Soil Erosion Analysis of Watersheds in Series
A thesis presented to
the faculty of the Russ College of Engineering and Technology of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
Andrew K. Lucas
June 2012
© 2012 Andrew K. Lucas: All Rights Reserved 2 This thesis titled
Soil Erosion Analysis of Watersheds in Series
by
ANDREW K. LUCAS
has been approved for
the Department of Civil Engineering and the Russ College of Engineering and Technology by
Tiao J. Chang
Professor of Civil Engineering
Dennis Irwin
Dean, Russ College of Engineering and Technology 3 Abstract
LUCAS, ANDREW K., M. S., May 2012, Civil Engineering
Soil Erosion Analysis of Watersheds in Series
Director of Thesis: Tiao J. Chang
The objective of this study is to determine the relationship between soil erosion and sedimentation within Wills Creek, Senecaville Lake, and Salt Fork watersheds of
Ohio. Both Senecaville Lake and Salt Fork Lake watersheds are entirely located within the watershed of Wills Creek Lake. Experimental results using the Revised Universal
Soil Loss Equation and a sediment delivery equation in conjunction with Geographic
Information Systems are compared to sedimentation reports prepared by the United
States Army Corps of Engineers. Results of this comparison show that the type of land cover has the highest impact on the amount of soil erosion, specifically the lands associated with cultivated crops. Furthermore, the sediment yield of a watershed is not accurately calculated based on average annual sedimentation and present RUSLE erosion potential.
Approved:
Tiao J. Chang
Professor of Civil Engineering 4 Acknowledgements
I would like to thank those involved in the research, development, and completion of my thesis. Specifically, I would like to thank Dr. Tiao J. Chang for his support throughout the development and research processes. I would like to thank Dr. Lloyd
Herman, Dr. James Dyer, and Dr. Teruhisa Masada for serving on my thesis committee and providing helpful insight, comments, and suggestions. I would also like to thank
Luisa Chinchilla, Brett Blevins, Josh Minnich, and Yanhui Fang for their support and guidance throughout the completion of my thesis. Finally, I would like to extend my thanks to any other friends and family members.
5 Table of Contents
Abstract……………………………………………………………………………... 3 Acknowledgements………………………………………………………………… 4 List of Tables………………………………….……………………………………. 7 List of Figures………………………………………………………………………. 8 List of Symbols and Abbreviations….……………………………………………... 11
Chapter I – Introduction…………………………………… ………………………. 14 I.1 Soil Erosion Types………………………………………………………... 15 I.2 Soil Erosion Estimation Models………………………………………….. 17 I.3 Nature of the Study……………………………………………………….. 20 I.4 Objective of the Study…………………………………………………….. 24
Chapter II – Literature Review……………………………………………………... 25 II.1 Improvements in Soil Loss Calculations………………………………… 25 II.2 RUSLE…………………………………………………………………… 27 II.2.1 Rainfall Runoff Erosivity Factor………………………………... 28 II.2.2 Soil Erodibility Factor…………………………………………... 30 II.2.3 Slope Length and Slope Steepness Factors……………………... 30 II.2.3.1 Slope Length Factor……………………………………. 31 II.2.3.2 Slope Steepness Factor…………………………………. 33 II.2.4 Cover-Management Factor……………………………………… 34 II.2.5 Support Practice Factor…………………………………………. 37 II.3 Use of Geographic Information Systems………………………………… 40 II.4 Sediment Delivery Ratio…………………………………………………. 41
Chapter III – Theory and Methodology…………………………………………….. 43 III.1 – Lake Sedimentation…………………………………………………… 43 III.2 – GIS Data Analysis…………………………………………………….. 44 III.2.1 – Bathymetry…………………………………………………… 44 III.2.2 – Watershed Delineation……………………………………….. 45 III.2.3 – RUSLE Factor Interpretation………………………………… 46 III.2.3.1 – R Factor……………………………………………… 46 III.2.3.2 – K Factor……………………………………………… 47 III.2.3.3 – LS Factor…………………………………..………… 47 III.2.3.4 – C Factor……………………………………………… 50 III.3 – Erosion Potential Determination……………………………………… 51
6 Chapter IV - Application and Results………………………………………………. 58 IV.1 Bathymetric Analysis…………………………………………………… 58 IV.2 Watershed Soil Erosion Model…………………………………………. 61 IV.3 Sediment Delivery………………………………………………………. 86 IV.4 RUSLE Factors Correlation to Annual Soil Erosion…………………… 89
Chapter V - Conclusions and Recommendations…………………………………... 96 V.1 Conclusions…………………………………………………………….... 96 V.2 Recommendations……………………………………………………….. 97
References…………… ………………………………………………………….….. 99
Appendix A - Soil Type Abbreviations and Percent Watershed Land Cover...... 105
Appendix B - C++ Program for Computing LS Factor (Van Remortel, Maichle, Hickey, 2004) …….……………………………… 141
Appendix C – L Factor Maps………………………………………………………. 157
Appendix D – S Factor Maps………………………………………………………. 161
Appendix E – Watershed Land Cover Maps……………………………………….. 165
Appendix F – C Factor with Percent Land Cover and Percent Erosion……………. 169
Appendix G – GIS Watershed Modeling Tutorial………………………………….. 177
7 List of Tables
Table I.3.1 Physical Features of Wills Creek Lake………………………….…...... 21 Table I.3.2 Physical Features of Senecaville Lake………………………………… 22 Table I.3.3 Physical Features of Salt Fork……………………………….....……... 22 Table III.2.3.1.1: R Factors for Watershed Counties………………………………. 46 Table III.2.3.4.1: C Factors for Land Cover…………………………………….…. 51 Table III.3.1 Average RUSLE Erosion Potential and RUSLE Factor Values…...... 57 Table IV.3.1 Maner (1958) SDR and Estimated Sediment Yield……………...….. 87 Table IV.3.2 Calculated SDR Based on USACE Annual Sedimentation…….…… 88 Table IV.4.1 RUSLE Factors Correlation to Annual Soil Erosion in
Wills Creek Lake Watershed...... 89 Table IV.4.2 RUSLE Factors Correlation to Annual Soil Erosion in
Senecaville Lake Watershed………………………………………...... 90 Table IV.4.3 RUSLE Factors Correlation to Annual Soil Erosion in Salt Fork Lake Watershed……………………………………..……...... 90 Table IV.4.4 RUSLE Factors Correlation to Annual Soil Erosion in Wills Creek Lake Contributing Area………………………………..…..….. 90 Table A.1 Soil Type Abbreviations and K Factor…………………………………. 105 8 List of Figures
Figure I.3.1 Map of Wills Creek Lake, Senecaville Lake, and Salt Fork Lake Watershed………………………………………...……. 23 Figure II.2.1.1: Ohio Rainfall-Runoff Erosivity Factor………………………...…... 29 Figure III.2.3.3.1 LS Computation Flowchart (Van Remortel, Maichle, & Hickey, 2004)……………….………………… 49 Figure III.3.1 Cumulative histogram of the erosion model for Wills Creek Lake Watershed………………………………….……………. 53 Figure III.3.2 Cumulative histogram of the erosion model for Senecaville Lake Watershed…………………………………………..……. 54 Figure III.3.3 Cumulative histogram of the erosion model for Salt Fork Lake Watershed…………………………………………………... 55 Figure III.3.4 Cumulative histogram of the erosion model for Wills Creek Lake contributing Area………………………………………... 56 Figure IV.1.1 Senecaville Lake 1937 and 1998 Bathymetric Maps………………... 59 Figure IV.1.2 Senecaville Lake 1998 Sediment Depth……………….…………..… 60 Figure IV.2.1 Wills Creek Lake Watershed Rainfall Runoff Erosivity Factor………………...…………………………………… 62 Figure IV.2.2 Senecaville Lake Watershed Rainfall Runoff Erosivity Factor………………………………...…………………… 63 Figure IV.2.3 Salt Fork Lake Watershed Rainfall Runoff Erosivity Factor…………………………………...………………… 64 Figure IV.2.4 Wills Creek Lake Contributing Area Rainfall Runoff Erosivity Factor…………………………………………...………… 65 Figure IV.2.5 Wills Creek Lake Watershed Soil Erodibility Factor………………... 67 Figure IV.2.6 Senecaville Lake Watershed Soil Erodibility Factor………………... 68 Figure IV.2.7 Salt Fork Lake Watershed Soil Erodibility Factor………...………… 69 Figure IV.2.8 Wills Creek Lake Contributing Area Soil Erodibility Factor………... 70 Figure IV.2.9 Wills Creek Lake Watershed Slope Length and Slope Steepness Factors………………...…………………………………... 72 Fig ure IV.2.10 Senecaville Lake Watershed Slope Length and Slope Steepness Factors…………………………………………………….. 73 Figure IV.2.11 Salt Fork Lake Watershed Slope Length and Slope Steepness Factors………………...…………………………………... 74 Figure IV.2.12 Wills Creek Lake Contributing Area Slope Length and Slope Steepness Factors………………………………………………... 75 Figure IV.2.13 Wills Creek Lake Watershed Cover-Management Factor…………. 77 Figure IV.2.14 Senecaville Lake Watershed Cover-Management Factor………...... 78 9 Figure IV.2.15 Salt Fork Lake Watershed Cover-Management Factor……….…. 79 Figure IV.2.16 Wills Creek Lake Contributing Area Cover-Management Factor…………………...…………………………... 80 Figure IV.2.17 Wills Creek Lake Watershed Potential Annual Erosion……….... 82 Figure IV.2.18 Senecaville Lake Watershed Potential Annual Erosion………...... 83 Figure IV.2.19 Salt Fork Lake Watershed Potential Annual Erosion………...…… 84 Figure IV.2.20 Wills Creek Lake Contributing Area Potential Annual Erosion…... 85 Figure IV.4.1 Wills Creek Lake Watershed Cover-Management Factor….………. 91 Figure IV.4.2 Senecaville Lake Watershed Cover-Management Factor…………... 92 Figure IV.4.3 Salt Fork Lake Watershed Cover-Management Factor…………….. 92 Figure IV.4 Wills Creek Lake Contributing Area Cover-Management Factor……………...………………………………..... 93 Figure IV.4.5 Wills Creek Lake Watershed Cover-Management Factor (99th Percentile)……………..……………………………………... 93 Figure IV.4.6 Senecaville Lake Watershed Cover-Management Factor (99th Percentile)…………………………………..………………... 94 Figure IV.4.7 Salt Fork Lake Watershed Cover-Management Factor (99th Percentile)…………………………………………..………... 94 Figure IV.4.8 Wills Creek Lake Contributing Area Cover-Management Factor (99th Percentile)………………………………………………..…... 95 Figure A.1 Soil Composition in Watersheds………………………………………. 118 Figure C.1 Wills Creek Lake Watershed Slope Length Factor……………………. 157 Figure C.2 Senecaville Lake Watershed Slope Length Factor……………………. 158 Figure C.3 Salt Fork Lake Watershed Slope Length Factor………………………. 159 Figure C.4 Wills Creek Lake Contributing Area Slope Length Factor…………… 160 Figure D.1 Wills Creek Lake Watershed Slope Steepness Factor………………… 161 Figure D.2 Senecaville Lake Watershed Slope Steepness Factor…………………. 162 Figure D.3 Salt Fork Lake Watershed Slope Steepness Factor…………………… 163 Figure D.4 Wills Creek Lake Contributing Area Slope Steepness Factor………… 164 Figure E.1 Wills Creek Lake Watershed Land Cover…………………………….. 165 Figure E.2 Senecaville Lake Watershed Land Cover……………………………... 166 Figure E.3 Salt Fork Lake Watershed Land Cover………………………………... 167 Figure E.4 Wills Creek Lake Contributing Area Land Cover…………………….. 168 Figure F.1 Wills Creek Lake Watershed Cover-Management Factor (50th Percentile)…………………………………………………..... 169 Figure F.2 Senecaville Lake Watershed Cover-Management Factor (50th Percentile)……………………………………………………. 169 10 Figure F.3 Salt Fork Lake Watershed Cover-Management Factor (50th Percentile)……………………………………………………. 170 Figure F.4 Wills Creek Lake Contributing Area Cover-Management Factor (50th Percentile)……………………………………………………. 170 Figure F.5 Wills Creek Lake Watershed Cover-Management Factor (75th Percentile)……………………………………………………. 171 Figure F.6 Senecaville Lake Watershed Cover-Management Factor (75th Percentile)……………………………………………………. 171 Figure F.7 Salt Fork Lake Watershed Cover-Management Factor (75th Percentile)……………………………………………………. 172 Figure F.8 Wills Creek Lake Contributing Area Cover-Management Factor (75th Percentile)……………………………………………………. 172 Figure F.9 Wills Creek Lake Watershed Cover-Management Factor (90th Percentile)……………………………………………………. 173 Figure F.10 Senecaville Lake Watershed Cover-Management Factor (90th Percentile)……………………………………………………. 173 Figure F.11 Salt Fork Lake Watershed Cover-Management Factor (90th Percentile)……………………………………………………. 174 Figure F.12 Wills Creek Lake Contributing Area Cover-Management Factor (90th Percentile)……………………………………………………. 174 Figure F.13 Wills Creek Lake Watershed Cover-Management Factor (95th Percentile)……………………………………………………. 175 Figure F.14 Senecaville Lake Watershed Cover-Management Factor (95th Percentile)……………………………………………………. 175 Figure F.15 Salt Fork Lake Watershed Cover-Management Factor (95th Percentile)……………………………………………………. 176 Figure F.16 Wills Creek Lake Contributing Area Cover-Management Factor (95th Percentile)……………………………………………………. 176 11 List of Symbols and Abbreviations
