual Runs

Run 1

Run 2

Run 3
21

5.2.1 Road Slope and Sediment

Road slope refers to the linear grade of 

the roadway. Road slope was one of the 

criteria used in choosing road segments to test. 

The 14 road segments tested were chosen in 

part because they represented a fairly wide 

distribution of slopes from 3.3% to over 20%. 

The relationship between road slope and 

average sediment production is illustrated in 

Figure 5.4. The correlation between road slope 

and sediment generation is very weak 

(R2=0.24), meaning that in this study, road 

slope had little determination on the average 

amount of sediment generated. Part of the 

reason slope was not a large factor may be the 

spatially limited rainfall event. The RainMaker

only produced rainfall on 100 linear feet of 

roadway. In a natural rain event, portions of 

roadway outside the test section would be 

receiving rainfall and generating runoff that 

would flow onto the test section. In this situation, an increase in road slope would cause 

increase velocities of runoff and increased erosion. While the rainfall simulator does a good job 

at simulating the impact erosion of rainfall, it lacks the ability to generate rainfall on a large 

enough area to simulate the extent of rill and gully erosion that occurs in a real storm. The 

slope for each road segment is included in Table 4.1. It is worth noting that road slope showed 

a slightly better correlation (R2increase from 0.24 to 0.32) to sediment production in the first run 

of the RainMaker that was completed before traffic passes (Figure 5.5). Sediment production 

from runs 2 and 3, done after traffic passes, had less correlation with road slope.

5.2.2 Road Width and Sediment

The width of the road was measured in two ways. First, the width of the actual traveled 

roadway was measured. This measurement was typically based on the presence or absence of 

vegetation. Vegetated areas beside the road that do not typically see traffic were considered 

berms. The width of the road including berms was also measured since the berms, although 

typically not driven on, can still contribute sediment to the test sections. Road widths ranged 

Figure 5.4: Road slope in relation to average sediment 

production. (Site A omitted because of grass cover)

R2 = 0.235

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50 60 70

Road Slope

Average Sediment Production

Correlation of Road Slope to Sediment

Figure 5.5: Road slope in relation to sediment production 

from RUN 1 only. (Site A omitted because of grass cover)

R2 = 0.3202

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50

Road Slope

RIN 1 Sediment Production

Correlation of Road Slope to Sediment RUN 1
22

from 10 feet to 15 feet in width, with an average of 12.0 feet. The road and berm combined 

width ranged from 11 feet to 17 feet with an average of 14.1 feet. The correlation of road width 

and “road+berm” width in relation to total sediment production is illustrated in Figure 5.6. 

Although neither factor showed a strong correlation with sediment production, the “road+berm” 

width did have a better correlation (R2=0.19) than road width alone (R2=0.02). The widths for 

each road segment are included in Table 4.1. Just as with road slope, it is worth noting that 

road width showed a better correlation (R2increase from 0.19 to 0.31) to sediment production in 

the first run of the RainMaker that was completed before traffic passes (Figure 5.7). Sediment 

production from runs 2 and 3, done after traffic passes, had less correlation with road width.

 

5.2.3 Road Use and Sediment

Road use, or traffic volume, was approximated using field observations and maps. The 

14 sites tested encompassed a wide array of traffic volumes. Some roads served as main 

access routes to the majority of the oil and gas wells and tank batteries. These roads routinely 

see traffic such as loaded tanker trucks, pickup trucks, and all-terrain vehicles. Some roads 

were secondary, only serving a handful of wells. Site A was on a gated road where the traffic 

level was low enough to allow grass to 

cover most of the roadway.

Each road segment was assigned a 

rating from 1 (lowest) through 5 (highest) to 

approximate its traffic level. Use levels 

were based on field observations of traffic 

and wear, and on the amount of wells the 

road accessed. The use level for each 

road segment is included in Table 4.1. The 

correlation between road use level and 

Figure 5.8: Road use, or traffic volume, in relation to 

average sediment production. 

R2 = 0.0614

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70

Road Use Level

Average Sediment Production

Correlation of Road Use to Sediment

Figure 5.6: Road width, with and without berm, in relation 

to average sediment production. (Site A omitted because 

of grass cover and indeterminate width)

R2 = 0.0189

R2 = 0.1949

8

9

10

11

12

13

14

15

16

17

18

0 10 20 30 40 50 60 70

Road Width (ft)

Average Sediment Production

Correlation of Road Width to Sediment

road width

road + berm width

Figure 5.7: Road width, with and without berm, in relation 

to RUN 1 sediment production. (Site A omitted because of 

grass cover and indeterminate width)

R2 = 0.0546

R2 = 0.3091

8

9

10

11

12

13

14

15

16

17

18

0 10 20 30 40 50

Road Width (ft)

RUN 1 Sediment Production

Correlation of Road Width to Sediment RUN 1

road width

road + berm width
23

sediment production was very weak (R2=0.06). Visual differences in road use can be seen in 

pictures in individual site results.

5.2.4 Road Strength and Sediment

The in-place strength of each road segment was 

measured using a dynamic cone penetrometer [DCP]. 

The DCP, pictured in Figure 5.9, consists of a pointed 

steel shaft with a standard weight that is free to slide up 

and down. Following ASTM standards (D6951/D6951M09), the weight is lifted and dropped repeatedly from a 

know height in order to slowly drive the pointed end of the 

shaft into the road surface. In simplest terms, the ease of 

which the shaft can be driving into the road can be 

correlated to the strength of the road. This strength is 

often expressed as the road’s “California Bearing Ratio”

[CBR]. The DCP was used to provide an inexpensive and 

quick approximation of the CBR for each 

site. The CBR rating was developed for 

measuring the load-bearing capacity of soils 

used for building roads. The stronger the 

surface is, the more resistant it is to 

penetration, the higher its CBR rating. A 

CBR of 3 equates to tilled farmland, a CBR 

of 4.75 equates to turf or moist clay, while 

moist sand may have a CBR of 10. High 

quality crushed rock has a CBR over 80. 

The standard material for this test is 

crushed California limestone which has a value of 100 (Salgado, 2003).

All CBR testing was done on the same day to eliminate soil moisture and weather as 

variables. The Cone Penetrometer was run at five random locations on each road segment to a 

depth of 100mm. The five data points were then averaged and used to determine the CBR for 

each site. CBRs ranged from a low of 31 for the grassed site (Site “A”), to a high of 141 (Site 

“T”). As illustrated in Figure 5.10, the CBR had almost no correlation with the actual amount of 

sediment produced from each site (R2=0.08).

However, when the amount of sediment increase between Run 1 and Run 3 for each 

site is compared to road hardness, a relationship is found. Figure 5.11 illustrates that roads with 

Figure 5.10: Road strength, expressed as California 

Bearing Ratio, in relation to average sediment production. 

(Site A omitted because of grass cover. No data for Site 

B & C)

R2 = 0.0808

40

60

80

100

120

140

160

0 10 20 30 40 50

California Bearing Ratio (CBR)

Average Sediment Production

Correlation of Strength (CBR) to Sediment

Figure 5.9: The dynamic cone 

penetrometer pictured here is used to 

measure road surface strength. 
24

a lower strength or CBR tend to be more 

effected (larger sediment increase) by the 

traffic that the sites were subjected to 

between runs (R

2

=0.61). Roads with a higher 

strength or CBR were less affected by traffic. 

The CBRs for each road segment are 

included in Table 4.1.

A correlation also exists between the 

use or traffic level assigned to each road, and 

the road strength expressed as the California Bearing Ratio. Figure 5.12 illustrates that as road 

use increases, the strength or CBR of the road surface increases as well (R2=0.84).

5.2.5 Road Composition and Sediment

As part of the effort to quantify the characteristics of each site, road surface samples 

Table 5.1: Composition of road surface samples. Samples are in order of increasing sediment production. 

