The Pennsylvania State University
The Graduate School
College of Engineering
RUNOFF AND SOIL LOSS FROM LABORATORY PLOTS
COVERED WITH ROLLED EROSION CONTROL PRODUCTS
IMPREGNATED WITH POLYACRYLAMIDES AND GYPSUM
A Thesis in
Agricultural and Biological Engineering
by
Jason M. Marete
© 2013 Jason M. Marete
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
December 2013
The thesis of Jason M. Marete was reviewed and approved* by the following:
James Hamlett
Associate Professor of Agricultural Engineering
Thesis Advisor
Jack Watson
Professor of Soil Science/Soil Physics
Albert Jarrett
Professor Emeritus of Biological Engineering
Peter Kleinman
Soil Scientist
USDA-ARS-Pasture Systems and Watershed Management Research Unit
Virendra M. Puri
Distinguished Professor of Agricultural and Biological Engineering
Chair of Graduate Program
Department of Agricultural and Biological Engineering
*Signatures are on file in the Graduate School.
ii
ABSTRACT
This study evaluated the effectiveness of polyacrylamide (PAM) and PAM plus gypsum
treatments on the effectiveness of coconut straw and coconut-wheat straw rolled erosion control
products (RECPs) in reducing runoff, sediment, and turbidity losses from small soil plots. Soil
boxes of clay loam soil were covered with the treated RECPs and a simulated rainfall intensity of
125 mm/hr was applied on soil plots that were set up at the USDA-ARS laboratory, located at
Penn State University, University Park, PA. PAM and gypsum were applied at rates of 46.4 kg
PAM/ha and 928 kg gypsum/ha, respectively. Runoff samples were collected at 2 minute
intervals after the start of runoff during the experiment. After 37 minutes of rainfall for the first
rainfall application and 27 minutes of rainfall for the subsequent three rainfall applications, four
response variables (time to runoff initiation, total runoff, sediment concentration, and turbidity)
were evaluated for each of the samples collected at 2 minute intervals. Two days were allowed
between subsequent rainfall applications to allow the soil to drain. Four replicates of each
treatment were evaluated for the collected samples.
The results show that the addition of PAM and PAM plus gypsum to the RECPs did not
reduce the total runoff or time to runoff initiation significantly during the first rainfall application
nor for the subsequent three rainfall applications. However, PAM applied on the erosion control
blankets reduced mean sediment concentrations and mean turbidity by 76 % and 95 % ,
respectively, for the first rainfall application compared to control (bare soil). For the subsequent
rainfall applications the reductions of mean sediment concentrations and turbidity were 82 % and
96 %, respectively, compared to the control. PAM plus gypsum reduced the mean turbidity by
up to 96 % for the first application as compared to the control and the sediment concentrations
by 70 % compared to the control. In the subsequent rainfall applications, PAM plus gypsum
iii
reduced turbidity and mean sediment concentrations by 97 % and 81 % compared to bare soil,
respectively. However, the reductions in turbidity and sediment concentrations were not
significant α=0.05, when PAM plus gypsum treatments were compared with those of PAM only
for the four rainfall applications. When PAM plus gypsum treatments were compared with those
of gypsum only treatments, there was reduction in both sediment concentration and turbidity for
the four rainfall applications; however, the turbidity reductions were significant while the
sediment concentrations reductions were not. Results indicate that PAM, PAM and gypsum
treatments on the RECPs reduced total soil loss and turbidity from Hagerstown soil, though only
turbidity reductions were significant when PAM plus gypsum treatments were compared with
PAM only treatments.
iv
TABLE OF CONTENTS
LIST OF TABLES……………………………………………………………………………...viii
LIST OF FIGURES……………………………………………………………….……………....x
ACKNOWLEDGEMENTS…………………………………………………………..………....xiv
CHAPTER 1 INTRODUCTION.……………………………...……………………..….…….….1
CHAPTER 2 LITERATURE REVIEW…………………………………………...…………..….4
2.1 Introduction……………………………………………………………………………4
2.2 Soil erosion from upland sources….…………………………...………………...……5
2.3 Rainfall simulators for soil erosion studies…………….……...…………………..…..6
2.4 Runoff and erosion control using rolled erosion control products…....….....….……..8
2.5 Runoff and erosion control using polyacrylamides (PAMs)……….....…………..…12
2.6 Using gypsum to control soil erosion………………………………………………..18
2.7 State of the art of runoff and sediment loss as affected by PAM or/and gypsum treated
RECPs……………………………………………………………………………………23
CHAPTER 3 GOALS, OBJECTIVES AND HYPOTHESIS…………………………..…….…26
3.1 Research goal…………………………………………………..……………………26
3.2 Objectives……………………………………………................................................26
3.3 Hypotheses………...…………………………………………………………………27
CHAPTER 4 METHODOLOGY……………………………...…………………………...……29
4.1 Introduction………………………………..…………………………………………29
v
4.2 General overview………………………………………………………………….…29
4.3 Phase 1-Initial setup, preparation, and selection of RECP, PAM and gypsum...……31
4.4 Preliminary run of the rainfall/runoff experiment…..….……………………………46
4.5 Methodology of the actual experiment…….………………………………………...58
4.6 Methodology of data analysis..……….…..………………………………………….62
CHAPTER 5 RESULTS AND DISCUSSION……….……………………………….…………75
5.1 Physical and chemical properties of the soil and water used in the experiment…….75
5.2 Simulator flow rate, operating pressure and rainfall distribution.…….……………..76
5.3 Initial analysis of data………………………………………………………………..84
5.4 Mean time to initial runoff.………………...……….………...…………………..….89
5.5 Total runoff…….……………………………………………………………….……94
5.6 Total sediment loss from plots.……………………..……………..………………..102
5.7 Turbidity…………….………………………..…………………………………….116
CHAPTER 6: SUMMARY AND CONCLUSIONS…….………..……….…………………...132
CHAPTER 7: RECOMMENDATIONS AND FUTURE RESEARCH……………………….136
REFERENCES………...………………………….…………….……………………………...139
APPENDIX A: Sample tables for recording mass of runoff, mass of sediment amounts and
turbidity ………………………………………………………………………………………...145
APPENDIX B: SAS model for statistical analysis of the runoff data……………………….…148
APPENDIX C: Converting the volume of rainfall (ml) collected in the beakers to rainfall
(mm)…………………………………………………………………………………………….154
APPENDIX D: Contour maps of rainfall below the simulator nozzle……………………...….156
vi
APPENDIX E: Results and discussion of the initial data analysis runoff /erosion studies…….158
vii
LIST OF TABLES
PAGE
Table 4.1 Sample table for entering data for the runoff response for RECP 1………………......69
Table 4.2 Fixed and random terms for the SAS program to analyse runoff…………………….71
Table 4.3 Table showing contrasts for testing the specific hypotheses of individual treatment...73
Table 5.1 Table showing the physical and chemical properties of the experimental soil…….....75
Table 5.2 Chemical analysis results for the rainfall water………………………..…………..….76
Table 5.3 Table showing the results of measurements for the gravimetric moisture content of the
experimental soil, as available prior to the experiment………………………………………….76
Table 5.4 Flow rate and operating pressure of simulator during rainfall simulator setup…....….77
Table 5.5 Results of operating pressure and the resulting flow rate…………………………..…78
Table 5.6 Data for rainfall collected from 8 erosion boxes for three replicates for a 10 minute
duration of simulated rainfall and the resulting Christensen’s coefficient of uniformity……..…78
Table 5.7 Results of coefficient of uniformity for the simulator at the three stages of the actual
experiment………………………………………………………………………………………..79
Table 5.8 Data of rainfall calculated from the isohyetal plots for the five soil-box positions…...83
Table 5.9 Table showing the descriptive statistics of the sediment concentrations (g/L) responses
for the first replicate…………………………………………………………………………...…88
Table 5.10 Mean time to runoff initiation of the four replicates for each of the four rainfall
applications………………………………………………………………………..………….….89
Table 5.11 Output of contrasts for testing the null hypotheses of no significant differences in
time to runoff initiation for the various treatments for each of the four rainfall applications (runs
1, 2, 3, and 4)…………………………………………………………………………………….93
Table 5.12 Results for the mean total runoff (mm) for the four rainfall application from the eight
treatments and the control……………………………………………………………………......95
Table 5.13 Output of contrasts for testing the differences in total runoff of the various treatments
for the four rainfall applications………………………………………………………………..102
viii
Table 5.14 Mean total sediment loss (TS) for the four replicates and % sediment reduction
compared to control for the eight treatments for the four rainfall applications (Runs 1, 2, 3, and
4)…………………….…………………...........................................................................……..103
Table 5.15 Mean sediment concentrations (g/L) for the rainfall applications (Runs 1, 2, 3, and 4)
based on the four replications...……………………………………………………………...…105
Table 5.16 Output of contrasts for testing the differences in sediment concentration of the
various treatments for the four rainfall applications……………………………………………110
Table 5.17 Output of treatment means for sediment concentrations..………………….….…...112
Table 5.18 Table showing the least square mean sediment concentrations of the treatments
groups that are being compared………………………………………………………………...113
Table 5.19 Mean turbidity and % reduction for the eight treatments and the control for the four
runs………………………………………………………………………………………...……116
Table 5.20 Table showing output of hypotheses test for the turbidity response of the various
treatments for the four rainfall applications…………………………………………………….125
Table 5.21 Table showing the mean turbidity of runoff for the treatments…………………….128
Table 5.22 Table showing the least square mean turbidity of the treatments groups being
compared………………………………………………………………………………………..129
ix
LIST OF FIGURES
PAGE
Figure 4.1 Flow chart for testing RECPs/PAM/gypsum runoff and erosion control
effectiveness………………………………………………………………………………….…..30
Figure 4.2 Image showing soil box and location of drainage holes………………………….…..33
Figure 4.3 Image of soil box showing the trough, pipe connection and the runoff routing tube...34
Figure 4.4 Image of wheat straw/ coconut fiber erosion control blanket (RECP 1)…………..…35
Figure 4.5 Image of coconut fiber erosion control blanket (RECP 2)…………………………...36
Figure 4.6 Image showing the water reservoir, filters and deionizers used to supply water to the
rainfall simulator…………………………………………………………………………………38
Figure 4.7 Image showing the simulator frame and the tarpaulin……………………………….39
Figure 4.8 Image showing nozzles and nozzle shut-off valves………………………………….41
Figure 4.9 Pictorial layouts of erosion boxes…………………………………………………….42
Figure 4.10 Pictorial layout of the collection beakers on the simulator floor used to measure
rainfall distribution (not to scale)………………………………………………………………...44
Figure 4.11 Image showing RECP 1 being cut with a pair of scissors……..……………………47
Figure 4.12 Image showing RECP 1 being sprinkled with PAM………………………………..48
Figure 4.13 Image showing the control and the treated soil samples laid on the simulator floor.50
Figure 4.14 Image showing bottles ready to collect the ensuing runoff…………………………51
Figure 4.15 Turbidimeter for measuring turbidity
(Ref:http://www.mi l l eranalyti cal.com/images/2020e. jpg)………………………………………………….54
Figure 4.16 Flow chart illustrating two total suspended solids methods for analyzing sediments
from a 0.5 L sample……………………….………………………………………………..……57
Figure 4.17 Flow chart for testing RECPs enriched with PAM and gypsum…………..……..…60
Figure 4.18 Methodology of data analysis for the responses…………………………………….64
Figure 4.19 An excel worksheet showing the summation (summarizing) of runoff responses for
the first run replicate 1…………………………………………………………..……….………65
Figure 4.20 Excel worksheet showing the calculation of total runoff for runoff data.……….….66
x
Figure 4.21 Excel worksheet showing a portion of data of the total runoff data set…………….67
Figure 5.1 Figure showing spatial distribution of depth of rainfall (mm) and the position of soil
boxes under the simulator nozzle at start of experiment (05/20/2011)…………………………..80
Figure 5.2 Figure showing spatial distribution of depth of rainfall (mm) and the position of soil
boxes under the simulator nozzle mid-way through experiment (06/30/2011).………………....81
Figure 5.3 Figure showing spatial distribution of depth of rainfall (mm) and the position of soil
boxes under the simulator nozzle at end of experiment (09/30/2011).…………………………..82
Figure 5.4 Graph showing the rainfall (mm) received in each soil box……………………..…..84
Figure 5.5 Graph showing the mean cumulative runoff for rainfall applications 2, 3 and 4, and
the first rainfall application for the first replicate……………………………………….……….85
Figure 5.6 Graph showing the mean turbidity for rainfall applications 2, 3 and 4, and the first
rainfall application for the first replication..……………………………………………………..87
Figure 5.7 Graph showing the relationship between the treatment method and the time to runoff
initialization for the four runs……………………………………………………………………90
Figure 5.8 Box plots of mean time to runoff initiation of the four replicates for the first rainfall
application (run 1)………………………………………………………………………………..91
Figure 5.9 Box plots of mean time to runoff initiation of the four replicates for the second rainfall
application (run 2)………………………………………………………………………………..91
Figure 5.10 Box plots of mean time to runoff initiation of the four replicates for the third rainfall
application (run 3)……………………………………………………………...………………...92
Figure 5.11 Box plots of mean time to runoff initiation of the four replicates for the fourth
rainfall application (run 4)………………………………………………………..……………...92
Figure 5.12 Graph showing the mean total runoff for the rainfall applications for the different
treatments…………………………………………………………………………………….…..95
Figure 5.13 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the first rainfall application………………………………………………………...96
Figure 5.14 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the second rainfall application (run 2)…………………………………………......97
Figure 5.15 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the third rainfall application (run 3)…………………………………………….….98
xi
Figure 5.16 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the third rainfall application (run 4)………………………………………………..98
Figure 5.17 Box plots of mean total runoff across all rainfall applications vs the eight treatments
and the control…………...………………………………………………………………………99
Figure 5.18 Box plots of mean total runoff vs PAM and non-PAM treatments for all runs and
replicates………………………………………………………………………………………..100
Figure 5.19 Box plot of total runoff vs PAM+G and PAM only treatments……………...........101
Figure 5.20 Graph showing the mean total sediment loss (for four replications) for each
treatment and for the four rainfall applications (runs 1, 2, 3, and 4). Errors bars represent
standard errors of mean sediment concentration for the respective treatments………………...103
Figure 5.21 Graph showing the % reduction in mean total sediment loads for treatments
compared to the control for four rainfall applications (runs 1, 2, 3, and 4)…………………….104
Figure 5.22 Graph showing the variation of mean sediment concentration with treatment for each
rainfall application (runs 1, 2, 3, and 4). Errors bars represent standard errors of mean sediment
concentration for the respective treatments…………………………………………………….106
Figure 5.23 Box plots of sediment concentration of all the replicates for the treatments…….107
Figure 5.24 The box plots of sediment concentrations for PAM and non-PAM treatments with
RECP1 and RECP2 for the four replications………………………………………...…………108
Figure 5.25 The box plots of sediment concentration of the four replicates for RECP1 and
RECP2 with PAM+G and PAM only……..……………………………………………….…...109
Figure 5.26 Graph of mean turbidity for the eight treatments and the control (across all 4
replications and all 4 rainfall applications)……………………………………………………..118
Figure 5.27 Graph of mean % turbidity reduction for the treatments (across all 4 replications and
all 4 rainfall applications)………………………………………………………………………118
Figure 5.28 Graph of mean turbidity (across all for replications) for the first rainfall application
(Run 1)….………………………………………………………………………………………119
Figure 5.29 Graph of mean turbidity (across all for replications) for the second rainfall
application (Run 2)…………………………………………………………………..…………120
Figure 5.30 Graph of mean turbidity (across all for replications) for the third rainfall application
(Run 3)..…………………………………………………………………………………….......120
xii
Figure 5.31 Graph of mean turbidity (across all for replications) for the fourth rainfall application
(Run 4)..…………………………………………………………………………………….......121
Figure 5.32 Box plots of turbidity for the 8 treatments and the control (each box plot is based on
16 responses)….…………………………………………………………………………….......122
Figure 5.33 Box plots of mean turbidity for PAM and non-PAM treatments (each box plot is
based on 32 responses)………………………………………………………………………….122
Figure 5.34 Box plots of turbidity vs PAM+G (box plot is based on 32 responses) and PAM (box
plot is based on 32 responses) only treatments…………………………………………………123
xiii
ACKNOWLEDGEMENTS
I would like to extend my thanks to the following:
My advisor, Dr. James Hamlett, for believing in me, for helping me professionally, encouraging
and being patient in the process.
Dr. Peter Kleinman for allowing me to use the facilities and materials at USDA-ARS laboratory
at Head House 4, Penn State University, University Park, PA.
My committee-Dr. Albert Jarrett, Dr. Jack Watson, and Dr. Peter Kleinman for their guidance,
support, and understanding in shaping this endeavor.
My funding sources from-Comparative Literature and Liberal Arts Department, Agricultural and
Biological Engineering Department, my family.
My loving wife, Mary, and my children, Cindy and Lynn, for accompanying and helping me
along this journey.
xiv
CHAPTER 1
INTRODUCTION
Sediment is one of the most common pollutants affecting surface water. Soil loss from
both agricultural and non-agricultural lands in the U.S. has been estimated at 4x109 metric tons
per year (Brady et al., 1996). Forested lands lose an average of 0.36 metric tons ha-1 yr-1 ,
agricultural lands lose an average of 5.5 metric tons ha-1 yr-1 , whereas construction sites average
73.3 metric tons ha-1 yr-1 (Faucette et al., 2007). Land disturbance for construction activity
exposes large areas of bare soil to water and wind erosion, thereby increasing the erosion rates
by 2 to 40,000 times the preconstruction and agricultural rates (Harbor, 1999). The eroded
sediments also carry nutrients, pesticides, motor oils and fuels, and other contaminants that
adsorb to the soil particles. The runoff that occurs during storm events tends to pick up mostly
clay-sized and silt-sized soil particles and carries them to the nearest water body, depositing
some along the way. Large particles such as gravel and sand settle quickly once runoff flow rate
slows. Clays and fine silts settle much more slowly and tend to stay suspended for longer periods
of time. The suspended particles result in turbidity and travel to downstream water bodies.
Turbidity reduces the productivity of affected waters, decreases their recreational value, and
increases treatment costs for industrial or drinking water plants (McLaughlin, 2006).
A recent assessment of the conditions of 16% of the rivers and streams in the U.S.
(USEPA, 2004) found that, 45% of the total length was classified as impaired for intended use;
of the 39% of the nation’s lakes, ponds, and reservoirs assessed, 64% were reported as impaired;
and of the 29% assessed bays and estuaries, 30% were reported as impaired. In these cases,
sediment and siltation were the leading causes of impairment (USEPA, 2004).
1
The urban population in the U.S. is estimated to be growing at 15.6% every decade (U.S.
Census Bureau, 2000). This increases the need for housing, transportation, and commercial
development, thereby leading to rapid expansion of metropolitan areas and changes in land use.
Between 1992 and 1997, 25 million acres of forests, rangeland, pasture, cropland, and wetlands
were converted to freeways, factories, malls and residential uses in the U.S. (U.S. Census
Bureau, 2000). Approximately the same area of vegetative cover and topsoil had to be removed
from sites or modified substantially during this construction.
Loss of topsoil from construction sites hinders the revegetation of disturbed areas. Also,
the resulting suspended sediments exported from sites can clog storm drainage systems, reduce
reservoir storage, and reduce stream algae from photosynthesizing, which may affect the lives of
aquatic biota. Removing the deposited sediments from the streams requires expensive dredging.
To remedy the onsite damage requires landscaping, revegetation, and other soil conservation
measures. Presently, regulations and damage costs do not give construction owners incentives to
remedy the problems. In addition, as with other types of non-point source pollution, it is hard to
clearly attribute responsibility for the damage to the water source because there are many
possible pollutant sources. Due to this difficulty, federal and state agencies have developed
guidelines for measures to mitigate construction site damages that affect water use, quality, and
levels of disturbance.
In 1987, amendments to the federal Clean Water Act mandated that storm water runoff,
erosion, and sediments originating from construction sites must be controlled (EPA, 2000). In
1990, the National Pollutant Discharge Elimination System (NPDES) Phase 1 Rules mandated
that all construction sites over 2 ha must have land disturbance activity permits and pollution
prevention plans. In 2003, NPDES Phase II went into effect, thereby extending the storm water
2
pollution prevention plan requirements to any land disturbing activity over 0.4 ha. The erosion
and sediment control plan must detail the area to be disturbed and possible measures to be used
in controlling sediment export from the site throughout the life cycle of the project. In
accordance with these policies the EPA designated certain rolled erosion control products
(RECPs) as storm-water best management practices (BMPs). As a result there is high demand for
RECPs as more contractors seek NPDES compliance. This has led to the tripling of RECP use
since 1999 and development of many sophisticated RECPs (Li et al., 2008). However, the
effective use of RECPs in conjunction with other BMPs is not well documented.
Federal and state agencies have set rules and regulations to limit the amount of runoff
from construction sites. Despite these federal regulations, sediment remains a primary pollutant
affecting the quality of surface waters. The use of RECPs to control soil loss from construction
sites is generally thought to be insufficient (Jennings et al., 2009). Interest exists in improving
efficiency of RECPs by impregnating them with amendments such as gypsum and
polyacrylamide (PAM). Addition of gypsum to the PAM solution reduces the viscosity of PAM
solution and establishes cation bridges between negatively-charged clay particles and PAM
molecules (Lee et al., 2010). The focus of the study reported herein was to further explore the
potential erosion control and sediment reduction benefits of using PAM and gypsum added to
rolled erosion control products.
3
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
The evaluation of construction site erosion and sedimentation control requires an
understanding of soil erosion processes and sediment transport dynamics. At present, the primary
best management practices (BMPs) used to combat erosion from construction sites include the
use of rolled erosion control products (RECPs), silt fences, silt traps and basins, check dams and
lined channel. This thesis focuses on rolled erosion control products. Rolled erosion control
products (RECPs) are temporary, degradable or long-term, non-degradable materials
manufactured or fabricated into rolls designed to reduce soil erosion and to assist in growth,
establishment, and protection of vegetation. RECPs are engineered to hold seed and trap soil and
sediments within their structure, thereby minimizing erosion caused by impact of precipitation
and water flow.
Polyacrylamides (PAMs) have been shown to increase the infiltration rate and reduce
runoff from exposed soils. The addition of a multivalent electrolyte (Ca2+) to PAM has been
shown to further reduce runoff volume and sediment yield on some soils (Flanagan et al., 2002,
and Peterson et al., 2002). However, studies by Lepore et al. (2009) showed that the addition of a
multivalent electrolyte (Ca2+) to PAM does not reduce runoff volume and sediment yield.
Gypsum has been used as the source of Ca2+. This literature review focuses on the erosion
processes and the research that has been done to develop appropriate methods of combating soil
erosion; more specifically on erosion control products treated with polyacrylamides (PAMs) and
gypsum to control soil erosion from construction sites.
4
2.2 Soil erosion from upland sources
Soil erosion is defined as the process by which soil particles are detached and then
transported to a new location. Natural erosion is the erosion of land in its natural environment
undisturbed by man. It includes the processes of weathering, erosion by normal rains, and
erosion that occurs from floods, earthquakes, landslides and glaciers.
Manmade accelerated soil erosion is the increase in soil erosion and sediment production over
and above the natural erosion. The primary forces or processes that detach and transport soil
particles are wind and water. Agents or activities that cause soil detachment include raindrop
impact on the soil surface, water flowing on the soil surface thereby detaching soil particles,
movement of wind over the soil surface, and primary and secondary tillage exacted on the soil
during seedbed preparation. Once the soil particles have been detached from the larger body of
soil, a transport agent or activity, predominantly flowing water or wind, must be applied to
transport these soil particles. Details on soil erosion agents, types, and methods of estimating soil
loss using the Revised Universal Soil Loss Equation (RUSLE) are available in most soil
conservation literature. Jarrett (2000) discusses the soil erosion processes, types, and the methods
of estimating soil losses.
As noted, water erosion, the most prevalent type of erosion, is primarily driven by rainfall
and runoff. A clear understanding of the role played by rainfall in soil erosion requires that the
rainfall is available at the required time and has the required characteristics. Hence, there are
challenges associated with carrying out experiments associated with natural rainfall that hinder a
comprehensive understanding of the erosion processes (Miller, 1987). These challenges include:
5
1. test conditions, such as rainfall intensity and duration, cannot be controlled
in the field, and interference by wind complicates the understanding of the
erosion mechanisms of each individual physical disturbance,
2. collecting all eroded soils for evaluation may be difficult,
3. runoff during or after the rainfall may not be timely sampled for constituent
analysis, and
4. cost of field experiments is relatively high. Laboratory-scale tests facilitate
the understanding of the effects of the rainfall factor on erosion control, and
rainfall simulators are required to provide the characteristics of natural
rainfall that are necessary to carry out the tests.
2.3 Rainfall simulators for use in runoff/erosion studies
Rainfall simulators are able to provide rainfall intensity, drop sizes and distribution,
rainfall impact energy, and spatial distribution of raindrops that are similar to the characteristics
of natural rainfall. Rainfall simulators have been extensively researched and cited in the literature
(Swanson, 1965; Meyer and Harmon, 1979; Brian and Luk, 1981; Hallock et al., 2002; Faucette
et al., 2007). The advantage of using rainfall simulators in runoff and erosion research is the
control and reproducibility the simulator provides over the rainfall variables. Rainfall can be
produced on demand, when necessary, and of character and for the duration required (Bowyer et
al., 1989). However, complications that arise in simulator use include limited plot size, edge
effects at plot boundaries, differences in drop size distribution and energy characteristics of
natural and simulated rainfall, and the intricate variability of natural rainfall compared with the
controlled nature of simulated rainfall (Bowyer et al., 1989). According to Bowyer et al. (1989),
a rainfall simulator for research should satisfy the following criteria :
1. Simulate the range of rainfall intensities required, which is controlled by the
aerial densities of raindrop production and rate of production.
6
2. Simulate the required energy conditions of the rainfall produced. Energy
conditions of the rainfall are controlled by drop size, aerial density of
rainfall production, and fall height.
3. Encompass the area of rainfall application required.
4. Be portable and easy to use.
Rainfall simulators have been used with much success throughout the last 75 years to
conduct research on infiltration, surface runoff, and soil erosion. Rainfall simulators have the
ability to create controlled and reproducible artificial rainfall, which in turn expedites data
collection (Humphrey et al., 2002) and allows comparison of soils and management variables
among locations (Sharpley et al., 1999). However, rainfall simulators have performance
limitations due to their inability to simulate all aspects of natural storms (Mech, 1965). These
limitations include plot size, simulated rainfall intensity, portability, and cost. Several different
simulators have evolved and differ primarily in the method of drop formation and intensity
control (Shelton, 1985). Mutchler and Hermsmeier (1965) and Bubenzer (1979) describe
simulators as being one of two types:

Simulators that produce rainfall from nozzles and

Simulators using drop formers such as hanging yarn or tubing tips.
Simulators using nozzles are generally preferred over drop-former simulators because they have
the capability of yielding greater intensities, are more portable, and can effectively cover larger
plot areas. Spray-nozzle simulators also produce more randomly distributed rainfall drop pattern
compared to drop forming simulators, which tend to produce raindrops of equal size in the same
locations.
7
According to Meyer (1965), Shelton et al. (1985), and Moore et al. (1983), simulators
should produce desired rainfall characteristics and satisfy design considerations. Desirable
rainfall characteristics address drop size distribution, fall velocity, kinetic energy, intensity,
uniformity and continuous application. Design considerations deal with water usage, operation
requirements, acceptable plot sizes, portability, and cost. Few simulators satisfactorily possess all
these desired traits. Often one trait must be sacrificed to produce the desired other features. The
intended use or the nature of the study often dictates the characteristics required of a rainfall
simulator (Humphrey et al., 2002). However, the simulator should be able to supply the required
rainfall intensity for the set rainfall duration.
2.4 Runoff and erosion control using rolled erosion control products (RECPs)
The use of surface erosion control products has been shown to reduce runoff and erosion
(Adams, 1966; Meyer et al., 1972; Foster et al., 1985; and Faucette et al., 2007). Runoff from
covered soil can be reduced to a fraction of that from uncovered soil and thus lead to reduced soil
erosion (Meyer et al., 1972). RECPs intercept and dissipate the energy of raindrops and prevent
soil surface crusting; they also split the overland flow of runoff into smaller water streams and
retain water at the surface allowing more time for water to infiltrate the soil (Gorman et al.,
2000). Adams (1966) found that soils covered with wheat straw mulch averaged less than 0.9
tons ha-1 soil loss compared with 18.3 tons ha-1 from uncovered soils during 215.6 mm of rain on
October 4, 1959. On a 20% construction slope of 46 m length and a storm of 62.5 mm/hr,
Meyers (1972) found that straw erosion control blankets (ECBs) yielded 4.5 kg ha-1 soil loss
compared to 90 kg ha-1 soil loss from bare soil.
8
By protecting the surface, RECPs prevent aggregate breakdown and delay the
development of surface sealing, thereby enhancing water infiltration into the soil. RECPs also
increase the surface roughness, thus reducing overland flow velocities and shear stress exerted on
the soil. Since their introduction in 1950, RECPs have played an important role in protecting
disturbed slopes from accelerated erosion (Thiesen, 1992). Thiesen (1992) and Lancaster et al.
(2003) stated that the Erosion Control Technology Council (ECTC) has developed a standardized
terminology for these products. This terminology, as listed by ECTC, and reported by Lancaster
et al. (2003) consists of:
1. Mulch control netting. The ECTC defines mulch control netting as
plane woven natural fiber or extruded geosynthetic mesh used as a
temporary, degradable RECP to anchor loose fiber mulches. The
mulch control netting is rolled out over the seeded or mulched area
and stapled in place. The products include straw netting and natural
aspen wood fiber.
2. Open weave textile The ECTC defines open weave textile as natural
or polymer yarns woven into a matrix, which is used to provide
erosion control and facilitate vegetation establishment. Open weave
textiles provide erosion control with or without the use of the
underlying loose mulch layer. They also display stronger and higher
tensile strength than mulch control netting and are employed where
higher tensile strengths are required.
3. Erosion control blanket. The ECTC defines erosion control blankets
as a degradable RECP composed of processed natural polymer
fibers mechanically, structurally or chemically bound together to
form a continuous matrix to provide erosion control and facilitate
vegetation establishment. They are usually made from straw, wood,
excelsior, coconut, propylene or a combination of these stitched or
glued together. These materials are also available with seed preincorporated into their structure. The erosion control blankets are
rolled out onto the soil surface and anchored with stakes or anchor
trenches.
4. Turf reinforcement mat. The ECTC defines turf reinforcement mat
as a long term, non-degradable RECP composed of UV-stabilized,
non-degradable synthetic fibers, filaments, nettings and/or wire
9
mesh designed for permanent and critical hydraulic applications
where design discharges exert velocities that exceed the shear stress
limits of mature natural vegetation.
Turf reinforcement mats
provide sufficient thickness, strength and void space to permit soil
filling, retention and the development of vegetation within the
matrix. Turf reinforcement mats can be rolled out on freshly-seeded
soil surfaces and allows the natural sedimentation to fill out the mat.
The vegetation grows through the underlying mat. Turf
reinforcement mats can also be rolled out and then in-filled with
fine soil, and the vegetation roots and anchors through the mat for
initial, as well, as permanent reinforcement.
The ECTC also classifies RECPs as:
1. Ultra-Short term products that last no more than 3 months and are
used where vegetation establishes fast.
2. Short-Term products that provide protection for longer than 3
months and up to 12 months.
3. Extended products that provide protection for longer than 12
months and up to 24 months.
4. Long-term products that provide protection for longer than 24
month and up to 36 months.
With the wide range of products being marketed, there is increased demand for research
on their performance. However, only limited research has been conducted on the effectiveness of
these products. Only demonstration studies that report a general failure or success of a product
are available, and these have usually been conducted by the manufacturer.
In their study over a period of 24 months, Bhatia et al. (2002) evaluated a 1130-meter
long constructed channel in Onondaga, New York, covered with RECPs. Ten different RECPs
donated by five distributers were used in the study. Four of the RECPs were erosion control
blankets and six were turf reinforcement mats. The study was conducted in an area characterized
by moderate to very rapid runoff with moderate to severe hazard of erosion. The rainfall ranged
between 34 to 138 mm/month and snowfall was 2.5 to 850 mm/month during the winter period.
10
The RECPs were rolled onto the channel, which had been broadcast seeded with perennial rye
grass as per the manufacturer’s recommendations.
The cross sections of the channel for each reach were evaluated based on observations of
vegetative growth and measured deformations of channel cross sections over a 22-month
inspection period. There were no visual signs of significant erosion along any of the reaches
where RECPs were installed. The study was not conducted under controlled conditions, and,
hence, conclusions on the factors that contributed to the lowering of soil losses from treated plots
cannot be definitively reached.
Benik et al. (2003) tested four different erosion control products on the slopes of a
highway sedimentation basin in Minnesota. The treatments were: a bare condition, disc-anchored
straw mulch, wood fiber blanket, and a bonded fiber matrix. After treatment with RECPs, the site
was planted with prairie seed, and the establishment of vegetation was monitored over the
growing period. Water was applied by use of a simulator and water quality samples collected for
each plot and the results analyzed. The results showed that the volumes of runoff collected from
plots treated with the RECPs were 64 % less compared to those from bare soil plots. The soil
losses from RECP plots were 58 % less compared to those from bare soil plots. Although the
results of the study applied to the four selected RECPs and for the site and specific conditions of
the study, the findings can be transferred to other products and conditions.
Sutherland et al. (1997) carried out a study in which 13 commonly used RECPs were
exposed to varying simulated rainfall intensities over several durations. The aim of the study was
to determine the RECPs that most reduced splash detachment and to identify the physical
attributes of RECPs that contributed to the erosion control success. In their experiment they
11
investigated the effects of the physical, mechanical, and hydraulic properties of each RECP.
These properties were supplied by the manufacturers. The RECP properties investigated were:

Light transmission, mass per area, mass density and thickness

Tensile strength and shear stress

Manning’s roughness coefficient, porosity, sorption and the maximum flow
velocity as provided by the manufacturers
Soil erosion boxes were treated with the 13 RECPs and simulated rainfall of 120 mm/hr was run
for 1 hour. The ensuing runoff was collected periodically and analyzed for sediment
concentrations and runoff rates. The runoff responses were then compared with the properties of
the RECP. The results indicated that there were differences in runoff rates and sediment
concentrations of runoff from the 13 RECPs. Thick RECPs yielded lower runoff amounts
compared to the thinner RECPs. RECPs that provided greater surface cover per unit area also
yielded less runoff than RECPs that provided less surface cover. The same results were observed
for sediment concentrations of the runoff. However, there were many variables that were being
tested with no controls for meaningful conclusions to be drawn. The data on properties of the
RECPs were provided by the manufacturers and could also not be verified independently.
2.5 Runoff and erosion control using polyacrylamides (PAMs)
The use of polyacrylamide (PAM) in land management has its origins in furrow
irrigation, where applying PAM to irrigated furrows reduced erosion by up to 94 % (Jennings et
al., 2009) and decreased the total amount of excess nutrients and hazardous biological pollutants
in surface runoff by 10 fold as compared to untreated irrigation furrows (Entry et al., 2002).
12
Polyacrylamide is a long-chain synthetic polymer that acts as a strengthening agent,
thereby binding soil particles together (Barvenik, 1998). The long PAM chains adsorb on soil
aggregates and bind the soil particles together, thereby increasing their resistance to splash by
raindrop impact and detachment by runoff (Jian et al., 2003). PAM includes a wide variety of
chemicals based on the acrylamide unit. Being a long-chain polymer, the acrylamide units can be
modified to produce a net positive, neutral, or negative charge on the PAM molecule. Cationic,
positively charged, PAMs are generally not used for turbidity control due to their potential
toxicity to the biota when applied in significant concentrations to aquatic ecosystems. Anionic,
negatively charged, forms are much less toxic and are widely used to address environmental
issues (McLaughlin et al., 2007).
Polyacrylamide acts as an effective chemical flocculent. Anionic PAM binds to
suspended sediments largely through cation-bridging, pulling particles together and forming
flocculants which settle as sediments. The reaction is rapid and irreversible. Therefore, little or
no residual PAM is expected to be found in the runoff once particles have settled after treatment
(Bhardwaj and McLaughlin, 2008). The effectiveness of PAM in flocculating sediments,
especially clays, is dependent on the soil chemistry. The dominant soils and their characteristics
will determine the type of PAM that will be most effective in treating turbid waters. Clay and silt
particles are smaller than sand particles and hence are easily suspended (McLaughlin et al.,
2007). Thus, PAM will be most effective in soils with high proportions of clay and silt particles
because PAM works by binding to suspended soil particles. A soil test performed by the PAM
manufacturer will provide the information necessary to make an accurate determination of the
type of PAM to be used on the tested soil.
13
According to Sojka and Lentz (1998) PAM is an environmentally friendly chemical.
Results from an acute toxicity test conducted by MACTEC Engineering Consultants, Inc., and
reported by Sojka and Lentz (1998) indicated a less than 50% acute mortality to the water flea
(Ceriodophnia dubia) in a 420 ppm test concentration. A lethal toxic concentration could not be
determined and the No-Observable-Effect-Concentration (NOEC) was determined to be 210 ppm
(BioTox Laboratory, 2008).
Weston et al. (2009) investigated the effect of PAM of solution concentrations of 0.18,
0.37, 0.75, 1.5, 3, 6 mg PAM/L on freshwater amphipod, Hylella Azteca, at the University of
California. In this investigation, 10 individual Hylella azteca were added in each of the solutions
and their status was analyzed after 10 days. After 10 days the animals were sieved out and the
toxicity of PAM formulations on Hylella azteca was evaluated. The results showed a 93-100 %
survival of the Hylella azteca at concentrations of 0.18 to 6 mg PAM/L. Hence, PAM did not
have a toxic effect on the organisms tested at the concentrations analyzed and can be used
without harming the organisms.
Polyacrylamide is available as a crystalline powder, an emulsion, and as a solid block or
“log” (McLaughlin, 2007). PAM can be sprayed, broadcast, or directly injected before or during
a runoff event. These application methods are usually costly. A more practical, passive
application method involves passing flowing water over wet PAM Floc Logs. Though effective,
the amount of PAM released cannot be controlled. PAM can also be applied using a wheel
applicator, which proportionally injects PAM based on the water flow rate (McLaughlin, 2007).
Regardless of the application method, uniform mixing of PAM with soil is needed to
ensure maximizing the exposure time of suspended particles to the PAM molecules
14
(McLaughlin, 2007). Once the sediment- laden runoff has been treated, deposition of most of the
fine clay particles will take place. PAM has also been shown to be effective when applied to
slopes either with or without additional ground covers. Such treatments were found to reduce
erosion rates by 98% and turbidity by up to 82% compared to untreated soil slopes (Jennings et
al., 2009).
As reported by Sojka and Lentz (1998), some disadvantages of using anionic PAM include:
1. PAM’s effectiveness is soil specific so leftover material may not be
effective if used at another site,
2. using PAM requires site-specific testing that may take several days to
complete,
3. overuse may clog soil pores, thereby decreasing infiltration, and
4. PAM is not effective when applied to pure sand or gravel with no fine
silts or clays, nor when applied over snow cover.
Bjorneberg and Aase (2000) investigated the combined effects of surface residue and PAM
on runoff, soil loss, and phosphorous loss from sprinkler- irrigated soil in the lab. In their
research, steel boxes were filled with loamy soil and treated with PAM at rates of 0, 2, and 4
kg/ha of PAM applied on the surface. Wheat straw was applied to provide 30% or 70% surface
cover on the soil. Simulated rainfall of intensity 80 mm/hr was then applied for 15 minutes. The
results showed that the PAM treatments reduced runoff by more than 85 % on straw-covered soil
but by only 40 % on bare soil. The results also indicated that runoff and phosphorous losses were
not statistically significantly different between the PAM application rates of 0, 2, and 4 kg/ha.
The sediment concentrations were reduced by 80 % as compared to the control by applying 4
kg/ha PAM, and by 50% by applying 2 kg/ha PAM as compared to the control.
15
In another laboratory experiment, Bjorneberg et al. (2000) investigated the effect of PAM
applied through sprinkler irrigation water on the soil loss from silty clay loam soil. The
researchers used soil that had passed through a 6.4 mm sieve, packed evenly in 1.2 m wide by
1.5 m long by 0.2 m deep erosion boxes. PAM was applied through irrigation water at 3 kg/ha
for initial (single) irrigation and at 1 kg/ha per application for multiple (3) irrigation applications.
Four replications were investigated for each treatment and the control. To allow the soil surface
to dry, 7 to 10 days elapsed between irrigation applications. The PAM-treated water was
obtained by mixing 210 l of well water with concentrated PAM solution (1920 g PAM/l). The
boxes were placed at a 6.5 % slope, and a rainfall of intensity 80 mm/hr was applied for 10
minutes. The single (3 kg/ha PAM) application and the multiple (1 kg/ha PAM) applications
both significantly (α=0.05) reduced runoff (60%) compared to the control (no PAM). However,
the reductions of runoff were less significant (40 %) in the remaining irrigations as compared to
the control.
Faucette et al. (2007) investigated how blending wood mulch with compost affected the
compost’s performance as erosion BMP relative to a straw blanket treated with PAM. The study
also investigated whether the particle size distribution of the ECB material affected runoff and
erosion. To determine the effectiveness of the ECB for runoff and erosion control, the storm
water quantity and quality analysis included total runoff volume, peak runoff rate, elapsed time
until runoff commencement, total sediment load, suspended solids load, average turbidity,
percentage of rain becoming runoff, nitrogen load, and phosphorous load. The researchers found
that the greater the percentage of compost used in an ECB, the lower the total runoff and the
lower the runoff rate. The PAM-treated straw blankets yielded more soluble phosphorous in
runoff than the untreated straw blankets. The research also showed that particle size distribution
16
of the ECB affected runoff, erosion, and vegetation establishment. The research, however, tested
only two erosion control products and not the many that are commonly used in the construction
industry.
Wilson et al. (2008) investigated the use of PAM-enhanced armoring of RECP. The
researchers used lab test plots of 1.2 m x 0.6 m x 0.075 m packed with well-graded soil from an
active construction site in Alabama (58.6 % sand, 12.5 % silt and 28.9 % clay). The soil was put
into the erosion plots in 2.5 cm lifts and compacted using hand tamps. The soil plots were then
set on a 25 % slope below the simulator. Three replicates of each of the following treatments
were used: (1) bare soil, (2) jute matting, and (3) jute matting with dry PAM applied at a rate of
31.2 kg/ha. Simulated rainfall of intensity 112 mm/hr was applied on the soil plots for 15
minutes, and the ensuing runoff was collected over the duration of the rainfall event and until
there was no more runoff. The results showed that jute matting with dry PAM reduced turbidity
by 98 %, and jute matting alone reduced turbidity by 74 % compared to the control. The jute
matting with dry PAM applied reduced soil loss by 98 % and jute matting without PAM by 98 %
compared to the control. The research thus showed that jute matting was effective at reducing
soil loss; and, using jute matting in conjunction with dry granular PAM (applied at 31.2 kg/ha)
was effective in reducing both turbidity and soil loss.
McLaughlin et al. (2009) conducted a study at a North Carolina road construction site.
The treatments carried out were in a drainage ditch adjacent to the road and consisted of rock
check dams, fiber check dams (FCDs), and FCDs treated with granulated PAM. The results
showed more than 20 % reductions in turbidity and suspended solids when using FCDs,
especially when they were treated with granulated PAM compared to untreated soil.
17
2.6 Using gypsum to control soil erosion
As noted, PAM stabilizes the soil by reducing repulsive forces among clay particles and
also acting as a bridge between soil particles, thereby binding the particles into an aggregate
(Ben-Hur, 1994). The benefits of PAM are enhanced by the introduction of an electrolyte source
(multivalent cation), and gypsum helps to create a cation-bridge for the polymer to adsorb to the
soil (Shainberg et al., 1990). Flanagan et al. (2002) indicated that the multivalent cations can be
introduced by application of phosphogypsum (PG). Gypsum has been widely used to enhance the
electrolyte concentration of the soil solution. Peterson et al. (2002) found that gypsum was a
better source of electrolyte than other calcium bases to use in combination with PAM because of
gypsum’s greater solubility.
Zhang et al. (1998) evaluated the effects of longevity of soil amendments, tillage, and
screen cover on runoff and interill erosion on a Cecil sandy loam soil in Georgia, U.S.A., under
natural rainfall conditions. The researchers used six field plots of size 3.5 m long by 0.9 m wide
with a slope of 6 %. During the first two-month period (July 10, 1991, to August 26, 1991), three
treatments (control, screen cover, and crust breaking shallow tillage) were studied and another
three treatments (control, PAM, and gypsum) were studied in the subsequent five-month period
(November 11, 1991, to March 9, 1992). The treatments were in duplicate and were randomly
allocated to the plots. Gypsum was applied on the soil surface as a powder at a rate of 5 Mg/ha.
PAM solution was prepared by dissolving 1 kg PAM/m3 in 2.5 mol gypsum/m3 . The solution
was then sprayed on the plot surface at a rate of 20 kg PAM/ha. The amounts of rainfall, runoff
and soil loss were measured after every storm event. The results were then statistically analyzed
using paired t-test and Duncan’s method. The results showed that gypsum reduced total runoff
18
by 67 % and PAM by 44 % compared with the control for a total depth of 328 mm rainfall. The
results also showed statistically significant reduction in runoff volume and soil loss for all the
rainfall storms on PAM and gypsum treated plots compared to the control. However, the
researchers had two replicates; hence the results could not be relied upon to be statistically
significant. The numbers of replicates in an experiment depend on the number of effects being
tested, level of significance of test and whether the test is one-tailed or two-tailed
(http://www.ndsu.edu/ndsu/horsley/ExptSize.pdf). Hence drawing conclusions from two replicates
will give inaccurate results. In addition, the experiments were done during two different seasons.
In experiments by Flanagan et al. (2002), PAM was found to be more effective at
controlling rill erosion than interill erosion. Treatments were a control, PAM sprayed at a rate of
80 kg/ha, and PAM plus gypsum applied at a rate of 80 kg/ha and 5 Mg/ha, respectively. PAM
plus gypsum treatment reduced runoff by 52 % and sediment yield by 91 % compared to the
control over the sequence of rainfall events. Flanagan et al. (2002) also noted a much more
prominent network of rills in the untreated control plots and in the PAM treated plots than in the
PAM plus gypsum treated plots.
Peterson et al. (2002) investigated the effectiveness of PAM application method (dry or
in solution) and effectiveness of two sources of Ca 2+ electrolytes. The cation sources used were
Nutra-Ash and Soiler-Lime. The research was conducted in a former quarry in West Lafayette,
Indiana. The soil surface was ploughed and formed to a slope of 20 %. Twelve plots of size 3 m
by 9.1 m were then constructed, and each plot was filled with about 12 m3 of soil. The plots were
then developed to a slope of 16 + 2 % and soil depth of 0.35 m to 0.25 m by leveling and
compacting the soil. The treatments in the study were (1) control, (2) PAM in liquid solution plus
19
Nutra-Ash, (3) granular PAM plus Nutra-Ash, and (4) PAM solution plus Soiler-Lime. PAM was
applied at 60 kg/ha, Soiler-lime at 4.3 Mg/ha, and Nutra-Ash at 80 Mg/ha. Simulated rain was
then applied to the plots at an intensity of 75 mm/hr for 1 hr followed by a dry break of 1 hr,
followed by a wet application of intensity 75 mm/hr for 1 hr and a break of 30 minutes, followed
by an application of intensity 100 mm/hr for 30 minutes. The time taken for continuous runoff to
be initiated was noted, and runoff samples were collected every 3 minutes after runoff initiation.
The researchers then analyzed the runoff for sediment concentration and mineral constituents.
Peterson et al. (2002) reported that treatments using an application of liquid PAM
solution were the most effective in reducing total runoff (62 % to 76 % reductions compared to
control) and sediment yields (93 % to 98 % reduction compared to control). They concluded that
spraying of PAM in solution was significantly more effective (α=0.05) in controlling runoff and
erosion than was the dry granular application for the rainfall events simulated in the study.
Jian et al. (2003) investigated effects of application of granular PAM and gypsum on the
infiltration rate and soil erosion from two soil types; silty loam and sandy clay. Each soil type
was packed in erosion boxes (0.2 m x 0.4 m x 0.04 m) evenly to maintain a bulk density of 1.32
g/cm3 and 1.17 g/cm3 for the silty loam and sandy clay, respectively. Granular PAM was applied
uniformly and mixed with the upper 5 mm of soil. PAM plus gypsum mix and gypsum alone
were applied by spreading the recommended rates on the soil surface. For each soil type eight
treatments were studied; (1) control, (2) 20 kg of dry PAM /ha, (3) 2 Mg of gypsum/ha (4) 4 Mg
of gypsum /ha, (5) 20 kg of dry PAM /ha plus 2 Mg of gypsum /ha (6) 20 kg of dry PAM /ha
plus 4 Mg of gypsum /ha (7) 40 kg of dry PAM /ha plus 2 Mg of gypsum /ha (8) 40 kg of dry
PAM /ha plus 4 Mg of gypsum /ha. Each treatment was applied in triplicates. The soil trays were
20
then placed at a slope of 15 % under a rainfall simulator and a rainfall intensity of 36 mm/hr was
applied for 2 hrs. Samples of runoff and infiltration water through the soil were collected
throughout the experiment for analysis. The results showed that for the silty loam there was an
increase in infiltration rate and a higher final infiltration rate (up to 4 times) for all the treatments
as compared with the control. A similar trend was also noted in the treated sandy clay soil. The
results showed that gypsum treatments in the two soil types reduced soil loss by 50 % compared
to the control and treatment due to PAM resulted in soil loss reductions of 15 % and 30 % for
silty loam and sandy clay, respectively. PAM plus gypsum treatments resulted in greater
reductions in runoff than PAM treatments alone for both soil types. However, there were no
significant reductions in soil loss for PAM plus gypsum treatments compared to PAM
treatments. The researchers hypothesized that the long PAM polymer chains adsorbed on the
external surfaces of soil aggregates and bonded together the soil particles that were far apart,
thereby, increasing their resistance to splash by raindrop impact and detachment by runoff. The
stretched chains also blocked the conducting pores between the soil particles and hence lowered
the infiltration rate. However, Jian et al. (2003) hypothesized that when an electrolyte was added
to PAM, the polymer chains are shorter and less effective in binding together soil particles that
are far apart, thus enabling more soil detachment. Hence, the efficiency of PAM in soil loss
reduction was reduced in the presence of gypsum.
Lepore et al. (2009) conducted a laboratory investigation on the effect of applying soil
amendments in a combined form and also compared runoff, sediment, sediment-bound P and N,
and dissolved Ca and S loads. Soil was packed evenly in erosion boxes measuring 0.48 m x 0.20
m x 0.09 m and compacted to give a bulk density of 1.2 g/cm3 . The treatments were (1) bare soil,
(2) lime, (3) lime plus PAM, (4) PAM-coated lime, (5) gypsum, (6) gypsum plus PAM, and (7)
21
PAM-coated gypsum, and all treatments were investigated in triplicates. Simulated rainfall at an
intensity 65 mm/hr was applied for 1 hr, and the ensuing runoff was collected throughout the
rainfall-runoff event. Samples were collected at 3 minute intervals and analyzed for sediment
loads, nutrient loads, mineral loads, and runoff volume. The results for sediment loads showed
the trend: loss from bare soil > lime plus PAM > PAM-coated lime for all replications.
Phosphorous and nitrogen concentrations also showed a similar trend to that of sediment loads.
For the gypsum treatment, sediment load reductions were observed in all the measurements as
compared to the control. However, the sediment load reductions between the PAM treatments
and PAM plus gypsum treatments were not significantly different (α=0.05). The results showed
that the cumulative runoff was similar for lime and the corresponding gypsum treatment.
However, the cumulative sediment yields were 1.2, 1.8, and 1.3 times greater for gypsum, PAMcoated gypsum, and gypsum plus PAM, respectively, than for the corresponding treatments with
lime.
Truman et al. (2010) conducted a field study in Dawson, Georgia, over a 2 year period
using soil type Kandiult. The soil constituents were 75 % sand, 16 % clay and 9% silt. The
objectives of the study were to identify rainfall partitioning and sediment delivery improveme nts
with surface applied gypsum and to assess the feasibility of using gypsum on agricultural land in
southern Georgia. During the two year period the field was disked, cultivated and planted with
cotton and peanuts in rotation. The field plots were 2 m wide and 3 m long on an average slope
of 1 %. The treatments were done in triplicate and included gypsum application rates of 0, 1.1,
2.2, 4.5, and 9 Mg/ha. Simulated rainfall was applied to each plot at a target intensity of 50
mm/hr for 1hr. Runoff was collected at 5 minute intervals during each rainfall event. The
sediment yield was calculated using gravimetric method, and the infiltration calculated by
22
difference (rainfall-runoff).The results were then adjusted for the evapotranspiration (ET) of the
crop. The results showed an increase in infiltration, a decrease in runoff, and a decrease in
sediment yield for all the gypsum treatments as compared to the control. The average increase in
infiltration was 26 %, average decrease in runoff was 40 %, and average decrease in sediment
yield was 24 % for plots treated with gypsum compared to the control. However, the results
showed no significant differences in infiltration, runoff, and sediment yield between the gypsum
application rates. Thus, there were no benefits of increasing the application rate of gypsum in
increasing infiltration, decreasing runoff, or decreasing the sediment yield of the field plots that
were investigated.
2.7 State of the art of runoff and sediment loss as affected by PAM or/and gypsum treated
RECPs
Considerable opportunity exists to combine rolled erosion control products (RECPs) and
flocculants to reduce surface runoff and erosion (Meyer et al., 1972; Bhatia et al., 2002; and
Benik et al., 2003). Gorman et al. (2000) explained that RECPs intercept and dissipate the energy
of raindrops and prevent soil crusting; they also split the overland flow of runoff into streams and
hold more water at the surface allowing more time for water to infiltrate the soil. Bjorneberg and
Aase (2000); Faucette et al. (2007); and Wilson et al. (2008) showed that PAM reduced turbidity
in the runoff. Michelle et al. (2004); Faucette et al. (2007); Bhardwaj and McLaughlin (2008);
and McLaughlin et al. (2009) indicated that there was significant reduction in turbidity and
suspended solids in the runoff when the RECPs that they investigated were treated with PAM.
Jian et al. (2003) suggested that the long PAM polymer chains are adsorbed on soil aggregates
and binds the soil particles far apart together, thereby, increasing their resistance to splash
23
raindrop impact and detachment by runoff. The stretched chains also block the conducting water
pores that are between the soil particles, resulting in low infiltration rate (McLaughlin et al.,
2009). Also the PAM polymers bound to suspended sediments largely through cation-bridging
and caused the particles to form flocculants that settled, thereby reducing soil loss (Bhardwaj and
McLaughlin, 2008). This resulted in reduced soil loss and increased runoff.
The benefits of using gypsum and PAM together to reduce runoff and soil loss have been
investigated in RECPs (for jute matting and wheat straw). Flanagan et al. (2002); Lepore et al.
(2009); and Peterson et al. (2002) showed that gypsum addition to PAM resulted in more
reductions of soil erosion and runoff as compared to addition of PAM alone. Shainberg et al.
(1990) hypothesized that the introduction of an electrolyte source (multivalent cation) helps to
create a cation-bridge for the polymer to adsorb to the soil. Hence the presence of Ca2+ ions in
the soil or PAM solution flocculated the soil particles more rapidly than PAM alone resulting in
reduced soil loss. However, studies by Jian et al. (2003) had also noted no reduction in sediment
loss in soil treated with PAM plus gypsum when compared with those treated with PAM only.
The researchers suggested that, when an electrolyte was introduced the PAM polymers were
shorter and less effective in binding together soil particles that were far apart (Jian et al., 2003).
Thus, the efficiency of the polymer in reducing soil losses was reduced when gypsum was
introduced in the soil solution.
The benefits of using gypsum and PAM on coconut fiber, propylene, and coconut
fiber/straw have not to date been widely reported in the literature. In addition there was no
reporting of the role that the method through which gypsum and PAM were introduced into the
RECPs played in the flocculation and subsequent reduction of soil loss. Studies by Jian et al.
24
(2003) have shown that impregnating RECP with PAM and gypsum resulted in no reduction in
sediment concentration, but in significant reduction in turbidity, when compared with RECPs
treated with PAM only. The mechanisms for these reductions in turbidity and no significant
reductions in sediment concentrations have not been fully explained. Also, the results from these
studies were for a few selected RECPs, such as open weave jute mats and wheat straw. In
addition, previous studies have not established the most effective method of applying the PAM
or gypsum plus PAM into the RECPs. There was also no information on the soil composition of
the sediments in runoff from PAM-treated RECPs, PAM plus gypsum treated RECPs, or from
untreated RECPs. Thus, further research is necessary to identify effective methods of applying
gypsum and PAM and to test how effective gypsum and PAM impregnated RECPs as opposed to
PAM and gypsum incorporated into the soil matrix are in reducing soil loss.
25
CHAPTER THREE
GOALS, OBJECTIVES, AND HYPOTHESES
Many studies have illustrated how RECPs are engineered to armor exposed soil surfaces
and capture suspended soil particles, thereby reducing erosion and sediment loss caused by the
impact of precipitation and water flow. Studies on the influence of PAM and gypsum on soil
water processes that affect soil erosion have also been conducted. However, further research is
needed to quantify the benefits of adding gypsum and PAM to the structure of the RECPs to
further address erosion and sediment loss.
3.1 Research Goal
The goal of this study was to determine the effectiveness of two alternative RECPs, in
conjunction with gypsum and PAM, in reducing runoff, sediment loss, and turbidity in runoff
from small packed soil boxes subjected to simulated rainfall. Two types of RECPs (coconutwheat RECP and coconut fiber RECP) were evaluated. Tests were conducted for RECPs alone,
RECPs impregnated with PAM only, RECPS impregnated with PAM plus gypsum, and RECPs
impregnated with gypsum only.
3.2 Objectives
Specific objectives were to:
i.
Setup, test and calibrate the rainfall simulator so that it delivers rainfall of near uniform
intensity over the area below the nozzle,
ii.
Determine the effectiveness of untreated and treated (PAM, PAM plus gypsum, and
gypsum) RECPs on runoff, turbidity and total suspended sediment in runoff from packed
soil boxes initially under dry soil conditions,
26
iii.
Determine the effectiveness of treated (PAM, PAM plus gypsum, and gypsum) RECPs
on runoff, turbidity and total suspended sediment in runoff from packed soil boxes
under wet soil conditions receiving three rainfall applications separated by two days each,
allowing for gravity drainage between rainfall events , and
iv.
Determine the effect of treated (PAM, PAM plus gypsum, and gypsum) RECPs on time
to runoff initiation from packed soil boxes for first and subsequent rainfall events,
3.3 Hypotheses
Based on the goal and objectives, the following hypotheses were evaluated:
1. Ho : Runoff volume, turbidity, and amount of sediment lost from the control (bare soil)
plots are not significantly different from the responses from plots covered with only
RECPs.
H1 : Runoff volume, turbidity, and amount of sediment lost from the control (bare soil)
plots are significantly different (α=0.05) from the responses from plots covered with only
RECPs.
2. Ho : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs with PAM only are not significantly different from the responses from plots
covered with only RECPs.
H1 : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs with PAM only are significantly different (α=0.05) from the responses for plots
covered with only RECPs.
3. Ho : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs with gypsum plus PAM are not significantly different from the responses from
plots covered with only RECPs.
27
H1 : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs with gypsum plus PAM are significantly different (α= 0.05) from the responses
from plots covered with only RECPs.
4. Ho : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs treated with gypsum plus PAM are not significantly different from the responses
from plots covered with RECPs with PAM only.
H1 : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs treated with gypsum plus PAM are significantly different (α= 0.05) from the
responses from plots covered with RECPs with PAM only.
5. Ho : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs treated with gypsum plus PAM are not significantly different from the responses
from plots covered with RECP plus gypsum only.
H1 : Runoff volume, turbidity, and amount of sediment lost from plots covered with
RECPs treated with gypsum plus PAM are significantly different (α= 0.05) from the
responses from plots covered with RECP plus gypsum only.
6. Ho : Time to runoff initiation for plots covered with treated RECPs (gypsum treated, PAM
and gypsum treated, PAM treated) for rainfall applications (1, 2, 3, and 4) is not
significantly different from the time to runoff initiation for untreated RECPs for rainfall
applications (1, 2, 3, and 4).
H1 : Time to runoff initiation for plots covered with treated RECPs (gypsum treated, PAM
and gypsum treated, PAM treated) for rainfall applications (1, 2, 3, and 4) is significantly
different from the time to runoff initiation for untreated RECPs for rainfall applications
(1, 2, 3, and 4).
28
CHAPTER FOUR
METHODOLOGY
4.1 Introduction
This section provides a description and summation of the experiments that were done to
meet the specific goal, objectives, and hypotheses of this research project, explains the
experimental procedures, and also illustrates how the data were collected and used to test the
hypotheses. Description of the experimental design, sampling methods, data collection, and data
analyses are also presented and discussed.
4.2 General overview.
As shown in Figure 4.1 the research methodology was in four phases. Phase 1 was the
preparation of the experiment and rainfall simulator testing. During this phase the equipment, the
RECPs, PAM, and gypsum were identified, evaluated, and acquired. The operating pressures of
the rainfall simulator were calibrated to deliver the desired rainfall intensity and acceptable
Christiansen rainfall uniformity coefficient.
The primary purpose of Phase 2 was to evaluate an acceptable method to apply PAM and
PAM plus gypsum on the RECPs. Sprinkling by hand was selected as the method of applying
PAM and PAM plus gypsum. A preliminary experiment was also performed during this phase to
test and confirm the operating pressure, rainfall intensity and uniformity of the rainfall simulator.
29
PHASE 1: PREPARATION
AND SELECTION
-Sel ection of s oil, RECPs , PAM,
gyps um; s etting up of ra infall
s i mulator a nd s imulator
ca l ibration-intensity,
di s tribution, pressure
-Setti ng up of
eros ion plots a nd
Trea tment of
RECPs wi th PAM,
gyps um
Appl ication of rainfall on erosion
boxes
Mea s ure responses from simulated
ra i nfall (runoff volume, turbidity,
s ediment concentrations)
PHASE 2: PRELIMINARY
EXPERIMENT
Ana l ysis of results for a pplication ra tes,
ra i nfall intensity a nd uniformity, erosion
box s l ope, and time interval.
Sel ection of a pplication pressure, erosion box
s l ope, l ength of ra infall a pplication, and ti me
i nterval for collecting runoff samples.
La boratory design of experiment (RECP only, RECP
pl us PAM, RECP pl us PAM pl us gypsum, a nd
control ).
Four
repl icates
a nd four
runs per
Appl y ra infall to the treated erosion boxes.
repl icate
PHASE 3: RECP, PAM AND
GYPSUM TESTING
Mea s ure responses from simulated ra infall
(runoff volume, turbidity, sediment
concentrations with time)
PHASE 4: DATA ANALYSIS
AND HYPOTHESIS
Sta ti stical evaluation of responses, result, a nd
concl usions
TESTING
Figure 4.1 Flow chart for runoff and erosion control effectiveness of RECPs/PAM/gypsum tests.
30
Phase 3 involved the design and performance of the laboratory experiments to collect the
necessary data to complete the research objectives and test the hypotheses. The experiment
consisted of four replicates of soil plots with the following treatments; 1) control of bare soil, 2)
covered with RECP 1 only, 3) covered with RECP 2 only, 4) covered with RECP 1 treated by
sprinkling with powdered PAM only, 5) covered with RECP 2 treated by sprinkling with
powdered PAM only, 6) covered with RECP 1 treated by sprinkling with powdered PAM plus
gypsum, 7) covered with RECP 2 treated by sprinkling with powdered PAM plus gypsum,8)
covered with RECP 1 treated by sprinkling with gypsum only, and 9) covered with RECP 2
treated by sprinkling with gypsum only. Rainfall simulation was applied on erosion boxes
subject to nine treatments and the ensuing runoff collected for analysis. Samples were
periodically every 2 minutes collected from time of runoff initiation and analyzed for runoff
volumes, turbidities, and sediment concentrations.
Phase 4 of the methodology involved data evaluation and hypothesis testing. In this phase
data were compiled, evaluated, and organized to facilitate statistical evaluation. Based on the
evaluation, the proposed hypotheses were tested. The final component of this phase was
summary of the results and the derivation of conclusions.
4.3 Phase 1-Initial setup, preparation, and selection of RECP, PAM and gypsum
4.3.1 Introduction
Phase 1 consisted of planning and preparing the equipment for conducting the research.
The planning involved acquisition of equipment and materials to be used. During this phase a
preliminary run of the experiment was completed. During the preliminary testing the flow rate,
31
rainfall distribution, operating pressure, and duration of rainfall application for the rainfall
simulator were determined. The slope of the experimental plots was also established.
4.3.2 Site and laboratory description
The lab space used was in the USDA-ARS laboratory at Head House 4, located at Penn
State University, University Park, PA. Most of the equipment required for the research was
available in the lab and included: erosion boxes, rainfall simulator, turbidity meter, weighing
balances, measuring cylinders, and an oven. In addition to the equipment, there were trained
personnel to assist with training on the operation of the equipment.
4.3.3 Preparation of soil for erosion experiments
The soil used in this experiment was from a soil originally mapped as a Hagerstown silt
loam and was obtained from the Agricultural Research Service (ARS) at State College,
Pennsylvania. Hagerstown soil belongs to the grey-brown alfisols group developed from the
decomposition of limestone in place (Jeffries et al., 2007). Two soil samples were collected
from the mixed soil and taken to the Agricultural Analytical Services Laboratory (AASL) at
Penn State University, University Park, PA for analysis. The soils to be used had been air-dried,
sifted through a 20 mm sieve and stored in containers in the USDA lab. To minimize variability,
the soils used in the experiment were mixed thoroughly prior to placement in the soil boxes. The
gravimetric moisture content was determined by collecting five soil samples from the
experimental soil and putting them in an oven at 105o C for at least 48 hours and then weighing
them periodically until mass of the sample was constant.
32
4.3.4 Soil boxes for simulator experiments
Setup of erosion boxes included selection of soil boxes, setting up the slope of the soil boxes,
and placing the test soil into the boxes. The boxes to be used were designed following the
USDA National Phosphorous Research Project protocol
(http://www.sera17.ext.vt.edu/Documents/National_P_protocol.pdf). As per the protocol, the
erosion boxes used had internal sizes 1.074 m long, 0.197 m wide and 0.06 m deep. The boxes
were constructed of aluminum sheet metal of thickness 1 mm. Drainage holes (5 mm diameter)
were located on the base of the box at the upper, mid, and lower locations as shown in Figure
4.2.
Al uminum sheets
Length of box 1.074 m
Dra i nage
hol es
Wi dth of box
0.197 m
Figure 4.2 Image showing soil box and location of drainage holes.
33
Surface runoff was collected at the down-slope end of the box equipped with a V-shaped
aluminum trough to route the runoff into a collection tube and containers. The 15.9 mm diameter
PVC pipe outlet was connected at the base of the trough with araldite as shown in Figure 4.3.
V-s haped Aluminum
trough
15.9 mm di a meter
PVC tubi ng
Ara l dite to connect
PVC to trough
Figure 4.3 Image of soil box showing the trough, pipe connection and the runoff routing tube.
Cheesecloth was placed on the bottom of the box, followed by addition of two quantities
of soil in 3 cm deep increments placed into the box. Packing of soil was done after each soil
addition, using a 4.54 kg steel rammer. To ensure that a uniform bulk density of 1.1 g/cm3 for all
soils was achieved, the same mass (14.0 + 0.5 kg) of soil was compacted evenly in each of the
soil boxes. A bulk density of 1.1 g/cm3 for the soil that was used in this research was considered
acceptable for a construction site. Soil was added until it was level with the lower lip of the
boxes.
34
4.3.5 Selection of RECPs, PAM and gypsum
Selection of RECP 1 and RECP 2 for erosion experiments
The RECPs used were supplied by Propex Geosynthetic Company, which is located at
6025 Lee Highway, Chattanooga, Tennessee, U.S.A. RECP 1 was an erosion control blanket
(ECB) with trade name Landlock CS2. RECP 1 consisted of 70 % wheat straw and 30 %
mattress-grade coconut fiber. The straw and coconut fibers were homogeneously blended and
distributed throughout the blanket. The fibers were mechanically bound and covered on both
sides with photodegradable polypropylene netting with mesh openings of approximately 11 mm
by 11 mm, as shown in Figure 4.4.
Wheat straw
and coconut
fiber
Propylene netting of
size 11 mm by 11 mm
Figure 4.4 Image of wheat straw/ coconut fiber erosion control blanket (RECP 1).
RECP 2 was an Erosion control blanket manufactured by Propex Geosynthetic Company
under the trade name Landlok C2. The erosion control blanket consisted of 100% mattress grade
35
coconut fiber, which was mechanically bound and covered on both sides by netting. The coconut
fiber was homogenously blended and distributed throughout the blanket. The netting was made
of ultra-violet stabilized polypropylene with mesh opening of approximately 16 mm by 16 mm,
as shown in Figure 4.5.
Coconut fiber
Polypropylene
netting spaced at
16 mm x 16 mm
Figure 4.5 Image of coconut fiber erosion control blanket (RECP 2).
Selection of PAM
The PAM used for this research was supplied by ACF Environmental Company. The
company has offices in most U.S. states. Here in Pennsylvania, the company has technical
centers in western, central and eastern Pennsylvania. The company supplies PAM under the trade
name Applied Polymer Systems (APS) silt-stops powder. PAM is a soil-specific soil conditioner
and hence the correct PAM has to be used for the specific soil. To facilitate the identification of
36
specific PAM to be used, soil samples were mailed to the company offices at Applied Polymer
Systems, Inc., 519 Industrial Drive, Woodstock, GA., 30189. The company analyzed the soil
samples and supplied the PAM for use in this experiment. The company did not supply
information about how they decided on the type of PAM for use on the Hagerstown soil.
Selection of gypsum
The gypsum used in this research was FGD (flue gas desulfurization) gypsum. FGD
gypsum is a byproduct in the coal industry and its chemical formula is calcium sulfate dihydrate
(CaSO 4 ·2H2 O). In agricultural operations gypsum is used as a soil conditioner. The gypsum used
as soil conditioner in experiments carried out by Flanagan et al. (2002) and Lepore et al. (2009)
was at least 95% (CaSO 4 ·2H2 O). The gypsum used in this experiment (95% (CaSO 4 ·2H2 O)) was
available in the USDA-ARS laboratory at Head House 4 in the form of granules stored in sealed
plastic containers. For use in the experiment the gypsum was sieved through a 250 micrometer
sieve and stored in sealed plastic bottles.
4.3.6 Flow rate and rainfall distribution of rainfall simulator
The rainfall simulator used was constructed following guidelines from USDA National
Phosphorous Research Project and following the design by Miller (1987), as described by
Humphrey et al. (2002). The simulator consists of:
1. A reservoir tank of capacity 850 liters for storing the water supply.
2. An electric water pump for supplying the required water pressure.
3. Filters and deionizers to provide clean and deionized water to the simulator.
4. Pressure regulator for establishing the required flow rate at the nozzle.
37
5. Valves for controlling the quantity of water flowing through the pipes and nozzle.
6. PVC and plastic pipes that deliver water to the rainfall simulator components.
7. The simulator nozzle (Fulljet ½ HH-SS30WSQ) placed in the center of the simulator
area and 3.05 m above the ground surface.
8. A tarpaulin that surrounds the simulator to minimize air disturbances and splashing.
850 liters water
tank
Irrigation water filters
Water deionizers
Figure 4.6 Image showing the water reservoir, filters and deionizers used to supply water to the
rainfall simulator.
38
Water is stored in a 4 m tall storage tank which has the outlet at the bottom. The water,
under pressure, passes through plastic pipes into water filters that remove the suspended
sediments and deionizers that remove the dissolved solids. The cleaned and deionized water
passes through an electric pump, pressure valves and an operating pressure gauge into the nozzle
for discharge. The operating pressure gauge is located at the outer end of the top middle frame of
the simulator, and a plastic pipe connects it to the simulator nozzle, which is located on the
middle frame and at the center of the simulator area. The tarpaulin is supported by the metal
frame and covers the three sides of the simulator area, as shown in Figure 4.7.
Metal frames supporting
tarpaulin, nozzles, valves and
pressure gauge
Tarpaulin for
minimizing wind effects
Figure 4.7 Image showing the simulator frame and the tarpaulin.
39
The operating pressure of the rainfall simulator was determined using the USDA National
Phosphorous Research Project guidelines as described in the protocol at
http://www.sera17.ext.vt.edu/Documents/National_P_protocol.pdf. The intensity of the
simulated rainfall was determined by measuring (weighing) collected masses of rain at specified
locations for a period of time. The rainfall distribution was analyzed using Christensen (1942)
coefficient of uniformity. The Christensen coefficient of uniformity (Cu) has been used to
measure the evenness of irrigation application by researchers in simulation studies (Miller, 1987;
Tossell et al., 1987; and Paul et al., 1983). Cu is a measure of the difference of individual
observations from the arithmetic mean of all observations according to:


C u  1001 



i n

 Ymean 

nYmean



Y
i 1
i
Where;

Cu= Christensen’s coefficient of uniformity (%)

Yi=individual rainfall amount at specific location

Ymean =mean rainfall amount across all i locations

n=number of measurements


=summation of n values
A value of 100% represents a perfectly uniform distribution and a smaller percentage
corresponds to a less uniform application.
40
Determination of flow rate and operating pressure
The pressure of the flow system to the nozzles was set by adjusting the pressure regulator
and observing the resulting pressure at the pressure gauge. A 3.05 m length of 3.9 cm diameter
PVC pipe was placed around the nozzle (shown in Figure 4.8) so that the water from the nozzle
was collected in a bucket over a period of time. The pressure was regulated until a flow rate of
125 mL/s was obtained.
Simulator nozzles
placed 3.05 m above
floor
Nozzle shut-off
valves
Figure 4.8 Image showing nozzles and nozzle shut-off valves.
Determination of rainfall intensity and uniformity distribution
The operating pressure was initially set at 31.7 KPa. This pressure is slightly higher than
the required pressure of 31.0 KPa as specified in the USDA National Phosphorous Research
41
Project guidelines as described in the protocol at
http://www.sera17.ext.vt.edu/Documents/National_P_protocol.pdf. This is because the pressure
tended to decrease by a small amount and to stabilize at 31.0 KPa after the simulator operated for
120 seconds. To ensure that the flow rate was 125 mL/s, the simulator was run for 120 s and the
water was collected and weighed. Once the flow rate was confirmed to be 125 + 0.5 mL/s, the
uniformity distribution test was conducted.
Soil boxes were laid side by side on the floor under the center of the simulator area as
shown in Figure 4.9. For this simulator rainfall distribution test 8 soil boxes (1.074 m long by
0.197 m wide) were placed side by side in the rainfall simulator area. The operating pressure was
adjusted to be 31.7 + 0.5 KPa. The 3.05 m long PVC pipe was used to divert the rainfall away
from the plot area for about 5 minutes (to make sure steady state was established) prior to start of
the test. The simulator was then run for 10 minutes. The runoff water collected from each erosion
box was weighed and tabulated.
Soi l boxes of size 1.074 m
by 0.197 m
Length i s 1.074 m
Si mulator nozzle at the
center of floor a rea and
3.05 m a bove simulator
0.537 m
fl oor.
Wi dth i s 1.60 m
Figure 4.9 Pictorial layout of erosion boxes under rainfall simulator nozzle.
42
Determination of rainfall intensity distribution
The operating pressure was set at 31.7 KPa. Plastic beakers of height 10.37 cm and top
diameter of 4.10 cm (Figure C-1) were placed on the simulator floor at spacing of 20 cm by 20
cm as shown in Figure 4.10. The beakers covered the area where the erosion boxes were placed
during the actual experiment at the specified spacing. There were 80 beakers. The simulator was
then run for 20 minutes. The pressure was maintained at 31.7 + 0.5 KPa throughout the
experiment. The water collected in each of the beakers was weighed using an electronic balance.
The rainfall intensity distribution was determined by analyzing the water collected during the 20
minutes of simulated rainfall. In addition a rainfall distribution map (isohyet) was plotted. Three
replicates were done and their mean calculated for the analysis. Analysis of rainfall distribution
was done before the main experiment, midway through the experiment, and at the end of the
experiment.
43
Figure 4.10 Pictorial layout of the collection beakers on the simulator floor used to measure
rainfall distribution (not to scale).
4.4 Preliminary run of the rainfall/runoff experiment
A preliminary rainfall-runoff experiment was conducted to refine major variables such as
the quantity of soil to be packed into the soil box to give a bulk density of 1.1 g/cm3 , the length
of the simulated rainfall event, time interval for collecting runoff samples, slope of boxes, and
44
characteristics of simulated rainfall. The preliminary run involved simulating rainfall over the
soil plot and collecting the ensuing runoff. During the rainfall simulator testing, the intensity was
determined. The duration of rain required to generate measureable quantities of runoff was also
determined. The simulated rainfall should generate quantities of runoff that can be weighed
accurately and later analyzed for turbidity and sediment concentrations.
The preliminary run also to assess whether the sizes of the soil boxes and the time
interval set for runoff collection were suitable. The collected runoff samples from the
experiments were analyzed for runoff amount, sediment concentration, and turbidity at the Water
Resources Laboratory, Agricultural and Biological Engineering Department (ABENG) at
Pennsylvania University for analysis. Results of the analyses showed measurable quantities of
runoff amount, sediment concentration, and turbidity as in the Bryan (1986) experiment. Thus it
was concluded that the duration and intervals that the researcher used can be used as the initial
sampling intervals. Bryan (1986), using a silty clay loam soil and a constant rainfall intensity of
77 mm/hr, obtained runoff after 4 minutes of simulated rainfall, and collected runoff samples for
analysis at 2 minute intervals for 20 minutes.
Packing of soil and setting the soil boxes
The soil for use in the research had been removed from the B-horizon of the soil profile.
Due to the disturbance of the soil during harvesting and haulage, the soil became loosely held
structure, which is unsuitable for a construction site. For the experimental soil to have the
characteristics similar to those of a construction site (have adequate strength, be incompressible,
and stable against volume change, compacted so that settlement does not occur after
construction), the soil was compacted by hand. The first step in acquiring soil with structure
45
similar to those of a construction site is to compact the soil to a bulk density higher than 1.0
g/cm3 for clayey loam soil. For the soil used in this research, a bulk density of 1.1 g/cm3 was
achieved by hand compaction using the equipment available in the lab. To achieve this bulk
density the soil was packed in soil boxes. When packing the soil into the boxes cheese cloth was
first placed at the base of the soil box. The cheese cloth prevented soil from being lost through
seepage from the bottom of the box. To achieve a bulk density of 1.1 g/cm3 , 14 kg of soil was
packed in the soil box. 7 kg of the soil was weighed and spread to the same depth throughout the
soil box. Using a piece of wood designed to fit inside the erosion box, the soil was evened out by
running the wood across the soil surface. The soil was then compacted evenly by hand using a
4.5 kg weight that was dropped from a height of 1 m. An additional vertical force of 100 N
(approximately) was applied on the rammer by hand to tamp the soil. A mallet was used to
compact the soil at the edges and corners of the soil boxes where the rammer could not access.
Once the soil was compacted to the target 3 cm throughout the soil box, the remaining 7 kg of
soil was put into the soil box and again compacted using the 4.5 kg weight and the mallet. The
box and the soil contents were weighed to ensure that the soil that was packed in the soil box was
14 kg.
The soil box exterior was then cleared of all the soil particles and other debris to make
sure any sediments collected during the event were removed from within the soil boxes being
tested. A 1.5 m long, 4 mm diameter tubing for collecting runoff was fixed onto the collecting
trough using araldite (adhesive). Both outside and inside of the tubing were then cleaned. Water
was drained from the tubing and the equipment was left to dry in the open. The box was labeled
with the treatment to be applied in the experiment.
46
Treating the soil with RECP
The RECP was placed on a table and unrolled to a suitable length for cutting. A pair of
scissors was used to cut a piece of the RECP of size 100 cm by 20 cm as shown in Figure 4.11.
This size covered the soil surface in the soil box. The RECP was laid on the soil surface making
sure that the RECP covered all the soil. The RECP was then pressed down by hand to remove
ripples and make sure that the RECP was intimately in contact with the soil, similar to how
placement occurs on a construction site.
Roll of RECP 1
Pair of scissors cutting through
RECP 1
Figure 4.11 Image showing RECP 1 being cut with a pair of scissors.
Treating the RECP with PAM by sprinkling
As reported in literature, the application rate of 40 kg/ha, 60 kg/ha and 31.2 kg/ha were
used by Jian et al. (2003), Peterson et al. (2002), and Wilson et al. (2008), respectively, in their
47
PAM treatment experiments. Thus the quantity of PAM to be applied on the erosion box surface
(1.094 m x 0.197 m) to give an application rate same as by Jian et al. (2003) was too small (1
gram) to be applied alone. PAM, for application on the RECP, was mixed with Hagerstown soil
before sprinkling. The dry soil was sieved through a 250 micron sieve. 19 grams of the soil was
weighed and spread out evenly on a clean white sheet of paper. One gram (application rate 46 kg
PAM/ha) of PAM was weighed using the electronic balance. The PAM was then spread on the
weighed soil using a spatula. The two were then mixed evenly using a spatula. The mixed
contents were put into beaker with sieve cover to dispense the PAM/soil mixture on the RECP.
All the 20 grams of PAM/soil mixture was then sprinkled on the RECP evenly, as shown in
Figure 4.12.
Plastic beaker with PAM
Soil box
RECP 1
Figure 4.12 Image showing RECP 1 being sprinkled with PAM.
48
Treating the RECP with PAM and gypsum by sprinkling
Previous gypsum application rates done on soil of similar texture as the soil in this
experiment were 1 Mg gypsum/ha, and 1 and 2 Mg gypsum/ha by Jian et al. (2003) and Truman
et al. (2010), respectively. To achieve an application rate of 1.0 Mg gypsum/ha, 22 grams of
gypsum was applied on the erosion box. 100 grams of dry gypsum was sieved through a 250
micron size sieve and placed in a sealed container. 22 grams of the gypsum was weighed using
an electronic balance and then spread on a clean sheet of paper. 1 gram of PAM powder was
weighed and then spread evenly on the gypsum. The PAM and the gypsum powder were then
mixed using a spatula until the two were evenly mixed. The mixture was then put into a beaker
with sieve cover to dispense the contents onto the RECP. All the 23 grams of PAM plus gypsum
was then sprinkled evenly on the RECP.
Treating the RECP with gypsum by sprinkling
Twenty two grams of the gypsum were weighed using an electronic balance and then put
into a beaker with sieve cover to dispense the contents onto the RECP. All the 22 grams of
gypsum was then sprinkled evenly on the RECP.
First rainfall application (run 1)
Wooden planks and empty soil boxes were used to set up 15% slopes for the boxes at the
five positions established earlier, as shown in Figure 4.13. The five positions were identified
during simulator testing as described in Section 4.3.6 and presented in Section 5.1. The boxes
containing the treated soil samples were placed on these positions making sure that the soil boxes
were at a slope of 15% by measuring the slope.
49
Twelve 500 mL plastic bottles were labeled from 1 to 12 and placed at the discharge end
of each of the 15 mm diameter tygon tubing and made ready to collect the runoff as shown in
Figure 4.14. Rain gauges were placed on the side of the boxes to measure the amount of rainfall.
Wooden
pl a nks
RECP 2
RECP 2
pl us PAM
Control
Ra i n
Ga uge
RECP 1
RECP 1 pl us
PAM
Figure 4.13 Image showing the control and the treated soil samples laid on the simulator
floor.
50
15 mm di a meter tygon tubing
500 ml pl astic bottles
Figure 4.14 Image showing bottles ready to collect the ensuing runoff.
The end of the tygon tubing was placed in the first bottle to collect the initial runoff. A
3.05 m long, 4 cm diameter PVC pipe was placed over the simulator nozzle with its lower end
leading into a bucket. The pump for the simulator was started and the simulated rain was routed
through the PVC pipe to the bucket for about three minutes. During this time the pressure was
adjusted to 31.7 + 0.5 KPa. Once the pressure was established at 31.7+ 0.5 KPa, the 4 cm
diameter PVC pipe was removed from the simulator nozzle so that the rain was now falling on
the treated soil boxes, and a stop watch was also started at the same time.
51
The bottles were observed for the initial runoff initiation. The time it took for the runoff
initiation for each treatment was noted. Two minutes after the runoff initiation, the five runoff
bottles labeled No 1 were removed from the tubing nozzle and replaced with bottles labeled No
2. If there was any runoff the bottles were capped and stored. The same procedure was repeated
for every 2 minute period until runoff was collected in bottle No 12. In case a bottle could not
accommodate all runoff before the 2 minutes expired, another bottle was put in the nozzle and
labeled bottle # B. Once tubing was inserted into the last bottle (No 12), the simulator nozzle was
covered with the 3.05 m long 4 cm diameter PVC pipe and the rain was routed into the bucket.
The stopwatch time was noted and the simulator pump was switched off. All the runoff was
allowed to drain into the last bottles. Bottle No 12 was also capped. The wet soil box was then
left in the open to drain and air dry. The second rainfall application (Run 2) was done on the
treated boxes after two days.
Weighing the runoff samples
The runoff samples were arranged on the table according to the erosion treatments and in
the order that the runoff was collected, beginning with bottle number 1 up to 12. Each bottle was
weighed using an electronic balance and the reading entered into a data table as shown in
Appendix A-1.
Measuring turbidity
Turbidity is a measure of the amount of light that is scattered and absorbed by particles in
a fluid. Turbidity gives an indication of the amount of suspended particles in a sample. The units
of turbidity are nephelometric turbidity units (NTUs), and the higher the turbidity the greater the
52
number of particles in the sample. A turbidimeter is used to measure turbidity and consists of a
nephelometer, with light that illuminates the sample, and a photoelectric detector that indicates
the intensity of light scattered. A sample turbidimeter is shown in Figure 4.15. The EPA method
180.1 as described by Papacosta (2002) was used to measure turbidity in this research. The
method is based on a comparison of the intensity of light scattered by the sample with the light
scattered by a reference suspension under the same conditions each day. The turbidimeter was
calibrated each day prior to taking measurements.
The turbidimeter was calibrated using formazine. Formazine is a solution of hydrazine
sulphate and a polymer with pure water. The liquid has a constant turbidity. Filtered and
deionized water were used to clean the meter and the sample bottle. The sample bottle was then
rinsed with formazine. Formazine was put into the sample bottle and its turbidity measured using
the turbidimeter. The turbidimeter reading of the formazine was then compared to the
manufacturer’s value. If the readings were different the turbidimeter was adjusted to conform to
the manufacturer’s value. The turbidimeter was calibrated every day before taking any turbidity
measurements.
53
Turbidity meter
cover
Slot for putting the bottle
containing test sample
Display screen
Figure 4.15 Turbidimeter for measuring turbidity (Ref:
http://www.milleranalytical.com/images/2020e.jpg).
After calibration, the runoff sample being analyzed was mixed thoroughly to disperse the
solids by shaking. The sample was rested for ten seconds to allow the air bubbles to escape.
Resting the sample for more than 10 seconds would have allowed some sediments to settle,
thereby giving NTU results that are lower than the actual ones. The sample was then poured into
54
a cuvette tube for testing. The results were read directly from the turbidimeter. For turbidities
exceeding 900 NTUs the sample was diluted with one or more volumes of turbidity-free water
until the turbidity measured was below 900 NTU. The turbidity of the original sample was then
computed from the turbidity of the diluted sample and the dilution factor as below;
NTUoriginal=A*(B+C)/C
A=NTU found in diluted sample
B=Volume of dilution water in milliliters
C=Volume of undiluted sub-sample in milliliters
The turbidity of the samples was entered into a table as shown in Appendix A-2.
Measuring suspended sediments
Suspended sediment concentration is often measured via gravimetric analysis of water
samples collected manually or by water samplers (Edward and Glysson, 1999; Bent et al., 2003;
Davis, 2005). Gravimetric analyses are determined using one of two analytical methods: the
suspended sediment concentration (SSC) method and the total suspended solids (TSS) method
(Gray et al., 2000). The SSC analysis involves measuring the dry weight of sediment from the
total volume of a water sample (Gray et al., 2000). The TSS method analyzes a sub-sample of the
larger sample. Analyzing suspended sediment concentration via the TSS method can produce
large errors if the concentration of sand-sized particles is greater than 25% (Gray et al., 2000). In
this research the runoff samples were large in both quantity and number and hence using the SSC
method was not practical. Also, the concentration of sand-sized particles in the runoff was not
55
analyzed; hence, assuming the concentration was less than 25 % may not be accurate. The TSS
procedure for analyzing sediment concentrations was used in this research for a 50 mL subsample, and a schematic of the test protocol is shown in Figure 4.16.
The procedure that was adopted for sediment concentration involved analyzing a portion
of the sample by evaporating to dryness in pre-weighed aluminum dishes. The water that was
used in the rainfall simulation experiment was not saline, and thus there were no salts that could
increase the errors significantly. The collected runoff sample to be analyzed was mixed
thoroughly by shaking to disperse the solids. A sub-sample of 50 milliliters was extracted from
the sample using a syringe. The sub-sample was put into a 50 milliliter aluminum dish labeled as
per the content. The same amount of sample (50 ml) was extracted from all the other bottles and
put into the corresponding labeled 50 ml aluminum dishes. The sub-samples were then put in
trays and the contents were put in an oven set at 105 o C for drying. After two days (48 hrs) ten
samples were taken randomly and weighed. The samples were put back in the oven. The samples
were weighed again after 12 hrs. If the mass of the samples had not decreased further from the
previous time of measurement, all the samples were removed from the oven and weighed. If the
mass had not stabilized, the samples were kept in oven for a further 12 hrs. This was repeated
again after 12 hrs until there was no decrease in mass of the ten samples. The results were
entered in a sample data sheet as shown in Appendix B.
56
Total sediment sample
Total suspended concentration
(Aluminum dish method)
Weigh the Aluminum Dish
Put 50 ml of sub sample into the
Aluminum Dish
Dry subsample in oven for 48 hrs at
103+2 o C
Weigh sediment and Aluminum
Dish
Concentration (mg/ml)=Sediment
mass /50
Figure 4.16 Flow chart illustrating the total suspended solids methods for analyzing sediments
from a 50 mL sub-sample.
57
The sub-samples were then put in trays and the contents were put in an oven set at 105 o C
for drying. After two days (48 hrs) ten samples were taken randomly and weighed. The samples
were put back in the oven. The samples were weighed again after 12 hrs. If the mass of the
samples had not decreased further from the previous time of measurement, all the samples were
removed from the oven and weighed. If the mass had not stabilized, the samples were kept in
oven for a further 12 hrs. This was repeated again after 12 hrs until there was no decrease in
mass of the ten samples. The results were entered in a sample data sheet as shown in Appendix
B.
Second rainfall application (run 2)
The runoff boxes were left to drain in the same positions for two days after the first
rainfall simulation experiment. After the two days, the 0.5 L bottles were placed at the discharge
side of the erosion boxes as shown in Figure 4.14 and the same procedure as the previous
protocol (First rainfall application) was used to collect and analyze the runoff.
Third and fourth rainfall application experiment (run 3 and run 4)
Run 3 was conducted after the soil boxes were left to drain for two days after Run 2, and Run 4
conducted 2 days after Run 3. In these runs the same experimental procedure was used as in Run
1.
4.5 Methodology of the actual experiment
The procedure for conducting the main experiment was the same as that of the
preliminary experiment. In all there were four replicates in this experiment. The procedure
involved setting up the erosion boxes, simulator set up, running the rainfall simulator and
58
collecting the ensuing runoff, analyzing the runoff and entering the results in the relevant tables.
A flow chart of the methodology is shown in Figure 4.17.
First rainfall application of the experiment (run 1)
Following the preliminary experiment to test the experimental setup and parameters the
actual experiment was conducted. This consisted of nine soil boxes each treated with the
respective treatments as described in the preliminary experiment (Section 4.4). The boxes were
labeled with the respective box number and the treatment, these being;