AR Average annual soil loss (tons/acre/year) 2 AS Watershed area in km a Coefficient varying with ridge height used in support practice factor for contouring b Empirical coefficient relating to the effectiveness of vegetative cover used in surface-cover subfactor b Coefficient varying with ridge height used in support practice factor for contouring B Credit for deposition Bur Mass density of live and dead roots found in the upper inch of soil
Bus Mass density of incorporated surface residue in the upper inch of soil
CR Cover-management factor used in RUSLE CSW Average annual soil loss from claypan soils for a specific rotation, slope steepness, slope length, and row direction used in Smith’s and Whitt’s soil loss equation c Coefficient varying with ridge height used in support practice factor for contouring Cb Relative effectiveness of subsurface residue in consolidation CC Canopy-cover subfactor Cf Surface-soil-consolidation factor cur Calibration coefficient representing the impacts of subsurface residues cus Calibration coefficient representing the impacts of subsurface residues d Coefficient varying with ridge height used in support practice factor for contouring EM Average annual topsoil loss (mm) EP Erosion potential of the watershed (tons) EI30 30-minute storm erosivity
EIt Sum of the EI30 percentages for the entire time period F Erodibility factor for a specific soil (mm)
Fc Fraction of land surface covered by canopy gp Sediment load at the end of the slope that would occur if the strips had no deposit H Distance raindrops fall after striking the canopy (ft.) K Soil erodibility factor LM Length of slope (m) LR Slope length factor used in USLE, RUSLE LS Maximum length of the watershed parallel to the main drainage channel used in SDR LS Combined slope length and slope steepness factor 12 M Product of primary particle size fractions m Dimensionless slope length exponent N Number of years n Number of periods used in summation OM Percent organic matter of the soil PM 30-minute, 2 year frequency rainfall for the area used in Musgrave equation PR Support practice factor used in USLE, RUSLE p Code for soil permeability Pb Base value of the support practice factor for contouring PLU Prior-land-use subfactor Pm Minimum support practice factor value
Pmb Minimum support practice factor value for a given ridge height with base conditions Ps Support practice factor value for strip cropping
Py Sediment delivery factor
Qk Computed runoff amount for the given soil and cover-management condition RM Vegetation cover RR Rainfall-runoff erosivity factor used in USLE, RUSLE RS Relief used in SDR Ru Surface roughness (in.)
SR Slope steepness factor SM Land slope (percent) sK Code for soils structure used in soil erodibility factor sS Terrace slope length used in sediment delivery factor SC Surface-cover subfactor sc Slope for which a value of Pb is desired SDR Sediment delivery ratio se Slope steepness above which contouring is ineffective SLR Soil loss ratio SM Soil-moisture subfactor sm Slope at which contouring has its greatest effectiveness
Sp Percentage of land area covered by surface cover SR Surface-roughness subfactor β Ratio of rill to interrill erosion θ Slope angle (degrees) λ Horizontal slope length (ft.) ARS Agriculture Research Service DTM Digital Terrain Model EPA Environmental Protection Agency 13 GIS Geographic Information Systems LASOD Global Assessment of Soil Degradation GPS Global Positioning System IAMG International Association for Mathematical Geosciences MUSYM Map Unit Symbol MWCD Muskingum Watershed Conservancy District NAD North American Datum NED Nation Elevation Dataset NRCS Natural Resources Conservation Service ODNR Ohio Department of Natural Resources RUSLE Revised Universal Soil Loss Equation USACE United States Army Corps of Engineers USDA United States Department of Agriculture USGS United States Geologic Survey USLE Universal Soil Loss Equation
14 Chapter I
Introduction
Global annual soil erosion is occurring at a much higher rate than soil replenishment (Favis-Mortlock, 2008). Streams and rivers are the means by which the soil is transported. Eventually, large amounts of soil deposits in local lakes and reservoirs will result in watersheds becoming prone to flooding. This erosion results in the destruction of prime cropland and forests. Annual soil erosion results in losing more than ten million hectares of viable croplands worldwide (Pimental et al., 1995). Deposition of soil in waterways decreases the sediment transport capacity. The decrease in transport capacity can ultimately lead to the destruction of aquatic wildlife habitats by dramatically increasing the total suspended solids. The natural habitats of the native organisms are limited when the suspended sediment becomes deposited in the waterways. Additionally, the turbid waters will reflect light that is required for healthy plant growth near the bottom of the water, and increase surface temperatures. The alteration in water temperature, then, has a negative impact on the life cycles of the aquatic organisms.
This chapter first discusses the types of soil erosion. Next, soil erosion estimation models and the implications of geographic information systems (GIS) are discussed. The nature of the study and the Revised Universal Soil Loss Equation (RUSLE) and its contributing factors will then be introduced. Finally, the objectives of the study will be introduced using erosion analysis of several watersheds within the Muskingum River watershed.
15 I.1 Soil Erosion Types
A variety of natural sources cause soil erosion. During rain events, sheet erosion caused the detachment and removal of soil. During this process, the soil particles are transported in an uncondensed, thin sheet of water. First, falling rain droplets cause soil to detach and ultimately erode from overland flow, better known as splash erosion. This process is most commonly observed on level terrain, under rainfalls of high intensity, and in areas with very little groundcover. When rain falls, its energy dissipates by striking vegetation before hitting the ground. If vegetation is not present, the falling rain droplets exert maximum force on the bare soil.
Water-induced soil erosion is also influenced by the gravitational effect of water flowing in rills (Barthes & Roose, 2002). A rill is a thin opening in the ground (similar to a small stream bed) that transports runoff during and after rain events. After rain events, the water that does not permeate the ground surface induces overland flow. This runoff typically occurs after rain events of high magnitude or after the ground has already been saturated. Small ponds and puddles form on the ground surface and ultimately fill to capacity and overtop, which causes concentrated overland flow. The focusing of the flow increases the likelihood of soil erosion, which produces rills. The detachment of soil in rills is directly related to the hydraulic shear stress of flow and the critical shear stress of the soil particles (Nearing et al., 1989).
As the water being transported through the rills increases and becomes more concentrated, gullies are formed. A gully is a more severe form of rill erosion and cannot be removed through normal tillage practices. A gully is defined as an area where channels at least 30 cm deep (NSW Department of Primary Industries, 2012) allow 16 running water to erode and transport soil. Upon formation, gullies are narrow with vertical sidewalls; however, the transported water allows for widening and lengthening
In addition, wind greatly influences soil detachment. Typically in level, dry areas with little vegetation, high wind events cause unprotected soil to be blown away. Small grain soil particles are easily transported, leaving behind the larger grain soils (sands), cobbles, and boulders. By eroding the finer silts and clays, the sandy soil is more susceptible to water-induced soil erosion. The fine soil particles that are suspended in the air by wind come back to the ground surface and detach more soil particles upon impact
(Wolfe & Nickling, 1993).
Another natural source of soil erosion is glaciation. Glaciers erode the ground surface as they move over the land, and drastically change the landscape. Deep valleys, lakes, and narrow hill peaks are common landforms created through glacial geomorphology. As a glacier moves over the ground surface, sediment is relocated. As opposed to water and wind-induced soil erosion, glacial erosion is not limited to fine grain soil particles. Glaciers abrade and pluck soil, cobbles, boulders, and bedrock, as they move over the ground. The eroded material is then deposited elsewhere, as the glaciers move along their paths.
Additionally, human activities are known to greatly accelerate the rate of erosion.
Soil erosion increases through the destruction of natural vegetation and the alteration of the ground contour (Meeuwig, 1970). The roots of vegetation physically hold the soil together. Without the roots, the detachment of soil through overland runoff is more easily achieved. Moreover, the leaves of plants shield the ground from direct contact from rain droplets. The low-lying vegetation helps to shield the ground surface from wind erosion. 17 As the ground surface is altered, the erosion potential increases. Farmers practice crop rotation and no-till practices to minimize the amount of soil erosion in fields. Global
Assessment of Soil Degradation (GLASOD) determined that 1,643 million hectares of land erode due to human influences on wind-induced and water-induced soil erosion
(Morgan, 2005).
Through the aforementioned natural sources of erosion, rills are formed in the soil. Once a rill becomes deep enough to interfere with tillage practices, it becomes a gully (Poesen et al., 1996). The interrill erosion occurring is primarily due to the rainfall intensity and slope (Meyer, 1981). Both rills and gullies aid in the transport of sediment and overland runoff. The sediment is transported from the rills and gullies to streams, rivers, and reservoirs. Through human interactions the erosion potential in rills and gullies can be greatly decreased.