Average

#2 3/4" 3/8" #4 #10 <#10 sand % silt % clay % Sediment (lbs)

A 18% 36% 19% 10% 7% 11% 5% 3% 3% 3.2 A

T 0% 32% 15% 14% 9% 27% 20% 4% 3% 7.4 T

S 13% 16% 24% 22% 15% 12% 6% 3% 3% 7.9 S

R 16% 28% 18% 15% 11% 11% 5% 3% 3% 11.1 R

K 50% 11% 12% 9% 6% 12% 7% 3% 2% 12.7 K

G 0% 18% 26% 19% 15% 22% 9% 8% 5% 13.0 G

I 0% 13% 16% 19% 25% 27% 12% 9% 6% 14.7 I

C 6% 28% 18% 20% 16% 13% 6% 4% 3% 14.9 C

L 38% 18% 12% 10% 8% 13% 7% 4% 2% 28.4 L

N 4% 32% 25% 17% 10% 12% 6% 4% 2% 29.7 N

E 8% 21% 8% 11% 12% 40% 29% 6% 5% 35.9 E

F 18% 23% 22% 12% 10% 16% 7% 5% 3% 42.7 F

M 16% 22% 16% 17% 13% 17% 8% 5% 3% 43.1 M

B 0% 25% 21% 17% 13% 24% 11% 8% 5% 60.1 B

Percent of Sample Retained on Seives Breakdown of <#10 Material

Site Site

Figure 5.11: The relationship of strength, expressed as 

California Bearing Ratio, to the percent of sediment 

increase from Run 1 to Run 3. (No data for Site B & C) 

0

20

40

60

80

100

120

140

160

-200% 0% 200% 400% 600% 800%

California Bearing Ratio (CBR)

% Increase in Sediment from Run 1 to Run 3

Correlation of Strength (CBR) to Sediment 

Increase from Run 1 to Run 3

Figure 5.12: The relationship of strength, expressed as 

California Bearing Ratio, to the traffic volume level. (No 

data for Site B & C) 

R2 = 0.8376

0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 Strength (CBR)

Road Traffic Level

Correlation of Road Traffic to Strength (CBR)

R

2

=0.61

Figure 5.13: The relationship of the percent sand, silt, 

and clay in road surface samples to average sediment 

production. (Site A omitted because of grass cover)

R2 = 0.0096

R2 = 0.18

R2 = 0.0521

0%

5%

10%

15%

20%

25%

30%

0 10 20 30 40 50 60 70

% Composition

Average Sediment Production

Correlation of <#10 Material to Sediment

% sand

% silt

% clay
25

were taken and analyzed for composition. The samples were obtained by excavating an area 

approximately two square feet by four inches deep in the surface of each site. Material sample 

locations were randomly located on each test section. These samples were taken to the soils 

lab at Penn State University for analysis. The samples were first screened to obtain the size 

distribution of the larger particles. Any particles passing through a #10 screen (1/10th of an inch 

nominal max size) were further analyzed to determine the percent sand, silt, and clay in the 

sample. Table 5.1 lists the composition of each sample.

There were no significant effects on sediment production that could be predicted by any 

one particle size or combination of particle sizes in the samples taken from the road surface.

Figure 5.13 shows that there is not a significant correlation between the amount of sand silt and 

clay in the road surface samples and average sediment production. 

5.3 Multi-Variable Analysis

No one variable showed a strong correlation with the amount of sediment produced from 

the road segments. This is most likely due to the high number of variables involved in 

determining sediment loss. There is, however, an interesting point of discussion when looking 

at the combined effect of road slope and width on sediment production both before and after 

traffic stresses. If road slope and road width are combined into a “slope/width” factor, it 

presents a stronger correlation to sediment production than either factor alone (Figure 5.14). 

The R2for “slope/width” correlation to sediment on run 1 was 0.51, compared to an R2of 0.32 

and 0.30 for road slope and width individually. 

One interesting result of this 

analysis is that the “slope/width” factor 

shows a much better correlation with 

sediment production from run 1 before 

traffic stresses. Runs 2 and 3, after traffic, 

show a much lower correlation. These 

observations can be combined with trends 

found in road strength (Section 5.2.4) to 

generate some general theories on 

sediment production. 

Initially, without traffic stress, the 

amount of sediment production from each road segment is best correlated with the slope and 

width of the road (as the slope and width of the road increases, sediment production increases 

(Figure 5.14)). For runs 2 and 3 of the RainMaker, each done after 20 vehicle passes, road 

slope and width played less of a role in determining sediment production. Instead, sediment 

Figure 5.14: Road “slope+width” factor, in relation to 

average sediment production for each run. (Site A omitted 

because of grass cover)

R2 = 0.5108

R2 = 0.2784

R2 = 0.1542

20

22

24

26

28

30

32

34

36

38

40

Combined "Slope/Width" Factor

0 20 40 60 80 100

Sediment Production (lbs)

Correlation of "Slope+Width" to Sediment 

run 1

run 2

run 3
26

production from runs 2 and 3 can best be explained by the road strength. Road strength was 

shown to be a good indicator of how much sediment would increase on the sites after being 

stressed by traffic (Figure 5.11). Roads with the lowest strength, measured by CBR, had the 

highest percentage of sediment increase after traffic stresses. Roads with the highest strength 

showed little sediment increase, and some even showed a decrease, in runs 2 and 3 after traffic 

stresses.

5.4 Sediment Production After Aggregate Placement

The second part of this study looked 

at sediment production after the placement 

of pit-run aggregate and Driving Surface 

Aggregate. As stated earlier, pit-run is a 

term used to describe a relatively cheap 

locally derived material of varying quality 

and DSA is a specially designed aggregate 

specification designed to achieve maximum 

compacted density to resist traffic and 

erosion. The DSA used in this study was 

limestone based, although limestone is not 

required. DSA is a processed commercial product that is typically more expensive than pit-run. 

Depending on the site location and aggregate source, DSA may also have to be hauled a 

substantial distance which also increases the cost. The Allegheny National Forest has been 

using DSA on roads adjacent to streams in an effort to reduce sediment pollution. More 

information about DSA is available from PennDOT specification MS-0450-0004 or from the 

Center’s website at www.dirtandgravelroads.org. Previous research conducted by the Center 

has showed that DSA reduced sediment production by over 90% for at least two years after 

placement when compared to native surfaced roads in Potter County. This research is also 

available on the Center’s website.

Figure 5.16 visually depicts each of the four sites for both the existing road surface and 

the new road aggregate. All four sites showed sediment reductions after new aggregate 

placement ranging from 39% to 95% compared to the existing road surface. It is worth noting, 

however, that the two sites where new pit-run was placed had significantly higher “existing road” 

sediment production than the two sites where DSA was placed. This was not planned and is 

simply a result random road location when Allegheny National Forest personnel performed work 

on the road system in early 2011. This higher “existing road” sediment production may have 

helped show a more significant sediment reduction for the new pit-run sites.

Figure 5.15: Summary of sediment reductions found 

between existing road surfaces and new aggregate 

surfaces.

0

10

20

30

40

50

60

B 

before

B new 

pit-run

F 

before

F new 

pit-run

G 

before

G DSA C 

before

C DSA

lbs sediment per 30 min

Summary of Sediment Production Rates 

Before and After New Aggregate
27

Figure 5.16: Visual comparison of the four sites in exiting condition during testing in 2010 and with new aggregate 

during testing in 2011. Average sediment production for three runs is included on each photo.

The two new pit-run sites examined in this part of the study averaged 26.1 

pounds of sediment per 30 minute event. This is slightly higher than the sediment production of 

24.7 pounds per event that was found when all 13 “existing condition” tests (excluding grass 

road on site A) were averaged. This means that while the pit-run sites did produce reductions in 

sediment compared to the “existing road” test for their individual sites, the sediment productions 

found were typical of other existing pit-run surfaces tested.

The two new DSA sites examined in this part of the study averaged 2.5 pounds of 

sediment per 30 minute event. This is significantly lower than all of the “existing road” testing 

done where sediment production averaged 24.7 pounds. Both DSA sites were significantly 

below the sediment production rates of even the lowest “existing road” test of 7.4 lbs found on 

site T. In fact, the sediment production from the two DSA sites was most similar to the grassed 

road tested on site A. The DSA on site C showed an extremely high sediment reduction of 

95%, which is similar to previous studies conducted by the Center. The DSA on site C also had 

the lowest sediment production figure found in the study by far, even including the grassed road

on Site A (0.8 lbs avg; 1.5 lbs, 0.5 lbs, 0.4 lbs in individual runs). It should also be noted that 
28

both DSA sites showed significant reduction in sediment production in sequential runs, an 

indication that traffic stresses between runs did not generate additional sediment from the 

aggregate. This discussion is illustrated in figure 5.17.

Figure 5.17: Summary of average sediment production by road type. All figures are in pounds of 

sediment produced per mile of road. Note the differing number of each type of sites that was 

available for use in obtaining average.