1-Control (bare soil)

2-RECP1 (70% wheat straw and 30% coconut fiber)

3-RECP1+PAM

4-RECP1+PAM +Gypsum

5-RECP1+Gypsum

6-RECP2 (100% coconut fiber)

7-RECP2+PAM

8- RECP2+PAM+Gypsum

9-RECP2+Gypsum
Box numbers 1 to 5 were placed under the simulator in positions 1 to 5 of the simulator area.
Rainfall gauges were placed on the side of each of the runoff boxes, as described in the
preliminary section. Following the procedure as described in Section 4.4, the simulator was
started and the ensuing runoff collected every 2 minutes after runoff initiation. The time to runoff
initiation for each runoff box was noted and the result entered in the data recording sheet.
59
PAM a nd Gyps um Tes ti ng
Si mulator s et up;
Eros i on Box Set-up;
-wa ter ta nk full,
Pres s ure a t 31.7 KPa
-compa cting soil,
a tta ch pvc pipe at
di s charge end
- trea ti ng a nd placing
RECPs on erosion
boxes
Repl i ca te 2, 3, 4
-pl acing erosion boxes
on s i mulator floor
-treating
serosion
Pl a ci ng
0.5 l
col l ector bottles on
di s cha rge s i de of
Run s imulated rainfall on the
eros ion boxes with treated RECPs
a nd Control (Run 1, 2, 3, 4)
Run 2, 3, 4
eros i on box
Col l ect a nd a nalyze runoff
for a mount, turbidity, and
Al l ow 2 da ys for
eros ion boxes to drain.
s ediment concentration
Conduct next run
Cl ea n a nd dry the erosion
Enter res ults in ta bles for
boxes and the plastic bottles
s ta tistical a nalysis
Figure 4.17 Flow chart for testing RECPs enriched with PAM and gypsum.
60
Once the run was complete, the simulator was stopped and the time noted. Depths of rain in
the rain gauges were noted, and the contents emptied. The five boxes were removed from the
simulator area and left to drain. Box numbers 6 to 9 were then placed in the simulator area with
box 6 placed at position 1; box 7 at position 2; box 8 at position 3; and box 9 at position 4. For
this portion of the experiment, position 5 did not have a soil box since only 4 treatments
remained to be tested. The procedure described in Section 4.3.6 was followed in starting the
simulator and collecting the ensuing runoff.
The bottles of collected runoff samples were taken to the lab for analysis to determine the
mass of runoff, turbidity, and sediment concentrations using the procedures described in Section
4.4. Data results were entered in the appropriate data tables.
Second rainfall application of the experiment (run 2)
After the two days of drying, the 0.5 L bottles for runoff collection were placed at the
discharge side of the soil boxes. The erosion boxes 1 to 5 were then placed at the positions where
they were during run 1. The rainfall gauges were placed on the sides of the boxes for measuring
the rainfall received at each soil box position. The top rims of the rain gauges were 5 + 1.0 cm
above the soil box. Hence, the rain gauges’ mouth being above the soil boxes could not
accurately measure the amount of rainfall received by the soil box. However, the rain gauge
readings were good approximation of the rainfall received. The procedure described in Section
4.4 was followed in starting the simulator and collecting the ensuing runoff. The time to runoff
initiation for each runoff box was noted and results recorded. Once the run was complete, the
simulator was stopped and the time noted. The bottles of runoff were then taken to the lab for
61
analysis to determine the mass of runoff, turbidity, and sediment concentrations using the
procedure described in Section 4.4.
Third and fourth rainfall application of the experiment (run 3 and run 4)
Run 3 was conducted after the soil boxes were left to drain for two days after run 2, and
run 4 conducted 2 days after run 3. In both rainfall applications the same procedure was used to
collect the same information as in runs 1 and 2.
4.6 Methodology of data analysis
Introduction
The data were entered into excel worksheets and then converted into metric units. The
data were then changed into formatted text (.prn) so that data could be entered into Statistical
Analysis System (SAS 9.3) software. In the initial analysis the data for each replicate were tested
for normality and equal variances using mini tab and excel, respectively. Initial data review
involved testing responses for each replicate and rainfall application for normality and equal
variances using minitab 16.1 statistical software. The excel F-test (α=0.05) was used to test for
equality of variance among the replicates for each treatment and rainfall application. The
procedure for testing for normality and equal variances is shown in Appendix B.
A flowchart for the methodology for the data analysis is shown in Figure 4.18. The
results of the statistical evaluations were used to determine whether to reject or accept the null
hypotheses. The statistical evaluations compared the results from the eight treatments and the
control, these being; RECP1, RECP2, RECP1 +PAM, RECP1+PAM +Gypsum, RECP2 +PAM,
RECP2+PAM +Gypsum, RECP1+Gypsum, RECP2+Gypsum, and the Control. As stated in
Section 4.3.4, RECP1 is an erosion control blanket consisting of 70% wheat straw and 30%
62
mattress-grade coconut fiber, and RECP2 is an erosion control blanket consisting of 100%
mattress-grade coconut fiber. The null hypothesis (Ho ) tested was that the mean of the measure
of interest (total runoff, sediment concentration, and turbidity) for the specific treatment was not
significantly different (α=0.05) from the mean of the measure of interest of alternative treatment.
For example; for the hypothesis comparing the turbidity of runoff for RECPs treated with PAM
with the turbidity of untreated RECPs, the null (Ho ) and the alternative (H1 ) hypotheses (α=0.05)
were written as:
Ho : Turbidity for RECP treated with PAM is not significantly different from that of non-PAM
treated RECP (TurbidityPAM=TurbidityNonPAM);
H1 : Turbidity for RECP treated with PAM is significantly different from that of non-PAM
treated RECP (TurbidityPAM≠TurbidityNonPAM).
The data obtained from the experiment consisted of 12 measurements of each response for every
treatment and rainfall application (Run). There were four rainfall applications for each of the
four replicates. The first run was the first runoff experiment that was done on the soil boxes.
After the first run, the boxes were left to drain for two days and the second run was carried out.
The third run was carried out after two days and the fourth run two days after the third. For
example, for treatment RECP 1 with PAM runoff data set, there were 12 observations for the
first replicate and the first run, 12 observations for first replicate and the second run, 12
observations for the first replicate and the third run, and 12 observations for the first replicate
and the fourth run.
63
Runoff, turbi di ty, s edi ment
concentration da ta , a s col l ected
from experi ment.
Enter da ta i n excel works heet
a nd convert to metri c uni ts
Ra i nfa l l
a mount
for ea ch
trea tment
Ti me to
runoff
i ni tiati on
(TRO)
For the four
repl i ca tes ,
ca l cul a te the
mea n turbidity of
runoff from each
trea tment a nd
run
For the four
repl i ca tes ,
ca l culate the total
runoff vol ume
(RO) from ea ch
trea tment and run
For the four
repl i ca tes ,
ca l cul a te the
total sediments
(TS) from ea ch
trea tment a nd
run
Arra nge works heets
a nd s ummarize da ta
Extra ct da ta and pl ot gra phs
of cumul a ti ve runoff, mea n
turbi di ty, tota l s edi ments ,
ti me to i ni ti a l runoff, a nd
ra i nfa l l .
Cha nge a nd s a ve da ta a s
forma tted text (.prn)
Import data into SAS
s oftwa re
Expl oratory Data
Ana l ys i s
Model analysis for
the res pons es
Tes t for the
fi xed effects
Tes t the Hypothes es
Figure 4.18 Methodology of data analysis for the experimental responses.
64
Compa re means
of trea tments
Hence, for treatment RECP 1 with PAM replicate 1 there were 48 runoff observations, 48
sediment observations, 48 turbidity observations, 4 rainfall observations, and 4 time to runoff
initiation observations. Each of the other 8 treatments had the same number of observations.
Results from each run were entered in separate excel Workbook files (.xlsx).
Representative response variables were then calculated for each run and replicate. For example,
the representative response variable for “turbidity” is the mean of the 12 responses. To calculate
the mean turbidity for treatment RECP 1 with PAM replicate 1 run 1, the 12 turbidity results
were added and divided by 12. For response “sediment” the mean was also used. However, for
the response variable “runoff” the cumulative value of the 12 responses was used. Figure 4.19
shows the calculation of total runoff for run 1 replicate 1.
Total runoff (mm)
Treatment
Responses (data)
(mm)
Figure 4.19 An excel worksheet showing the summation (summarizing) of runoff responses for
the first run replicate 1.
65
Figure 4.20 shows the single values of "total runoff" for each treatment for Run 1 Replication 1. The values in the row "Total runoff" were used in the analysis.
Total runoff (mm)
Treatment
Runoff data (mm)
Figure 4.20 Excel worksheet showing the calculation of total runoff for runoff data.
The same procedure was used to calculate the other 15 run and replication combinations
of runoff data. Rainfall was also converted into metric units and included for each treatment.
The final part of data preparation was the preparation of the runoff working dataset. For the
"runoff" working dataset, each observation had a row including values of "treatment",
"replication", "run", "rainfall" and the response "total runoff" as shown in Figure 4.21. The
“rainfall” was the amount received on the soil box (area 1.097 m x 0.197 m) during the rainfall
application time (37 minutes for the first run and 27 for runs 2, 3, and 4).
66
Figure 4.21 Excel worksheet showing a portion of data of the total runoff data set.
Before importing the data into SAS 9.3 (http://webapps.psu.edu/Apps/SAS/) software,
the file type was changed by saving the excel workbook file (.xlsx) as a formatted text file (.prn).
The saved data were then imported into the SAS software. SAS programs were then compiled
and used to analyze the data.
Similarly, for responses "turbidity" and "sediment", the 12 measurements of every
treatment were summarized into one value, in each run within each replication. As indicated for
turbidity, the summarized value was the mean and for the sediments the summarized value was
the mean sediment concentration.
67
4.6.1 Data preparation
The SAS software was used for statistical analysis of the data obtained from the soil
treatments as described in Section 4.4. The hypotheses for testing were as stated in Section 3.3.
The data from these soil treatments consisted of nine treatments in four replications with four
runs each and 12 observed measurements of any response (runoff, turbidity, and sediment
concentration). Also there was one measurement each for the response variables time to runoff
initiation and rainfall amount for each run. The 12 measurements (runoff, sediment
concentration, and turbidity) were analyzed in ways that did not increase the degrees of freedom
in the model, which would cause pseudo-replication error. Pseudo-replication errors are
introduced in data sets when data are treated as independent when they are not. Thus the 12
measurements were analyzed as interdependent variables to reduce the effect of the errors.
These 12 measurements of given response were converted to one value for purposes of
statistical analysis. Thus, for the response variable runoff, the total cumulative runoff was the
measurement in each run within each replication. Similarly, for response “turbidity” and
“sediment” the mean of the 12 measurements for every treatment, in each run within each
replication, was the mean value. Hence, the cumulative data for the response runoff for RECP 1
was presented in an excel worksheet as shown in Table 4.1.
The data were imported into the SAS software and used to conduct exploratory data
analysis and hypotheses testing. The program for reading the runoff data into SAS is shown in
Appendix B-1. In SAS software, the code for running the software is displayed in the "Editor"
window. The corresponding code is run by highlighting the SAS software and clicking the
"SUBMIT" icon, which is in the tool bar as indicated in Appendix B-2.
68
Table 4.1 Sample table for entering data for the runoff response for RECP 1.
Treatment Replicate
Run
RECP1
1
1
RECP1
1
2
RECP1
1
3
RECP1
1
4
RECP1
2
1
RECP1
2
2
Rainfall
Total
runoff
4.6.2 Exploratory data analysis (EDA)
The tool that was used for the EDA was the box-and-whisker-diagram, also known as the
box plot. The box plot graphically depicts data for each treatment: endpoints of whiskers
extending to the bottom and the top of the graph indicate the minimum and maximum values
except for the potential outliers; three horizontal lines forming the box represent the lower
quartile, the median, and the upper quartile from bottom to top, respectively; the diamond in the
box is the location of the mean; asterisks indicate outliers if they exist.
The SAS procedure, "PROC BOXPLOT", was used to create a box plot of the "total
runoff" by the "treatment". The procedure for creating a box plot using SAS software is
explained and shown in Appendix B-3
For the hypothesis comparing runoff volume for RECPs treated with PAM with the
runoff volume from that of non-PAM treated RECPs , a box plot of “run-off” against treatments
“PAM” and “non-PAM” was created as shown in Appendix B-4.
69
.
For the hypothesis testing the difference in runoff volume for RECPs treated with PAM
plus gypsum to that of RECPs treated with PAM only, a box plot of “run-off” against treatments
“PAM+G” and “PAMonly” was created. The program for the hypothesis test is shown in
Appendix B-5.
Another EDA was conducted to determine whether “rainfall”, which was assumed to be
uniform in space should be considered in the statistical analysis. A graph of “rainfall” recieved at
each erosion box location for the rainfall application was plotted. In the expriment all the
replicates received rainfall of intensity 125 mm/hr for 37 minutes for the first rainfall
applications, and of intensity 125 mm/hr for 27 minutes for the second, third and fourth rainfall
applications. The results of the EDA for the rainfall would show whether location of box
(treatment) affected the runoff; an indication of whether rainfall should be considered a
covariate. The program for the analysis is shown in Appendix B-6.
The results obtained after running the EDA SAS program for runoff were analysed and
conclusions drawn. Similarily, exploratory data analysis of the turbidity and the sediment
concentration used the same SAS program but with the data set runoff replaced by mean
turbidity and mean sediment concentrations, respectively.
4.6.3 Model and analysis for the runoff data
The experiment was conducted in a randomized complete block design. Each treatment
appeared equally in each block.. The different sets of soil samples used for each replication were
a blocking factor. In addition, since each treatment level was located within each block of the
design, the blocks were considered to be complete. The replications had a blocking effect which
reduced variability. Fixed effects have no differences between them; unlike random effects
whose variation occurs randomly. The SAS model that was used in the analysis contained both
70
fixed and random effects. The fixed effects were “treatment”, “run”, and their interaction
“treatment*run”, and “rainfall” was used as a covariate (determined after performing EDA). The
random effects were “replication”(the blocking factor) and the interaction term
“replicate*treatment”. The model terms for the SAS software are as shown in Table 4.2.
Table 4.2 Fixed and random terms for the SAS program to analyse runoff responses.
Model terms
Description
Treatment
Fixed, consists of 8 levels and the control
Run
Fixed, consists of 4 levels, measures the time effect
Replication
Random, a blocking factor
Rainfall
Covariate
Run*treatment
Fixed, interaction
Replicate*treatment
Random, interaction
To compare treatment effects, a contrast was used in the statistical analysis. In a contrast,
the linear combination of cell means have coefficients that sum to zero. Each treatment level is
accompanied with a number that classifies the treatment level into a specific group. Treatments
are classified into three groups:

Positive number classifies the treatment level into one treatment group

Negative number classifies the treatment level into the other treatment group
71

“Zero” indicates that the treatment level is not included in this comparison (not
related with the hypothesis)
Contrasts are determined based on the hypothesis to be tested. For the hypothesis that
runoff volume for RECPs treated with PAM will not be significantly different from runoff
volume of non-PAM treated RECPs, the mean of the PAM treatment group is compared with
the mean of the non-PAM treatment group. Thus “-1” is applied for a treatment without PAM
and “1” is applied for a treatment containing PAM. The control treatment is assigned “0”.
Similarly, for the hypothesis that runoff volume for RECPs treated with PAM plus
gypsum will not be significantly different from that of RECPs treated with PAM only, a “-1” is
applied for treatment with PAM only, a “1” is used for treatment containing PAM plus gypsum,
and “0” is used for other treatment levels. Contrasts for testing the hypotheses thus become as
shown in Table 4.3.
To test the hypothesis runoff volume for RECPs treated with PAM will not be
significantly different from the runoff from non-PAM treated RECPs, the “PROC MIXED”
procedure in SAS was used. The coding for this procedure is shown in Appendix B-7. To test the
hypothesis runoff volume for RECPs treated with PAM plus gypsum will not be significantly
different from the runoff from PAM only treated RECPs the SAS procedure in Appendix B-8
was used. To test the hypothesis runoff volume for RECPs treated with gypsum will not be
significantly different from the runoff from Non-gypsum treated RECPs, the SAS procedure in
Appendix B-9 was used. Similarily, for the model of the data analysis of the turbidity, sediment
concentration, and time to runoff initiation, the same SAS program was used but with the data set
runoff replaced by mean turbidity, mean sediment concentrations, time to initial runoff,
respectively.
72
Table 4.3 Table showing contrasts for testing the specific hypotheses of individual treatment.
Contrasts for
Hypotheses
RECP1
RECP1
+G
RECP1
+PAM
RECP1
+PAM+G
RECP2
RECP2+
G
RECP2+
PAM
RECP2+
PAM+G
PAM vs nonPAM
-1
-1
1
1
-1
-1
1
1
PAM+G vs
PAMonly
0
0
-1
1
0
0
-1
1
G vs non-G
-1
1
-1
1
-1
1
-1
1
PAM +G vs
G only
0
-1
0
1
0
-1
0
1
PAM only vs
RECPs
-1
0
1
0
-1
0
1
0
Gypsum only
vs RECPs
-1
1
0
0
-1
1
0
0
4.6.4 Comparing the means of treatments
Comparison of means was applicable where there was significant difference between the
treatments. The test determined which treatment group had the smallest mean runoff. By
comparing the means of both groups, the group with the lowest mean was the most effective in
reducing runoff volume. For example, if the contrast for "PAM vs non-PAM" is determined as
significant (at α=0.05), the mean for PAM treatments and mean of non-PAM treatments were
calculated separately. If, on comparing the two results, the mean for non-PAM was less than the
mean for the PAM treatment the conclusion was that treatments without PAM performed more
effectively in reducing the runoff volume than treatments with PAM.
The comparisons were determined across all the rainfall applications (all the runs), and
separately on rainfall applications 1, 2, 3, and 4 (runs 1, 2, 3, and 4). The SAS procedure for
comparing the cumulative runoff for treatments PAM vs Non-PAM, PAM+G vs PAM only, G
73
vs Non G, and PAM vs G only for rainfall application 4 (run 4) are outlined in Appendix B-10.
The LSMEANS statement shown in Appendix B-11 was used to run the SAS code for the
calculation and comparison of the means of treatments for the turbidity and the sediment
concentration.
74
CHAPTER 5
RESULTS AND DISCUSSION
5.1 Physical and chemical properties of the soil and water that was used in the experiment
The results of the soil and water tests are shown in Tables 5.1 and 5.2, respectively.
Using the USDA textural triangle the soil was classified as clay loam soil
(http://soils.usda.gov/technical/aids/investigations/texture/).
Table 5.1 Table showing the physical and chemical properties of the experimental soil.
Soil Property
Sample 01
Sample 02
Sand content (%)
25.0
23.5
Silt (%)
41.0
41.9
Clay (%)
34.0
34.6
Extractable Sodium (ppm)
41.2
38.7
Soil pH
6.1
6.5
Phosphorus (P)
( ppm)
181
155
Potassium (K)
( ppm)
376
346
Magnesium (Mg)
(ppm)
325
293
CEC (meq/100g)
21.1
16.0
Salts (mmhos/cm)
0.48
0.42
Organic matter (%)
0.0 (no organic
matter)
0.0 (no organic
matter)
As shown in Table 5.2 total suspended sediments in the water supplied to the rainfall
simulator were less than 1 mg/L, and hence one can conclude that sediments measured in the
runoff were not contributed substantially from the rainfall water. The total dissolved solids and
75
the electrical conductivity were also low and were deemed not to affect the PAM (Lentz et al.,
2002).
Table 5.2 Chemical analysis results for the rainfall water.
Analysis
Units
Sample No W1
Sample No W2
4.6
5.1
pH
Total Dissolved Solids
mg/L
<20
<20
Total Suspended Solids
mg/L
<1
1
mmhos/cm
0.033
0.030
Electrical Conductivity (EC)
The results of the gravimetric moisture tests for the soil samples are as shown in Table
5.3. The mean gravimetric moisture content of the test soil used in the experiment was 1.6 %.
Table 5.3 Table showing the results of measurements for the gravimetric moisture content of the
experimental soil, as available prior to the experiment.
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Mass (g) of sample before drying (Mw)
23.71
22.10
23.96
22.37
27.47
Mass (g) of dry sample (Md)
23.38
21.73
23.56
22.01
27.03
Mass (g) of moisture in sample (Mw-Md)
0.38
0.37
0.40
0.36
0.44
%Moisture in sample (Mw-Md)/Md%
1.63
1.70
1.70
1.64
1.63
Mean
1.66
5.2 Simulator flow rate, operating pressure and rainfall distribution
The first step in the rainfall simulator study was to determine the operating pressure that
delivers the target flow rate. In this research the target flow rate for the SS30WSQ nozzle was
125 mL/s (Humphrey et al., 2002). In their experiments, Humphrey et al. (2002) had used flow
rates of between 119 to 128 mL/s and achieved a coefficient of uniformity of 85% and above for
the SS30WSQ nozzle. Once the target flow rate was established the rainfall intensity and
distribution were checked. To achieve the target flow rate of 125 mL/s, an operating pressure of
76
33.0 kPa was initially set, which stabilized at 31.7 kPa, after running the simulator for five
minutes. Hence a target flow rate of 125 mL/s was approximately achieved.
Determination of flow rate and operating pressure
Flow rates at various nozzle pressures were determined by collecting the volume of water
discharged for a specified time interval. The operating pressure gauge measured pressure in bars,
which were then converted to kilopascals (kPa). Results of the three trials of flow rate for
operating pressures of 35.9, 33.1, and 31.0 kPa are shown in Table 5.4.
Table 5.4 Flow rate and operating pressure of simulator during rainfall simulator setup.
Operating pressure
(kPa)
35.9
33.1
31.0
Mass of water collected from simulator nozzle
(kg)
First Trial
16.2
15.6
11.2
Second Trial
16.0
15.4
11.0
Third Trial
16.2
15.4
11.0
Average
16.13
15.47
11.07
Time
(s)
Flow Rate
(mL/s)
120
120
90
134
129
123
To achieve the target flow rate of 125 mL/s, an operating pressure of 33.0 kPa was initially set,
which stabilized at 31.7 kPa, after running the simulator for five minutes. Hence a target flow
rate of 125 mL/s was approximately achieved.
Determination of rainfall intensity and uniformity distribution received in erosion boxes
The aim was to determine the uniformity distribution of the rainfall for the area to be
used for the experiments below the nozzle. The operating pressure that produced the target flow
rate (125 mL/s) was set. During the experiment the operating pressure varied from 31.7 kPa at
the start to 30.3 kPa at the end, as shown in Table 5.5. The change in operating pressure was due
to the height of water column in the supply tank (the height decreased as water was being
pumped out during the experiment). However, an acceptable rainfall flow rate of 125+ 1 mL/s
was maintained.
77
Table 5.5 Results of operating pressures and the resulting flow rates.
Operating Pressure
(kPa)
Mass of Water
(kg)
Time
(s)
Flow Rate
(mL/s) *
31.03
31.03
30.34
14.95
15.10
15.05
120
120
120
124.6
125.8
125.4
* Rainfall flow rate (mL/s) = Volume of collected water (mL)/Time (seconds) = mass of
water collected (kg)/Density of water (kg/m3 ) x 1000000 mL/m3 /Time (s) .
Results of the rainfall distribution for the area encompassing the eight boxes arranged
side by side on the simulator floor were calculated, and the results are tabulated in Table 5.6.
Christensen’s coefficient of uniformity was calculated for each of the three rainfall applications
as described in Section 4.3. The results are shown in Table 5.6.
Table 5.6 Data for rainfall collected from 8 erosion boxes for three replicates for a 10 minute
duration of simulated rainfall and the resulting Christensen’s coefficient of uniformity .
First rainfall application
Box No
1
2
3
4
5
6
7
8
M ass
of
Water
(kg)
1.95
2.15
2.15
2.50
2.45
2.45
2.40
2.05
Rainfall
intensity
(mm/hr)*
54.3
59.9
59.9
69.6
68.2
68.2
66.8
57.1
Second rainfall application
Christensen’s
coefficient of
uniformity
%
91.7
M ass
of
Water
(kg)
1.85
2.05
2.25
2.30
2.35
2.30
2.05
1.95
Rainfall
intensity
(mm/hr)
51.5
57.1
62.6
64.0
65.4
64.0
57.1
54.6
Christensen’s
coefficient of
uniformity
%
93.4
Third rainfall application
M ass
of
Water
(kg)
1.85
2.0
2.20
2.25
2.25
2.30
2.4
1.95
* Rainfall intensity = collected water (mm)/Time (hr)
10 mins=10 mins/(60 mins/hr)=1/6 hr
Collected water (mm3 )=collected water (kg)/Density of water (1000 kg/m3 )x
(1000x1000x1000) (mm/m)3 =Collected water (kg)x1000000 mm3
Surface area of the soil boxes (mm2 )=1.094 m x 0.197 m =0.216 m2 x (1000x1000)
(mm/m)2 =216000 mm2 .
Hence, collected water (mm)=Co llected water (kg)x1000000 mm3 /216000
mm2 =4.63xcollected water (kg) mm
Therefore rainfall intensity =4.63x Collected water (kg)/(1/6 hr) mm/hr=27.8x Collected
water (kg) mm/hr.
78
Rainfall
intensity
Christensen’
s coefficient
(mm/hr)
51.5
55.7
61.3
62.6
62.6
64.0
66.8
54.3
%
92.4
As indicated in Section 4.3, values of coefficient of uniformity greater than 90% obtained
in this experiment are above the 85% coefficient of uniformity recommended for rainfall
simulators of this type (Miller, 1987). The experiment was repeated at the start of the actual
experiment (05/20/2011), in the middle of the experiment (06/30/2011), and at the end of the
experiment (09/30/2011). The results are tabulated in Table 5.7. The results at the start of the
experiment, midway through the experiment, and at the end of the experiment are all above
recommended value of 85% (Miller, 1978).
Table 5.7 Results of coefficient of uniformity for the simulator at the three stages of the actual
experiment.
Period
experiment
Coefficient of uniformity for the rainfall applications (%)
First rainfall
application
Before start of the
experiment
Date: 05/20/2011
M idway through
the experiment
Date: 06/30/2011
End of experiment
Date: 09/30/2011
S econd rainfall
application
Third rainfall
application
Mean coefficient
of uniformity
85.17
89.23
91.14
88.33
87.34
87.65
89.45
88.14
91.89
86.45
88.34
88.89
Isohyet maps for the simulator.
The data for the mass of water collected in the beakers was used to plot isohyets showing
the rainfall distribution at the start of the experiment, midway through the experiment, and at the
end of the experiment. The rainfall data for sample collected locations were converted from mass
to depth units by developing a formula for calculating volume of a truncated cone as explained in
Appendix C. The isohyets of the rainfall simulator at the start of the experiment, midway through
the experiment, and at the end of the experiment are shown in Figures 5.1, 5.2, and 5.3,
respectively. The visual appearance of the maps show that the rainfall distribution at the three
79
stages of the simulation experiments (start, middle, and at the end) are slightly different but show
similar overall distribution with the greatest intensity at the center of the plot area (directly under
the nozzle) the least near the edges of the plot area. Using the same rainfall data, other rainfall
contour maps were plotted using Minitab 16.1 (http://webapps.psu.edu/App/minitab/) and are
presented in Appendix E. The soil boxes were allocated positions under the simulator as shown
in Figures 5.1, 5.2 and 5.3. The isohyet method was used to calculate the depth of rainfall
received by each soil box location, and the data are shown in Table 5.8 and Figure 5.4. The
simulator floor fitted five soil boxes leaving space to fit rain gauges on the side of each soil box.
The results show that rainfall received is larger at position 3 than other positions for the three
stages of the experiment. There is also variation of the amount of rainfall received at each soil
box position and during the three stages of the experiment.
80
Erosion box
position number
Position of
simulator nozzle
25
28
1.6
31
1
34
1
1
37
140
1.2
1
Distance on the y-axis (m)
43
1
31
0.8
1
1
1
2
3
1
1
5
4
28
1
1
1
0.4
25
1
0
1
0
0.4
0.8
1.2
Distance on the x-axis (m)
1.6
2.0
1
Figure 5.1 Figure showing spatial distribution of rainfall (mm) depth after 20 minutes of
simulated rainfall and the positions of the soil boxes under the simulator nozzle at the start of the
experiment (05/20/2011).
81
1.6
Position of
simulator nozzle
Erosion box
position
31
34
37
1
1
1
40
43
1
40
37
1
1
46
1
1.2
491
34
Distance on the y-axis (m)
1
1
31
0.8
1
28
1
5
4
3
2
1
25
0.4
1
0
1
0
1
0.4
1.2
0.8
1.6
2.0
Distance on the x-axis (m)
1
1
Figure 5.2 Figure showing spatial distribution of rainfall (mm) depth after 20 minutes of
simulated rainfall and the positions of the soil boxes under the simulator nozzle at midway
through the experiment (06/30/2011).
82
Figure 5.3 Figure showing spatial distribution of rainfall (mm) depth after 20 minutes of
simulated rainfall and the positions of the soil boxes under the simulator nozzle at the end of the
experiment (09/30/2011).
83
Table 5.8 Data of rainfall calculated from the isohyetal plots for the five soil-box positions.
Mean of rainfall received after 20 minutes (mm)
Positions
of erosion
box
start of
experiment
(05/20/2011)
Midway in
experiment
(06/30/2011)
End of the
experiment
(09/30/2011)
Mean
1
29.7
35.4
37.1
34.1
2
42.4
41.8
39.1
41.1
3
42.8
43.6
40.5
42.3
4
39.8
40.8
37.4
39.3
5
35.1
34.0
33.5
34.2
Before
Midway
End
Mean
50
Rainfall after 20 mins (mm)
45
40
35
30
25
20
15
10
5
0
1
2
3
4
5
Position of soil box
Figure 5.4 Graph showing the rainfall (mm) received in each soil box after 20 minutes at start of
experiment, midway through experiment and at end of experiment.
84
5.3 Initial analysis of data
Data for each replicate were analyzed for cumulative runoff, time to runoff initiation,
turbidity and sediment concentration. Excel was used to sum, average and plot the results, and
minitab statistical program was used to evaluate the number of responses, standard errors and the
standard deviations of responses from each treatment. The results for the first replicate for runoff
and turbidity are shown in Figures 5.5 and 5.6, respectively. The results of replicates 2, 3, and 4
are shown in Appendix E (E-1, E-3, and E-5).
Control
RECP 2 + PAM
RECP 2 + G
RECP 1 + PAM
RECP 1 + G
mean RECP 2
mean RECP 2 + PAM + G
mean RECP 1
RECP 1 + PAM + G
RECP 2
RECP 2 + PAM + G
RECP 1
RECP 1 + PAM + G
mean Control
mean RECP 2 + PAM
mean RECP 2 + G
mean RECP 1 + PAM
mean RECP 1 + G
30
Mea n cumm. runoff responses
for runs 2, 3, 4
Cumulative runoff (mm)
25
20
Cumm.
runoff
res ponses
for run 1
15
10
5
0
0
#
5
10
15
20
25
Time after rainfall initiation (mins)
30
35
40
‘T reatment mean’=mean total runoff from second, third and fourth runoff for the treatment.
Figure 5.5 Graph showing the cumulative runoff for the first rainfall application and the mean
cumulative runoff for rainfall applications 2, 3 and 4 for the first replicate.
85
Visual observation of the cumulative runoff data for replicates 1, 2, 3, and 4, as presented
in Figure 5.5 and in Appendix E (E-7, E-8, and E-9), show that there is similarity in that the time
to runoff initiation varies with position (treatment) of soil box and the rainfall application. The
runoff from first rainfall application for all replicates is smaller than the mean runoff of the
subsequent rainfall applications (2, 3, and 4). Also, the first rainfall application for all the
replicates shows larger variations in cumulative runoff among the treatments than those of the
subsequent rainfall applications.
Similarly, visual observations of turbidity data for replicates 1, 2, 3 and 4, presented in
Figure 5.6 and Appendix E (E-7, E-8, and E-9), show larger turbidity of the control than for
other treatments. Also, the graphs show lower variations in turbidity for PAM and PAM plus
gypsum treated RECPs compared with other treatments. The outputs of the descriptive statistics
for the sediment concentrations of the first replicate are shown in Table 5.9. The sediment
concentrations of the second, third, and fourth replicates are shown in Appendix E (E-7, E-8, and
E-9). The results show that the mean sediment concentration of the control is much larger (>2.0
g/L) than that of all the other treatments for the replicates in all the rainfall applications. The
results show small variations in the mean, standard error, and standard deviation for each
treatment within the rainfall application across the replicates. Hence, the mean of the four
replicates for each treatment will be used in further comparison of sediment concentrations.
86
Control
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
RECP 1 + G
Mean Sb Control
Mean SbRECP 2
Mean Sb RECP 2 + PAM
Mean Sb RECP 2 + PAM + G
Mean Sb RECP 1
Mean Sb RECP 1 + G
Mean Sb RECP 1 + PAM + G
Mean Sb RECP 1 + PAM
1600
1400
Turbidity for control
1200
Turbidity (NTUs)
1000
800
600
Turbi dity for RECPs
400
200
Turbidity for PAM,gypsum
treated RECP
0
0
5
10
15
20
25
30
35
40
Time after rainfall initiation (mins)
*
Mean sb treatment’= mean turbidity of the second, third and fourth rainfall application.
Figure 5.6 Graph showing the mean turbidity for rainfall applications 2, 3 and 4, and the first
rainfall application for the first replication.
87
Table 5.9 Table showing the descriptive statistics of the sediment concentrations (g/L) responses for the first replicate.
Treatment
Rainfall application 1
Control
RECP2
RECP2+PAM
RECP2+PAM+G
RECP2+G
RECP1
RECP1+PAM
RECP1+PAM+G
RECP1+G
N
11
11
12
8
7
12
11
8
6
E
1
1
0
4
5
0
1
4
6
Mean
2.21
0.81
0.41
1.10
1.36
1.34
0.71
0.50
1.13
S.E
0.08
0.09
0.05
0.08
0.07
0.65
0.19
0.07
0.70
Rainfall application 2
S.D
0.26
0.29
0.17
0.22
0.18
2.25
0.62
0.19
0.19
N
11
11
11
11
11
12
12
12
12
E
1
1
1
1
1
0
0
0
0
Mean
2.10
0.66
0.29
0.67
0.95
0.44
0.24
1.08
0.75
S.E
0.20
0.07
0.05
0.05
0.08
0.47
0.05
0.54
0.04
Rainfall application 3
S.D
0.66
0.22
0.16
0.18
0.27
0.16
0.18
1.87
0.14
N
11
11
11
11
11
12
12
12
12
E
1
1
1
1
1
0
0
0
0
Mean
3.61
0.87
1.83
0.90
1.06
0.87
0.55
0.92
0.12
S.E
0.13
0.17
1.10
0.10
0.12
0.06
0.08
0.10
0.43
Rainfall application 4
S.D
3.03
0.05
0.26
0.33
0.58
0.43
0.06
0.45
0.00
N
11
11
11
11
11
12
12
12
12
E
1
1
1
1
1
0
0
0
0
Mean
4.14
0.79
0.88
0.87
1.03
0.95
0.59
0.92
0.93
S.E
0.11
0.18
0.09
0.09
0.12
0.07
0.08
0.10
0.12
N is the number of responses; E is the number of observations with no runoff, S.E. is the standard error, and S.D. is the standard deviation.
88
S.D
0.38
0.59
0.29
0.29
0.40
0.23
0.28
0.35
0.40
5.4 Mean time to initial runoff
The time to runoff initiation was averaged for each treatment and each rainfall
application separately for the four replicates. The results are shown in Table 5.10 and plotted in
Figure 5.7. In analyzing the time to initial runoff for all treatments, it was noted that a longer
time to initial runoff occurred for the first rainfall application (>10 minutes) and lower for
subsequent rainfall applications (<4 minutes) (rainfall applications two, three and four). This can
be attributed to the fact that the soil was dry during the first rainfall application and an initial part
of simulated rain was needed to wet the soil thus allowing infiltration of all rainfall during this
initial period. During the next runs (occurring two days after the previous run) the soil was
initially wet and a shorter time of rainfall (<4 minutes) was needed to produce runoff. Addition
of RECPs and treating the RECPs with PAM or with PAM plus gypsum did not appear to have
any sizable effect on the mean time to runoff initiation for any of the rainfall applications.
Table 5.10 Mean time to runoff initiation (based on four the replicates) for each of the 4 rainfall
applications.
Treatment Method
Control
Time to runoff initiation (minutes); mean of four replicates
Rainfall
Rainfall
Rainfall
Rainfall
application
application
application
application
number 1
number 2
number 3
number 4
10.58
2.48
1.49
1.58
RECP 2
11.13
2.94
2.60
2.31
RECP 2 + PAM
14.31
2.72
2.57
2.17
RECP 2 + PAM + G
11.96
3.00
2.86
2.85
RECP 2 + G
10.42
3.11
2.68
2.73
RECP 1
10.98
2.38
2.63
2.53
RECP 1 + PAM
10.47
2.40
2.70
2.25
RECP 1 + PAM + G
10.79
3.14
2.70
2.59
RECP 1 + G
10.62
2.95
2.91
2.95
89
Time to Runoff initiation (minutes) Run 1
Time to Runoff initiation (minutes) Run 2
Time to Runoff initiation (minutes) Run 3
Time to Runoff initiation (minutes) Run 4
Time to runoff initiation (minutes)
16
Stardand error bars
14
12
10
8
6
4
2
0
Control
RECP 2
RECP 2 + RECP 2 + RECP 2 +
PAM
PAM + G
G
RECP 1
RECP 1 + RECP 1 + RECP 1 +
PAM
PAM + G
G
Treatment method
Figure 5.7 Graph showing the relationship between the treatment method and the time to runoff
initiation for the four runs. Bars represent the mean time to runoff initiation and the standard
error bars indicate the uncertainty in measuring time to runoff initiation for the treatments.
Box plots for time to runoff initiation for the four rainfall applications were produced
using SAS, and the results are shown in Figures 5.8, 5.9, 5.10, and 5.11. Each box plot represents
the analysis of the mean time to runoff initiation for 4 observations, from the four replicates. For
example, for the first rainfall application, the control had 4 observations (1 each from replicate 1,
2, 3, and 4). The other 8 treatments also had 4 observations each.
Based on visual comparisons in Figure 5.8, there is little difference in mean time to
runoff initiation due to treatment effects in the first rainfall application. However, the control
appears to have a lower mean time to runoff initiation when compared to other treatments for the
subsequent runs (2, 3, and 4) as in Figures 5.9, 5.10, and 5.11. This is attributed to the fact that
90
RECPs hold more water at the surface allowing more time for water to infiltrate the soil (Gorman
et al., 2000). The RECPs also intercept and dissipate the energy of raindrops, and reduces the
runoff flow velocity. Hence, less water is available for runoff and more time is needed for runoff
to reach the downslope discharge point. Also, there appears to be no large differences in mean
time to runoff initiation among the treatments for the first and the subsequent runs.
Figure 5.8 Box plots of mean time to runoff initiation of the four replicates for the first rainfall
application (run 1).
Figure 5.9 Box plots of mean time to runoff initiation of the four replicates for the second
rainfall application (run 2).
91
Figure 5.10 Box plots of mean time to runoff initiation of the four replicates for the third rainfall
application (run 3).
Figure 5.11 Box plots of mean time to runoff initiation of the four replicates for the fourth
rainfall application (run 4).
5.4.1 Statistical analysis of time to initial runoff initiation
To test the hypothesis that time to runoff initiation for plots covered with treated RECPs
(gypsum treated, PAM and gypsum treated, PAM treated) for the first rainfall application is not
significantly different from the time to runoff initiation for untreated RECPs for first rainfall
application, the means of treated RECPs were compared with those of untreated RECPs. The
results of the contrast tests for differences in time to runoff initiation are as shown in Table 5.11.
92
The results show that there is no significant difference in time to runoff initiation when treated
RECPs are compared with untreated RECPs. Hence, addition of PAM, PAM plus gypsum, and
gypsum only did not significantly affect the time to runoff initiation of the RECPs. The result is
different from those observed by Zhang et al. (1998) who observed significant increase in time to
runoff initiatiation due to PAM, PAM plus gypsum, and gypum alone when compared to
untreated soil. However, Zhang et al. had applied gypsum in solid and PAM in solution form to
soil as opposed to this research where PAM and gypsum were applied in solid form to RECP.
Table 5.11 Output of contrasts for testing the null hypotheses of no significant differences in time
to runoff initiation for the various treatments for each of the four rainfall applications (runs 1, 2,
3, and 4).
Hypotheses ;
H0 :Null Hypothesis
H1 :Alternative
Hypothesis
Hypothesis
test
H0 : XP AM=XNonP AM
H1 :XP AM≠XNonPAM
H0 : XP AM+G =XP AMonly
H1 :XP AM+G ≠XP AMonly
RUN 1
RUN 2
RUN 3
RUN 4
PAM vs
Non PAM
0.2235
0.5642
0.9216
0.6414
Not significant for all
runs
PAM + G vs
PAM only
0.5195
0.1598
0.4244
0.2027
Not significant for all
runs
0.6295
0.1186
0.2013
0.2434
Not significant for all
runs
0.5581
0.5098
0.9263
0.3935
Not significant for all
runs
0.2550
0.8755
0.8187
0.9927
Not significant for all
runs
0.9475
0.3729
0.2887
0.6634
G vs
Non G
H0 : XG =XNonG
H1 :XG ≠XNonG
H0 : XP AM+G =XGonly
H1 :XP AM+G ≠XGonly
H0 : XP AM=XRECP s
H1 :XP AM≠XRECP s
H0 : XG =XRECP s
H1 :XG ≠XRECP s
Significance at 0.05
level of significance
p-value
PAM + G vs
G only
PAM only
vs
RECPs only
Gypsum
only vs
RECPs only
Not significant for all
runs
5.5 Mean total runoff
The runoff volume of each treatment was accumulated for each rainfall application (run)
throughout the rainfall duration. The resulting total runoff from each of the four replicates was
averaged and the results are shown in Table 5.12. A bar graph of the results is shown in Figure
93
5.12. When compared with the control (bare soil), all treatments resulted in less total runoff after
37 minutes of simulated rainfall during the first rainfall application (run 1). More total runoff
occurred for all treatments for the subsequent rainfall applications (run 2, 3 and 4) when
compared with the runoff from the first rainfall application. This was despite the rainfall duration
being less in the subsequent applications (27 minutes). The differences can be attributed to the
fact that the soil was initially dry during the first run whereas for the subsequent runs the soil had
received rainfall two days prior to the subsequent run. RECP 1 treatments have slightly larger
total runoff than corresponding RECP 2 treatments. The difference was caused by the ability of
RECP 2 to provide greater surface cover per unit area than RECP 1, and hence yielding less
runoff (Sutherland et al., 1997). There did not appear to be large differences in total runoff
among the various treatments for the first rainfall application. Also there did not appear to be
major differences in total runoff between treatments for the subsequent rainfall applications (runs
2, 3 and 4).
Table 5.12 Results for the mean total runoff (mm) for the four rainfall applications (runs 1, 2, 3,
4) from the eight treatments and the control.
Runoff depth for
application 1
(mm)
Runoff depth for
application 2
(mm)
Runoff depth for
application 3
(mm)
Runoff depth for
application 4
(mm)
Control
18.2
23.2
25.0
25.3
RECP2
14.1
22.5
22.3
22.9
RECP2+PAM
13.0
22.5
23.5
23.1
RECP2+PAM+Gypsum
14.8
23.4
23.9
23.8
RECP2+Gypsum
17.2
23.7
24.3
24.2
RECP1
16.2
23.8
25.8
24.0
RECP1 +PAM
16.3
24.9
25.2
25.7
RECP1+PAM+Gypsum
14.9
24.1
24.7
25.4
RECP1+Gypsum
17.1
29.1
25.8
25.9
Treatment
94
Mean total runoff (mm)
Run 1
Run2
Run 3
Run 4
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
Treatment
Figure 5.12 Graph showing the mean total runoff (across four replications) with error bars for
the four rainfall applications (runs 1, 2, 3, 4) for the different treatments. The error bars indicate
the standard error of the mean total runoff for the respective treatment.
Cumulative runoff for the first and subsequent rainfall applications
The accumulated runoff data over the duration of the rainfall simulation for each rainfall
application (run) and the averaged resulting runoff with time from each of the four replicates are
shown in Figures 5.13, 5.14, 5.15 and 5.16. As seen in Figure 5.13, cumulative runoff of the