I.2 Soil Erosion Estimation Models
To estimate the amount of water-induced soil erosion potential in an area, several erosion potential models have been proposed. The most common model is the Universal
Soil Loss Equation (USLE), which was developed first in the 1930’s specifically for the
Corn Belt in the United States; however, the equation can be adapted for other areas
(Renard et al., 1991). The equation was developed to not only take several erosion factors into account, but also the effect of soil conservation practices. The resulting equation is expressed by:
18 I.1
where = average annual soil loss (tons/acre/year), = rainfall-runoff erosivity factor,
K = soil erodibility factor, LS = combined slope length and slope steepness factor, = cover-management factor, and P = support practice factor (Yoder et al., 2004).
Each of the USLE factors helps to determine the potential of soil. The rainfall- runoff erosivity factor measures the kinetic energy associated with falling rain droplets in storms throughout the year multiplied by the maximum 30-minute intensity. Small rain events are not included in this analysis. The rainfall-runoff erosivity factor greatly varies by location. The soil erodibility factor represents the tendency of soil to erode. Clays have low values because of their low erosion potential, while silts and fine sands more likely to erode have high values. The combined slope length and slope steepness factor represent the role that terrain plays in soil erosion. Slope length is the ratio of soil loss from measured slope length to a 72.6 ft. length on the same soil type and gradient. Areas with slopes much greater than 72.6 ft. will results in a greater LS factor. The slope steepness represents the correlation between slope steepness and erosion. Slope steepness is the ratio of soil loss from the measured gradient to a nine percent slope subjected to the same conditions. The cover-management factor expresses the relationship between soil loss and land cover. The factor was developed in conjunction with crop and farming practices. The support practice factor shows the effects of soil loss remediation, typically in farmlands (Renard et al., 1991).
Based on the USLE, the Revised Universal Soil Loss Equation was developed to better estimate annual soil loss potential. Improvements were made from USLE through 19 several adjustments in factor determination. More gauge stations are used to determine the rainfall-runoff erosivity factor. The soil erodibility factor accounts for variation between seasons. Previous land use is considered when determining the cover- management factor. The equation to determine slope length and slope steepness has been modified to more accurately estimate rill and interrill erosion. Support practice factors have been determined for a variety of conservation techniques (Renard et al., 1991).
Currently, the RUSLE is the most widely used model to estimate soil erosion in large areas due to its compatibility with GIS. The factors can be spatially represented and analyzed (Mitasova et al., 1996).
The sediment delivery ratio is desired due to the fact that not all of the soil eroded becomes deposited in a reservoir. After the erosion potential is determined using the
RUSLE, the sediment delivery ratio is calculated for each watershed. An equation to determine sediment delivery ratio was developed using watershed length and relief variables by Maner (1958), which is expressed by:
( ) ( ) I.2
where SDR = sediment delivery ratio (%), = maximum length of the watershed measured parallel to the main drainage channel, and = relief (difference between average elevation and outlet elevation) of the watershed (Khanbilvardi & Rogowski,
1984).
As the length of the watershed increases, sediment that reaches the mouth of the watershed decreases. Conversely, as relief increases, the percentage of sediment reaching 20 the mouth of the watershed increases. Therefore, it is important to determine whether the watersheds should be modeled separately or as one large watershed. The calculated values for delivered sediment are compared to the United States Army Corps of
Engineers (USACE) measured values to determine the most appropriate modeling method. Sediment delivery can be affected by many factors; therefore, accurate modeling is nearly impossible (Walling, 1983). An equation to determine the sediment delivery ratio is expressed by:
I.3
where = sediment yield (tons) and = erosion potential of the watershed (tons).
I.3 Nature of Study
This thesis investigates the relationship of potential soil erosion and sediment deposition in the watersheds that are in series, one containing the other. Senecaville Lake watershed and Salt Fork Lake watershed are both entirely located within Wills Creek
Lake watershed. Senecaville Lake and Salt Fork Lake eventually empty into Wills Creek
Lake that discharges into the Ohio River via the Muskingum River.
Wills Creek Lake is located near Wills Creek, Ohio, and its contributing drainage area extends into Coshocton, Noble, Muskingum, Monroe, Guernsey, Belmont, Harrison, and Tuscarawas counties. The water from Wills Creek Lake flows into the Muskingum
River via Wills Creek. Wills Creek Lake is not a typical lake. It was designed by the 21 USACE to reduce flood damage. A maximum of 196,000 acre-feet of water can be stored in the lake. On average, the lake maintains water at an average elevation of 742 ft.
(USACE, 2011b). The physical features of Wills Creek Lake are shown in Table I.3.1.
Senecaville Lake is located in Senecaville, Ohio, and its contributing watershed area ranges throughout Guernsey, Belmont, Noble, and Monroe counties. Senecaville
Lake was designed by the United States Army Corps of Engineers on Wills Creek for flood control. The dam was built in 1937 to hold a maximum of 88,500 acre-ft. of water.
Currently, the lake’s water is at an average elevation of 832 ft. with an average out flows of 398 ft3/s (USACE, 2011a). The physical features of the lake are shown in Table I.3.2.
Salt Fork Lake is managed by the Ohio Department of Natural Resources
(ODNR) in Cambridge, Ohio, and its drainage area extends into Tuscarawas, Harrison,
Guernsey, and Belmont counties. Salt Fork Creek connects to Wills Creek downstream of the reservoir, and meets Wills Creek Lake thirty-two miles downstream. The lake was first designed in 1956 as a source of water for Cambridge, Ohio and to prevent floods; however, its vast area became part of a 17,229 acre state park in 1960 (ODNR, 2011).
The physical features of Salt Fork Lake are shown in Table I.3.3.
Table I.3.1 Physical Features of Wills Creek Lake
Lake Length 36,960 feet 11,300 meters Lake Breadth 1,600 feet 500 meters Maximum Depth 22 feet 6.7 meters Water Surface Area (normal pool) 900 acres 3.64 km2 Shoreline Length 19.6 miles 31.5 km Lake Elevation (normal pool) 742 feet 226 meters Lake Elevation (spillway) 779 feet 237 meters Watershed Area 842 mile2 2181 km2 22 Table I.3.2 Physical Features of Senecaville Lake
Lake Length 26,900 feet 8,200 meters Lake Breadth 3,500 feet 1,100 meters Maximum Depth 27 feet 8.2 meters Water Surface Area (normal pool) 3,550 acres 14.37 km2 Shoreline Length 45.9 miles 73.8 km Lake Elevation (normal pool) 832 feet 254 meters Lake Elevation (spillway) 857 feet 261 meters Watershed Area 118 mile2 306 km2
Table I.3.3 Physical Features of Salt Fork
Lake Length 74,400 feet 22,700 meters Lake Breadth 7,600 feet 2,300 meters Maximum Depth 35 feet 10.7 meters Water Surface Area (normal pool) 2,800 acres 11.33 km2 Shoreline Length 61.9 miles 99.6 km Lake Elevation (normal pool) 800 feet 244 meters Lake Elevation (spillway) 820 feet 250 meters Watershed Area 158 mile2 409 km2
23
Figure I.3.1 Map of Wills Creek Lake, Senecaville Lake, and Salt Fork Lake
Watersheds
24 I.4 Objective of the Study
The objectives of this study are to estimate the erosion potentials of watersheds in series using the RUSLE and to investigate their relationships of sediment delivery. Using
Senecaville Lake and Wills Creek Lake survey data from the US Army Corps of
Engineers, surveyed sedimentation rates were compared to the calculated sedimentation rates using the RUSLE. Additionally, based on the results from the RUSLE, areas of high erosion were identified.
Factors of the RUSLE equation were analyzed to determine their respective impacts on potential erosion estimations. Furthermore, areas subjected to the highest amounts of erosion were inspected to determine correlation of the attributes of the highest erosion factor to the potential annual erosion. From the analysis the most dominant erosion element was identified.
25 Chapter II
Literature Review
II.1 Improvements in Soil Loss Calculations
Ewald Wollny was one of the first scientists to investigate water-induced soil erosion in Germany (Menard, 1985). Wollny primarily focused on hillslope steepness, vegetation, and type of soil. Later, erosion research began on rangelands in Utah
(Sampson & Weyl, 1918) due to the adverse effects that soil erosion has on farmlands. In this study, Samson and Weyl suggested corrective measures that could be taken to limit soil erosion in the western United States. H. H. Bennett and W. R. Chapline (Bennett &
Chapline, 1928) determined that in order to decrease the damage cause by soil erosion, ground vegetation must be improved and protected from wildfires and farmland grazing should be monitored.
In the 1930’s, Cook used data to empirically derive equations depicting water- induced soil erosion. From Cook’s analysis, erosion was determined to be caused by the soil’s susceptibility to erosion, erosivity caused by rainfall and surface runoff, and the ground surface protection. Zingg (1940) was able to incorporate the effects of slope length and slope steepness on soil erosion. A year later, Smith enhanced the model by including factors associated with crops and support practice factors for the Midwestern
United States. From this equation, Browning (1947) was able to include soil erodibility and management. The first empirical sheet erosion equation to include a factor representing rainfall was the Musgrave equation (Musgrave, 1947): 26
II.1.1
where = average annual topsoil loss (mm), F = erodibility factor for a specific soil
(mm), = vegetation cover factor, SM = land slope (percent), LM = length of slope (m), and PM = 30-minute, 2 year frequency rainfall for the area.
Later, Smith and Whitt (1948) developed an equation that included soil loss ratios for several support practice factors; however, this equation did not include a rainfall- runoff erosivity factor and limited the use to only claypan soils in Missouri. Smith and
Whitt’s resulting equation for annual soil erosion is:
II.1.2
where = average annual soil loss from claypan soils for a specific rotation, slope steepness, slope length, and row direction.
A factor representing rainfall was required for accurate analysis of various areas.
At the time, most soil loss equations focused on erosion in the Corn Belt of the United
States. Researchers at Purdue University were able to determine a rainfall factor for the eastern United States. From this new factor, Wischmeier, Smith, and other researchers from the Agriculture Research Service (ARS) and Soil Conservation Service published the Universal Soil Loss Equation in 1965 in the USDA Agriculture Handbook 282. After publication, the equation was improved to be used in more areas. The amended USLE
(known as RUSLE) was later published in the Agriculture Handbook 537.
27 II.2 RUSLE
Although the USLE and the RUSLE use the same factors to compute average annual soil erosion, many improvements have been made since the beginning of research and development of the USLE by Wischmeier and Smith in the 1950’s. In 1997 the
RUSLE was published by the United States Department of Agriculture (USDA). Several important modifications were made to the formula. Computers are incorporated to assist with the evaluation of the RUSLE and subfactors, 1,200 gauge locations were added in the western United States to assist in determining the rainfall-runoff erosivity factor
(resulting in more accurate isoerodent maps), corrections to account for splash erosion on flat terrain and ponded water, seasonal variation (freezing and thawing) in the soil erodibility factor, subfactors indicating prior land use to determine the cover- management factor, slope length and steepness equations with direct relation to interrill erosion, slope length and steepness equations to calculate values for various shapes and slope alignments, and modified support practice factor values (Renard et al., 1991).
The RUSLE is continually updated by the USDA – Agriculture Research Service
(ARS) to more accurately predict soil loss. The RUSLE 1.06c and the RUSLE 2 are the two most current versions of the equation. Both of these equations are influenced by the same factors; however, the most current equations are more accurate because the variables can be very specific to reflect the exact circumstances. In addition, factors in the
RUSLE 2 are calculated daily.