1,306

1,378

167 133

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

Existing Roads 

(13 sites)

New Pit-run 

(2 sites)

Grass 

(1 site)

New DSA 

(2 sites)

Pounds of sediment per road mile

Average Sediment Productions per Road-mile for Each 

Road Type for a Single 30-minute 0.6" Rain Event

run 1

run 2

run 3

AVG
29

CHAPTER 6: Conclusions

The construction and maintenance of access roads is an often 

overlooked environmental impact of oil and gas development. Access

roads can represent a significant change in land use patterns, especially in 

forested watersheds. These roads, almost all of which are unpaved, not 

only generate significant sediment, but also add to the hydrologic 

connectivity of the watershed. The purpose of this study was to quantify 

sediment runoff rates from existing shallow oil well access road in the 

Allegheny National Forest.

It is important to note that the sediment figures obtained in this study 

should be considered conservative. Sediment production rates from an 

actual rain event of similar magnitude can be expected to be higher 

because:

 The rainfall simulator produces rainfall on 100’ of roadway. In actual 

rain events, runoff from upslope sections of roadway would have 

entered all of the test section. In addition to rainfall falling directly on

other sections of the road, many sites would also experience water 

running onto the road from adjacent banks, well pads, and other 

areas in a natural event. This additional volume of water would have 

caused more erosion on each of the test sections. 

 The size and velocity of raindrops from the RainMaker were within

the range of natural rain events. Both drop size and velocity, 

however, were towards the low end of the expected range. Larger 

drop size and higher velocity would also contribute to increased 

sediment detachment.

 The rainfall simulator was only run during the summer and after two 

days of dry weather. Running the simulator in other times of year, 

such as during spring thaw, may yield more sediment.

The Center’s rainfall simulator showed that even a modest 

rainstorm (0.6” in 30 minutes, 2 month-recurrence) causes these roads to 

produce significant amounts of sediment. The average sediment 

production from these roads before traffic stresses was 840 pounds per 

mile for a single 30 minute event. If traffic stresses are introduced onto the 

road, the average sediment production increases 83% to 1,540 pounds per 

mile for a single 30 minute event. Figure 6.1: Testing 

on site “E”.
30

Looking beyond the immediate 

study watersheds, there were 9,764

existing wells within the Allegheny 

National Forest in 2009. These wells are 

accessed by a network of 1,695 miles of 

road, not including ANF owned roads

(USDA-FS Unpublished, 2010a). 

Combining this information with the 

results of the study indicate that a single 

30 minute rainfall event of 0.6” can be 

estimated to generate over 1,106 tons of 

sediment from existing oil and gas roads 

in the Allegheny National Forest. 

Projections are that the network of 1,695

miles of existing oil and gas roads will increase to 2,258 miles by 2020 (USFS Unpublished, 

2010a). This increases the estimated sediment production from 1,106 tons to over 1,470 tons 

per storm event from oil and gas roads in the forest in 2020. 

In order to minimize sediment delivery to streams, roads should be built away from 

streams and avoid stream crossings. Also, roads should be maintained to avoid directing road 

runoff to streams. Results reported by Bloser and Scheetz [2008] show significant sediment 

reductions ranging from 31% to 94 % by applying individual road drainage control practices that 

reduce and control the volume of road runoff. These practices include raising the roads profile, 

installing grade breaks, adding additional drainage outlets, and berm removal. The combined 

sediment reduction effects of these practices have not been studied, but can be expected to be 

greater than that of the individual practices.

Results reported by Bloser and Scheetz [2008] can be used to compare the sediment 

production results found on the oil access roads in this study with the Center’s previous 

RainMaker findings on State Forest and Municipal roads. Please note that the earlier study was 

completed with the Center’s “first-generation” RainMaker. The storm intensities of the first and 

second generation RainMakers are very similar (0.55” per 30 minutes for old and 0.62” per 30 

minutes for new), and the procedures used were virtually identical, allowing some 

generalizations to be made by comparing results of the two studies. The Center’s previous 

research was completed on five “as-is” roads, two municipal owned, and three State Forest 

owned. The averaged sediment productions in the previous study ranged from 0.7 pounds to 

12.2 pounds, with an average of 5.6 pounds per 30 minute event. The 14 oil access roads in 

this study ranged from 3.2 pounds to 60 pounds, with an average of 25 pounds per 30 minute 

Figure 6.2: This image, taken in the Grunder watershed in late 

March after a natural rain event, illustrates the effect of poor 

road material, excessive moisture, and vehicle traffic on 

sediment generation. (photo courtesy ANF)
31

event. The oil access roads in this study produced an average of 450% more sediment than the 

public roads in the Center’s previous study, despite only a 13% increase in rainfall intensity in 

the new study. This greater sediment production is likely due to the lack of consideration of 

drainage structures, road shape, and quality surface materials. Municipal and Forestry roads 

are open to the public and must meet user standards, including the passage of passenger cars. 

These access roads have no user standards except to be passable by truck or All Terrain 

Vehicle.

One of the secondary goals of this study was to begin to determine which road factors 

played a role in determining the amount of sediment generated by the various road segments. 

While none of the individual factors tested (slope, CBR, etc) showed a strong relationship to the 

amount of sediment produced, there were a number of noteworthy observations:

 There was a significant “First Flush” effect, in which sediment concentrations in runoff 

were significantly higher at the beginning of each test, and slowly decreased over each 30 

minute event (Details in Figure 5.1).

 Roads that served as “trunk lines” or accessed more well pads had a higher CBR 

indicating they have much more compacted surfaces. These roads also showed the 

lowest percentage of sediment increases after being stressed by traffic. Roads with lower 

traffic showed large increases in sediment production after traffic stresses. 

 Site “A” was similar to all other sites, except that the low amount of traffic on the road 

allowed the establishment of grass over most of the road surface. Site “A” had the lowest 

CBR and was the most impacted by traffic stresses. Traffic removed some grass cover 

and created rutting, causing sediment production to increase over 600% between run 1 

and run 3. Despite its relatively steep slope (14.7%), Site A produced significantly less 

sediment than the other sites (3.2 pounds on Site A versus 24.7 pound average for other 

sites). Furthermore, during Run 1 of the RainMaker before traffic, sediment production 

from this site was only 1.0 pound in 30 minutes. This would suggest that significant 

sediment reductions could be achieved by establishing grass on access roads with low 

traffic volumes.

 Without traffic stress, the best indicators of sediment production from the roads tested

were road slope combined with road width. If the road is stressed by traffic, then sediment 

production becomes less dependent on road width and slope, and more dependent on 

road strength as measured by the California Bearing Ratio.

The second component of this study looked at changes in sediment production after the 

placement of new pit-run and Driving Surface Aggregate surfaces. While both new pit-run 

surfaces reduced sediment compared to the “existing road” tests, the amount of sediment 

produced was consistent with the average sediment production for all 13 “existing condition” 
32

road tests as illustrated in figure 5.17. Because pit-run material is simply excavated instead of 

mined and processed, there is little quality control and the product is highly variable. The use of 

pit-run material represents the traditional approach to road maintenance for this part of 

Pennsylvania.

The sediment production rates found for the two DSA sites was approximately one-tenth 

that of the pit-run surfaces. This is consistent with previous research done by the Center that 

showed sediment production rates well below 1 pound per run for at least two years after DSA 

placement on two separate sites in Potter County, PA, in 2008. DSA has been in use since 

2000 in Pennsylvania and was designed to resist erosion and to provide a longer lasting road 

surface and reduce runoff pollution to nearby streams. From this study and previous testing, it 

is clear that Driving Surface Aggregate significantly reduces sediment production compared to 

traditional “locally derived materials”.

It is also important to note that the two DSA sites tested both showed a significant decrease 

in sediment in sequential individaul runs despite being subjected to traffic between runs. This 

shows that DSA provides greater surface durability and structural support for traffic loads. This

is evidence that a hard and well graded aggregate is less likely to break down into finer particles 

under traffic and subsequently leave the road composite in storm run-off or as dust. The likely 

result is significantly longer maintenance cycles related to required grading and/or re-graveling 

(i.e. – a longer elapsed time between needed reshaping of the road surface or replenishment of 

surface material). Along with the potential for significant long term environmental benefits, the 

long-range economic benefits should be considered when selecting road surface materials. In 

prioritizing the selection of different road surface materials, factors such as anticipated volume 

and type of traffic (mining and timber hauling), and proximity to surface waters might be 

emphasized.

While the environmental and traffic resistance road benefits of DSA may be apparent, its 

use does represent a significant cost increase over locally derived materials such as pit-run. 