control is larger than that of all the other treatments. This is attributed to the fact that RECPs
intercept and dissipate the energy of raindrops and prevents soil surface crusting. The RECP
also splits the overland flow of runoff into smaller ‘water streams’ and holds more water at the
surface allowing more time for water to infiltrate the soil (Gorman et al., 2000). Thus, more
water infiltrates the soil and hence less is available for runoff. The results are consistent with
those obtained by Benik et al. (2003), Sutherland et al. (1997), and Adams (1966). Treating the
RECPs with PAM or PAM plus gypsum did not appear to have large effect on the cumulative
runoff for the first rainfall application.
95
Control
RECP2
RECP2+PAM
RECP2+PAM+Gypsum
RECP2+Gypsum
RECP1
RECP1 +PAM
RECP1+PAM+Gypsum
RECP1+Gypsum
Cummulative runoff (mm)
40
30
20
10
0
0
5
10
15
20
25
30
35
40
Time after rainfall initiation (minutes)
Figure 5.13 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the first rainfall application (run 1) (based on mean of four replicates).
As seen in Figures 5.14, 5.15, and 5.16 there is no sizable difference (<5 mm) in
cumulative runoff between any treatments for any of the second, third, or fourth rainfall
applications. However, the cumulative runoff of the control is slightly greater than all the other
treatments over the first 10 minutes after rainfall initiation. The sudden increase in mean
cumulative runoff of RECP1+ gypsum (Figure 5.14) probably came from the runoff reading for
rainfall application 2 (run 2) 20 minutes after runoff initiation after twenty minutes which was
higher than any other reading (4.58 mm) compared to <2.5 mm for all others.
96
Cummulative runoff (mm)
Control
RECP2
RECP2+PAM
RECP2+PAM+Gypsum
RECP2+Gypsum
RECP1 +PAM
RECP1
RECP1+PAM+Gypsum
RECP1+Gypsum
40
30
20
10
0
0
5
10
15
20
25
30
Time from rainfall initiation (mm)
Figure 5.14 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the second rainfall application (run 2).
Control
RECP2
RECP2+PAM
RECP2+PAM+Gypsum
RECP2+Gypsum
RECP1
RECP1 +PAM
RECP1+PAM+Gypsum
RECP1+Gypsum
Cummulative runoff (mm)
40
30
20
10
0
0
5
10
15
20
25
30
Time after rainfall initiation (minutes)
Figure 5.15 Graph showing the relationship between cumulative runoff and time after rainfall
initiation for the third rainfall application (run 3).
97
Control
RECP2
RECP2+PAM
RECP2+PAM+Gypsum
RECP2+Gypsum
RECP1
RECP1 +PAM
RECP1+PAM+Gypsum
RECP1+Gypsum
Cummulative runoff (mm)
40
30
20
10
0
0
5
10
15
20
25
30
Time from rainfall initiation (minutes)
Figure 5.16 Graph showing the relationship between the mean cumulative runoff and time after
rainfall initiation for the fourth rainfall application (run 4).
Box plots for total runoff for the four rainfall applications were produced using SAS and
the results are shown in Figure 5.17. Each box plots represents the analysis of the mean of total
runoff for 16 responses, from the four replicates and four rainfall applications. For example, for
the control there are 4 responses for the first rainfall application (1 each from replicate 1, 2, 3,
and 4) and 12 responses for second, third, and fourth rainfall application (3 each from replicate 1,
2, 3, and 4). Based on visual comparisons in Figure 5.17, there is little difference in total runoff
due to treatment effects. Treatments containing gypsum had the largest range in total runoff.
Also total runoff for RECPs alone and RECP 1 with PAM plus gypsum had smaller mean total
runoff than the other treatments.
98
Figure 5.17 Box plots of mean total runoff across all rainfall applications vs the eight treatments
and the control.
Figure 5.18 presents box plots of non-PAM treated RECPs compared to PAM treated
RECPs. The box plots compare the means of the total runoff of all the treatments with PAM
(RECP 1 plus PAM, RECP 1 plus PAM plus gypsum, RECP 2 plus PAM, RECP 2 plus PAM
plus gypsum) with the means of total runoff of non-PAM treated RECPs (RECP 1, RECP 1 plus
gypsum, RECP 2, RECP 2 plus gypsum). The number of observations for non-PAM treated
RECPs were 64 and the number of observations for the PAM treated RECPs were 64. Figure
5.18 shows that the means of the total runoff for PAM treated RECPs are nearly the same as
those of non-PAM treated RECPs; thereby, implying that there is little difference in runoff due
to the PAM treatment.
99
Total runoff (grams)
64
64
Treatments
Figure 5.18 Box plots of mean total runoff for PAM and non-PAM treatments for all the runs
and replicates.
Figure 5.19 shows box plots of total runoff for RECP for treatments with “PAM+G” and
“PAMonly”. The box plots compare the means of the total runoff of all the treatments with PAM
plus gypsum (RECP 1 plus PAM and gypsum, and RECP 2 plus PAM and gypsum) with the
means of total runoff of PAMonly treated RECPs (RECP 1 plus PAM and RECP 2 plus PAM).
Both PAM plus gypsum treated RECPs and PAM treated RECPs had 32 responses. As seen, the
means of the two treatments are almost the same; thereby, implying that there is little difference
Total runoff (grams)
between the mean total runoff produced by the PAM plus gypsum and PAM only treatments.
32 responses
32 responses
Treatments
Figure 5.19 Box plots of total runoff vs PAM+G and PAM only treatments.
100
5.5.1 Statistical analysis for the runoff data
Based on the null hypothesis that runoff volume for RECPs treated with PAM is not
significantly different from runoff volume of non-PAM treated RECPs, the means of the PAM
treated RECPs were compared with the mean runoff of non-PAM treated RECPs. The output of
the contrasts is shown in Table 5.13. The table shows the output of contrasts of cummulative
runoff for the tests of each of the four rainfall applications (runs 1, 2, 3 and 4), and the contrasts
when tests were run across the four rainfall applications. The contrast tables shows that the pvalue is greater than the specified significance level (α= 0.05) for all the tests. Thus, we cannot
reject the null hypothesis that there are no significant differences in total cumulative runoff for
all the treatments across all runs. This result is different from that of Faucette et al. (2007), who
found that there was significant difference in runoff amount from plots receiving PAM treated
wood mulch and untreated mulch. However, wood mulch is structurally different from RECP 1
and RECP 2.
101
Table 5.13 Output of contrasts for testing the differences in total runoff of the various treatments
for the four rainfall applications.
Hypotheses ;
H0 :Null Hypothesis
H1 :Alternative
Hypothesis
Hypothesis
test
H0 : XP AM=XNonP AM
H1 :XP AM≠XNonPAM
H0 : XP AM+G =XP AMonly
H1 :XP AM+G ≠XP AMonly
H0 : XG =XNonG
H1 :XG ≠XNonG
p-value
OVERALL*
Significance at
0.05 level of
significance
RUN 1
RUN 2
RUN 3
RUN 4
PAM vs
Non PAM
0.9547
0.5564
0.9424
0.7976
0.9998
Not significant
for all runs
PAM + G vs
PAM only
0.5683
0.9690
0.7309
0.9512
0.8189
Not significant
for all runs
0.4934
0.3473
0.7855
0.9952
0.2785
Not significant
for all runs
G vs
Non G
H0 : XP AM+G =XGonly
H1 :XP AM+G ≠XGonly
PAM + G vs
G only
0.9710
0.2505
0.5457
0.4666
0.5798
Not significant
for all runs
H0 : XP AM=XRECP s
H1 :XP AM≠XRECP s
PAM only
vs
RECPs only
0.9076
0.7446
0.6134
0.7152
0.5804
Not significant
for all runs
Gypsum
Not significant
only vs
0.8880
0.6854
0.5645
0.3346
0.9998
for all runs
RECPs only
*
Output for the p-values when contrast test is made of mean total runoff for all the rainfall applications .
H0 : XG =XRECP s
H1 :XG ≠XRECP s
5.6 Total sediment loss from plots
The sediment transported off the plots over the duration of the rainfall for each rainfall
application (run) was assessed. The resulting total sediment loads from each of the four
replicates were averaged for each run and the results are shown in Table 5.14 and Figures 5.20
and 5.21.
102
Table 5.14 Mean total sediment loss (TS) (for the four replicates) and % sediment reduction
compared to control for the eight treatments for the four rainfall applications (Runs 1, 2, 3, and
4).
Tre atment
Run 1
TS
(tons/ha)
Run 2
%
Reduction
*
TS
(tons/ha)
Run 3
%
Reduction
TS
(tons/ha)
Run 4
%
Reduction
TS
(tons/ha)
%
Reduction
Control
228.15
RECP 2
45.94
79.86
60.61
89.50
77.71
84.37
114.83
82.94
RECP 2 + PAM
37.30
83.65
28.57
95.05
87.07
82.49
102.72
84.74
63.28
72.26
64.40
88.84
66.37
86.65
102.80
84.73
RECP 2 + PAM
+G
RECP 2 + G
577.08
497.21
673.08
95.53
58.13
89.97
84.41
92.46
81.40
124.84
81.45
RECP 1
63.56
72.14
56.00
90.30
96.38
80.62
123.98
81.58
RECP 1 + PAM
54.02
76.32
45.29
92.15
80.60
83.79
114.65
82.97
61.23
73.16
78.41
86.41
84.74
82.96
127.46
81.06
88.46
61.23
99.93
82.68
93.12
81.27
130.17
80.66
RECP 1 + PAM
+G
RECP 1 + G
*% Reduction=(Total sediment loss from control plot- T otal sediment loss from treatment plot)/(Total sediment loss from control plot)x100.
Total sediment loss (Metric tons/Ha)
Run 1
Run 2
Run 3
Run 4
RECP 2 + RECP 2 + RECP 2 +
PAM PAM + G
G
RECP 1
800
700
600
500
400
300
200
100
0
Control
RECP 2
RECP 1 + RECP 1 + RECP 1 +
PAM PAM + G
G
Treatment method
Figure 5.20 Graph showing the mean total sediment loss (for four replications) for each
treatment and for the four rainfall applications (runs 1, 2, 3, and 4). Errors bars represent
standard errors of total sediment concentration for the respective treatments.
103
% reduction of sediment loss
Run 1
Run 2
Run 3
Run 4
120
100
80
60
40
20
0
Control
RECP 2
RECP 2 + RECP 2 + RECP 2 +
PAM
PAM + G
G
RECP 1
RECP 1 + RECP 1 + RECP 1 +
PAM
PAM + G
G
Treatment
Figure 5.21 Graph showing the % reduction in mean total sediment loads for treatments
compared to the control for four rainfall applications (runs 1, 2, 3, and 4).
The largest soil losses from the plots were observed in the control, with the lowest losses
for control occurring during the first rainfall application. As seen in Table 5.14, for the four
rainfall applications, RECP treatments alone showed reductions in sediment losses of 72% to
90% compared to the control. RECP treatments were effective in reducing soil loss because they
reduced soil detachment by raindrops and trapped soil and sediments within the RECP material,
thereby minimizing erosion (Adams, 1966; Meyer et al., 1972). This result is consistent with
results obtained by Bhatia et al. (2002) and Faucette et al. (2007). For the four rainfall
applications, PAM treatment of the RECP reduced the total sediment loss by between 76% to
95% compared to the control. The reduction in soil loss was because the PAM polymers bound
to suspended sediments largely through cation-bridging and caused the particles to form
flocculants that settled on the plots, thereby reducing soil loss (Bhardwaj and McLaughlin,
2008).
104
RECP treatments with PAM plus gypsum reduced the total sediment loss by 72% to 89%
compared to the control for the four rainfall applications, and treatment of RECP with gypsum
only reduced soil loss by 58% to 84% compared to the control. This result is consistent with
those obtained by Bjorneberg and Aase (2000). The gypsum supplied more Ca 2+ that were
attracted to the soil particles that in turn attracted anionic PAM polymer, thereby creating
cationic bridges between soil and PAM polymers (Wallace, 1996). This enhances flocculation,
which stabilizes the soil aggregates and thus decreases soil detachment and hence lowers the
amount of sediments in the runoff (Lee et al., 2010). However, as noted later, the reductions in
this study were not significant.
Sediment concentrations
The resulting mean sediment concentrations from each of the four replicates and
graphical presentation of the results are shown in Table 5.15 and Figure 5.22, respectively.
Table 5.15 Mean sediment concentrations (g/l) for the rainfall applications (Runs 1, 2, 3, and 4)
based on the four replications.
105
Figure 5.22 Graph showing the variation of mean sediment concentration with treatment for the
rainfall applications (Runs 1, 2, 3, and 4). Errors bars represent standard errors of mean
sediment concentration for the respective treatments.
When compared with the control (bare soil), all the treatments decreased the sediment
concentration for all four rainfall applications. However, there were slight differences in
sediment concentrations for individual rainfall applications on any given treatment. The
difference in sediment concentrations is due in part to the cover provided by the RECP and the
stabilization of soil particles by the treatments with PAM or PAM plus gypsum.
As explained in Section 4.4 the summarized data for sediment concentrations were
further analyzed using the SAS code. The exploratory data analysis of the sediment
concentrations for each treatment and the control for all the rainfall applications and replicates
was conducted, and the box plots of sediment concentrations for the 8 treatments and the control
are shown in Figure 5.23. The box plot for each treatment represents the means of sediment
concentrations for 16 responses. From the box plots there appear to be substantial differences
between the control and each of the treatments. There also appear to be slight differences among
106
the treatments. The treatments with PAM have lower mean values of sediment concentrations.
Since the differences in sediment concentrations among treatments are small, comparing two
Sediment concentration (g/L)
treatments at a time would provide a more detailed analysis.
Figure 5.23 Box plots of sediment concentrations of all the replicates for the treatments.
To compare the differences in sediment concentrations between PAM treated RECPs and
non-PAM treated RECPs, exploratory data analysis was done on the sediment concentration data
(using SAS code as described in Section 4.3). The box plots for comparing the means of
sediment concentrations of PAM treated RECPs and non-PAM treated RECPs both had 64
responses and are provided in Figure 5.24. From the box plots, the mean sediment concentration
for PAM-treated RECPs is slightly lower than those of non-PAM treated RECPs
107
Sedi ment concentration (g/L)
Treatments
Figure 5.24 Box plots of mean sediment concentrations of PAM and non-PAM treatments of the
four replicates.
The box plot of sediment concentrations for RECPs treated with PAM plus gypsum
versus RECPs treated with PAM only is provided in Figure 5.25. Each of the treatments being
compared had 32 responses. The box plot shows that the mean sediment concentrations of the
two treatments are almost the same. The mean sediment concentration of PAM+G treatment has
the larger variation.
108
Sedi ment Concentration (g/L)
Trea tment
Figure 5.25 Box plots of sediment concentration of the four replicates for RECPs with PAM+G
vs PAM only.
5.6.1 Statistical analysis of the sediment concentration data
As explained in section 4.3, contrasts for testing the sediment concentration hypotheses
were performed using the SAS code. The contrast results for testing the sediment concentration
hypotheses across all the rainfall applications (runs) and for the four rainfall applications (run 1,
2, 3, and 4) separately are shown in Table 5.16. The table shows that the p-value for the test of
sediment concentration is less than the specified significance level (α=0.05) for PAM treated
RECPs when compared with untreated RECPs for run 1 and 2 but not for runs 3 and 4. This
result is consistent with the results obtained by Flanagan et al. (2002), Wilson et al. (2008), and
McLaughlin et al. (2009). As explained in Section 2.5, PAM binds suspended sediments largely
through cation-bridging, pulling particles together and forming flocculants which settle, thereby,
reducing the sediment concentration of the runoff (McLaughlin et al., 2007). The lack of
109
significant difference between the two treatments for runs 3 and 4 could be because the PAM
polymers were removed from the soil by runoff during rainfall application 1 and 2. The PAM
polymer could also have lost its effectiveness after being in solution for more than 4 days.
Table 5.16 Output of contrasts for testing the differences in sediment concentration of the
various treatments for the four rainfall applications.
Hypotheses ;
Hypothesis
test
p-value
H0 :Null Hypothesis
Significance at 0.05
level of significance
H1 :Alternative
Hypothesis
RUN 1
RUN 2
RUN 3
RUN 4
OVERALL*
H0 : XP AM=XNonPAM
H1 :XP AM≠XNonP AM
PAM vs
non PAM
0.0056
0.0094
0.4697
0.0403
0.0060
Significant for all
the runs and overall,
but not run 3.
H0 : XP AM+G =XP AMonly
H1 :XP AM+G ≠XP AMonly
PAM + G
vs PAM
only
0.0833
0.0773
0.8913
0.5188
0.1768
Not significant for
all runs and the
overall
0.0735
0.0429
0.5470
0.3271
0.0701
Significant for run 2
only
H0 : XG =XNonG
H1 :XG ≠XNonG
G vs
non G
H0 : XP AM+G =XGonly
H1 :XP AM+G ≠XGonly
PAM + G
vs G only
0.1055
0.0911
0.4114
0.1159
0.0423
Not siginificant for
runs 1, 2, 3,and 4,
but significant for
overall.
H0 : XP AM=XRECP s
H1 :XP AM≠XRECP s
PAM only
vs
RECPs
only
0.016
0.0341
0.8349
0.1627
0.0434
Significant for runs
1,2 and the overall
but not runs 3 and 4
Gypsum
Not significant for
H0 : XG =XRECP s
only vs
0.4033
0.2157
0.4569
0.4369
0.1925
all runs
H1 :XG ≠XRECP s
RECPs
only
*
Output for the p-values when contrast test of mean sediment concentrations for all rainfall applications.
110
The p-value of PAM plus gypsum treatments when compared with PAM only treatments
was greater than α=0.05 for all the rainfall applications. Also the p-value of PAM plus gypsum
treatments when compared with gypsum only treatments was greater than α=0.05 for all the
rainfall applications except the overall. This result is different from those obtained by Flanagan
et al. (2002); Lepore et al. (2009); and Peterson et al. (2002) who showed that gypsum addition
to PAM resulted in significant (α=0.05) reductions of soil erosion and runoff as compared to
addition of PAM alone. Flanagan et al. (2002) hypothesized that the increased amount of
eloctrolytes (Ca2+ ions) supplied by gypsum attracted anionic PAM, thereby enhancing
flocculation that stabilized the soil aggregates and thus decreased soil detachment and reduced
the sediment concentration even more than PAM alone.
However, Jian et al. (2003) suggested that addition of gypsum to PAM may reduce the
efficiency of PAM in reducing soil losses. Jian et al. (2003) suggested that when an electrolyte
was introduced to low electrolyte water, the resulting PAM polymers were shorter and coiled.
These polymers were less effective in binding together soil particles that were far apart than the
long PAM polymers that results when PAM alone is mixed with soil solution. Thus, the
efficiency of the polymer in reducing soil losses was reduced.
The p-value of sediment concentration for gypsum treated RECPs when compared with
untreated RECPs is greater than (α=0.05) for all the rainfall applications indicating no significant
effect of gypsum addition alone.
5.6.2 Comparing the mean sediment concentrations for the treatments
As shown in Figure 5.23 there appears to be a difference among the treatments for the
sediment concentrations in runoff when comparing all the rainfall applications (runs). The
111
LSMEANS (least square means) procedure in SAS was used to assess the reductions in sediment
concentrations of runoff for the various treatments. The resulting means of all the treatments
using the least square procedure are shown in Table 5.17.
Table 5.17 Output of treatment means for sediment concentration.
Mean sediment concentrations (g/L)
T reatment
First rainfall
application (run 1)
Second rainfall
T hird rainfall
Fourth rainfall
application (run 2) application (run 3) application (run 4)
Overall*
RECP 1
0.89
0.49
0.65
0.79
0.70
RECP 1+G
1.01
0.55
0.79
0.83
0.80
RECP 1+PAM
0.68
0.38
0.51
0.70
0.57
RECP 1+PAM+G
0.85
0.51
0.65
0.76
0.67
RECP 2
1.07
0.49
0.70
0.78
0.76
RECP 2+G
1.18
0.67
0.78
0.88
0.88
RECP 2+PAM
0.59
0.18
0.78
0.64
0.55
RECP 2+PAM+G
0.89
0.39
0.68
0.69
0.69
Overall* is the mean sediment concentration for all the rainfall applications.
As indicated in Section 4.6.5, the results of least square means can be used to compare
the means of the treatment groups. Table 5.18 shows the mean sediment concentrations of the
groups being compared. For example when comparing PAM + G vs G only treatments, the mean
sediment concentration of “PAM + G” treatments is the mean sediment concentration of all
treatments that have “PAM + G” treatment (RECP 1+PAM+G, RECP 2+PAM+G) and mean
sediment concentration of the “G only” treatment is the mean sediment concentration of all
treatments that have “G only” treatment (RECP 1+G, RECP 2+G).
112
Table 5.18 Table showing the mean of sediment concentrations for the treatment groups being
compared.
Hypothesis
test
Comparison
Least square means of sediment concentrations (g/L)
RUN 1
RUN 2
RUN 3
RUN 4
OVERALL*
0.75
0.37
0.66
0.70
0.62
1.04
0.55
0.73
0.82
0.79
0.44
0.23
0.33
0.36
0.34
0.64
0.28
0.65
0.67
0.56
0.98
0.53
0.73
0.79
0.76
0.81
0.39
0.66
0.73
0.68
0.87
0.45
0.67
0.73
0.68
1.10
0.61
0.79
0.86
0.84
0.64
0.28
0.65
0.67
0.56
0.98
0.49
0.68
0.79
0.73
1.10
0.61
0.79
0.86
0.84
0.98
0.49
0.68
0.79
0.73
Significance at 0.05
level of significance
(from Table 5.16)
PAM
PAM vs
non PAM
Non-PAM
PAM + G
vs PAM
only
PAM + G
PAM only
Significant for all the
runs and overall, but
not run 3.
Not significant for all
runs and the overall
Gypsum
G vs
non G
non Gypsum
Significant for run 2
only
PAM + G
PAM + G
vs G only
PAM only
vs
RECPs
only
Gypsum
only vs
RECPs
only
Gypsum
only
PAM only
RECPs only
Gypsum
RECPs only
Not siginificant for
run 1, 2, 3,and 4, but
significant for
overall.
Significant for runs
1,2 and the overall
but not runs 3 and 4
Not significant for all
runs
PAM vs non-PAM
Table 5.18 shows that the mean sediment concentration of treatments with PAM is less
than that of those without PAM for all the runs (1, 2, 3, and 4) as well as the overall. This result
is consistent with the results obtained by Flanagan et al. (2002), Wilson et al. (2008), and
McLaughlin et al. (2009). As explained in Section 2.5, PAM binds suspended sediments largely
113
through cation-bridging, pulling particles together and forming flocculants which settle, thereby,
reducing the sediment concentration of the runoff (McLaughlin et al., 2007). Thus, the
treatments with PAM perform more effectively in reducing sediment concentration in runoff than
treatments without PAM.
PAM only vs RECPs only
Table 5.18 shows that the mean sediment concentration for treatments with PAM only is
less than for RECP only treatments for all the runs (1, 2, 3, and 4) and the overall. This result is
consistent with the results obtained by Flanagan et al. (2002), Wilson et al. (2008), and
McLaughlin et al. (2009). The mechanism for this reduction is as explained in Section 2.5.
G vs Non-G
The results show that the sediment concentration for gypsum treatments is greater than the
sediment concentration for non-gypsum treatments for all the runs (1, 2, 3, and 4) as well as the
overall. This result is rather surprising, as the literature suggests that addition of gypsum should
reduce the sediment concentration (Lepore et al., 2009, Jian et al., 2003, and Peterson et al.,
2002). A possible explanation for the difference could be due to gypsum releasing Ca2+ into the
soil solution when dissolved. The Ca2+ ions prevented clay dispersion, hence preventing seal
formation on the soil surface (Peterson et al., 2002). This could have lead to high infiltration rate
and hence a more concentrated runoff . More concentrated runoff implies higher sediment
concentration for gypsum treatments. However, the reductions in sediment concentration was not
significant at α=0.05 as noted later in this study.
114
PAM+G vs PAM only
The results in Table 5.18 show that sediment concentration for RECPs with PAM plus
gypsum were greater than for RECPs with PAM only, for the four runs (1, 2, 3, and 4) as well as
the overall. This is despite the treatment of RECPs with PAM + G not being significantly
different (α=0.05) when compared with PAM only treatments for the four runs (1, 2, 3, and 4) as
well as the overall. Hence, RECPs with PAM + G performed slightly more effectively than those
with PAM only in reducing sediment concentration. This result is consistent with results
obtained by Flanagan et al. (2002), Lepore et al. (2009), and Peterson et al. (2002). The lack of
significance could have been due to the fact that in the Flanagan et al. (2002), Lepore et al.
(2009), and Peterson et al. (2002) studies the soil was treated directly, whereas in this study the
RECPs, rather than the soil matrix, were treated. Furthermore, addition of gypsum to PAM
resulted in shorter PAM polymer chains that are less effective in binding together soil particles
(Jian et al 2003). This resulted in soil particles that were less resistant to splash by raindrop
impact and detachment by runoff. Also, fewer particles settled and, hence, addition of gypsum to
PAM resulted in more sediment concentration in the runoff.
PAM+G vs G only
The table shows that the sediment concentration of RECPs with PAM plus gypsum was
less than that of RECPs treated with gypsum only for runs (1, 2, 3, and 4) as well as the overall.
This result is consistent with the results obtained by Jian et al. (2003), Peterson et al. (2002a),
and Peterson et al. (2002b). The reduced sediment concentration was likely caused by PAM
stabilization of soil by reducing repulsive forces among clay particles and also acting as a bridge
between soil particles, thereby bonding the particles into an aggregate that settled easily (BenHur, 1994). Additionally, gypsum helped to create a cation-bridge for the PAM polymer to
115
adsorb to the soil by introducing the multivalent Ca2+ to the soil, thereby enhancing settling
(Shainberg et al., 1990).
5.7 Turbidity
Turbidity (NTUs) data for each treatment were averaged across all the samples taken
during the rainfall simulation for each rainfall application (run). The resulting mean turbidity
from each of the four replicates was then averaged. The percentage reduction in turbidity (%
reduction) of any treatment compared to the control was calculated and the results are shown in
Table 5.19. Graphs of turbidity and turbidity reduction are shown in Figures 5.26 and 5.27,
respectively.
Table 5.19 Mean turbidity and % reduction of turbidity for the eight treatments and the control
for the four runs.
Mean turbidity (NTUs) of treatments
Treatment
*
%Reduction *
Run 1
Run 2
Run 3
Run 4
Mean turbidity
Control
1230.05
1506.40
1306.43
1472.71
1378.89
RECP 2
125.35
261.24
190.58
100.05
169.30
87.72
RECP 2 + PAM
8.18
16.08
13.87
18.88
14.25
98.97
RECP 2 + PAM + G
14.73
8.18
13.20
7.25
10.84
99.21
RECP 2 + G
85.29
85.14
64.88
97.84
83.29
93.96
RECP 1
207.85
361.91
105.67
136.15
202.89
85.29
RECP 1 + PAM
6.89
7.94
21.30
12.88
12.25
99.11
RECP 1 + PAM + G
5.24
6.78
6.26
10.27
7.14
99.48
RECP 1 + G
54.05
64.84
78.66
52.26
62.45
% Reduction=(Control mean turbidity-Treatment mean turbidity)/( Control mean turbidity)x100.
95.47
The results in Table 5.19 show that the largest turbidity occurred for the control, and the
turbidities were lowest for the first rainfall application (run 1) as compared to subsequent rainfall
applications. The RECP 1 and RECP 2 treatments showed reductions in mean turbidity of 85%
and 87% respectively, compared to the control. As with the results for the sediment
116
concentrations, RECP 1 and RECP 2 treatments were effective in reducing turbidity because they
lessened soil detachment by rain energy and trapped sediments within their structure (Adams,
1966; Meyer et al., 1972). PAM treatments of the RECP 1 and RECP 2 reduced the mean
turbidity by 99% compared to the control. This result is consistent with the results obtained by
Jennings et al. (2009), Bjorneberg and Aase (2000), and McLaughlin (2007). The reduction in
turbidity resulted due to the PAM polymers binding to suspended sediments and forming
flocculants, which increased settling, thereby reducing turbidity (Bhardwaj and McLaughlin,
2008). Treatments with PAM plus gypsum also reduced the mean turbidity by 99% compared to
the control.
There was minimal decrease in turbidity reduction when RECP 1 and RECP 2 were
treated with PAM plus gypsum as compared to RECP 1 and RECP 2 treated with PAM only. The
slight decrease was likely caused by gypsum helping to create a cation-bridge for the PAM
polymer to adsorb to the soil by introducing the multivalent Ca2+ in the soil electrolyte, thereby
increasing deposition of soil particles (Shainberg et al., 1990). This also explains the slight
increase in turbidity reduction observed for RECP 1 plus gypsum and RECP 2 plus gypsum
compared to untreated RECP 1 and RECP 2.
117
Turbidity (NTUs)
1600
1400
1200
1000
800
600
400
200
0
Control RECP 2 RECP 2 + RECP 2 + RECP 2 + RECP 1 RECP 1 + RECP 1 + RECP 1 +
PAM PAM + G
G
PAM PAM + G
G
Treatment
Figure 5.26 Graph of mean turbidity for the eight treatments and the control (across all 4