28 II.2.1 Rainfall-Runoff Erosivity Factor
The rainfall-runoff erosivity factor indicates the effect that the local climate has on splash, sheet, and rill erosion. Data for the factor are collected from 1,200 gauge stations on fifteen day intervals. Erosivity factors are interpolated for areas between gauging stations and calculated based on similar climates of similar areas (Renard &
Freimund, 1994). Soil erosion occurs due to the compounding effect of several storms.
Currently, the equation does not comply well with precipitation other than rain.
The resulting equation that is used to create the isoerodent maps (Renard et al.,
1997) is:
∑ ( ) II.2.1.1
where = rainfall-runoff erosivity factor, (EI30)i = 30-minute storm erosivity for storm i, j = number of storms in an N year period, and N = number of years. In the above equation, storms exhibiting less than one-half of an inch of rain are excluded, with the exception of storms with a quarter of an inch in a fifteen minute time frame.
In Ohio, the rainfall-runoff erosivity factor varies from 95-155 (Ohio Department of Natural Resources, 2000), with the highest values occurring in the southwestern portion. A detailed map of the area is shown in Figure II.2.1.1. In the United States, values vary from 0-700 (southern Louisiana having the highest values). 29
Figure II.2.1.1: Ohio Rainfall-Runoff Erosivity Factor
30 II.2.2 Soil Erodibility Factor
The soil erodibility factor is used to predict the likelihood of soil detachment, erosion, and runoff. The soil erodibility factor is established from a unit plot (72.6 ft. on a
9% slope) of fallow land that has been tilled (Renard et al., 1997). Wischmeier and
Smith’s (1978) equation to determine the soil erodibility factor when silt represents less than 70% of the soil is:
( ) ( ) ( ) II.2.2.1
where K = soil erodibility factor, OM = percent organic matter of the soil, M = product of primary particle size fractions, sK = code for soil structure, and p = code for soil permeability. More than half of soils return a factor of less than 0.3 (Renard et al., 1997).
Soils that have higher amounts of fine sand or clay result in lower soil erodibility factor values. Soils that are high in sand infiltrate water more easily and are difficult to transport. Clayey soils have a tendency to stick together, which results in a low chance of erodibility. Soils with a high silt or loess content are the most likely to erode due to low infiltration rates and ease of transportation.
II.2.3 Slope Length and Steepness Factors
Generally, the slope length and slope steepness factors are joined into one factor.
The combined slope length and steepness factors represent rill to interrill erosion and the susceptibility of erosion due to landscape influences of a certain point. Lengths at which 31 surface runoff will concentrate vary; however, it generally occurs in less than 400 ft.
(Renard et al., 1997). The combined factor equals 1.0 for a unit plot on topographies that are 72.6 ft. long at a 9% slope (Renard et al., 1997). Additionally, the orientation of the slope can alter the value for the slope length and slope steepness factor. A convex slope can result in potential erosion values 30% higher than comparable flat and concave slopes
(Renard et al., 1997). Overall, the slope steepness factor influences the erosion potential more than the slope length of a hill (Agassi, 1996).
Compared to the USLE values for the combined slope length and slope steepness factors, the RUSLE results are generally higher. The RUSLE models a hillslope of varying lengths and steepness separately; whereas the USLE would combine the slope length and average the slope steepness in order to use a single model. Additionally, the development of the factors led to applicability for steep slopes and curved slopes. The
RUSLE’s method demonstrates a more accurate depiction of field soil erosion.
When modeling the slope length and slope steepness factors with the USLE, many field measurements would need to be taken to determine the values for each of the erosion factors. Measurements of the slope length and slope angle would need to be taken on each gradient in the study area. Currently, computer programs are capable of running algorithms that accurately determine the topography factors for large study areas. The slope length and slope steepness factors are then spatially analyzed in a GIS program.
II.2.3.1 Slope Length
The slope length factor is used to determine the susceptibility of soil erosion due to the span of the hillslope. Compared to the other factors, soil erosion is least affected by 32 slope length (Renard et al., 1991). The factor represents the ratio of the field erosion to a
72.6 ft. slope consisting of the same soil and slope steepness. When developing the slope length factor equation, slopes greater than 18% were excluded. The equation used to determine the slope length factor (Renard et al., 1997) is:
II.2.3.1.1 ( )
where = slope length factor, = horizontal slope length (ft.), and m = dimensionless slope length exponent (Wischmeier & Smith, 1978). The equation to determine the slope length exponent (Foster et al., 1997) is:
II.2.3.1.2
where = ratio of rill to interrill erosion. The slope length exponent increases as susceptibility to erosion increase. The equation to determine the ratio of rill to interrill erosion (McCool et al., 1989) is:
II.2.3.1.3
( )
where = slope angle (degrees).
33 II.2.3.2 Slope Steepness
The slope steepness factor represents the effect of slope steepness on soil erosion potential. The factor represents a ratio of field erosion potential to erosion potential of a
9% hillslope subjected to the same field conditions. Experiments show that the amount of runoff, and therefore erosion, dramatically changes at a slope of 9% because the rate of runoff is not influenced by slope steepness when evaluating slopes steeper than 9%. The equations used to determine the slope steepness (McCool et al., 1987) are:
II.2.3.2.1
II.2.3.2.2
where = slope steepness factor. In addition, the previous equations do not accurately depict erosion for hillslopes that are shorter than 15ft. The equation used to determine the slope steepness for slopes shorter than 15 ft. (McCool et al., 1987) is:
( ) II.2.3.2.
The previous equation is not representative of hillslopes that do not experience rill erosion. In order for rill erosion to occur, a slope length of at least 15 ft. must be observed. For recently tilled soil exposed to surface flow, the equations used (McCool et al., 1987) are:
34 II.2.3.2.4
II.2.3.2.5 ( )
II.2.4 Cover-Management Factor
The cover-management factor is used to determine the impact of the implementation of cropping and management practices on the erosion potential of the soil. The field erosion potential is compared to reference land that has been tilled and left unsown. In areas of cyclical vegetative growth due to a changing climate, a soil loss ratio must be calculated for different seasons to accurately represent the vegetative subfactors.
Generally, the soil loss ratio is calculated in 15 day increments but smaller intervals can be used in areas of drastically changing environment. Due to the numerous contributing factors affecting cover-management, subfactors are required. The equation used to determine the ratio of soil loss in the field to that of reference conditions (Laflen et al.,
1985) is:
II.2.4.1
where SLR = soil loss ratio, PLU = prior-land-use subfactor, CC = canopy-cover subfactor, SC = surface-cover subfactor, SR = surface-roughness subfactor, and SM = soil-moisture subfactor. The soil loss ratio is subject to change throughout the year due to changes in vegetal growing season. 35 The prior-land-use subfactor represents the effects of antecedent moisture conditions (previous crops and tillage methods) of the land area on soil erosion.
Furthermore, vegetal decomposition is incorporated into the calculation. The equation used to determine the prior-land-use subfactor (Renard et al., 1997) is:
II.2.4.2 [( ) ( )]
( )
where = surface-soil-consolidation factor, = relative effectiveness of subsurface residue in consolidation, and = calibration coefficients representing the impacts of subsurface residues, = mass density of live and dead roots found in in the upper inch of soil, and = mass density of incorporated surface residue in the upper inch of soil.
The canopy-cover subfactor represents the reduction in rainfall energy due to vegetative cover. When rain strikes the canopy instead of directly impacting the ground surface, energies and erosion potentials are decreased. The equation to determine the canopy-cover subfactor is:
II.2.4.3
where Fc = fraction of land surface covered by canopy, and H = distance raindrops fall after striking the canopy (ft.). 36 The surface-cover subfactor represents the reduction in runoff transport. Ponded areas cause a decrease in soil area that is susceptible to rainsplash erosion. The equation used to determine the surface-cover subfactor (Renard et al., 1997) is:
( ) II.2.4.4
where b = empirical coefficient relating to the effectiveness of vegetative cover, Sp = percentage of land area covered by vegetative surface cover, and Ru = surface roughness
(inches). The surface roughness is based on the standard deviation of the surface elevation (excluding changes due to slope).
The soil-roughness subfactor represents only the random, natural roughness of the soil. Roughness caused by tillage is not considered in this factor. When considering soils subjected to identical field conditions, rougher soils will experience less erosion than smooth soils. The equation to determine the surface-roughness subfactor (Renard et al.,
1997) is:
( ) II.2.4.5
The soil loss ratios and corresponding storm erosivity percentage are then used to calculate the cover-management factor. The equation used in the computation of the cover-management factor (Renard et al., 1997) is:
37 II.2.4.6
where SLR = soil loss ratio, EIi = percentage of the annual or crop EI during that time period, n = number of periods used in the summation, and EIt = sum of the EI percentages for the entire time period.
II.2.5 Support Practice Factor
The support practice factor represents the effects of implemented plans to help limit the soil erosion. For the purposes of this study, the effects of the support practice factor were omitted due to a lack of widespread policies and spatial information. The factor is defined as the ratio of the implemented support practice factor to identical conditions under upslope and downslope tillage. After implementing no-tillage practices on farmlands, soil losses can be expected to decrease by 78% (Fu et al., 2006). Contour tillage is a relatively inexpensive method of soil erosion reduction; therefore, it is commonly implemented. Contouring decreases erosion potential due to the ridges that are created along hillsides. The tillage is performed parallel to the ridges, causing surface runoff to flow up and down the ridges, rather than create channelization within the ridges.
The equation to determine the support practice factor for contouring (Renard et al., 1997) is:
( )( ) II.2.5.1
38 where Pb = base value of the support practice factor for contouring, Pm = minimum support practice value, and Pmb = minimum support practice factor value for a given ridge height with base conditions.
Furthermore, the support practice factor is influenced by the height of the ridges.
Less soil erosion is likely to occur in ridges where crops are planted parallel to the apex of the ridge (Van Doren et al., 1950). The equations for the curves dealing with ridge heights (Renard et al., 1997) are:
( ) II.2.5.2
( ) II.2.5.3
II.2.5.4
where a, b, c, and d = coefficients varying with ridge height, sm = slope at which contouring has its greatest effect, sc = slope for which a value of Pb is desired, sm = slope at which contouring has its greatest effectiveness, and se = slope steepness above which contouring is ineffective. The equation used to determine the minimum support practice factor value (Renard et al., 1997) is:
II.2.5.5 ( )
39 where Qk = computed runoff amount for the given soil and cover-management condition.
Another common method for soil erosion reduction is the implementation of stripcropping, buffer strips, or filter strips. Stripcropping is a method of growing crops in small bands, where the crops are rotated each subsequent growing season. Buffer strips are lands with permanent vegetation and are generally used at the edges of a tilled field in order to reduce surface runoff. Filter strips are similar to buffer strips; however, they are generally located within the riparian corridor of waterways. The equation used to determine the support practice factor for stripcropping (Renard et al., 1997) is:
( ) II.2.5.6
where Ps = support practice factor value for strip cropping, gp = sediment load at the end of the slope that would occur if the strips had no deposition (based on field data), and B = credit for deposition. The equation assumes that uniform erosion and deposition takes place along the strip and that no surface runoff or sediment leaves the strip.