This is especially true in locations such as the Allegheny National Forest where DSA must be 

imported significant distances. In 2011, DSA was approximately 2.5 times more expensive than

pit-run for locations in the Allegheny National Forest. It is up to individual road managers and 

departments to weight the benefits of DSA against the increase in cost. Over the past decade 

as aggregate costs have risen, Allegheny National Forest personnel have been focusing their 

DSA usage to environmentally sensitive locations. These include stream crossings and 

streamside locations where road runoff to the stream is unavoidable. DSA is typically used in 

approximately 300 foot stretches around such streamside locations. DSA has also been used 

on roads experiencing higher traffic loads and volumes since it tends to hold up better to those 

stresses. Sections of road that are not near streams and do not have excessive traffic have 
33

continued to be surfaced with cheaper materials such as pit-run. Additonally, consideration 

should be given to avoiding direct road discharge into streams throug the use of imporved 

drainage practices, regardless of the road surface selected. These drainage improvements may 

prove to be a more cost-effective way to reduce sediment pollution than the use of DSA alone. 

However, it is clear that the Allegheny National Forest’s use of DSA in locations where direct 

runoff to the stream is unavoidable is an effective practice to reduce sediment pollution.

Future Research

This study was an effort to begin quantifying the sediment pollution generated by oil access 

roads on the Allegheny National Forest. The results of the study, with average sediment 

production of 1,300 pounds per mile for a single 30 minute 0.61” storm, illustrate the magnitude

of the problem. There are many other potential topics that should be considered for future 

study:

 Analysis of the connectivity of the road drainage network and the stream network to 

determine how much of the sediment that is produced can be expected to reach a stream.

 Obtain additional data from other test sites within the ANF. The 14 sites tested in this 

study represent only 1,400 linear feet of roadway, or about 0.02% of the known existing of 

oil access road in the Allegheny National Forest. More test sites will yield more accurate 

sediment production figures, and may help to determine which road characteristics are 

closely tied to sediment production. Even nearby roads, if owned by a different company, 

may have different sediment production rates because a different set of standards and 

practices are used to maintain the road.

 Obtain additional data from other test sites outside the ANF. Local geology and climate 

play a large role in determining sediment generation from unpaved roads. In addition, 

other activities, such as wind farms and Marcellus gas drilling, require access roads of 

varying densities and sizes where sediment production has not been quantified.

 In addition to testing “existing” roads, another beneficial study would be to determine 

sediment reductions achieved by implementing Best Management Practices (BMP) to 

reduce sediment generation on roads. By running the RainMaker on an existing road, 

then running it again at various timeframes after BMP installation, the long term 

performance of the BMP in reducing sediment pollution could be quantified. For instance, 

grade brakes, broad-based dips and maintaining road crown direct runoff to ditches and 

the forest floor and are effective at reducing runoff on the road surface. They also reduce 

the length of roads that are hydrologically connected to streams.

 Run the rainfall simulator on different aggregate such as 2A or 2RC that are widely used 

for road surfacing throughout Pennsylvania. This can begin to address the question of 
34

was it actually “DSA” that caused the sediment reductions, or simply the use of a graded 

limestone aggregate. It is probable that both 2A and 2RC would also have significantly 

lower sediment productions than pit-run. It would be useful to compare their sediment 

productions to those of DSA found here and in previous studies.
35

REFERENCES

ANF (Allegheny National Forest). 2007. Final Environmental Impact Statement. Appendix F

http://www.fs.fed.us/r9/forests/allegheny/projects/forest_plan_revision/FEIS-LRMP/ (last 

accessed 10/26/2010)

Aron, G., D. Wall, E. White, C. Dunn, D. Kotz. 1986. Field Manual of PA DOT Storm Intensity-DurationFrequency charts PDT-IDF. Pennsylvania State University, 1986

Coe, D.B.R. 2006. Sediment Production and Delivery from Forest Roads in the Sierra Nevada, California. 

M.S. Thesis, Colorado State University. 2006

Evans, B., S. Sheeder, K. Corradini, and W. Brown, 2003a. AVGWLF User’s Guide, Version 5.0.2. 

Pennsylvania State University, Environmental Resources Research Institute, University Park, PA.

Food and Agriculture Organization of the United Nations, 1993. Field Measurement of Soil Erosion 

and Runoff. http://www.fao.org/documents/show_cdr.asp?url_file=/ docrep/T0848E/t0848e11.htm (Last Accessed April, 2006)

Foltz, R.B. 1993. Sediment Processes in Wheel Ruts on Unsurfaced Forest Roads. Ph.D Dissertation. 

University of Idaho, Moscow, 1993

Kahklen, Keith, 2001. A Method for Measuring Sediment Production from Forest Roads. USDA, Forest 

Service. 

MacDonald L.H., R.W. Sampson, and D.M. Anderson. 2001. Runoff and Road Erosion at the Plot and 

Road Segment Scales, St. John, US Virgin Islands. Earth Surface Processes and Landforms.

Novotny, V., 2003. Water Quality: Diffuse Pollution and Watershed Management. John

Wiley & Sons, Inc., New York.

Pennsylvania Department of Transportation, 1999. Criteria for Applicability of Hydrologic and 

Hydraulic Methodologies. http://www.dot.state.pa.us/bridge/standards/Pubs/ 4319911.pdf (Last 

Accessed April, 2005)

Pennsylvania Department of Environmental Protection, 2004. Pennsylvania’s Chesapeake Bay 

Tributary Strategy. http://www.dep.state.pa.us/hosting/pawatersheds/chesapeakebay/ 

docs/TribStrategy (Last Accessed July, 2007)

Rogers,R.R. A Short Course in Cloud Physics, 2 ed. Pergamon Press, 1979

Salgado, R., S. Yoon. 2003. Dynamic Cone Penetration Assessment for Subgrade Assessment. Civil 

Engineering Joint Transportation Research Program, Purdue University, 2003.

Scheetz, B.E., S. Bloser. 2008. Environmentally Sensitive Maintenance Practices for Unpaved Roads: 

Sediment Reduction Study. Final Report Prepared for Chesapeake Bay Commission

U.S. Department of Agriculture, Forest Service. 2010. unpublished Programmatic Effects of Private Oil 

and Gas Activity on the Allegheny National Forest. Pages 4-7.

U.S. Department of Agriculture, Forest Service. 2010a. Attachments 2 for unpublished Programmatic 

Effects of Private Oil and Gas Activity on the Allegheny National Forest. Page 23.

Ziegler A.D., R.A. Sutherland, and T.W. Giambelluca. 2000. Partitioning Total Erosion on Unpaved 

Roads into Splash and Hydraulic Components: The roles of Interstorm Surface Preparation and 

Dynamic Erodibility. Water Resources Research. 
36

APPENDIX A – RainMaker Data

The runoff rates and sediment concentration for all sites in this study are on the 

following tables. Sites are in order of increasing average sediment production, as listed 

throughout this report.
37

Appendix A: RainMaker Data: SITE A 3.2 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 11.03 1 5.4 Kd1.01 1,210

5 7.12 1 8.4 Kd1.05 720

10 4.31 1 13.9 Kd1.10 420

15 3.38 1 17.8 Kd1.15 210

20 2.69 1 22.3 Kd1.20 125

25 2.97 1 20.2 - - 72

30 9.63 1 6.2 Kd1.30 19

35 21.41 1 2.8 - - 19

40 39.28 1 1.5 - - 19

45 103.47 1 0.6 - - 19

50 139.44 1 0.4 - - 19

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.47 1 4.8 Kd2.01 5,000

5 4.88 1 12.3 Kd2.05 1,280

10 3.16 1 19.0 Kd2.10 410

15 2.60 1 23.1 Kd2.15 250

20 2.88 1 20.8 Kd2.20 182

25 2.71 1 22.1 - - 134

30 4.81 1 12.5 Kd2.30 85

35 11.31 1 5.3 - - 85

40 22.50 1 2.7 - - 85

45 36.13 1 1.7 - - 85

50 54.50 1 1.1 - - 85

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 13.87 1 4.3 Kd3.01 11,400

5 4.72 1 12.7 Kd3.05 3,900

10 2.97 1 20.2 Kd3.10 1,350

15 2.41 1 24.9 Kd3.15 870

20 2.34 1 25.6 Kd3.20 680

25 2.48 1 24.2 - - 410

30 4.84 1 12.4 Kd3.30 140

35 12.44 1 4.8 - - 140

40 24.07 1 2.5 - - 140

45 39.34 1 1.5 - - 140

50 101.05 1 0.6 - - 140

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 3.55 Run 1 6.20 Run 2 3.60
38