replications and all 4 rainfall applications).
% turbidity reduction
105
100
95
90
85
80
75
Control
RECP 2 RECP 2 + RECP 2 + RECP 2 + RECP 1 RECP 1 + RECP 1 + RECP 1 +
PAM PAM + G
G
PAM PAM + G
G
Treatment
Figure 5.27 Graph of mean % turbidity reduction for the treatment compared with the control
(across all 4 replications and all 4 rainfall applications).
The average turbidity over the duration of the rainfall simulation for each rainfall
application (run) from the four replicates were used to plot graphs shown in Figures 5.28, 5.29,
5.30, and 5.31. As seen in Figures 5.28, 5.29, 5.30, and 5.31 the mean turbidity of runoff over the
rainfall application period from the control treatment is larger than that of all the other treatments
118
for all the rainfall applications (runs). This is attributed to the fact that RECPs intercept and
dissipate the energy of raindrops. The RECP also splits the runoff flowing over the surface into
small water streams and holds more water at the surface. This allows more time for water to
infiltrate the soil and for sediments to settle, thereby reducing the turbidity (Gorman et al., 2000).
Visual observation of Figures 5.28, 5.29, 5.30, and 5.31, show that the mean turbidities of runoff
from RECPs treated with PAM only, RECPs with PAM plus gypsum, and RECPs plus gypsum
only are lower than from untreated RECPs for all the rainfall applications.
CONTROL
RECP 2
RECP 2 + PAM
RECP2+Pam & Gypsum
RECP2+G
RECP1
RECP 1 + PAM
RECP 1 + PAM + G
RECP 1 + G
Turbidity (NTUs)
2000
1500
1000
500
0
0
5
10
15
20
25
30
35
40
Time after rainfall initiation (minutes)
Figure 5.28 Graph of mean turbidity (across all four replications) for the first rainfall
application (Run 1).
119
CONTROL
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
RECP1+G
Turbidity (NTUs)
2000
1500
1000
500
0
0
5
10
15
20
25
30
Time from rainfall initiation(minutes)
Figure 5.29 Graph of mean turbidity (across all four replications) for the second rainfall
application (Run 2).
CONTROL
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
RECP 1 + G
Turbidity (NTUs)
2000
1500
1000
500
0
0
5
10
15
20
25
Time after rainfall initiation (minutes)
Figure 5.30 Graph of mean turbidity (across all four replications) for the third rainfall
application (Run 3).
120
30
Turbidity (NTUs)
2000
1500
1000
500
0
0
5
10
15
20
25
30
Time from rainfall initiation (minutes
CONTROL
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
RECP 1 + G
Figure 5.31 Graph of mean turbidity (across all four replication) for the fourth rainfall
application (Run 4).
The summarized data of the turbidity of runoff was analyzed using SAS and the box plots
of turbidity for the 8 treatments and the control are as shown in Figure 5.32. Each treatment
represents mean turbidity for the 16 responses (4 replicates and 4 runs for each replicate). From
the box plots there appears to be a substantial difference between the mean turbidity of the
control when compared with the other treatments. It appears that treatments of RECPs plus
PAM+G and RECPs plus PAM only have the lowest mean turbidity. Also RECPs without PAM
or gypsum have greater variations in mean turbidity than treatments with PAM.
121
Turbidity (NTUs)
Figure 5.32 Box plots of turbidity for the mean of 8 treatments and the control (each box plot is
based on 16 responses).
The box plots of “turbidity” for “PAM” and “non-PAM” treatments for the four
replicates are provided in Figure 5.33. The box plots for comparing the means of PAM treated
RECPs (RECP 1+PAM, RECP 1+PAM+G, RECP 2+PAM, RECP 2+PAM+G) with non-PAM
treated RECPs (RECP 1, RECP 1 +G, RECP 2, RECP 2+ G) had 64 responses each. From the
box plots, the mean turbidity for PAM-treated RECPs is substantially lower than for non-PAM
treated RECPs, implying that there is a differencein the two treatment groups. The low mean
turbidity of PAM-treated RECPs was likey caused by anionic PAM polymers attracting soil
particles through “cationic bridges” formed by eloctrolytes in the water (Wallace et al., 1986).
The attraction enhanced floculation, which stabilizes the soil aggregates and thus decreases soil
detachment by rainfall and runoff, thereby reducing the turbidity (Lee et al., 2010).
122
Turbidity (NTUs)
Treatment
Figure 5.33 Box plots of mean turbidity for PAM and non-PAM treatments (each box plot is
based on 32 responses).
The box plots of “turbidity” of “PAM+G” and “PAMonly” treatments for the
four replicates are presented in Figure 5.34. The box plot for comparing the means of PAM +G
treated RECPs (RECP 1+PAM+G, RECP 2+PAM+G) with PAM-only treated RECPs (RECP
1+PAM, RECP 2+PAM) had 32 and 32 responses, repectively. The box plots show that the
mean of “PAM+G” is slightly smaller than that of “PAM only”, and the PAM only treatments
have a larger variation in turbidity than PAM+G treatments. The lower turbidities for the
PAM+G-treated RECPs were caused by the increased amount of eloctrolytes (Ca 2+ ions)
supplied by gypsum that in turn attracted anionic PAM, thereby enhancing flocculation that
stabilized the soil aggregates and thus decreased soil detachment and reduced the turbidity even
more (Flanagan et al., 2002).
123
Turbidity (NTUs)
Treatment
Figure 5.34 The box plot of turbidity vs PAM+G (box plot is based on 32 responses) and PAM
only (box plot is based on 32 responses) treatments.
5.7.1 Statistical analysis of the turbidity data
As was explained in Section 4.6.3 in analysis of runoff data, the PROC MIXED
procedure was used to analyse the turbidity data. Also, as noted in Section 4.4, turbidity is a
measure of the amount of light that is scattered and absorbed by particles in a fluid. Hence,
turbidity gives an indication of the amount of suspended particles in a sample, unlike sediment
concentration which indicates the amount (usually mass) of particles in a sample. High turbidity
units (NTUs) in a sample point to a greater number of particles in suspension impinging the light
scattering in the sample. Since turbidity and sediment concentration are different measures, one
could expect different results in effectiveness of treatments. Output of the contrasts for the
hypotheses tests of turbidity for the four rainfall applications and overall (all runs combined) are
shown in Table 5.20.
124
Table 5.20 Table showing output of hypotheses tests for the turbidity response of the various
treatments for the four rainfall applications.
Hypotheses ;
H0 :Null Hypothesis
H1 :Alternative
Hypothesis
Hypothesis
test
p-value
Significance at 0.05
level of significance
OVERALL*
RUN 1
RUN 2
RUN 3
RUN 4
PAM vs
Non PAM
<.0001
<.0001
<.0001
<.0001
PAM + G vs
PAM only
0.8679
0.7989
0.9084
0.9689
0.8492
Not significant for all
runs and the overall
<.0001
0.0130
0.1647
0.2878
0.0007
Significant for run 1, 2
but not 3 and 4
Significant for all the
runs and overall.
H0 :XP AM=XNo
nP AM
H1 :XP AM≠XNo
<.0001
Significant for all the
runs and overall.
nP AM
H0 : XP AM+G =XP AMonly
H1 :XP AM+G ≠XP AMonly
H0 : XG =XNonG
H1 :XG ≠XNonG
G vs
Non G
H0 : XP AM+G =XGonly
H1 :XP AM+G ≠XGonly
PAM + G vs
0.0001
0.0033 0.0006
<.0001
<.0001
G only
PAM only
H0 : XP AM=XRECP s
vs
<.0001
<.0001
<.0001
<.0001
<.0001
Significant for all the
H1 :XP AM≠XRECP s
RECPs only
runs and overall.
Gypsum
H0 : XG =XRECP s
Significant for runs 1,2
only vs
<.0001
0.0018
0.0374
0.1294
<.0001
H1 :XG ≠XRECP s
3 and the overall
RECPs only
*
Output for the p-values when contrast test of turbidity for all the rainfall applications are conducted together.
PAM vs Non-PAM
The mean turbidity from PAM-treated RECPs is significantly different from the mean
turbidity from non-PAM treated RECPs for all the rainfall applications as well as overall. This
result is consistent with the results obtained by Faucette et al. (2007), Wilson et al. (2008), and
McLaughlin et al. (2009). The difference in mean turbidity is caused by the ability of anionic
PAM polymers to attract cationic soil particles in solution. This enhances floculation and
stabilization of soil aggregates and thus decreases soil detachment and hence lowers the turbidity
(Lee et al., 2010). The results show that treating RECPs with PAM is effective in lowering the
turbidity. As noted in Section 5.6, the sediment concentration for PAM treated RECPs was less
than that of non-PAM treated RECPs indicating that the sediment concentration and the turbidity
tests show same relative effectiveness of PAM treatments.
125
PAM only vs untreated RECPs
The mean turbidity from PAM-treated RECPs is significantly different from the mean
turbidity from untreated RECPs for all the rainfall applications and the overall. This result is
consistent with the results obtained by Faucette et al. (2007), Wilson et al. (2008), and
McLaughlin et al. (2009) and the mechanism for the difference is as explained in Section 5.6.
PAM+G vs PAM only
The mean turbidity from PAM+G treated RECPs is not significantly different from mean
turbidity from RECPs treated with PAM only for all the rainfall applications and overall. This
result is not consistent with results obtained by Peterson et al. (2002) and Truman et al. (2010).
However, studies by Jian et al. (2003) had noted no significant reduction in turbidity in soil
treated with PAM plus gypsum when compared with those treated with PAM only. The
researchers attributed this to the fact that the low electrolyte rain water used resulted in
straightened PAM polymer chains, while in gypsum treatments coiled PAM polymer chains
resulted due to presence of Ca2+. The straightened PAM polymer chains without gypsum may
have been more effective at binding soil particles as PAM alone resulted in less soil loss than the
short, and possibly coiled, polymer chains from gypsum treated PAM.
G vs Non-G
The mean turbidity from gypsum treated RECPs is significantly different from mean
turbidity from non-gypsum treated RECPs for the first two rainfall applications (run1 and 2) but
not for the last two rainfall application (run 3 and 4). The lack of any significant difference in
mean turbidity between the two treatments for the third and fourth rainfall applications could be
due to all the gypsum having been transported by runoff during the first and second rainfall
applications. The mechanism for the difference in significance between the treatments is as
126
explained in Section 5.6.1 and the results were consistent with those obtained by Lepore et al.
(2009), Jian et al. (2003), and Peterson et al. (2002). However, as noted in Section 5.6, where the
sediment concentration of runoff for Non-G treated RECPs was larger than that of G treatments,
the sediment concentration test is not consistent with the turbidity test.
PAM+G vs G only
The mean turbidity from PAM + gypsum treated RECPs is significantly different from
mean of turbidity from gypsum treated RECPs for all the rainfall applications as well as the
overall. This result is consistent with the results obtained by Jian et al. (2003), Peterson et al.
(2002a), and Peterson et al. (2002b) and the mechanism for the difference is consistent with that
for sediment concentration, as explained in Section 5.6. Also, as noted in Section 5.6, the
sediment concentration of runoff for PAM + G treated RECPs was smaller than that of G only
treatments, and results of the sediment concentration test are consistent with the turbidity test.
5.7.2 Comparing the means of treatments for the turbidity
As shown in Table 5.20 there were differences among the treatments on the resulting
turbidity. A LSMEANS SAS procedure was used to further explore the turbidity effects and
results are shown in Table 5.21. Table 5.22 shows the mean turbidity of the groups being
compared.
127
Table 5.21 Table showing the mean turbidity of runoff for the treatments.
Turbidity (NTUs)
First rainfall
application (run 1)
Second rainfall
application (run 2)
Third rainfall
application (run 3)
Fourth rainfall
application (run 4)
Overall*
RECP 1
244.50
159.03
74.72
76.16
138.77
RECP 1+G
104.33
54.87
44.20
46.33
62.44
RECP 1+PAM
22.77
12.86
7.32
7.09
12.67
RECP 1+PAM+G
10.46
2.88
9.99
7.26
6.84
RECP 2
245.05
144.96
80.39
70.50
135.41
RECP 2+G
109.07
91.36
61.66
71.74
83.57
RECP 2+PAM
14.86
5.97
7.57
7.09
8.96
RECP 2+PAM+G
20.77
5.16
7.50
7.60
10.34
Treatment
*
Overall is mean turbidity for all the rainfall applications together.
128
Table 5.22 Table showing the mean of turbidity for the treatment groups being compared.
Hypothesis
test
Comparison
treatments
Least square means of turbidity (NTUs)
RUN 1
RUN 2
RUN 3
RUN 4
OVERALL*
PAM
17.22
6.72
8.10
7.26
9.70
Non-PAM
175.74
112.56
65.24
66.18
105.05
PAM + G
15.62
4.02
8.75
7.43
8.59
PAM only
18.82
9.42
7.45
7.09
10.82
Gypsum
61.16
38.57
30.84
33.23
40.80
PAM vs
non PAM
PAM + G
vs PAM
only
G vs
non G
PAM + G
vs Gypsum
only
PAM only
vs
RECPs
only
Gypsum
only vs
RECPs
only
Significance at 0.05
level of significance.
(based on Table 5.20)
Significant for all the
runs and overall.
Not significant for all
runs and the overall
Significant for run 1,
2 but not 3 and 4
non Gypsum
131.80
80.71
42.50
40.21
74.00
PAM + G
12.66
4.43
8.78
7.18
7.90
Gypsum
only
106.70
73.12
52.93
59.04
73.01
PAM only
18.82
9.42
7.45
7.09
10.82
RECPs only
244.78
152.00
77.56
73.33
137.09
106.70
73.12
52.93
59.04
73.01
244.78
152.00
77.56
73.33
137.09
Gypsum
RECPs only
Significant for all the
runs and overall.
Significant for all the
runs and overall.
Significant for runs
1,2 3 and the overall.
PAM vs non PAM and PAM only vs RECPs only
Table 5.22 shows that the mean turbidities of runoff from PAM treatments were smaller than
those of non-PAM treatments for all the rainfall applications as well as overall. Also the PAM
only treatments had much smaller mean turbidity of runoff than those of RECP only treatments.
129
These results clearly show that the turbidity for PAM treatments is less than the turbidity for
non-PAM treatments. The mechanism for this reduction is as explained in Section 5.7.1.
G vs Non-G
The mean turbidity for gypsum treatments was less than the mean turbidity of nongypsum treatments for runs 1, 2, 3, and 4 as well as overall. However, the reductions were
significant in run 1 and 2 but not run 3 and 4. Thus the treatments with gypsum perform more
effectively in reducing turbidity than treatments without gypsum in the first 2 rainfall
applications than in the subsequent runs (run 3 and 4). As explained in Section 5.6 the lack of
significant reduction in turbidity for gypsum treatments in the later runs was probably due to the
Ca2+ ion being transported by the runoff in the first and second event. This result is also
consistent with results obtained by Peterson et al. (2002) and the mechanism for the reduction is
as explained in Section 5.6.
PAM+G vs G only
The mean turbidity for PAM plus gypsum treatments was less than that of gypsum only
treatments for all the runs (run 1, 2, 3, and 4) as well as overall. This result is also consistent with
results obtained by Peterson et al. (2002), Jian et al. (2003), and Truman et al. (2010), and the
mechanism for this reduction in turbidity is as explained in Section 5.6.
PAM+G vs PAM only
From Table 5.22 the mean turbidity for PAM + G treatments was smaller than that of
PAM only treatments for run 1, 2 and overall, but not for run 3 and 4. However, as Table 5.20
shows these reductions were not significant (α=0.05). The results show that turbidity of treatment
with PAM plus gypsum was slightly less than that of PAM only, and hence, PAM plus gypsum
treatments performed slightly more effectively than PAM only treatments in reducing turbidity
130
for both the first and the subsequent rainfall applications. However, as Table 5.20 shows, these
reductions in turbidity were not significant. The results were consistent with those obtained by
Peterson et al. (2002), Jian et al. (2003), and Truman et al. (2010).
131
CHAPTER 6
SUMMARY AND CONCLUSIONS
Control of soil erosion in disturbed areas is a critical consideration in protecting the
quality of surface waters. To this end, it is important that methods that reduce soil erosion from
disturbed areas be investigated and, if found to be effective, be adopted. Gypsum and PAM
amendments applied to soils have been used to help reduce erosion. In this rainfall simulation
study PAM and gypsum treated RECPs were applied on small soil plots. The goal of this study
was to determine the effectiveness of using a coconut-wheat RECP and a coconut fiber RECP in
conjunction with PAM and gypsum in reducing runoff and sediment discharged with runoff from
disturbed soil plots. The study focused on the effectiveness of RECPs impregnated with PAM
only, RECPs impregnated with PAM plus gypsum, and RECPs alone in reducing runoff,
sediment loss, and turbidity of runoff from small plots under simulated rainfall conditions. The
responses of time to runoff initiation, runoff amount, turbidity, and sediment concentration of
runoff from treated and untreated soil plots were evaluated. The means of the responses from the
control (bare soil) were compared with those of the treated soil plots and comparisons among
various PAM and PAM+ G treatments were made based on 0.05 significance level.
The study evaluated the effects of PAM and PAM plus gypsum amendments when
applied by hand on two erosion control blankets (a 30% wheat straw and 70% coconut fiber
RECP, and a 100% coconut fiber RECP) used as soil cover. Soil boxes of clay loam soil were
covered with the RECPs, and a simulated rainfall of intensity 125 mm/hr for duration of 37
minutes for first rainfall application and 27 minutes for the subsequent rainfall applications was
applied on soil plots in the laboratory. PAM and gypsum were applied at rates of 46.4 kg
PAM/ha and 928 kg gypsum/ha, respectively. After 37 minutes of rainfall for the first rainfall
132
application and 27 minutes of rainfall for the subsequent three rainfall applications, four response
variables (time to runoff initiation, total runoff, sediment concentration, and turbidity) were
evaluated. Two days were allowed between subsequent rainfall applications to allow the soil to
drain. Four replicates of each treatment were evaluated.
Applying PAM to the RECP impacted runoff, turbidity and sediment yield when
simulated rainfall was applied. Both turbidity and sediment concentration were significantly
reduced when RECPs were treated with PAM as compared to RECPs alone. Further reduction of
the turbidity was noted when the RECP was treated with PAM plus gypsum. There was no
significant reduction in sediment concentration when RECPs were treated with PAM plus
gypsum compared with PAM only treatment. Also, there was no significant reduction of total
runoff when the RECPs were treated with PAM compared with a treatment with PAM plus
gypsum. There were also no major differences in time to runoff initiation when treated RECPs
were compared with untreated RECPs.
As indicated by the runoff analysis, the cover provided by the RECP 1 and RECP 2 did
not significantly (α=0.05) affect runoff from the treatments. Addition of PAM and PAM plus
gypsum to the RECPs did not reduce the total runoff significantly (α=0.05) during the first
rainfall application nor for the subsequent rainfall applications as compared to control. Time to
runoff initiation was greater in the first rainfall applications compared to the subsequent rainfall
applications because the soil was dry (after packing the soil boxes) during the first rainfall
application and, hence, the initial period of about 10 minutes simulated rain in the first
application was used to wet the soil. During the subsequent rainfall applications the soil was
initially wet; thus less total rain was needed for soil wetting. However, there was no significant
133
(α=0.05) difference in time to runoff initiation among the treatments for the first rainfall
application nor among treatments for the subsequent rainfall applications. The shorter time to
runoff initiation on the control treatment compared to the other treatments during the first rainfall
application was caused by the absence of a protective soil cover (RECP).
PAM applied to the erosion control blankets (RECPs) lead to reduced mean sediment
concentrations and mean turbidity by 76 % to 84 % and 95 % to 96 %, respectively, for the first
rainfall application compared to control (bare soil). For the subsequent rainfall applications the
reductions of mean sediment concentrations and turbidity were 82 % to 96 % and 96 % to 97 %,
respectively, compared to the control. The reduction in soil loss likely resulted because when the
PAM polymers are adsorbed on the external soil aggregates they bond the soil particles together
(though the particles are far apart), thereby increasing their resistance to splash by raindrop
impact and detachment by runoff (McLaughlin et al., 2009). Resulting suspended sediments
formed flocculants that settled, thereby reducing soil loss from plots.
PAM plus gypsum reduced the mean turbidity by up to 96 % for the first rainfall
application as compared to the control and the sediment concentrations by 70 to 80 % compared
to the control. In the subsequent rainfall applications, PAM plus gypsum reduced turbidity and
mean sediment concentrations by 97 to 99 % and 81 to 88 % compared to bare soil, respectively.
The gypsum provided more Ca2+ ions which attracted to the soil particles, which in turn attracted
anionic PAM polymer, thereby creating cationic bridges between soil and PAM polymers
(Wallace, 1996). The high concentration of Ca2+ ions due to the presence of gypsum enhanced
flocculation of clay particles, which stabilizes the soil aggregates and thus decreases soil
detachment and lowers the amount of sediments in the runoff (Lee et al., 2010). Addition of
gypsum to PAM may have resulted in shorter and coiled PAM polymers (Jian et al., 2003). The
134
short polymers are less effective in enhancing interparticle bonding, thus enabling more soil
detachment by raindrop impact. Hence, adding PAM and gypsum on RECP increased soil losses
compared to adding PAM alone. However, the reductions in sediment concentrations and
turbidity were not significant (α=0.05) when PAM + gypsum treatments were compared with
PAM only treatments.
135
CHAPTER 7
RECOMMENDATIONS AND FUTURE RESEARCH
The problems of runoff from construction sites have been recognized, and RECPs have
been shown to reduce erosion. The results from these studies were for two RECPs (a 30% wheat
straw and 70% coconut fiber, and one consisting of 100% coconut fiber) that were treated alone,
with PAM, with PAM plus gypsum, and with gypsum. The application of PAM and gypsum
treatments was by hand. The experiment was conducted in the laboratory under a rainfall
simulator (size 2.0 m by 2.0 m) applying rainfall on small soil boxes (size 1.094 m by 0.197 m).
This study showed that treating the RECP with PAM or gypsum or a combination of the two did
not have a significant effect on runoff amount or time to runoff initiation when treated RECPs
were compared with untreated RECPs. However, directly impregnating the RECPs with PAM
resulted in reduced turbidity and total suspended solids, but treating the RECP with PAM did not
reduce the sediment concentration significantly when compared to treating with PAM alone. The
effects of PAM plus gypsum applications on the sediment concentrations appeared to contradict
some literature (Lepore et al., 2009, Jian et al., 2003, and Peterson et al., 2002). Thus, further
research is necessary in the following areas:
Carrying out the study in natural conditions
The project timeline did not allow the study to be carried out under natural conditions.
Hence, the effects of natural conditions of undisturbed soil, rainfall, and wind were not
investigated. Research should be expanded to investigate whether these factors will affect the
results. In addition, erosion takes place in the open and not in laboratory and an investigation of
the treatments under natural and open conditions on actual construction sites is desirable.
136
Investigating other erosion control products
As noted, this study investigated only two RECPs (30% wheat straw and 70% coconut
fiber and 100% coconut fiber). However, there are many RECPs that are used in protecting
disturbed slopes from accelerated erosion, and there is increased demand for research on their
performance. Similar studies should thus be performed on these and various other types of
RECPs.
Selecting a method of applying PAM and gypsum on RECPs
Further research is necessary to identify the most effective methods of applying gypsum
and PAM on RECPs. Sprinkling by hand, as was done in this experiment, is not a practical
method of applying the treatments on disturbed slopes where the size is more than very small
plots. In addition, studies on effectiveness of PAM plus gypsum applied in solution onto the
RECPs need to be investigated.
Effect of PAM, and PAM plus gypsum-treated RECPs at different application rates and slope
Further research is necessary to investigate the effect of PAM and gypsum treatments on
RECPs when applied at different rates and on different land slopes.
Effect of PAM, PAM and gypsum-treated RECPs on sediment-bound P and N
Sediment-bound P and N contaminants are of considerable interest in erosion and
sediment loss studies. Thus, further studies of PAM/gypsum-treated RECPs on these
contaminants are of interest and should be conducted.
The longevity of PAM and PAM plus gypsum treated RECPs
The benefits of PAM and PAM plus gypsum treated RECPs in reducing soil loss has
been established. However, further research in the longevity of these benefits is an important
137
management issue. Results of this study indicate that the effectiveness of PAM applied to RECPs
is reduced with subsequent rainfall application, but studies should evaluate this in more detail.
138
REFERENCES
Adams, J. E. 1966. Influence of mulches on runoff, erosion, and soil moisture depletion. Soil
Science Society of America Proceedings 30:110-114.
Akbarzadeh, A., R. Taghizadeh, H. G. Refahi, H. Rouhipour, and M. Gorji. 2009. Application of
soil conditioners and man-made erosion control materials to reduce erosion risk on
sloping lands. African Journal of Plant Science 3(4):74-84.
Bartholomew, N. 2002. Polyacrylamide for turbidity control in runoff: Effects of
polyacrylamide, soil, and solution properties. MS thesis. Raleigh, North Carolina: North
Carolina State University, Department of Agricultural and Biological Engineering.
Barvenik, F. W. 1994. Polyacrylamide characteristics related to soil applications. Soil Science
Journal 158:235-243.
Benik, S.R., B. N. Wilson, D. D. Biesboer, B. Hansen, and D. Stenlund. 2003. Performance of
erosion control products on highway embankment. Transactions of American Society of
Agricultural Engineers 46(4):1113-1119.
Bhatia, S. K., J. L. Smith, D. Lake, and D. A. Walosky. 2002. Technical and economic
evaluation of geosynthetic rolled erosion control products in highway drainage channels.
Geosynthetic International 9(2):125-148.
Bhardwaj, A. R., and R. A. McLaughlin. 2008. Simple polyacrylamide dosing systems for
turbidity reduction in stilling basins. Transactions of American Society of Agricultural
Engineers 51(5):1653-1662.
Bio Tox Laboratory. 2008. Report for acute toxicity testing of applied polymer systems. Test
period February 5-7 2008. Accessed on June 25, 2010 at: http://www.siltstop.com/pdf/EZ%20PAM%20Acute%20Flea%20Test.pdf
Bjornberg, D. L., and J. K. Aase. 2000. Controlling sprinkler irrigation runoff erosion and
phosphorous with straw and polyacrylamide. Transactions of American Society of
Agricultural Engineers 16(5):501-504.
Bjornberg, D. L., J. K. Aase, and D. T. Westermann. 2000. Multiple polyacrylamide applications
for conrolling sprinkler irrigation runoff and erosion. Transactions of American Society
of Agricultural Engineers 43(6):1545-1551.
Bowyer, T. A. S., and T. P. Burt. 1989. Rainfall simulator for investigating soil response to
rainfall. Soil Technology Journal 2:1-16.
Bryan, L., and H. Luk. 1979. Laboratory experiments on variations of soil erosion under
139
simulated rainfall. Geoderma 26(1981):245-265.
Bubenzer, G. D., and L. D.Meyer. 1979. Simulation of rainfall and soils for laboratory
research. Transactions of American Society of Agricultural Engineers 8(1):73-75.
Christiansen, J. E. 1942. Irrigation by sprinkling. University of California Agriculture
Experiment Station Bulletin 670.
Entry, J. A., R. E. Sojka, M. Watwood, and C. Ross. 2002. Polyacrylamide preparations for
protection of water quality threatened by agricultural runoff contaminants. Environment
Pollution 120:191-200.
Federal Register. USEPA. 2011.76(79). Accessed on 06/20/2012 at
http://www.epa.gov/npdes/regulations/cgp_2011frnotice.pdf..
Faucette, L. B., J. Governo, C. F. Jordan, B. G. Lockaby, H. F. Carino, and P. Gorverno. 2007.