Terracing is a common method of reducing soil erosion on farmlands. When crops are grown in a mountainous or sloping area, terraces are developed at different elevations. Because this method results in several levels of flat terrain with shorter slope lengths, surface runoff is able to infiltrate at a higher rate. The equation used to determine the support practice factor for terracing (Renard et al., 1997) is:
( ) II.2.5.7 40 where Py = sediment delivery factor. After analyzing field data from USDA research stations, Foster and Ferreira (1981) concluded that the grade of the terrace directly relates to the sediment delivery. The equations used to determine the sediment delivery factor
(Renard et al., 1997) are:
II.2.5.8
II.2.5.9
where s = terrace slope length (%).
II.3 Use of Geographic Information Systems
GIS programs are used to efficiently store, display, and analyze spatial and temporal data. Because the data is also defined by time, patterns and changes are with respect to time are analyzed by GIS. Within GIS, data can be stored as either a raster or a vector. A raster is represented by a grid of pixels with unique data. Vectors are points, lines, and polygons (each with respective data).
The RUSLE is an ideal method for analyzing soil loss potential due to its compatibility with GIS. When modeling erosion in GIS, specific soil erosion parameters in the RUSLE can be investigated together or separately. Cells within a raster accurately define the variables of the RUSLE for a given area. The rainfall runoff erosivity, soil erodibility, cover-management, and support practice factors are readily available from the
USDA, United States Geologic Survey (USGS), and the Environmental Protection 41 Agency (EPA). Additionally, based on elevation data from the USGS, the slope length and slope steepness factors are calculated based on an algorithm.
II.4 Sediment Delivery Ratio
A small percentage of the soil eroded within a watershed reaches the outlet point due to deposition in waterways and hillslopes (Walling, 1983). The sediment delivery ratio represents the amount of eroded soil that reaches the mouth of the watershed compared to the total amount of soil that has been eroded. Studies performed in the southeastern United States by Maner (1958) show that the percent of eroded soil reaching the mouth of the watershed is related to the size of the watershed (a larger watershed having a lower sediment delivery ratio).
Due to the large number of factors that influence the sediment delivery ratio, no equation is fully accepted to accurately predict the amount of sediment that will be deposited at the mouth of a watershed. Therefore, several empirical equations have been created in order to aid in deposition prediction. The equations are specific to the area.
Models generally account for climate and land cover characteristics. The sediment delivery ratio for any watershed is affected by relief, watershed area, stream orders, stream lengths, bifurcation ratio, relief-length ratio, and drainage density (Roehl, 1962).
Additionally, large, unpredictable storm events have a high impact on the delivery of the sediment (Piest et al., 1972).
Further investigations must be performed in order to more accurately predict the delivery ratio. In addition to the influences of the physical watershed characteristics, channel conditions must also be accounted for. The amount of sediment reaching the 42 mouth of the watershed will be related to the capacity of the stream networks.
Additionally, perennial waterways are capable of carrying a higher sediment load than intermittent streams. Similarly, vegetated stream channels with shallow flow are not capable of delivering a high amount of sediment or large particles of sediment (Walling,
1983).
43 Chapter III
Theory and Methodology
III.1 Lake Sedimentation
Senecaville Lake and Wills Creek Lake have been most recently surveyed by the
United States Army Corps of Engineers in 1998 and 2002, respectively. The elevations of the bottoms of each of the lakes were determined using a Global Positioning System
(GPS) and a fathometer. A fathometer is an instrument that uses echo sounding in order to determine water depth. A wave is sent through the water, reflects off the bottom of the lake bed, and returns to the source. Several layers of soil beneath the water reflect the wave back to the fathometer; therefore many layers of sediment are able to be analyzed.
Based on the known waves speed through water and using the time the time taken for the wave to return to the source, the depth of water, according to the desired soil layer, is calculated.
A Digital Terrain Model (DTM) of the lake bottom is made using the surveyed contours. Then, using the original lake surveys, the total depth of sediment is found by subtracting the original lake survey from the most recent resurvey. Similarly, recent sedimentation can be calculated by subtracting the most recent resurvey from a previous resurvey of the lake. In order to determine the distribution of sedimentation, each of the lakes was divided into one mile segments. Using these bathymetric maps, areas where high amounts of sedimentation occur are found and investigated.
Sedimentation rates for reservoirs are calculated and compared to the contributing watershed area. From the most recent resurveys, Wills Creek Lake and Senecaville Lake 44 have annual sedimentation rates of 0.123 acre-feet/square mile (USACE, 2003) and 1.45 acre-feet/square mile (USACE, 2003), respectively. Both sedimentation rates are currently not damaging to the lakes; however, Senecaville Lake experiences significantly more soil deposition than other reservoirs in the area. Similar reservoirs are deposited with 0.3 acre-feet/square mile of drainage area (USACE, 1998). Based on the yearly sedimentation rates obtained from the resurvey results, additional resurveys for Wills
Creek Lake and Senecaville Lake were recommended for 2007 (USACE, 2003) and 2003
(USACE, 1998). However, no resurveys have been conducted as of early 2012.
III.2 GIS Data Analysis
Data were imported into ESRI ArcGIS 9.3 from the USACE, USGS seamless server, EPA, Natural Resources Conservation Service (NRCS), and the International
Association for Mathematical Geosciences (IAMG) in order to model the soil erosion potentials. Depending on the source, the data were imported as an ArcInfo interchange file, tagged image format, file system raster, or shapefile. A thorough list of the steps used to calculate the RUSLE are detailed in Appendix G.
III.2.1 Bathymetry
Using the USACE original survey and resurvey data, bathymetric maps of
Senecaville Lake were constructed. The original 1937 survey of the lake shows the original lake bottom elevations before operation, while the most recent survey in 1998 shows the most current elevations. The ArcInfo interchange files were each converted to a file system raster using ArcCatalog so that each file could be added into ArcGIS. Once 45 in ArcGIS, the minus function within the spatial analyst tools was used. Within this function, the original survey of Senecaville Lake was subtracted from the resurvey to determine the amount of sediment that has been accrued.
III.2.2 Watershed Delineation
One-third arc second National Elevation Dataset (NED) were downloaded from the USGS seamless server website. Due to the size of the contributing area, multiple raster files were downloaded to show the elevations of the surrounding area. Once each of the raster files was brought into ArcGIS, the project raster tool within data management tools was used to transform the raster datasets to North American Datum
(NAD) 1983 Zone 17 North using bilinear interpolation. Next, the raster files were made into one large file using the mosaic to new raster function.
In order to delineate the watersheds for Wills Creek, Senecaville, and Salt Fork
Lakes, the sinks in the previously made elevation raster file were filled using the fill command within spatial analyst tools. In order to determine the direction the water flows within the watershed, flow direction was computed based on elevation changes between adjacent cells. The flow accumulation function was then used to calculate the number of cells contributing flow to a given cell (all of the cells within the watershed drain into the cell at the mouth of the watershed). To create the pour point (outlet point for drainage area) of each watershed, a point shapefile was created in ArcCatalog with the same projection as the NED (NAD 1983 17 N). Using the editor in ArcMap, a new feature was created for the pour point using the sketch tool. The pour point was established by placing the crosshairs on the mouth of the watershed. After saving the edits, the snap 46 pour point command was used to move to pour point to the cell with the highest flow accumulation within 12.5 meters. Finally, by inputting the flow direction raster and pour point, the watershed function was used to delineate the watershed.
III.2.3 RUSLE Factor Interpretation
III.2.3.1 R Factor
The 2001 national atlas counties were downloaded from the USGS seamless server. A window large enough to include all of the eight contributing counties was chosen to download the shapefile. Once the shapefile was added to ArcMap, a new field was added to the attribute table to display the R factors for each county. The associated R factors were obtained from the Ohio Department of Natural Resources (ODNR) (ODNR,
2000) and are shown in Table III.2.3.1.1.
Table III.2.3.1.1: R Factors for Watershed Counties
County R-value Coshocton 115 Noble 125 Guernsey 120 Belmont 120 Harrison 115 Monroe 125 Muskingum 120 Tuscarawas 115
47 III.2.3.2 K Factor
Using the NRCS, the soil data were downloaded for each of the eight watershed counties. Through extensive surveys conducted in each of the contributing counties, soil profiles were studied and properties were recorded. The NRCS determined the associated
K factor value based on the soil texture, organic matter content, permeability, and rock fragment content. Once downloaded, the table displaying the physical properties was opened using Microsoft Access and exported to Microsoft Excel. Redundant and unnecessary information was omitted using pivot tables. Only the map unit symbol and soil erodibility factor were displayed within the Excel file. The newly created table was then added to ArcMap and joined with the corresponding county layer. Once this process was completed for each of the counties, the layers were merged using the field displaying the soil erodibility factor. The layer was then clipped to fall within each of the watershed boundaries.
III.2.3.3 LS Factor
The slope length and slope steepness factors were computed using the aid of Van
Remortel, Maichle, and Hickey’s (2004) C++ program downloaded from IAMG. The program calculates the slope length and steepness factors based on the criteria established by the RUSLE in the USDA Handbook 703. In order to use the program, the watershed’s clipped NED was first converted to an American Standard Code for Information
Interchange (ASCII) text file using the raster to ASCII function within ArcMap.
Once the C++ began analyzing the text file, all the sinks within the watershed were filled, similar to the fill function within ArcMap. The fill function is an iterative 48 process which fills in sinks up to the maximum specified depth. The cell was first filled to the lowest elevation of the eight adjacent cells. Then, the flow direction was calculated between adjacent cells. In order to compute the slope length factor, the non-cumulative slope length was calculated according the outflow direction of the cell. Next, points at which flow begins were established (ridges). Then, the contributing downhill distances were summed. The distance between adjacent cells is 25 meters or 35.36 meters depending on the location. The summation was stopped at areas of sedimentation.
Additionally, the slope steepness factor was calculated using the equations developed by
McCool et al. in 1987.
After running the C++ program, sixteen files (including slope length, slope steepness, and combined LS factors) were output. The files were then converted from data files to text files. Then, the files were individually converted to raster files in
ArcMap. The combined LS raster file displays the attributes multiplied by 100 in order to display two decimal places. The attributes were then divided by 100 to accurately display the value of the factor.
49
Figure III.2.3.3.1 LS Computation Flowchart (Van Remortel et al., 2004)
50 III.2.3.4 C Factor
The file displaying the cover-management factor was downloaded from the USGS seamless server as a tagged image file format. Originally, the file was projected using
USA contiguous Albers equal area conic, but was projected as NAD 1983 17 N to match the other files in ArcMap. Next, the layer was reclassified to remove all of the attributes with no data. A new field displaying the cover-management factor was then added using the editor within ArcMap. The associated C factor values were primarily obtained from the Ohio EPA (2007); however, the cover-management factor associated with barren land was derived from Yue-Qing et al., (2009), Lee & Lee (2006), and Raghunath (2002). The barren land was assumed to be erodible soil, not rock. Each study determined that the associated C factor value for barren land ranged from 0.5-1.0. Therefore, an average value of 0.75 was approximated for this study. The Ohio EPA determined the C factor values based on the effects of leaves and branches, soil surface mulch, and roots and stems on soil detachment and runoff. Table III.2.3.4.1 displays the C factor values used in the RUSLE analysis.