Appendix A: RainMaker Data: SITE T 7.4 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 10.31 1 5.8 Kd1.01 7,400

5 4.84 1 12.4 Kd1.05 2,910

10 4.04 1 14.9 Kd1.10 2,110

15 3.85 1 15.6 Kd1.15 1,890

20 3.67 1 16.3 Kd1.20 1,640

25 3.55 1 16.9 - - 1,105

30 66.87 1 0.9 Kd1.30 570

35 1.00 0 0.0 - - 570

40 1.00 0 0.0 - - 570

45 1.00 0 0.0 - - 570

50 1.00 0 0.0 - - 570

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 6.47 1 9.3 Kd2.01 4,200

5 3.57 1 16.8 Kd2.05 2,160

10 3.35 1 17.9 Kd2.10 1,390

15 3.18 1 18.9 Kd2.15 1,210

20 3.40 1 17.6 Kd2.20 1,450

25 3.22 1 18.6 - - 1,235

30 6.96 1 8.6 Kd2.30 1,020

35 16.66 0.132 0.5 - - 1,020

40 1.00 0 0.0 - - 1,020

45 1.00 0 0.0 - - 1,020

50 1.00 0 0.0 - - 1,020

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.95 1 10.1 Kd3.01 10,300

5 3.96 1 15.2 Kd3.05 2,200

10 3.68 1 16.3 Kd3.10 1,780

15 3.61 1 16.6 Kd3.15 1,390

20 3.61 1 16.6 Kd3.20 1,420

25 3.59 1 16.7 - - 1,200

30 7.30 1 8.2 Kd3.30 980

35 12.09 0.132 0.7 - - 980

40 78.68 0.132 0.1 - - 980

45 1.00 0 0.0 - - 980

50 1.00 0 0.0 - - 980

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 2.11 Run 1 5.40 Run 2 2.25
39

Appendix A: RainMaker Data: SITE S 7.9 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.67 0.132 1.7 Kd1.01 3,900

5 3.38 1 17.8 Kd1.05 1,500

10 3.13 1 19.2 Kd1.10 780

15 3.08 1 19.5 Kd1.15 940

20 2.89 1 20.8 Kd1.20 690

25 2.86 1 21.0 - - 750

30 12.78 1 4.7 Kd1.30 810

35 6.08 0.132 1.3 - - 810

40 11.41 0.132 0.7 - - 810

45 18.53 0.132 0.4 - - 810

50 27.69 0.132 0.3 - - 810

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.97 1 15.1 Kd2.01 8,500

5 3.09 1 19.4 Kd2.05 2,530

10 3.02 1 19.9 Kd2.10 1,620

15 2.72 1 22.1 Kd2.15 1,140

20 2.68 1 22.4 Kd2.20 1,240

25 2.76 1 21.7 - - 1,120

30 7.53 1 8.0 Kd2.30 1,000

35 3.38 0.132 2.3 - - 1,000

40 6.25 0.132 1.3 - - 1,000

45 11.87 0.132 0.7 - - 1,000

50 26.66 0.132 0.3 - - 1,000

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.36 1 13.8 Kd3.01 8,600

5 3.42 1 17.5 Kd3.05 3,200

10 3.06 1 19.6 Kd3.10 1,450

15 3.06 1 19.6 Kd3.15 1,190

20 3.09 1 19.4 Kd3.20 1,140

25 2.98 1 20.1 - - 1,070

30 6.35 1 9.4 Kd3.30 1,000

35 3.40 0.132 2.3 - - 1,000

40 7.87 0.132 1.0 - - 1,000

45 14.32 0.132 0.6 - - 1,000

50 15.75 0.132 0.5 - - 1,000

60 - - 0 - - - Run 3 2.66 Run 1 5.00 Run 2 3.09

FLOW RATE CONCENTRATION
40

Appendix A: RainMaker Data: SITE R 11.1 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 9.10 0.132 0.9 Kd1.01 3,300

5 3.47 1 17.3 Kd1.05 5,500

10 3.50 1 17.1 Kd1.10 1,970

15 3.34 1 18.0 Kd1.15 1,700

20 3.08 1 19.5 Kd1.20 1,670

25 3.29 1 18.2 - - 1,235

30 10.82 1 5.5 Kd1.30 800

35 9.04 0.132 0.9 - - 800

40 21.45 0.132 0.4 - - 800

45 42.22 0.132 0.2 - - 800

50 93.30 0.132 0.1 - - 800

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.52 1 13.3 Kd2.01 5,300

5 3.43 1 17.5 Kd2.05 2,410

10 3.27 1 18.3 Kd2.10 2,040

15 3.58 1 16.8 Kd2.15 1,970

20 3.22 1 18.6 Kd2.20 2,010

25 3.19 1 18.8 - - 1,690

30 10.10 1 5.9 Kd2.30 1,370

35 7.10 0.132 1.1 - - 1,370

40 16.45 0.132 0.5 - - 1,370

45 30.28 0.132 0.3 - - 1,370

50 53.09 0.132 0.1 - - 1,370

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.75 1 16.0 Kd3.01 6,000

5 3.44 1 17.4 Kd3.05 4,800

10 2.92 1 20.5 Kd3.10 3,100

15 3.12 1 19.2 Kd3.15 2,440

20 3.09 1 19.4 Kd3.20 1,990

25 3.03 1 19.8 - - 1,965

30 7.31 1 8.2 Kd3.30 1,940

35 6.00 0.132 1.3 - - 1,940

40 15.16 0.132 0.5 - - 1,940

45 27.88 0.132 0.3 - - 1,940

50 46.53 0.132 0.2 - - 1,940

60 - - 0 - - - Run 3 2.00 Run 1 3.00 Run 2 2.50

FLOW RATE CONCENTRATION
41

Appendix A: RainMaker Data: SITE K 12.7 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 6.33 0.132 1.3 Kd1.01 3,700

5 4.17 1 14.4 Kd1.05 4,000

10 2.83 1 21.2 Kd1.10 2,120

15 3.17 1 18.9 Kd1.15 1,410

20 2.96 1 20.3 Kd1.20 1,330

25 2.98 1 20.1 - - 1,070

30 6.10 1 9.8 Kd1.30 810

35 4.13 0.132 1.9 - - 810

40 6.97 0.132 1.1 - - 810

45 8.50 0.132 0.9 - - 810

50 10.71 0.132 0.7 - - 810

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 6.50 1 9.2 Kd2.01 7,600

5 2.80 1 21.4 Kd2.05 6,800

10 2.90 1 20.7 Kd2.10 3,200

15 2.88 1 20.8 Kd2.15 2,320

20 2.57 1 23.3 Kd2.20 1,580

25 2.55 1 23.5 - - 1,375

30 4.10 1 14.6 Kd2.30 1,170

35 9.50 1 6.3 - - 1,170

40 3.15 0.132 2.5 - - 1,170

45 4.41 0.132 1.8 - - 1,170

50 5.25 0.132 1.5 - - 1,170

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 11.90 1 5.0 Kd3.01 7,900

5 2.59 1 23.2 Kd3.05 5,900

10 2.55 1 23.5 Kd3.10 2,680

15 2.68 1 22.4 Kd3.15 2,000

20 2.70 1 22.2 Kd3.20 1,730

25 2.30 1 26.1 - - 1,310

30 3.40 1 17.6 Kd3.30 890

35 6.50 1 9.2 - - 890

40 2.75 0.132 2.9 - - 890

45 3.88 0.132 2.0 - - 890

50 5.32 0.132 1.5 - - 890

60 - - 0 - - - Run 1 2.50 Run 2 2.00 Run 3 1.83

FLOW RATE CONCENTRATION
42

Appendix A: RainMaker Data: SITE G 13.0 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.00 1 5.0 Kd1.01 9,300