Erosion control and storm water management from straw with PAM, mulch, and
compost blankets of varying particle sizes. Journal of Soil and Water Conservation
63(6):404-413.
Flanagan, D. C., K. Chaudhari, and L. D. Norton.2002. Polyacrylamide soil amendment effects
on runoff and sediment yield on steep slopes: Part 1. Simulated rainfall conditions.
Transactions of American Society of Agricultural Engineers 45(5):1327-1337.
Foster, G. R., R. A. Young, M. J. M. Romken, and C. A. Onstad. 1985. Processes of soil erosion
by water. Pages 137-162. R.F. Foller and B. A. Stewart (eds). Soil Science Society of
America.
Gorman, J. M., J. C. Sencinder, D. J. Horwarth, R. N. Singh, and R. E. Keefer. 2000. Erodibility
of fly ash as a topsoil substitute in mineland reclamation. Journal of Environmental
Quality 29:805-811.
Gray, J. R., G. D. Gysson, L. M. Turcious, and G. E. Schwarz. 2000. Comparability of
suspended-sediment concentration and total suspended solids data. Water Resources
Investigations 00-4191.Reston Virginia. United States Geologic Survey.
Hallock, B. G., R. Steven, S. Misty, C. Michael. 2002. Plant establishment with rainfall
simulators for erosion control. Association conference 33, Orlando Florida. February 25
to March 3, 2002. Accessed on April 23, 2011 at
http://www.owp.csus.edu/research/papers/papers/PP017.pdf.
Harbor, J., and S. R. Chris. 2001. Soil erosion tools from point to regional scales-the role of
140
geomorphologists in land management and research. Geomorphology 47(2002):189-209.
Hayes, S. A., R. A. McLaughlin, and D. L. Osmond. 2005. Polyacrylamide use for erosion and
turbidity control on construction site. Journal of Soil and Water Conservation 60(4):193199.
Herlihy, T. M. 1992. Operation and design of fixed-nozzle, variable- intensity, solenoidcontrolled rainfall simulator. Masters thesis. University Park, PA: Pennsylvania State
University, Department of Agricultural and Biological Engineering.
Jarrett, A. R. 2002. Water Management. Dubuque, Iowa: Kendall/Hunt Publishing Co.
Jennings, G. D., R. A. McLaughlin, and S. E. King. 2009. Improving construction site runoff
quality with fiber check dams and polyacrylamide. Journal of Soil and Water
Conservation 64:144-154.
Jian, Y., T. Lei, I. Stainberg, A. I. Mamedov, and G. J. Levy. 2003. Infiltration and erosion in
soil treated with dry PAM and gypsum. Soil Science Society of America Journal 67(2):
630-636.
Lancaster, T., and D. N. Austin. 2003. Classifying rolled erosion control products: a current
perspective. Geotechnical Fabrics Report 12(8):16-22.
Laws, J. O. 1941. Measurement of the fall velocity of water drops and raindrops.
Transactions of the American Geophysical Union 22:709-721.
Lee, S. S., C. J. Gantzer, A. L. Thomson, and S. H. Anderson. 2010. Polyacrylamide and
gypsum amendments for erosion and runoff control on two soil series. Journal of Soil
and Water Conservation 65(4): 233-242.
Lentz, R. D., R. E. Sojka, and B. E. Markey. 2002. Fate and efficiency of polyacrylamide applied
in furrow irrigation: Full advance and continuous treatment. Journal of Environmental
Quality 31:661-670.
Lepore, B. J., A. M. Thompson, and A. Petersen. 2009. Impact of polyacylamide delivery
method with lime or gypsum for soil and nutrient stabilization. Journal of Soil and Water
Conservation 64(3):223-231.
McLaughlin, R. A., and B. Tabitha. 2006. Evaluation of erosion control products with and
without added polyacrylamide. Journal of American Water Resources Association 42(3):
675-684.
141
McLaughlin, R. A., and N. Bartholomew. 2007. Soil factors influencing suspended sediments
flocculation by polyacrylamide. Soil Science Society of American Journal 71(2):537544.
McLaughlin, R. A., S. E. King, and G. D. Jennings. 2009. Improving construction site runoff
quality with fiber dams and polyacrylamide. Journal of Soil and Water Conservation
64(2):144-154.
Mech, S. J. 1965. Limitations of simulated rainfall as a research tool. Transactions of American
Society of Agricultural Engineers 8(1):66-72.
Meyer, L. D., and D. L. McCune. 1958. Rainfall simulator for runoff plots. Agricultural
Engineering 39:644-648.
Meyer, L. D. 1965. Simulator of rainfall for erosion research. Transactions of American Society
of Agricultural Engineers 8(1):63-65.
Meyer, L. D., C. B. Johnson, and G. R. Foster. 1972. Stone and woodchip mulches for erosion
control on construction sites. Journal of Soil and Water Conservation 27:264-269.
Meyer, L. D., and W. C. Harmon. 1979. Multiple rainfall simulator for erosion research on row
side slopes. Transactions of American Society of Agricultural Engineers 22(1):01000103.
Michelle, L. S., S. Mostaghimi, M. Amanda, A. F. Katherine, H. V. David, M. Aida, and
W.M. Phillips. 2004. Effectiveness of polyacrylamides (PAM) in improving runoff water
from construction sites. Journal of American Water Resources Association 40(1):53-66.
Miller, W. P. 1987. A solenoid-operated, variable intensity rainfall simulator. Soil Science
Society of America Journal 51:832-834.
Moore, I. D., M.C. Hirsch, and B. J. Barfield.1983. Kentucky rainfall simulator. Transactions of
American Society of Agricultural Engineers 26(4):67-68.
Mutchler, C. K., and L. F. Hermsmeier. 1965. A review of rainfall simulators. Transactions of
American Society of Agricultural Engineers 8(1):67-68.
Papacosta, K. 2002. Turbidity calibration standards evaluated from a different perspective.
Turbidity and other surrogates workshop. April 30-May 2. 2002. Reno. Nevanda.
Peterson, J. R., D. C. Flanagan, and J. K. Tishmack. 2002. Polyacryamide and gypsiferous
material effect on runoff and erosion under simulated rainfall. Transactions of American
Society of Agricultural Engineers 45(4):1011-1019.
142
Peterson, J. R., D. C. Flanagan, and J. K. Tishmack. 2002. PAM application method and
electrolyte source effects on plot-scale runoff and erosion. Transactions of American
Society of Agricultural Engineers 45(6):1859-1867.
Raskin, L., A. Deapoli, and M. J. Singer. 2005. Erosion control materials used on construction
sites in California. Journal of Soil and Water Conservation 60(4):187-192.
Robert, E. P., E. C. Shirley, and W. L. Donald. 2007. Construction site erosion and sediment
controls: planning, design and performance. DEStech Publications, Inc. Lancaster,
Pennsylvania, USA.
Sutherland, R. A. 1998. Rolled erosion control systems for hill slope surface protection; a critical
review, synthesis and analysis of available data. Land Degradation and Development
9:465-486.
Sutherland, R. A., A. D. Ziegler, and L.T. Tran. 1997. Influence of rolled control systems on
temporal rain splash response –A laboratory simulation experiment. Land Degradation
and Development 8:139-157.
Sharpley, A., T. Daniel, R. Wright, P. Kleinman, T. Sobecki, R. Parry, and B. Joern. 1999.
National research project to identify sources of agricultural phosphorus loss. Better
Crops. 18:12-14.
Sharpley, A., and P. Kleinman, 2003. Effect of rainfall simulator and plot scale on
overland flow and phosphorus transport. Journal of Environmental Quality 32(6):21722179.
Shelton, C. H., R. D. von Bernuth, and S. P. Rajbhandari. 1985. A continuous-application
rainfall simulator. Transactions of American Society of Agricultural Engineers 28(4):
1115-1119.
Sojka, R. E., and R. D. Lentz. 1998. Water erosion with multiple applications of
polyacrylamide. Soil Science Society of America Journal 62:56-66.
Stainberg, I., D. Warrington, and P. Rengasamy.1990. Water quality and PAM interactions in
reducing surface sealing. Soil Science Society of America Journal 149(2):301-307.
Swanson, F. 1965. Impact of clear-cutting and road construction on soil erosion by landslides in
Caslade range, Oregon. U.S. Department of Agriculture, Forest Service Research. Paper
No PNW-188.
Thiessen, M. S. 1992. The role of geosynthetic in erosion and sediment control: an
overview. Geotextile and Geomembrane 11:535-550.
143
Tossell, R. W., W. T. Dickinson, R. P. Rudra, and G. J. Wall. 1987. A portable rainfall simulator.
Canada Agricultural Research 43:1-9.
Truman, L. R., R.C. Nuti, and J. D. Dean. 2010. Feasibility of using FGD gypsum to conserve
water and reduce erosion from an agricultural soil in Georgia. Catena. Elsiever 234-239.
United States Code. 1972. Clean Water Act. Ln 33 U.S.C. 1251 et seq.
USEPA.1992. Storm water management for construction activities. In developing pollution
prevention plans and best management practices. Summary guidelines. USEPA 833-R92001. Washington, D.C.: U.S. Environmental Protection Agency.
USDA. 1996. National handbook of water quality monitoring. Part 600 National water quality
handbook. USDA 450-vi-NHWQM. Washington, D.C.: United States Department of
Agriculture, Natural Resources Conservation Services.
USDA NRCS. 2007 National research project for simulated rainfall-surface runoff studies
Protocol. Accessed on April 23, 2011 at
http://www.sera17.ext.vt.edu/Documents/National_P_protocol.pdf.
Novotny, V., and G. Chesters. 1989. Delivery of sediments and pollutants from non-point
sources: A water quality perspective. Journal of Soil and Water Conservation 44(6):
568-576.
Wallace, G. A., and A. Wallace. 1986. Control of soil erosion by polymeric soil conditioners.
Soil Science Society of America Journal 141:363-367.
Weston, D. P., R. D. Lentz, M. D. Cahn, R. C. Ogle, A. K. Rothert, and M. J. Lydy. 2009.
Toxicity of anionic PAM formulations when used for erosion control in agriculture.
Journal of Environmental Quality 38:238-247.
Wilson, W. T., W. C. Zech, T. P. Clement, and A. L. Shoemaker. 2008. Polymer-enhanced soft
armoring: An erosion and sediment control measure for construction hill slopes. MS
thesis. Auburn, Alabama: Auburn University, Department of Civil Engineering.
Wischmeier, W. H., and D. D. Smith. 1958. Rainfall energy its relationship to soil loss. America
Geophysical Union 39:284-291.
Zhang, X. C., W. P. Miller, M. A. Nearing, and L. D. Norton. 1998. Effects of surface treatment
on surface sealing, runoff, and interill erosion. Transactions of American
Society of Agricultural Engineers 41(4):989-994.
144
APPENDICES
APPENDIX A: Sample Tables
Appendix A-1: Table for recording the mass of runoff from each treatment.
Replicate No________________________
Date of Rainfall Simulation____________
Rainfall application No(Run No)__________
Time of Experiment_________________
M ass of runoff (grams)
Time after Runoff initiation (min)
0
2
4
6
Time to
Rainfall
Box No &
Treatment
runoff
Amount
initiation
(inches)
(minutes)
1.______
2._______
3.______
4._______
5.______
7.______
8.______
9.______
145
8
10
12
14
16
18
20
Appendix A-2: Table for recording the turbidity of runoff from each treatment runoff sample.
Replicate No________________________
Run No____________________________
Turbidity of sample after runoff initiation
(NTUs)
Time after Runoff initiation
0
2
4
(minutes)
Box No & Treatment
1.___________
2.___________
3.__________
4._________
5._________
6.___________
7.___________
8.___________
9.___________
146
6
8
10
12
14
16
18
20
Appendix A-3: Table for recording the mass of sediments from 50 ml of runoff from each runoff
sample.
Replicate No________________________
Run No____________________________
M ass ((grams) of sediments of 50 ml sub-sample after runoff initiation
Time after Runoff initiation
0
2
4
6
(minutes)
Box No & Treatment
1.______
2.______
3.______
4.______
5.______
6.______
7.______
8.______
9.______
147
8
10
12
14
16
18
20
APPENDIX B: SAS model for statistical analysis of the runoff data.
Appendix B-1: SAS program for reading the total runoff data.
Appendix B-2: Program showing how to run a SAS code.
Appendix B-3: SAS program for creating a box plot of the "total runoff" by all 8 treatments and
control.
148
Appendix B-4: SAS program for creating box plot of "total runoff" against treatments "PAM"
and "non-PAM".
Appendix B-5: SAS program for creating box plot for box plot of "total runoff" against
treatments "PAM plus G" and "PAM only".
149
Appendix B-6: SAS program for creating graph of "rainfall" applied on each treatment in Run 1
of Replication 1.
Appendix B-7: SAS program for testing the hypothesis runoff for RECPs treated with PAM will
not be significantly different from the runoff from non-PAM treated RECPs.
150
Appendix B-8: SAS program for testing the hypothesis runoff for RECPs treated with PAM plus
gypsum will not be significantly different from the runoff from PAM only treated RECPs.
Appendix B-9: SAS program for testing the hypothesis runoff for RECPs treated with gypsum
will not be significantly different from the runoff from untreated RECPs.
151
Appendix B-10: SAS program showing the program for comparing the cumulative runoff for
treatments PAM vs Non-PAM, PAM+G vs PAM only, G vs Non G, and PAM vs G only for
rainfall application 4 (Run 4).
152
Appendix B-11: SAS model for calculating the means of treatments for the runoff.
153
APPENDIX C: Converting the volume of rainfall (ml) collected in the beakers to rainfall (mm).
The volume the plastic beakers used is given by the formula;
V = π × h × (b²+a²+b×a) ⁄ 3
Figure C-1: Truncated cone that was used to convert rainfall (mL) to mm.
For the beakers used in rainfall distribution studies; a=3.115 cm, b=4.095 cm, and h=10.37 cm
giving a total volume of 4260 mL.
For height, H and top radius B the volume is given by the formula;
V=3.142/3xHx(3.1152 +3.115xB+B2 ).
Equation 1
B=3.115+0.0945/H.
Equation 2
By using similar triangles relationship in Figure C-1;
B-3.115=0.98/10.37.
Equation 3
Substituting equations 2 and 3 in equation 1 and simplifying the equation we get;
V=30.49H+0.925H2 +0.00935H3 .
Equation 4
154
Equation 4 was used to calculate the height of volume of water in the beaker by iteration and trial
and error.
Figure C-2: Figure showing the derivation of formula using similar triangles.
155
APPENDIX D: Contour maps of rainfall below the simulator nozzle.
Appendix D-1: Contour map of the rainfall depth (mm) on the simulator area before the start of
the experiment (05/20/2011).
<30.0
30.0-35.0
35.0-40.0
40.0-45.0
>45.0
Appendix D-2: Contour map of the rainfall depth (mm) on the simulator area midway through
the experiment (06/30/2011).
<30.0
30.0-35.5
35.5-41.0
41.0-45.5
>45.5
156
Appendix D-3: Contour map of the rainfall depth (mm) on the simulator area at the end of the
experiment (09/30/2011).
157
APPENDIX E: Results and Discussions of the initial Data Analysis.
E-1: Graph showing the mean cumulative runoff for rainfall applications 2, 3 and 4, and the first
rainfall application for the second replication.
Control
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
RECP 1 + G
mean Control
mean RECP 2
mean RECP 2 + PAM
mean RECP 2 + PAM + G
mean RECP 2 + G
mean RECP 1
mean RECP 1 + PAM
RECP 1 + PAM + G
mean RECP 1 + G
30
25
Cumulative runoff (mm)
20
15
10
5
0
0
5
10
15
20
25
Time after rainfall initiation (mins)
158
30
35
40
Appendix E-2: Graph showing the mean turbidity for rainfall applications 2, 3 and 4, and the
first rainfall application for the second replication.
RECP 1 + G
Control
RECP 2
RECP 2 + PAM
RECP 2 + PAM + G
RECP 2 + G
RECP 1
RECP 1 + PAM
RECP 1 + PAM + G
Mean RECP 1 + G
Mean Control
mean RECP 2
MeanRECP 2 + PAM
Mean RECP 2 + PAM + G
Mean RECP 2 + G
Mean RECP 1
Mean RECP 1 + PAM
Mean RECP 1 + PAM + G
2000
1800
1600
Turbidity (NTUs)
1400
1200
1000
800
600
400
200
0
0
5
10
15
20
25
Time from rainfall initiation (mins)
159
30
35
40
Appendix E-3: Graph showing the mean cumulative runoff for rainfall applications 2, 3 and 4,
and the first rainfall application for the third replicate.
Control
RECP2
RECP2+Pam
RECP2+Pam & Gypsum
RECP2+Gypsum
RECP1
RECP1+Pam
RECP1+Pam & Gypsum
RECP1+Gypsum
mean Control
mean RECP2
mean RECP2+Pam
mean RECP2+Pam & Gypsum
mean RECP2+Gypsum
mean RECP1
mean RECP1+Pam
mean RECP1+Pam & Gypsum
meanRECP1+Gypsum
35
30
Cumulative runoff (mm)
25
20
15
10
5
0
0
5
10
15
20
25
Time after rainfall initiation (min)
160
30
35
40
Appendix E-4: showing the mean turbidity for rainfall applications 2, 3 and 4, and the first
rainfall application for the third replicate.
RECP1+Pam & Gypsum
RECP1+Gypsum
Control
RECP2
RECP2+Pam
RECP2+Pam & Gypsum
RECP2+Gypsum
RECP1
RECP1+Pam
mean RECP1+Pam & Gypsum
mean RECP1+Gypsum
mean Control
mean RECP2
mean RECP2+Pam
mean RECP2+Pam & Gypsum
mean RECP2+Gypsum
mean RECP1
mean RECP1+Pam
1800
1600
1400
Turbidity (NTUs)
1200
1000
800
600
400
200
0
0
5
10
15
20
25
Time after rainfall initiation (mins)
161
30
35
40
Appendix E-5: Graph showing the mean cumulative runoff for rainfall applications 2, 3 and 4,
and the first rainfall application for the fourth replicate.
CONTROL
RECP2
RECP2+P
RECP2+P+G
RECP2+G
RECP1
RECP1+P
RECP1+P+G
RECP1+G
Mean CONTROL
Mean RECP2
Mean RECP2+P
Mean RECP2+P+G
Mean RECP2+G
Mean RECP1
Mean RECP1+P
Mean RECP1+P+G
Mean RECP1+G
35
30
Cumulative runoff (mm)
25
20
15
10
5
0
0
5
10
15
20
25
Time after rainfall initiation (mins)
162
30
35
40
Appendix E-6: Graph showing the mean turbidity for rainfall applications 2, 3 and 4, and the
first rainfall application for the fourth replicate.
RECP1+P
RECP1+P+G
RECP1+G
CONTROL
RECP2
RECP2+P
RECP2+P+G
RECP2+G
RECP1
Mean RECP1+P
Mean RECP1+P+G
Mean RECP1+G
Mean CONTROL
MeanRECP2
Mean RECP2+P
Mean RECP2+P+G
Mean RECP2+G
Mean RECP1
1800
1600
1400
Turbidity (NTUs)
1200
1000
800
600
400
200
0
0
5
10
15
20
25
Time after rainfall initiation (mins)
163
30
35
40
Appendix E-7: Table showing the descriptive statistics of the sediment concentrations (g/L) responses for the second replicate.
Rainfall application 1
Rainfall application 2
N
E
Mean
S.E
S.D
N
E
Mean
S.E
Control
12
0
2.34
0.17
0.58
12
0
3.21
RECP2
12
0
0.68
0.08
0.29
12
0
RECP2+PAM
10
2
0.34
0.08
0.26
12
RECP2+PAM+G
9
3
1.02
0.09
0.28
RECP2+G
12
0
1.06
0.05
RECP1
11
1
0.30
RECP1+PAM
11
1
RECP1+PAM+G
8
RECP1+G
11
Treatment
Rainfall application 3
Rainfall application 4
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
0.17
0.58
12
0
3.70
0.20
0.70
12
0
3.88
0.30
1.03
0.09
0.05
0.16
12
0
0.94
0.08
0.24
12
0
0.94
0.09
0.29
0
0.02
0.02
0.07
12
0
0.78
0.08
0.28
12
0
0.76
0.07
0.23
12
0
0.18
0.07
0.25
12
0
1.10
0.10
.036
12
0
0.97
0.10
0.34
0.16
12
0
0.33
0.13
0.46
12
0
1.15
0.06
0.19
12
0
1.14
0.05
0.18
0.09
0.31
12
0
0.02
0.02
0.05
12
0
0.72
0.08
0.29
12
0
0.69
0.08
0.27
0.42
0.12
0.39
12
0
0.06
0.04
0.13
12
0
0.86
0.08
0.28
12
0
0.88
0.09
0.30
4
0.52
0.17
0.49
12
0
0.09
0.05
0.18
12
0
0.92
0.14
0.49
12
0
0.91
0.12
0.42
1
1.22
0.11
0.37
12
0
0.52
0.11
0.38
12
0
1.48
0.11
0.38
12
0
1.25
0.16
0.56
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
164
Appendix E-8: Table showing the descriptive statistics of the sediment concentrations (g/L) responses for the third replicate.
Rainfall application 1
Rainfall application 2
N
E
Mean
S.E
S.D
N
E
Mean
S.E
Control
12
0
2.29
0.08
0.29
12
0
3.17
RECP2
12
0
0.80
0.14
0.47
12
0
RECP2+PAM
10
2
0.33
0.17
0.53
12
RECP2+PAM+G
12
0
0.83
0.14
0.48
RECP2+G
12
0
1.03
0.13
RECP1
12
0
0.88
RECP1+PAM
12
0
RECP1+PAM+G
12
RECP1+G
12
Treatment
Rainfall application 3
Rainfall application 4
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
0.12
0.42
12
0
3.52
0.17
0.59
12
0
3.38
0.15
0.52
0.31
0.12
0.42
12
0
0.34
0.07
0.25
12
0
0.35
0.06
0.21
0
0.04
0.04
0.14
12
0
0.18
0.06
0.21
12
0
0.34
0.07
0.23
12
0
0.24
0.1
0.34
12
0
0.37
0.10
0.35
12
0
0.33
0.06
0.22
0.44
12
0
0.47
0.14
0.49
12
0
0.30
0.11
0.38
12
0
0.40
0.11
0.39
0.14
0.48
12
0
0.52
0.10
0.36
12
0
0.22
0.06
0.21
12
0
0.49
0.09
0.31
0.62
0.07
0.25
12
0
0.55
0.06
0.22
12
0
0.20
0.03
0.11
12
0
0.64
0.05
0.17
0
0.67
0.12
0.40
12
0
0.42
0.09
0.32
12
0
0.47
0.07
0.24
12
0
0.39
0.07
0.24
0
0.63
0.08
0.28
12
0
0.20
0.08
0.28
12
0
0.23
0.06
0.21
12
0
0.26
0.54
0.19
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
165
Appendix E-9: Table showing the descriptive statistics of the sediment concentrations (g/L) responses for the fourth replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
3.93
0.10
0.35
12
0
5.10
0.27
0.93
12
0
4.33
0.13
0.44
12
0
5.27
0.05
0.19
RECP2
12
0
0.73
0.13
0.46
12
0
0.87
0.09
0.32
12
0
0.71
0.09
0.31
12
0
1.01
0.09
0.32
RECP2+PAM
12
0
0.97
0.67
2.32
12
0
0.37
0.06
0.19
12
0
0.32
0.05
0.18
12
0
0.60
0.06
0.20
RECP2+PAM+G
12
0
0.71
0.11
0.39
12
0
0.50
0.10
0.33
12
0
0.34
0.09
0.30
12
0
0.61
0.09
0.32
RECP2+G
12
0
1.30
0.11
0.36
12
0
0.95
0.09
0.31
12
0
0.63
0.09
0.31
12
0
0.94
0.08
0.27
RECP1
12
0
0.95
0.05
0.18
12
0
0.95
0.04
0.14
12
0
0.84
0.06
0.20
12
0
1.02
0.03
0.11
RECP1+PAM
12
0
0.89
0.12
0.41
12
0
0.65
0.06
0.21
12
0
0.47
0.09
0.31
12
0
0.69
0.10
0.35
RECP1+PAM+G
12
0
0.99
0.10
0.35
12
0
0.63
0.08
0.26
12
0
0.58
0.07
0.25
12
0
0.82
0.09
0.30
RECP1+G
12
0
1.25
0.08
0.27
12
0
0.77
0.11
0.37
12
0
0.61
0.10
0.34
12
0
0.69
0.10
0.35
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
166
Appendix E-10: Table showing the descriptive statistics of the turbidity (NTU) responses for the first replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
11
0
1148.7
34.4
114.1
11
0
1357.3
54.6
181.2
11
0
1186.5
44.4
181.2
11
0
1227.7
41.2
136.7
RECP2
11
0
214.5
20.2
67.1
11
0
130.2
7.3
24.2
11
0
84.6
4.5
24.2
11
0
72.1
1.3
4.2
RECP2+PAM
11
0
15.7
1.5
5.0
11
0
6.3
0.6
2.1
11
0
5.3
0.7
2.1
11
0
5.4
0.3
1.0
RECP2+PAM+G
8
3
42.8
14.5
41.1
11
0
7.6
1.4
4.6
11
0
9.0
3.0
4.6
12
0
7.2
0.9
2.9
RECP2+G
7
4
120.1
7.4
19.1
11
0
88.6
8.1
26.9
12
0
70.3
8.1
26.9
12
0
77.0
3.0
10.5
RECP1
7
4
290.1
17.0
58.8
12
0
251.1
6.3
21.8
11
0
151.6
4.8
21.8
12
0
133.9
3.6
12.4
RECP1+PAM
12
0
16.1
5.1
17.7
12
0
3.2
0.3
1.0
12
0
5.6
1.7
1.0
12
0
2.6
0.2
0.9
RECP1+PAM+G
12
2
10.5
1.9
5.9
12
0
3.3
0.4
1.3
12
0
3.8
0.5
1.3
12
0
4.1
0.3
1.1
RECP1+G
6
6
123.1
13.8
33.8
12
0
45.6
5.9
20.5
12
0
44.4
7.8
20.5
12
0
37.7
5.0
17.2
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
167
Appendix E-11: Table showing the descriptive statistics of the turbidity (NTU) responses for the second replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1525.3
40.3
139.7
12
0
1845.3
38.5
133.5
12
0
1283.6
34.2
118.6
12
0
1369.0
60.0
207.71
RECP2
12
0
194.5
17.2
59.4
12
0
126.1
14.3
49.4
12
0
90.6
8.5
29.5
12
0
72.1
3.9
13.5
RECP2+PAM
10
2
26.4
7.3
23.2
12
0
9.7
1.7
5.82
12
0
9.8
0.6
1.9
12
0
10.2
0.9
3.1
RECP2+PAM+G
9
3
18.4
4.6
13.9
12
0
6.1
1.5
5.0
12
0
6.4
0.8
2.9
12
0
4.7
0.6
2.2
RECP2+G
12
0
20.0
1.2
4.0
12
0
132.2
4.3
14.8
12
0
88.2
6.3
21.7
12
0
98.1
4.4
15.1
RECP1
12
0
850.7
89.2
308.9
12
0
73.5
6.7
23.1
12
0
44.4
3.5
12.1
12
0
51.6
1.7
5.7
RECP1+PAM
11
1
16.4
5.7
19.0
12
0
5.2
0.5
1.7
12
0
6.9
0.6
2.1
12
0
4.4
0.3
1.0
RECP1+PAM+G
10
2
10.8
1.5
4.9
12
0
3.8
0.3
1.1
12
0
7.0
0.7
2.6
12
0
5.0
0.3
1.0
RECP1+G
11
1
82.5
8.2
27.1
12
0
61.9
4.5
15.7
12
0
47.9
4.0
13.7
12
0
43.2
2.8
9.5
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
168
Appendix E-12: Table showing the descriptive statistics of the turbidity (NTU) responses for the third replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1140.9
36.3
125.8
12
0
1622.0
54.7
189.6
12
0
1350.8
52.2
180.9
12
0
1112.0
39.5
136.7
RECP2
12
0
339.8
20.7
71.6
12
0
244.6
6.7
23.2
12
0
95.6
6.8
23.5
12
0
82.1
2.2
7.6
RECP2+PAM
10
2
19.1
2.2
7.0
12
0
15.3
0.6
2.0
12
0
9.1
0.6
2.1
12
0
12.9
0.8
2.7
RECP2+PAM+G
12
0
21.3
2.8
9.8
12
0
14.1
2.8
9.6
12
0
10.2
3.9
13.6
12
0
7.2
0.4
1.3
RECP2+G
12
0
104.2
9.7
33.5
12
0
70.5
4.3
14.9
12
0
42.6
3.1
10.6
12
0
42.3
3.2
11.1
RECP1
12
0
212.0
18.9
65.3
12
0
120.6
6.5
22.5
12
0
42.1
3.2
11.1
12
0
47.9
2.7
9.5
RECP1+PAM
12
0
38.6
9.2
31.8
12
0
28.2
10.2
35.3
12
0
8.4
0.4
1.2
12
0
10.0
0.6
2.1
RECP1+PAM+G
12
0
10.6
2.1
7.4
12
0
5.2
0.4
1.3
12
0
4.2
0.2
0.6
12
0
5.1
0.3
0.9
RECP1+G
12
0
104.9
3.4
11.7
12
0
99.0
5.6
19.3
12
0
57.9
4.3
14.9
12
0
52.9
5.7
19.9
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
169
Appendix E-13: Table showing the descriptive statistics of the turbidity (NTU) responses for the fourth replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1402.5
26.2
90.8
12
0
1721.6
85.7
296.8
12
0
1361.5
34.9
120.8
12
0
1405.3
23.1
80.0
RECP2
11
1
228.0
19.6
65.1
12
0
66.8
5.6
19.4
12
0
54.5
3.4
11.9
12
0
61.4
2.0
6.9
RECP2+PAM
12
0
30.5
3.8
13.1
12
0
8.4
1.2
4.1
12
0
14.9
1.1
3.9
12
0
21.7
1.0
3.5
RECP2+PAM+G
12
0
10.0
3.1
10.8
12
0
3.8
0.5
1.7
11
1
8.7
1.5
4.8
12
0
6.6
0.5
1.8
RECP2+G
12
0
192.7
19.9
68.8
12
0
73.7
3.8
13.1
10
2
46.6
4.5
14.2
12
0
69.9
1.7
5.9
RECP1
12
0
223.4
17.1
59.3
12
0
181.4
30.2
104.5
12
0
64.0
4.0
13.7
12
0
75.8
2.83
9.8
RECP1+PAM
12
0
18.1
1.6
5.5
12
0
7.0
0.2
0.7
12
0
11.3
0.7
2.5
12
0
15.1
1.0
3.4
RECP1+PAM+G
12
0
13.6
2.2
7.8
12
0
5.5
0.3
1.2
12
0
10.3
1.0
3.3
12
0
11.7
0.6
2.2
RECP1+G
12
0
113.0
19.0
65.8
12
0
28.3
2.8
9.8
12
0
23.6
2.2
7.6
12
0
44.2
3.1
10.8
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
170
Appendix E-14: Table showing the descriptive statistics of the total runoff (mm) responses for the first replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
11
0
1.35
0.09
0.31
11
0
1.83
0.03
0.10
12
0
1.79
0.16
0.57
11
0
1.92
0.04
0.12
RECP2
11
0
1.53
0.09
0.31
11
0
2.18
0.03
0.09
12
0
1.79
0.19
0.67
11
0
2.24
0.02
0.06
RECP2+PAM
10
0
1.38
0.12
0.34
11
0
2.01
0.02
0.07
12
0
1.95
0.19
0.66
11
0
2.11
0.02
0.07
RECP2+PAM+G
8
3
0.95
0.17
0.47
11
0
1.73
0.05
0.17
12
0
1.67
0.19
0.67
11
0
1.87
0.07
0.22
RECP2+G
12
0
0.78
0.18
0.62
11
0
1.51
0.07
0.25
12
0
1.47
0.17
0.60
11
0
1.48
0.16
0.53
RECP1
12
0
1.24
0.12
0.41
12
0
1.99
0.02
0.07
12
0
2.04
0.03
0.09
12
0
2.02
0.02
0.62
RECP1+PAM
12
0
1.44
0.18
0.61
12
0
2.12
0.02
0.06
12
0
2.27
0.01
0.05
12
0
2.23
0.03
0.12
RECP1+PAM+G
12
0
0.96
0.21
0.74
12
0
1.80
0.03
0.10
12
0
1.98
0.05
0.16
12
0
1.87
0.05
0.17
RECP1+G
11
1
0.60
0.20
0.65
12
0
1.56
0.08
0.26
12
0
1.64
0.09
0.31
12
0
1.59
0.09
0.31
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
171
Appendix E-15: Table showing the descriptive statistics of the total runoff (mm) responses for the second replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1.55
0.04
0.13
12
0
1.93
0.03
0.11
12
0
2.20
0.12
0.40
12
0
2.38
0.13
0.45
RECP2
12
0
1.37
0.09
0.29
12
0
2.24
0.03
0.09
12
0
2.21
0.02
0.08
12
0
2.30
0.05
0.17
RECP2+PAM
10
2
1.11
0.08
0.26
12
0
2.02
0.04
0.15
12
0
2.01
0.03
0.11
12
0
2.06
0.01
0.04
RECP2+PAM+G
9
3
0.89
0.08
0.23
12
0
1.59
0.10
0.35
12
0
1.61
0.08
0.28
12
0
1.69
0.03
0.12
RECP2+G
12
0
1.37
0.06
0.21
12
0
2.05
0.02
0.08
12
0
2.15
0.03
0.10
12
0
2.12
0.03
0.11
RECP1
12
0
1.11
0.11
0.38
12
0
1.99
0.03
0.09
12
0
2.25
0.04
0.13
12
0
2.14
0.07
0.25
RECP1+PAM
11
1
1.21
0.07
0.25
12
0
2.01
0.03
0.09
12
0
2.06
0.01
0.04
12
0
2.19
0.01
0.04
RECP1+PAM+G
10
2
0.67
0.12
0.37
12
0
1.71
0.03
0.11
12
0
1.70
0.02
0.08
12
0
1.82
0.01
0.04
RECP1+G
11
1
1.34
0.07
0.23
12
0
2.05
0.01
0.05
12
0
2.04
0.03
0.09
12
0
2.20
0.03
0.09
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
172
Appendix E-16: Table showing the descriptive statistics of the total runoff (mm) responses for the third replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1.59
0.06
0.21
12
0
2.16
0.07
0.26
12
0
2.39
0.15
0.51
12
0
2.30
0.15
0.53
RECP2
12
0
1.07
0.08
0.29
12
0
1.60
0.05
0.16
12
0
1.73
0.05
0.17
12
0
1.72
0.04
0.12
RECP2+PAM
10
2
0.87
0.08
0.24
12
0
1.49
0.03
0.11
12
0
1.63
0.02
0.06
12
0
1.55
0.03
0.11
RECP2+PAM+G
12
0
1.34
0.08
0.28
12
0
2.05
0.06
0.21
12
0
2.38
0.20
0.70
12
0
2.17
0.02
0.09
RECP2+G
12
0
1.57
0.10
0.36
12
0
2.21
0.04
0.15
12
0
2.29
0.03
0.12
12
0
2.39
0.03
0.09
RECP1
12
0
1.52
0.09
0.32
12
0
2.04
0.07
0.23
12
0
2.33
0.22
0.76
12
0
2.37
0.02
0.10
RECP1+PAM
12
0
1.16
0.11
0.36
12
0
1.79
0.03
0.11
12
0
1.87
0.04
0.13
12
0
1.90
0.02
0.08
RECP1+PAM+G
12
0
1.23
0.18
0.64
12
0
1.93
0.05
0.16
12
0
2.01
0.04
0.14
12
0
2.04
0.06
0.22
RECP1+G
12
0
1.84
0.06
0.20
12
0
2.44
0.07
0.25
12
0
2.49
0.09
0.32
12
0
2.56
0.13
0.44
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
173
Appendix E-17: Table showing the descriptive statistics of the total runoff (mm) responses for the fourth replicate.
Treatment
Rainfall application 1
Rainfall application 2
Rainfall application 3
Rainfall application 4
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
N
E
Mean
S.E
S.D
Control
12
0
1.35
0.05
0.19
12
0
1.92
0.05
0.16
12
0
1.96
0.05
0.18
12
0
1.98
0.15
0.50
RECP2
12
0
0.79
0.12
0.40
12
0
1.62
0.01
0.05
12
0
1.51
0.05
0.19
12
0
1.56
0.01
0.04
RECP2+PAM
12
0
1.53
0.08
0.27
12
0
2.09
0.05
0.17
12
0
2.26
0.07
0.23
12
0
2.14
0.01
0.04
RECP2+PAM+G
12
0
2.18
0.17
0.60
12
0
2.52
0.05
0.16
12
0
2.30
0.19
0.65
12
0
2.35
0.04
0.14
RECP2+G
12
0
1.89
0.05
0.17
12
0
2.21
0.03
0.09
12
0
2.18
0.06
0.21
12
0
2.21
0.02
0.08
RECP1
12
0
1.40
0.07
0.25
12
0
1.84
0.06
0.20
12
0
1.97
0.06
0.21
12
0
1.47
0.02
0.05
RECP1+PAM
12
0
1.68
0.07
0.23
12
0
1.32
0.08
0.27
12
0
2.21
0.03
0.09
12
0
2.25
0.12
0.43
RECP1+PAM+G
12
0
2.18
0.20
0.68
12
0
1.53
0.07
0.24
12
0
2.56
0.12
0.43
12
0
2.73
0.22
0.75
RECP1+G
12
0
1.85
0.09
0.30
12
0
2.33
0.02
0.06
12
0
2.44
0.06
0.20
12
0
2.29
0.07
0.23
N is the number of responses; E is the number of observations where no runoff was recorded, S.E. is the standard error, and S.D. is the standard deviation.
174