51 Table III.2.3.4.1: C Factors for Land Cover
Description C Open Water 0 Developed, Open Space 0 Developed, Low Intensity 0 Developed, Medium Intensity 0 Developed, High Intensity 0 Barren Land (Rock/Sand/Clay) 0.75 Deciduous Forest 0.001 Evergreen Forest 0.001 Mixed Forest 0.001 Shrub/Scrub 0.0065 Grassland/Herbaceous 0.003 Pasture/Hay 0.004 Cultivated Crops 0.1175 Woody Wetland 0.0065 Emergent Herbaceous Wetland 0.0065
III.3 Erosion Potential Determination
Before erosion analysis, each of the layers displaying erosion contribution was changed to have 25 x 25 meter (625 m2) grid size in order for comparison with Bayes
(2000) and Beekman (2001). Once all the layers associated with the RUSLE were clipped to the watershed boundary and the primary display field was set appropriately, the average annual soil loss was calculated. The support practice factor was assumed to equal
1.0 because no methods of limiting soil erosion have been incorporated into the area.
Using the raster calculator, the average annual soil erosion was calculated by multiplying the layers displaying the R, K, LS, and C factors. Each of these layers was multiplied by the smallest factor of ten that would eliminate the digits after the decimal point, so the calculation would yield more accurate results. The layer was then changed to 52 floating point and divided by the aggregate value which the individual layers of the
RUSLE had previously been multiplied by, resulting in up to 3.5 million data values for a single watershed.
Exporting the data to Microsoft Excel allowed for the 50th, 75th, 90th, 95th, and 99th percentiles of soil loss to be computed. Using these data points shown in the subsequent figures, the RUSLE results were reclassified to display the statistically significant locations of soil erosion for each watershed. 53
Figure III.3.1 Cumulative histogram of the erosion model for Wills Creek Lake Watershed 54
Figure III.3.2 Cumulative histogram of the erosion model for Senecaville Lake Watershed
55
Figure III.3.3 Cumulative histogram of the erosion model for Salt Fork Lake Watershed 56
Figure III.3.4 Cumulative histogram of the erosion model for Wills Creek Lake contributing area 57 Additionally, the averages of the RUSLE erosion potentials and contributing soil loss factors were found for each watershed. From the computation, it is concluded that
Senecaville Lake watershed is subject to the highest erosion in the area, and the contributing watershed area for Wills Creek Lake is subject to the lowest.
Table III.3.1 Average RUSLE Erosion Potential and RUSLE Factor Values
RUSLE RUSLE Watershed (tons/acre/year) (kg/m2/year) R K LS C Wills Creek 1.451 0.3253 120.6 0.3319 3.130 0.01523 Senecaville 1.817 0.4073 124.3 0.3575 3.915 0.01106 Salt Fork 1.489 0.3338 119.8 0.3185 2.972 0.01639 Wills Creek Contributing 1.348 0.3022 120.1 0.3309 3.010 0.01563
58 Chapter IV
Application and Results
IV.1 Bathymetric Analysis
The original bathymetric map of Senecaville Lake was made using the 1937 topographic survey conducted by the SCS and the Muskingum Watershed Conservancy
District (MWCD). The cross sections of the lake were taken at one-mile increments. The original bathymetric map of Senecaville Lake is shown in Figure IV.1.1. Unfortunately, bathymetric data for Wills Creek Lake and Salt Fork Lake were unavailable.
The 1998 resurvey of Senecaville Lake was conducted by the USACE using the
DTM/Bed Contour method. The map shows that Senecaville Lake no longer extends as far southeast as it had immediately after construction in 1937. Cross sections of the resurvey were also taken at one-mile increments. The 1998 resurvey is also shown in
Figure IV.1.1.
The distribution of the sediment in Senecaville Lake was determined by subtracting the 1998 resurveyed lake from the original 1937 lake survey. From this map displaying sediment depth, it is estimated that 10,100 acre-feet of sediment has been deposited in the lake in the past 62 years, resulting in 1.45 acre-feet per year per square mile of drainage area. As seen in Figure IV.1.2, high amounts of sediment deposition have occurred along the narrow stream channel in the middle of the lake, where the
Seneca Fork feeds into the reservoir, and at the dam. Alternately, erosion typically occurred along the banks due to the variation of lake elevation, waves, wind, and large rain events. The areas of erosion are shown as negative values in Figure IV.1.2. 59
Figure IV.1.1 Senecaville Lake 1937 and 1998 Bathymetric Maps 60
Figure IV.1.2 Senecaville Lake 1998 Sediment Depth
61 IV.2 Watershed Soil Erosion Model
The annual erosion potential for each watershed was calculated based on the parameters (rainfall runoff erosivity, soil erodibility, slope length, slope steepness, and cover-management) in the RUSLE. The ODNR, NRCS, and Ohio EPA aided in determining the values of each of these contributing factors through site investigations of soil, land cover, topography, and environment.
The rainfall runoff erosivity factor was determined by the Ohio EPA to accurately display the amount of rainfall and peak erosivity of storm events. Each of the counties in the study area was designated with an R factor indicative of the storm events of the area.
In Ohio, the R factor varies from 95–155; however, the values only range from 115–125 for the counties in the study area. The R factor has the smallest amount of variation in the watersheds compared to the other factors in the RUSLE. In a larger study area, the R factor would be expected to have a higher influence on the soil erosion due to the higher degree of variation. Senecaville Lake watershed has the highest rainfall runoff erosivity factor, while Wills Creek Lake contributing drainage area has the lowest. Maps displaying the R factor in each of the watersheds are shown in Figures IV.2.1, IV2.2,
IV2.3, and IV.2.4.
62
Figure IV.2.1 Wills Creek Lake Watershed Rainfall Runoff Erosivity Factor 63
Figure IV.2.2 Senecaville Lake Watershed Rainfall Runoff Erosivity Factor
64
Figure IV.2.3 Salt Fork Lake Watershed Rainfall Runoff Erosivity Factor 65
Figure IV.2.4 Wills Creek Lake Contributing Area Rainfall Runoff Erosivity Factor
66 The soil erodibility factor was determined from studies conducted by the NRCS.
Each of the counties was associated with soil tables that presented the susceptibility of every type of soil in the area to erosion and runoff. Coarse sandy or clayey soils have the lowest susceptibility to erosion and runoff, while soils with high silt contents have high K factors because they are easily transported throughout the watershed. Westmoreland silt loam (K=0.37) is the most prominent soil in Wills Creek Lake watershed and Wills Creek
Lake contributing area. Westmoreland silt loam (K=0.37) and Lowell-Gilpin silt loam
(K=0.37) are the most prominent soils in Senecaville Lake watershed and Salt Fork Lake watershed, respectively.
In each of the watersheds, the soil erodibility factor ranges from 0.00-0.55.
Senecaville Lake watershed has the highest average K factor value, while Salt Fork Lake watershed has the lowest average value. The soils in the Senecaville Lake watershed are being eroded and transported more often than the other studied areas. Therefore,
Senecaville Lake watershed is characterized by silty soils. Table A.1 shows the soil type abbreviations, soil description, and associated K factor for each of the soils located in the study area. Similarly, Figure A.1 shows the percent land cover for each soil type within the four watersheds. The range of the percent area in each watershed has been changed in some of the figures in order to better identify the associated percentage on the y-axis.
Maps displaying the K factor in each of the watersheds are shown in Figures IV.2.5,
IV.2.6, IV.2.7, and IV.2.8. 67
Figure IV.2.5 Wills Creek Lake Watershed Soil Erodibility Factor 68
Figure IV.2.6 Senecaville Lake Watershed Soil Erodibility Factor 69
Figure IV.2.7 Salt Fork Lake Watershed Soil Erodibility Factor 70
Figure IV.2.8 Wills Creek Lake Contributing Area Soil Erodibility Factor
71 The slope length and slope steepness factors were calculated using the DTM obtained from the USGS seamless server. Using the aid of Van Remortel, Maichle, and
Hickey’s (2004) C++ program (shown in Appendix B), the slope length, slope steepness, and combined slope length and slope steepness factors are displayed in ArcGIS. Salt Fork
Lake watershed has the highest LS factor values, while Senecaville Lake watershed has the lowest. Therefore, the terrain in the Salt Fork Lake watershed subjects the soils to higher rates of erosion than the other watersheds. Additionally, the slopes in Salt Fork
Lake watershed are heavily weighted by slopes greater than 9%. Wills Creek Lake watershed, Senecaville Lake watershed, and Wills Creek Lake contributing area each have approximately 70% of the slopes greater than 9% steepness, while Salt Fork Lake contains 82% of slopes greater than 9% steepness.
After displaying the LS factor, the waterways and surrounding banks are easily identified. The lakes and waterways have the lowest values, while the adjacent banks have considerably higher values. It can be seen from looking at the maps that the highest
LS factor values are located in the Northwest and Southeast portions of the study area.
Maps displaying the LS factor overlaid on the elevation data in each of the watersheds are shown in Figures IV.2.9, IV.2.10, IV.2.11, and IV.2.12. Additional maps displaying the L factor and S factor are shown in Appendix C and Appendix D, respectively.
Senecaville Lake watershed had a slightly higher average L factor at 1.27, while the other watersheds each had an average value of 1.24. Similarly, Senecaville Lake watershed also had the highest average S factor at 2.82 and Salt Fork Lake watershed had the lowest average value at 2.19. 72
Figure IV.2.9 Wills Creek Lake Watershed Slope Length and Slope Steepness Factors
73
Figure IV.2.10 Senecaville Lake Watershed Slope Length and Slope Steepness Factors
74
Figure IV.2.11 Salt Fork Lake Watershed Slope Length and Slope Steepness Factors
75
Figure IV.2.12 Wills Creek Lake Contributing Area Slope Length and Slope Steepness
Factors
76 The cover-management factor was determined by joining the land cover obtained from the USGS seamless server and values from studies conducted by the Ohio EPA and other independent land cover studies (Yue-Qing et al., 2009; Lee & Lee 2006;
Raghunath, 2002). Then, by reclassifying the data, the layer was displayed by the C factor rather than the land cover type. The C factor better indicates the amount of erosion to occur in the study area.
Wills Creek Lake contributing drainage area has the highest value for the cover- management factor, while Senecaville has the lowest value. Therefore, the type of vegetal land cover in the contributing area of Wills Creek Lake allows the soil to erode more easily. The combined amount of area of pasture/hay and cultivated crops in Wills Creek
Lake contributing area is 27.90% (considerably higher than the other watersheds). A key problem in soil erosion is farmlands. As a result, strip cropping, buffer strips, terracing, and other methods to limit soil erosion are often implemented on farmed areas. Maps displaying the C factor in each of the watersheds are shown in Figures IV.2.13, IV.2.14,
IV.2.15, and IV.2.16. Furthermore, the land cover associated with each watershed is detailed in Appendix E.
77
Figure IV.2.13 Wills Creek Lake Watershed Cover-Management Factor
78
Figure IV.2.14 Senecaville Lake Watershed Cover-Management Factor
79
Figure IV.2.15 Salt Fork Lake Watershed Cover-Management Factor
80
Figure IV.2.16 Wills Creek Lake Contributing Area Cover-Management Factor
81 After completing analysis for each of the factors within each of the watersheds, the RUSLE’s average annual sediment erosion potential was calculated in ArcGIS. For each of the watersheds, the potential erosion was symbolized into six different classes according the 50th, 75th, 90th, 95th, and 99th percentiles in order to identify the areas subjected to the highest amount of soil erosion.