5 5.94 1 10.1 Kd1.05 6,600

10 2.65 1 22.6 Kd1.10 3,700

15 2.85 1 21.1 Kd1.15 2,540

20 2.65 1 22.6 Kd1.20 2,610

25 2.85 1 21.1 - - 2,435

30 6.62 1 9.1 Kd1.30 2,260

35 23.15 1 2.6 - - 2,260

40 9.95 0.132 0.8 - - 2,260

45 23.01 0.132 0.3 - - 2,260

50 50.35 0.132 0.2 - - 2,260

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 7.66 1 7.8 Kd2.01 8,700

5 3.03 1 19.8 Kd2.05 3,500

10 2.75 1 21.8 Kd2.10 2,840

15 2.58 1 23.3 Kd2.15 1,990

20 2.58 1 23.3 Kd2.20 2,150

25 2.50 1 24.0 - - 2,030

30 6.08 1 9.9 Kd2.30 1,910

35 28.23 1 2.1 - - 1,910

40 7.80 0.132 1.0 - - 1,910

45 15.23 0.132 0.5 - - 1,910

50 27.00 0.132 0.3 - - 1,910

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 8.31 1 7.2 Kd3.01 4,100

5 3.12 1 19.2 Kd3.05 2,490

10 2.91 1 20.6 Kd3.10 1,840

15 2.64 1 22.7 Kd3.15 1,670

20 2.54 1 23.6 Kd3.20 1,370

25 2.71 1 22.1 - - 1,515

30 5.50 1 10.9 Kd3.30 1,660

35 22.96 1 2.6 - - 1,660

40 7.43 0.132 1.1 - - 1,660

45 13.00 0.132 0.6 - - 1,660

50 15.47 0.132 0.5 - - 1,660

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 2.51 Run 1 2.90 Run 2 2.53
43

Appendix A: RainMaker Data: SITE I 14.7 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 14.34 1 4.2 Kd1.01 16,100

5 3.79 1 15.8 Kd1.05 8,900

10 3.02 1 19.9 Kd1.10 2,150

15 3.08 1 19.5 Kd1.15 2,150

20 2.94 1 20.4 Kd1.20 1,320

25 2.86 1 21.0 - - 1,355

30 3.91 1 15.3 Kd1.30 1,390

35 4.66 0.132 1.7 - - 1,390

40 12.25 0.132 0.6 - - 1,390

45 27.90 0.132 0.3 - - 1,390

50 55.55 0.132 0.1 - - 1,390

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.81 1 10.3 Kd2.01 11,500

5 3.15 1 19.0 Kd2.05 5,100

10 2.87 1 20.9 Kd2.10 3,700

15 2.85 1 21.1 Kd2.15 2,740

20 2.95 1 20.3 Kd2.20 1,850

25 2.72 1 22.1 - - 1,795

30 4.53 1 13.2 Kd2.30 1,740

35 3.97 0.132 2.0 - - 1,740

40 10.13 0.132 0.8 - - 1,740

45 20.22 0.132 0.4 - - 1,740

50 37.25 0.132 0.2 - - 1,740

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.13 1 11.7 Kd3.01 8,400

5 3.03 1 19.8 Kd3.05 4,100

10 2.67 1 22.5 Kd3.10 2,330

15 2.77 1 21.7 Kd3.15 1,850

20 2.68 1 22.4 Kd3.20 1,600

25 2.60 1 23.1 - - 1,590

30 3.75 1 16.0 Kd3.30 1,580

35 3.78 0.132 2.1 - - 1,580

40 8.91 0.132 0.9 - - 1,580

45 21.15 0.132 0.4 - - 1,580

50 32.50 0.132 0.2 - - 1,580

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 1.75 Run 1 2.03 Run 2 1.90
44

Appendix A: RainMaker Data: SITE C 14.9 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 20.78 1 2.9 Kd1.01 5,300

5 4.09 1 14.7 Kd1.05 7,000

10 3.25 1 18.5 Kd1.10 3,500

15 2.90 1 20.7 Kd1.15 2,410

20 2.60 1 23.1 Kd1.20 1,450

25 2.50 1 24.0 - - 925

30 11.12 1 5.4 Kd1.30 400

35 5.40 0.132 1.5 - - 400

40 8.71 0.132 0.9 - - 400

45 9.62 0.132 0.8 - - 400

50 11.72 0.132 0.7 - - 400

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.10 1 5.0 Kd2.01 9,000

5 3.22 1 18.6 Kd2.05 5,000

10 2.88 1 20.8 Kd2.10 3,200

15 2.55 1 23.5 Kd2.15 2,640

20 2.69 1 22.3 Kd2.20 1,820

25 2.50 1 24.0 - - 1,560

30 4.37 1 13.7 Kd2.30 1,300

35 17.31 1 3.5 - - 1,300

40 4.69 0.132 1.7 - - 1,300

45 6.70 0.132 1.2 - - 1,300

50 7.93 0.132 1.0 - - 1,300

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.72 1 12.7 Kd3.01 8,900

5 3.25 1 18.5 Kd3.05 5,700

10 2.64 1 22.7 Kd3.10 3,900

15 2.85 1 21.1 Kd3.15 2,910

20 3.09 1 19.4 Kd3.20 2,150

25 3.10 1 19.4 - - 2,165

30 4.00 1 15.0 Kd3.30 2,180

35 5.00 0.132 1.6 - - 2,180

40 6.50 0.132 1.2 - - 2,180

45 7.98 0.132 1.0 - - 2,180

50 8.28 0.132 1.0 - - 2,180

60 - - 0 - - - Run 3 3.33 Run 1 4.67 Run 2 2.33

FLOW RATE CONCENTRATION
45

Appendix A: RainMaker Data: SITE L 28.4 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 19.59 1 3.1 Kd1.01 18,800

5 6.00 1 10.0 Kd1.05 7,200

10 2.50 1 24.0 Kd1.10 2,560

15 2.19 1 27.4 Kd1.15 2,160

20 2.60 1 23.1 Kd1.20 1,350

25 2.94 1 20.4 - - 1,050

30 6.50 1 9.2 Kd1.30 750

35 15.54 1 3.9 - - 750

40 31.59 1 1.9 - - 750

45 63.87 1 0.9 - - 750

50 145.00 1 0.4 - - 750

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 7.81 1 7.7 Kd2.01 35,000

5 5.12 1 11.7 Kd2.05 14,600

10 3.31 1 18.1 Kd2.10 10,200

15 2.90 1 20.7 Kd2.15 7,600

20 2.92 1 20.5 Kd2.20 5,800

25 3.71 1 16.2 - - 4,065

30 7.97 1 7.5 Kd2.30 2,330

35 19.06 1 3.1 - - 2,330

40 5.50 0.132 1.4 - - 2,330

45 9.22 0.132 0.9 - - 2,330

50 14.81 0.132 0.5 - - 2,330

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 14.97 1 4.0 Kd3.01 22,200

5 4.04 1 14.9 Kd3.05 14,100

10 3.09 1 19.4 Kd3.10 9,400

15 2.68 1 22.4 Kd3.15 7,300

20 2.70 1 22.2 Kd3.20 6,200

25 2.75 1 21.8 - - 5,000

30 6.84 1 8.8 Kd3.30 3,800

35 20.78 1 2.9 - - 3,800

40 43.97 1 1.4 - - 3,800

45 71.02 1 0.8 - - 3,800

50 107.50 1 0.6 - - 3,800

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 3.67 Run 1 1.00 Run 2 3.50
46

Appendix A: RainMaker Data: SITE N 29.7 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.32 1 13.9 Kd1.01 4,900

5 3.81 1 15.7 Kd1.05 3,700

10 3.13 1 19.2 Kd1.10 2,630

15 2.42 1 24.8 Kd1.15 2,470

20 2.26 1 26.5 Kd1.20 2,320

25 2.01 1 29.9 - - 1,780

30 8.09 1 7.4 Kd1.30 1,240

35 31.64 1 1.9 - - 1,240

40 9.67 0.132 0.8 - - 1,240

45 15.88 0.132 0.5 - - 1,240

50 23.07 0.132 0.3 - - 1,240

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.14 1 14.5 Kd2.01 33,000

5 2.30 1 26.1 Kd2.05 9,500

10 2.26 1 26.5 Kd2.10 4,600

15 2.26 1 26.5 Kd2.15 3,900

20 2.25 1 26.7 Kd2.20 3,100

25 1.98 1 30.3 - - 3,100

30 3.01 1 19.9 Kd2.30 3,100

35 2.73 0.132 2.9 - - 3,100

40 5.04 0.132 1.6 - - 3,100

45 8.37 0.132 0.9 - - 3,100

50 12.13 0.132 0.7 - - 3,100

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.75 1 12.6 Kd3.01 6,200

5 2.40 1 25.0 Kd3.05 10,000

10 1.93 1 31.1 Kd3.10 4,700

15 1.99 1 30.2 Kd3.15 3,600

20 1.91 1 31.4 Kd3.20 2,840

25 1.76 1 34.1 - - 2,755

30 1.92 1 31.3 Kd3.30 2,670

35 2.20 0.132 3.6 - - 2,670

40 5.04 0.132 1.6 - - 2,670

45 5.30 0.132 1.5 - - 2,670

50 9.55 0.132 0.8 - - 2,670

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 0.40 Run 1 3.00 Run 2 1.20
47