Senecaville Lake Watershed has the highest average potential of soil erosion at
0.1700 tons/acre/year, while Wills Creek Lake Contributing Area, Salt Fork Lake, and
Wills Creek Lake watersheds have comparable average values of 0.1105-0.1197 tons/acre/year. Senecaville Lake watershed also had the highest average values for the R,
K, and LS, factors; however, it had the lowest average C factor value. Maps displaying the RUSLE annual soil erosion potential in each of the watersheds are shown in Figures
IV.2.17, IV.2.18, IV.2.19, and IV.2.20.
82
Figure IV.2.17 Wills Creek Lake Watershed Potential Annual Erosion
83
Figure IV.2.18 Senecaville Lake Watershed Potential Annual Erosion
84
Figure IV.2.19 Salt Fork Lake Watershed Potential Annual Erosion
85
Figure IV.2.20 Wills Creek Lake Contributing Area Potential Annual Erosion
86 IV.3 Sediment Delivery
Using the USACE lake surveys of Senecaville Lake and Wills Creek Lake, studies conducted by Bayes (2000), the Maner (1958) equation, and the annual sediment erosion as predicted by the RUSLE, sediment delivery and sediment yield were calculated for each of the four watersheds. From the USACE lake surveys, the volume of sediment was calculated and then converted to weight using the following equation:
( ) IV.3.1
where is the volume of soil, is the weights of soil solids, is the moisture content of the soil, is the specific gravity of the soil solids, and is the unit weight of water.
Based on studies conducted by Bayes (2000), the moisture content of the soil was estimated to be 20% (0.2) and the specific gravity of the soil solids was estimated to be
2.7.
Based on the USACE bathymetric surveys of Wills Creek Lake and Senecaville
Lake, the recorded sedimentation was compared to estimated sediment yield provided by the Maner (1958) sediment delivery ratio (Equation I.2). The results are shown in Tables
IV.3.1 and IV.3.2. Overall, Senecaville Lake watershed had the highest estimated SDR from Equation I.2. Additionally, based on the RUSLE annual erosion potential and the observed sedimentation, Senecaville Lake watershed also had the highest SDR and annual sedimentation. Similar to other studies conducted by the United States Department 87 of Agriculture (USDA) (1975), Vanoni (1975), and Boyce (1975), the SDR decreases as the size of the watershed increases.
Table IV.3.1 Maner (1958) SDR and Estimated Sediment Yield
Potential Length of Maner Sediment Relief Watershed erosion watershed SDR yield (m/m) (tons/ year) (m) (tons/tons) (tons/year) Wills Creek 780,772 68,795 1.26 0.11 85,885 Lake Watershed Senecaville Lake 137,197 23,281 1.27 0.27 37,043 Watershed Salt Fork Lake 151,125 31,951 1.22 0.20 30,225 Watershed Wills Creek Lake 487,363 57,382 1.24 0.13 63,357 Contributing Area
Additionally, three power relationships were analyzed; Vanoni (1975) (Equation
IV.3.2), Boyce (1975) (Equation IV.3.3), and USDA (1975) (Equation IV.3.4). Each of the SDR equations is based on hundreds of studies of watersheds located in the United
States. Various locations in the United States were chosen in the studies in order to create a universal equation capable of predicting sediment delivery. The variation in equations is due to the amount of factors that impact sediment delivery.
IV.3.2
IV.3.3
88
IV.3.4
2 where AS = watershed area (km ).
Table IV.3.2 Calculated SDR Based on USACE Annual Sedimentation
RUSLE Vanoni (1975) Boyce (1975) USDA (1975) Watershed USACE SDR SDR SDR SDR (tons/tons) (tons/tons) (tons/tons) (tons/tons) Wills Creek Lake 0.211 0.190 0.066 0.254 Watershed Senecaville Lake 2.830 0.231 0.096 0.301 Watershed Salt Fork Lake - 0.223 0.090 0.292 Watershed Wills Creek Lake Contributing 0.338 0.181 0.060 0.254 Area
The calculated results based on the RUSLE annual potential erosion and USACE sedimentation reports predict that more soil will be deposited into Senecaville Lake than will be potentially eroded in the watershed. Therefore the aforementioned equations cannot be compared with the results of this study. Because the land cover has drastically changed in Ohio since the dams for the lakes were built, the sediment delivery ratios are not indicative of the current sediment deposition.
Additionally, when compared to the lake sedimentation the RUSLE potential erosion because the RUSLE does not investigate bank and gully erosion, which may be the dominating factors (Church & Slaymaker, 1989; Dedkov, 2004). The RUSLE only investigates sheet and rill erosion which may only contribute a small portion of the study area’s erosion. 89 IV.4 RUSLE Factors Correlation to Annual Soil Erosion
Correlation matrices were made in order to compare the soil erosion to the contributing factors for each watershed. Furthermore, the association of these factors was computed for the 50th, 75th, 90th, 95th, and 99th percentiles using a 95% confidence interval. Overall, the cover-management factor was, by far, the most influential erosion factor. As the data were limited to only areas of higher erosion, the influence of the cover-management factor declined in each watershed. Generally, the correlation of the other soil erosion factors to the annual soil erosion remained approximately zero for each percentile in the four watersheds.
Table IV.4.1 RUSLE Factors Correlation to Annual Soil Erosion in Wills Creek Lake
Watershed
Erosion Percentile R LS K C 0% -0.0132 0.0880 0.0698 0.6302 50% -0.0212 -0.0109 0.0214 0.5995 75% -0.0175 -0.0419 -0.0044 0.5381 90% 0.0168 0.0749 -0.0031 0.3691 95% 0.0104 0.1039 -0.0001 0.2998 99% -0.0091 0.0096 -0.0144 0.2675
90 Table IV.4.2 RUSLE Factors Correlation to Annual Soil Erosion in Senecaville Lake
Watershed
Erosion Percentile R LS K C 0% 0.0448 0.0975 0.0855 0.6430 50% 0.0259 -0.0359 0.0234 0.6152 75% 0.0176 -0.0979 0.0092 0.5618 90% 0.0141 0.0064 0.0077 0.3942 95% 0.0113 0.1205 0.0122 0.2174 99% 0.0027 0.0129 0.0005 0.2447
Table IV.4.3 RUSLE Factors Correlation to Annual Soil Erosion in Salt Fork Lake
Watershed
Erosion Percentile R LS K C 0% -0.0398 0.0821 0.1095 0.5737 50% -0.0262 -0.0363 0.0427 0.5268 75% -0.0109 -0.0579 0.0120 0.4245 90% 0.0039 0.1574 0.0177 0.1580 95% 0.0083 0.1017 0.0053 0.1774 99% 0.0060 0.0205 -0.0057 0.1937
Table IV.4.4 RUSLE Factors Correlation to Annual Soil Erosion in Wills Creek Lake
Contributing Area
Erosion Percentile R LS K C 0% -0.0413 0.0846 0.0508 0.5291 50% -0.0351 -0.0023 0.0158 0.5075 75% -0.0261 -0.0276 -0.0105 0.4619 90% -0.0061 0.0579 -0.0162 0.3461 95% -0.0100 0.1031 -0.0103 0.2786 99% -0.0040 0.0078 -0.0175 0.2343
91 Due to the high influence of cover-management factor has on soil erosion; the C factor was investigated further. In addition to investigating the erosion of the entire watershed, the 50th, 75th, 90th, 95th, and 99th percentiles were examined. While deciduous forest was the prominent type of land cover for each of the watersheds, the vast majority of the erosion occurred in cultivated crops. As the investigated percentile of erosion increased, the amount of land cover associated with cultivated crops also increased.
Ultimately, the high contribution of cultivated crops to soil erosion is due to the higher C factor associated with the croplands. Barren land had the highest C factor (0.75), but also made up less than one percent of all land cover. Therefore, the high percentage of erosion in barren lands was not observed in the watersheds. Figures showing the 50th, 75th, 90th, and 95th percentiles of erosion are shown in Appendix F.
Wills Creek Lake Watershed Cover-Management Factor 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.1 Wills Creek Lake Watershed Cover-Management Factor
92 Senecaville Lake Watershed Cover-Management Factor 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.2 Senecaville Lake Watershed Cover-Management Factor
Salt Fork Lake Watershed Cover-Management Factor 80 70 60 50 40
Percent 30 Land Cover 20 10 Erosion 0
Figure IV.4.3 Salt Fork Lake Watershed Cover-Management Factor
93 Wills Creek Lake Contributing Area Cover- Management Factor 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.4 Wills Creek Lake Contributing Area Cover-Management Factor
Wills Creek Lake Watershed Cover-Management Factor(99th Percentile) 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.5 Wills Creek Lake Watershed Cover-Management Factor (99th Percentile)
94 Senecaville Lake Watershed Cover-Management Factor (99th Percentile) 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.6 Senecaville Lake Watershed Cover-Management Factor (99th Percentile)
Salt Fork Lake Watershed Cover-Management Factor (99th Percentile) 80 70 60 50 40
Percent 30 20 Land Cover 10 Erosion 0
Figure IV.4.7 Salt Fork Lake Watershed Cover-Management Factor (99th Percentile)
95 Wills Creek Lake Contributing Area (99th Percentile) 80 70 60 50 40
Percent 30 Land Cover 20 10 Erosion 0
Figure IV.4.8 Wills Creek Lake Contributing Area Cover-Management Factor (99th
Percentile)
96 Chapter V
Conclusions and Recommendations
V.1 Conclusions
The objective of this study was to investigate the soil erosion potentials of the watersheds in series using the RUSLE and relationships between varied parameters of the
RUSLE and the soil erosion potential. Based on the USACE sedimentation survey data, the sediments deposited in the lakes were estimated. It is found that the sediment delivery ratios do not coincide with those found in the literature.
The sedimentation in Senecaville Lake was primarily observed within the main channel in the center of the lake, at the dam, and where Seneca Fork feeds the lake. On the other hand, the soil erosion of the banks caused the lake to expand laterally along the minor axis over the past 62 years. Additionally, higher sediment deposition occurred on the Southwest shoreline of the lake, where the elevation is lowest. The areas displaying the highest sediment depth would likely be the targeted areas for dredging.
Through the annual soil erosion estimation, as calculated by the RUSLE, the cover-management factor was found to have the highest correlation to soil erosion potential. Furthermore, land cover associated with cultivated crops contributed the most to the soil erosion estimation in all the watersheds studied. Areas subjected to the highest potential erosion were identified and expressed as locations needed for possible remediation. Cultivated crops comprised the highest percentage of land cover out of the areas of 99th percentile erosion potential. Because the support practice of the RUSLE was assumed to be one, the resulting estimated annual soil loss is higher than observed. 97 The high sediment delivery ratios from the results of the RUSLE annual erosion potential and USACE sedimentation surveys could partially be due to the change in land cover in the study area. The land cover is very significant when analyzing sediment delivery (Wasson, 1994). The study area supported large surface strip mining operations in the 1960’s. During this time, the land cover could have resulted in more soil erosion.
Hence, the large amount of the sediment estimated based on the USACE sedimentation surveys may have been linked to this condition.