Appendix A: RainMaker Data: SITE E 35.9 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 10.00 1 6.0 Kd1.01 3,300

5 4.05 1 14.8 Kd1.05 2,770

10 3.54 1 16.9 Kd1.10 1,700

15 3.13 1 19.2 Kd1.15 1,470

20 3.03 1 19.8 Kd1.20 1,340

25 2.70 1 22.2 - - 1,210

30 8.87 1 6.8 Kd1.30 1,080

35 35.40 1 1.7 - - 1,080

40 13.37 0.132 0.6 - - 1,080

45 27.52 0.132 0.3 - - 1,080

50 60.06 0.132 0.1 - - 1,080

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 6.62 1 9.1 Kd2.01 38,000

5 3.25 1 18.5 Kd2.05 13,600

10 2.97 1 20.2 Kd2.10 8,800

15 2.78 1 21.6 Kd2.15 5,000

20 2.95 1 20.3 Kd2.20 4,500

25 2.80 1 21.4 - - 3,250

30 11.25 1 5.3 Kd2.30 2,000

35 36.19 1 1.7 - - 2,000

40 11.84 0.132 0.7 - - 2,000

45 21.22 0.132 0.4 - - 2,000

50 33.53 0.132 0.2 - - 2,000

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.62 1 16.6 Kd3.01 63,000

5 3.04 1 19.7 Kd3.05 18,800

10 2.80 1 21.4 Kd3.10 9,000

15 2.95 1 20.3 Kd3.15 6,400

20 2.80 1 21.4 Kd3.20 6,000

25 2.75 1 21.8 - - 4,600

30 12.80 1 4.7 Kd3.30 3,200

35 40.06 1 1.5 - - 3,200

40 11.68 0.132 0.7 - - 3,200

45 22.10 0.132 0.4 - - 3,200

50 36.28 0.132 0.2 - - 3,200

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 3.00 Run 1 2.50 Run 2 3.17
48

Appendix A: RainMaker Data: SITE M 42.7 lbs average sediment production

Run 3 could not be complete due to water truck problems. Data in this report for run 3 

was extrapolated based on the average sediment increase between runs 2 and 3 for the 

other 13 sites.

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.39 1 4.8 Kd1.01 26,000

5 4.81 1 12.5 Kd1.05 14,300

10 3.52 1 17.0 Kd1.10 6,300

15 3.35 1 17.9 Kd1.15 4,700

20 3.38 1 17.8 Kd1.20 3,600

25 3.28 1 18.3 - - 2,710

30 5.62 1 10.7 Kd1.30 1,820

35 26.53 1 2.3 - - 1,820

40 13.93 0.132 0.6 - - 1,820

45 39.50 0.132 0.2 - - 1,820

50 99.59 0.132 0.1 - - 1,820

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.55 1 10.8 Kd2.01 40,000

5 4.15 1 14.5 Kd2.05 19,500

10 3.25 1 18.5 Kd2.10 11,800

15 3.11 1 19.3 Kd2.15 8,300

20 3.13 1 19.2 Kd2.20 8,100

25 3.09 1 19.4 - - 5,700

30 6.37 1 9.4 Kd2.30 3,300

35 4.71 0.132 1.7 - - 3,300

40 13.65 0.132 0.6 - - 3,300

45 29.90 0.132 0.3 - - 3,300

50 52.75 0.132 0.2 - - 3,300

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 1

 2.21

Run 2

 3.57
49

Appendix A: RainMaker Data: SITE F 43.1 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 9.38 1 6.4 Kd1.01 26,100

5 3.91 1 15.3 Kd1.05 20,800

10 3.18 1 18.9 Kd1.10 12,100

15 3.05 1 19.7 Kd1.15 9,700

20 2.96 1 20.3 Kd1.20 7,800

25 2.72 1 22.1 - - 5,900

30 11.09 1 5.4 Kd1.30 4,000

35 5.00 0.132 1.6 - - 4,000

40 14.28 0.132 0.6 - - 4,000

45 26.81 0.132 0.3 - - 4,000

50 74.72 0.132 0.1 - - 4,000

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.65 1 10.6 Kd2.01 13,800

5 2.87 1 20.9 Kd2.05 9,000

10 2.59 1 23.2 Kd2.10 6,600

15 2.50 1 24.0 Kd2.15 7,300

20 2.35 1 25.5 Kd2.20 8,200

25 2.50 1 24.0 - - 6,450

30 6.72 1 8.9 Kd2.30 4,700

35 26.03 1 2.3 - - 4,700

40 13.03 0.132 0.6 - - 4,700

45 27.91 0.132 0.3 - - 4,700

50 47.97 0.132 0.2 - - 4,700

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.78 1 15.9 Kd3.01 16,900

5 3.02 1 19.9 Kd3.05 8,600

10 2.90 1 20.7 Kd3.10 6,700

15 2.75 1 21.8 Kd3.15 7,200

20 2.72 1 22.1 Kd3.20 7,200

25 2.51 1 23.9 - - 5,700

30 8.13 1 7.4 Kd3.30 4,200

35 41.47 1 1.4 - - 4,200

40 14.25 0.132 0.6 - - 4,200

45 26.25 0.132 0.3 - - 4,200

50 44.35 0.132 0.2 - - 4,200

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 2.67 Run 1 2.90 Run 2 2.50
50

Appendix A: RainMaker Data: SITE B 60.1 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.70 1 10.5 Kd1.01 21,300

5 3.90 1 15.4 Kd1.05 9,400

10 3.23 1 18.6 Kd1.10 7,900

15 3.00 1 20.0 Kd1.15 7,300

20 2.91 1 20.6 Kd1.20 6,400

25 3.70 1 16.2 - - 3,780

30 18.00 1 3.3 Kd1.30 1,160

35 6.75 0.132 1.2 - - 1,160

40 16.00 0.132 0.5 - - 1,160

45 32.50 0.132 0.2 - - 1,160

50 57.00 0.132 0.1 - - 1,160

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.70 1 16.2 Kd2.01 42,000

5 2.85 1 21.1 Kd2.05 21,000

10 2.50 1 24.0 Kd2.10 13,000

15 2.52 1 23.8 Kd2.15 8,800

20 2.28 1 26.3 Kd2.20 6,700

25 2.35 1 25.5 - - 4,125

30 6.04 1 9.9 Kd2.30 1,550

35 3.00 0.132 2.6 - - 1,550

40 5.77 0.132 1.4 - - 1,550

45 10.25 0.132 0.8 - - 1,550

50 17.43 0.132 0.5 - - 1,550

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.85 1 15.6 Kd3.01 46,000

5 2.70 1 22.2 Kd3.05 24,100

10 2.25 1 26.7 Kd3.10 14,700

15 2.00 1 30.0 Kd3.15 10,900

20 2.20 1 27.3 Kd3.20 7,200

25 2.11 1 28.4 - - 4,945

30 6.41 1 9.4 Kd3.30 2,690

35 2.65 0.132 3.0 - - 2,690

40 5.55 0.132 1.4 - - 2,690

45 9.69 0.132 0.8 - - 2,690

50 15.26 0.132 0.5 - - 2,690

60 - - 0 - - - Run 3 3.40 Run 1 6.33 Run 2 3.55

FLOW RATE CONCENTRATION
51

Appendix A: RainMaker Data: SITE F AFTER NEW PIT RUN

15.4 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 9.38 1 6.4 Kd1.01 6,200

5 3.91 1 15.3 Kd1.05 13,800

10 3.18 1 18.9 Kd1.10 7,800

15 3.05 1 19.7 Kd1.15 4,200

20 2.96 1 20.3 Kd1.20 2,560

25 2.72 1 22.1 - - 2,025

30 11.09 1 5.4 Kd1.30 1,490

35 5.00 0.132 1.6 - - 1,490

40 14.28 0.132 0.6 - - 1,490

45 26.81 0.132 0.3 - - 1,490

50 74.72 0.132 0.1 - - 1,490

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.65 1 10.6 Kd2.01 7,200

5 2.87 1 20.9 Kd2.05 4,200

10 2.59 1 23.2 Kd2.10 2,870

15 2.50 1 24.0 Kd2.15 1,630

20 2.35 1 25.5 Kd2.20 1,450

25 2.50 1 24.0 - - 1,315

30 6.72 1 8.9 Kd2.30 1,180

35 26.03 1 2.3 - - 1,180

40 13.03 0.132 0.6 - - 1,180

45 27.91 0.132 0.3 - - 1,180

50 47.97 0.132 0.2 - - 1,180

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.78 1 15.9 Kd3.01 4,000

5 3.02 1 19.9 Kd3.05 2,680

10 2.90 1 20.7 Kd3.10 2,150

15 2.75 1 21.8 Kd3.15 1,550

20 2.72 1 22.1 Kd3.20 1,550

25 2.51 1 23.9 - - 1,265

30 8.13 1 7.4 Kd3.30 980

35 41.47 1 1.4 - - 980

40 14.25 0.132 0.6 - - 980

45 26.25 0.132 0.3 - - 980

50 44.35 0.132 0.2 - - 980

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 2.00 Run 1 2.50 Run 2 2.00

Note that because the road project 

affected the drainage pattern of 

the road, less water was running at 

the sample point for each of the 4 

“after aggregate” tests. For this 

reason, flow volumes from the 

“existing road” tests (in red) were 

used in combination with the “after 

aggregate” sediment 

concentrations in order to 

determine the total volume of 

sediment that was attributable to 

the aggregate and not lost water 

volume.
52

Appendix A: RainMaker Data: SITE G AFTER NEW DSA

4.2 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.00 1 5.0 Kd1.01 4,500

5 5.94 1 10.1 Kd1.05 2,790

10 2.65 1 22.6 Kd1.10 2,020

15 2.85 1 21.1 Kd1.15 1,400

20 2.65 1 22.6 Kd1.20 1,070

25 2.85 1 21.1 - - 580

30 6.62 1 9.1 Kd1.30 89

35 23.15 1 2.6 - - 89

40 9.95 0.132 0.8 - - 89

45 23.01 0.132 0.3 - - 89

50 50.35 0.132 0.2 - - 89

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 7.66 1 7.8 Kd2.01 2,010

5 3.03 1 19.8 Kd2.05 1,410

10 2.75 1 21.8 Kd2.10 1,020

15 2.58 1 23.3 Kd2.15 78

20 2.58 1 23.3 Kd2.20 85

25 2.50 1 24.0 - - 228

30 6.08 1 9.9 Kd2.30 370

35 28.23 1 2.1 - - 370

40 7.80 0.132 1.0 - - 370

45 15.23 0.132 0.5 - - 370

50 27.00 0.132 0.3 - - 370

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 8.31 1 7.2 Kd3.01 1,370

5 3.12 1 19.2 Kd3.05 850

10 2.91 1 20.6 Kd3.10 760

15 2.64 1 22.7 Kd3.15 940

20 2.54 1 23.6 Kd3.20 610

25 2.71 1 22.1 - - 430

30 5.50 1 10.9 Kd3.30 250

35 22.96 1 2.6 - - 250

40 7.43 0.132 1.1 - - 250

45 13.00 0.132 0.6 - - 250

50 15.47 0.132 0.5 - - 250

60 - - 0 - - -

FLOW RATE CONCENTRATION

Run 3 2.50 Run 1 5.00 Run 2 2.50

Note that because the road project 

affected the drainage pattern of 

the road, less water was running at 

the sample point for each of the 4 

“after aggregate” tests. For this 

reason, flow volumes from the 

“existing road” tests (in red) were 

used in combination with the “after 

aggregate” sediment 

concentrations in order to 

determine the total volume of 

sediment that was attributable to 

the aggregate and not lost water 

volume.
53

Appendix A: RainMaker Data: SITE B AFTER NEW PIT-RUN

36.8 lbs average sediment production

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 5.70 1 10.5 Kd1.01 14,800

5 3.90 1 15.4 Kd1.05 8,400

10 3.23 1 18.6 Kd1.10 4,160

15 3.00 1 20.0 Kd1.15 3,410

20 2.91 1 20.6 Kd1.20 3,120

25 3.70 1 16.2 - - 1,990

30 18.00 1 3.3 Kd1.30 860

35 6.75 0.132 1.2 - - 860

40 16.00 0.132 0.5 - - 860

45 32.50 0.132 0.2 - - 860

50 57.00 0.132 0.1 - - 860

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.70 1 16.2 Kd2.01 39,000

5 2.85 1 21.1 Kd2.05 9,800

10 2.50 1 24.0 Kd2.10 5,300

15 2.52 1 23.8 Kd2.15 3,670

20 2.28 1 26.3 Kd2.20 3,650

25 2.35 1 25.5 - - 3,440

30 6.04 1 9.9 Kd2.30 3,230

35 3.00 0.132 2.6 - - 3,230

40 5.77 0.132 1.4 - - 3,230

45 10.25 0.132 0.8 - - 3,230

50 17.43 0.132 0.5 - - 3,230

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 3.85 1 15.6 Kd3.01 43,200

5 2.70 1 22.2 Kd3.05 12,400

10 2.25 1 26.7 Kd3.10 6,200

15 2.00 1 30.0 Kd3.15 4,900

20 2.20 1 27.3 Kd3.20 4,680

25 2.11 1 28.4 - - 3,050

30 6.41 1 9.4 Kd3.30 1,420

35 2.65 0.132 3.0 - - 1,420

40 5.55 0.132 1.4 - - 1,420

45 9.69 0.132 0.8 - - 1,420

50 15.26 0.132 0.5 - - 1,420

60 - - 0 - - - Run 3 2.20 Run 1 4.00 Run 2 2.50

FLOW RATE CONCENTRATION

Note that because the road project 

affected the drainage pattern of 

the road, less water was running at 

the sample point for each of the 4 

“after aggregate” tests. For this 

reason, flow volumes from the 

“existing road” tests (in red) were 

used in combination with the “after 

aggregate” sediment 

concentrations in order to 

determine the total volume of 

sediment that was attributable to 

the aggregate and not lost water 

volume.
54

Appendix A: RainMaker Data: SITE C AFTER NEW DSA

0.8 lbs average sediment production

Time To 

Runoff Minutes - -

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 20.78 1 2.9 Kd1.01 1,160

5 4.09 1 14.7 Kd1.05 1,410

10 3.25 1 18.5 Kd1.10 260

15 2.90 1 20.7 Kd1.15 170

20 2.60 1 23.1 Kd1.20 120

25 2.50 1 24.0 - - 86

30 11.12 1 5.4 Kd1.30 51

35 5.40 0.132 1.5 - - 51

40 8.71 0.132 0.9 - - 51

45 9.62 0.132 0.8 - - 51

50 11.72 0.132 0.7 - - 51

60 - - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes Time (sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 12.10 1 5.0 Kd2.01 280

5 3.22 1 18.6 Kd2.05 150

10 2.88 1 20.8 Kd2.10 110

15 2.55 1 23.5 Kd2.15 92

20 2.69 1 22.3 Kd2.20 73

25 2.50 1 24.0 - - 56

30 4.37 1 13.7 Kd2.30 39

35 17.31 1 3.5 - - 39

40 4.69 0.132 1.7 - - 39

45 6.70 0.132 1.2 - - 39

50 7.93 0.132 1.0 - - 39

60 0 - 0 - - -

Time To Minutes Time Volum Flow Sampl Lab TSS 

Time To 

Runoff Minutes

Time 

(sec)

Volum

e (gal)

Flow 

(gpm)

Sampl

e ID

Lab 

Code

TSS 

(mg/l)

0 - - 0 - - -

1 4.72 1 12.7 Kd3.01 190

5 3.25 1 18.5 Kd3.05 105

10 2.64 1 22.7 Kd3.10 81

15 2.85 1 21.1 Kd3.15 64

20 3.09 1 19.4 Kd3.20 57

25 3.10 1 19.4 - - 45

30 4.00 1 15.0 Kd3.30 32

35 5.00 0.132 1.6 - - 32

40 6.50 0.132 1.2 - - 32

45 7.98 0.132 1.0 - - 32

50 8.28 0.132 1.0 - - 32

60 - - 0 - - - Run 3 6.50 Run 1 8.00 Run 2 6.25

FLOW RATE CONCENTRATION

Note that because the road project 

affected the drainage pattern of 

the road, less water was running at 

the sample point for each of the 4 

“after aggregate” tests. For this 

reason, flow volumes from the 

“existing road” tests (in red) were 

used in combination with the “after 

aggregate” sediment 

concentrations in order to 

determine the total volume of 

sediment that was attributable to 

the aggregate and not lost water 

volume.