V.2 Recommendations
After the 2002 survey of Wills Creek Lake, the small lake was estimated to lose the minimum capacity in 2018 due to sedimentation (USACE, 2011b). Because of the short timeline, a resurvey of the lake should be conducted in order to estimate the current capacity. Similarly, Senecaville Lake was estimated to lose minimum capacity in 2198
(USACE, 2011a). Although this is not an immediate threat, a resurvey of the lake should be conducted in order to assess the impacts on wildlife and recreational activities.
Based on the results of the calculated sediment delivery ratios, the annual sediment yield cannot be determined based on average sedimentation and present potential erosion values calculated from the RUSLE. In order to obtain a more accurate result, sedimentation surveys should be conducted on consecutive years. Using the consecutive annual surveys and the RUSLE erosion potential, an accurate equation comparing the accrued lake sedimentation to the annual erosion potential can be derived.
The annual precipitation data for that study period must be noted in order to extrapolate the results to further studies. 98 In order to more accurately determine the annual soil erosion, factors with a finer resolution could be used particularly for areas of higher erosion potential. Smaller grid cells would more accurately estimate erosion potential due to less interpolation needed for computation. When analyzing the combined LS factors, higher ridges and lower valleys are omitted when using larger cell sizes because the average height is selected for each cell. However, to accomplish this, spatial data of finer grids for cover-management factor would be also needed and lead to further understanding of land cover impacts on erosion.
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Table A.1 Soil Type Abbreviations and K Factor MUSYM Soil Description K AaB Aaron silt loam, 2-6 percent slopes 0.37 AaC Aaron silt loam, 8-15 percent slopes 0.37 AaC2 Aaron silt loam, 8-15 percent slopes, eroded 0.37 AaD2 Aaron silt loam, 15-25 percent slopes, eroded 0.37 AbB Aaron silt loam, 2-8 percent slopes 0.37 AbC Aaron silt loam, 8-15 percent slopes 0.37 AbC2 Aaron silt loam, 8-15 percent slopes, eroded 0.37 AcB Aaron-Upshur complex, 2-6 percent slopes 0.37 AcC Aaron silt loam, 8-15 percent slopes 0.37 AeC Allegheny variant loam, 8-15 percent slopes 0.32 AfB Alford silt loam, 2-6 percent slopes 0.43 AfC2 Alford silt loam, 2-6 percent slopes, eroded 0.43 AgC Alford silt loam, 8-15 percent slopes 0.32 BaB Barkcamp gravelly sandy loam, 0-8 percent slopes 0.24 BaD Barkcamp gravelly sandy loam, 8-40 percent slopes 0.24 BaF Barkcamp very stony sandy loam, 40-70 percent slopes 0.24 BcB Barkcamp clay loam, 0-8 percent slopes 0.32 BcC Berks channery silt loam, 8-15 percent slopes 0.17 BcD Barkcamp clay loam, 8-25 percent slopes 0.32 BcE Berks channery silt loam, 25-40 percent slopes 0.17 BcF Berks channery silt loam, 4-70 percent slopes 0.17 BeB Bethesda silt loam, 0-8 percent slopes 0.43 BeC Berks channery silt loam, 8-15 percent slopes 0.17 BeD Bethesda silt loam, 8-25 percent slopes 0.17 BeD2 Bethesda silt loam, 8-25 percent slopes, eroded 0.17 BeE Berks channery silt loam, 25-40 percent slopes 0.17 BeF Berks channery silt loam, 40-70 percent slopes 0.17 BfC Berks channery silt loam, 8-15 percent slopes 0.17 BfE Bethesda clay loam, 25-40 percent slopes 0.43 BfF Bethesda channery loam, 40-70 percent slopes 0.28 BgB Bethesda loam, 0-8 percent slopes 0.43 BgD Bethesda loam, 8-25 percent slopes 0.43 BgE Bethesda loam, 25-40 percent slopes 0.43 BhB Bethesda shaly silty clay loam, 0-8percent slopes 0.28
106 Table A.1 (continued)
BhD Bethesda shaly silty clay loam, 8-25 percent slopes 0.28 BhE Bethesda shaly silty clay loam, 25-40 percent slopes 0.28 BhF Bethesda very cobbly silty clay loam, 40-70 percent slopes 0.28 BkC Bethesda channery clay loam, 8-15 percent slopes 0.28 BkD Brookside silty clay loam, 15-25 percent slopes 0.37 BkE Brookside silty clay loam, 25-40 percent slopes 0.37 BkF Bethesda channery clay loam, 25-70 percent slopes 0.28 BmB Bethesda channery loam, 0-8 percent slopes 0.28 BmF Bethesda channery loam, 25-70 percent slopes 0.28 BnD Berks-Guernsey complex, 15-25 percent slopes 0.43 BnF Bethesda channery clay loam, 25-70 percent slopes 0.28 BoB Bethesda shaly silt loam, 1-15 percent slopes 0.28 BoD Bethesda shaly silt loam, 15-25 percent slopes 0.28 BoF Bethesda very shaly silty clay loam, 25-70 percent slopes 0.28 BpD Bethesda channery silty clay loam, 8-25 percent slopes 0.43 BpF Bethesda channery silty clay loam, 25-70 percent slopes 0.28 BrC2 Brookside silty clay loam, 8-15 percent slopes, eroded 0.17 BrD Brownsville channery clay loam, 15-25 percent slopes 0.20 BrE Brownsville channery clay loam, 25-35 percent slopes 0.20 BrF Brownsville channery silt loam, 35-70 percent slopes 0.20 BsC Brookside silty clay loam, 8-15 percent slopes 0.37 BsC2 Brookside silty clay loam, 8-15 percent slopes, eroded 0.37 BsD Brookside silty clay loam, 15-25 percent slopes 0.37 BsD2 Brookside silty clay loam, 15-25 percent slopes, eroded 0.37 BsE Brookside silty clay loam, 25-40 percent slopes 0.37 BtC Brookside-Vandalia complex 8-15 percent slopes 0.37 BtD Brookside-Vandalia complex 15-25 percent slopes 0.37 BtD2 Brookside-Vandalia complex 15-25 percent slopes, eroded 0.37 BtE Brookside-Vandalia complex 25-40 percent slopes 0.37 BtE2 Brookside-Vandalia complex 25-40 percent slopes, eroded 0.37 BwD2 Brooke silty clay loam, 12-18 percent slopes, moderately eroded 0.37 BwE2 Brooke silty clay loam, 18-35 percent slopes, moderately eroded 0.37 Ca Chagrin loam, occasionally flooded 0.32 CaB Captina silt loam, 2-6 percent slopes 0.43 CaD2 Captina silt loam, 2-6 percent slopes, moderately eroded 0.43 CbD Chili gravelly loam, 12-18 percent slopes 0.24 CcA Chavies loam, 0-2 percent slopes 0.24 CdA Caneada silt loam, 0-2 percent slopes 0.43 107 Table A.1 (continued)
CfA Chili loam, 0-2 percent slopes 0.32 CfB Chili loam, 2-6 percent slopes 0.32 CfC Chili loam, 6-15 percent slopes 0.32 Cg Chagrin silt loam, occasionally flooded 0.32 Ch Chagrin silt loam, occasionally flooded 0.32 ChA Cidermill silt loam, 0-2 percent slopes 0.37 ChB Chili gravelly loam, 3-8 percent slopes 0.37 ChD Clarksburg channery silt loam, 15-25 percent slopes 0.28 CkC Clarksburg silt loam, 6-15 percent slopes 0.37 CkD Clarksburg silt loam, 15-25 percent slopes 0.37 CnC Coshocton silt loam, 8-15 percent slopes 0.37 CnC2 Coshocton silt loam, 8-15 percent slopes, eroded 0.37 CoB Coshocton silt loam, 2-6 percent slopes 0.37 CoC2 Coshocton silt loam, 6-15 percent slopes, eroded 0.37 CoD Coshocton silt loam, 15-25 percent slopes 0.37 CoE Coshocton silt loam, 25-35 percent slopes 0.37 CpB Coshocton silt loam, 3-8 percent slopes 0.37 CpC2 Coshocton silt loam, 6-15 percent slopes, very stony, eroded 0.37 CrC Claysville-Guernsey silty clay loams, 8-15 percent slopes 0.28 CrD Coshocton-Rigley complex, 15-25 percent slopes 0.37 CrD2 Coshocton-Rigley complex, 15-25 percent slopes, eroded 0.37 CrE Coshocton-Rigley complex, 25-35 percent slopes 0.37 CsB Coshocton silt loam, 3-8 percent slopes 0.37 CsC Coshocton silt loam, 8-15 percent slopes 0.37 CsC2 Coshocton silt loam, 8-15 percent slopes, eroded 0.37 CsD Coshocton-Westmoreland complex, 15-25 percent slopes 0.37 CsE Coshocton-Westmoreland complex, 25-35 percent slopes 0.37 CtD Coshocton-Guernsey very stony silt loams, 15-25 percent slopes 0.28 CtE Coshocton-Westmoreland complex, 25-40 percent slopes 0.37 CuB Culleoka silt loam, 3-8 percent slopes 0.32 CuC Culleoka silt loam, 8-15 percent slopes 0.32 CvD Coshocton-Guernsey very stony silt loams, 15-25 percent slopes 0.28 DeC Dekalb channery sandy loam, 6-15 percent slopes, stony 0.17 DhC Dekalb loam, 8-15 percent slopes 0.24 DkB Dekalb loam, 3-8 percent slopes 0.24 DkC Dekalb loam, 8-15 percent slopes 0.24 DkC2 Dekalb loam, 8-15 percent slopes, eroded 0.24 DkD Dekalb loam, 15-25 percent slopes 0.24
108 Table A.1 (continued)
DkD2 Dekalb loam, 15-25 percent slopes, eroded 0.24 DkE Dekalb loam, 25-40 percent slopes 0.24 DkE2 Dekalb loam, 25-40 percent slopes, eroded 0.24 DkF Dekalb channery loam, 40-70 percent slopes 0.17 DmE Dekalb channery loam, 25-40 percent slopes 0.17 DmF Dekalb channery loam, 40-70 percent slopes 0.17 DnF Dekalb moderately channery loam, 40-70 percent slopes 0.17 Dp Dumps 0.00 Ds Dumps, mine 0.00 EbB Elba silty clay loam, 3-8 percent slopes 0.43 EbC Elba silty clay loam, 8-15 percent slopes 0.43 EbD Elba silty clay loam, 15-25 percent slopes 0.43 EbD2 Elba silty clay loam, 15-25 percent slopes, eroded 0.43 EbE Elba silty clay loam,, 25-40 percent slopes 0.43 EbE2 Elba silty clay loam,, 25-40 percent slopes, eroded 0.43 EbF2 Elba silty clay loam, 40-70 percent slopes, eroded 0.43 EdD2 Elba-Guernsey silty clay loams, 15-25 percent slopes, eroded 0.43 EdE2 Elba-Guernsey silty clay loams, 25-35 percent slopes, eroded 0.43 EkF Elba-Berks complex, 40-70 percent slopes 0.43 ElB Elkinsville silt loam, 3-8 percent slopes 0.49 ElC Elkinsville silt loam, 8-15 percent slopes 0.49 ElD Elkinsville silt loam, 15-25 percent slopes 0.49 EnB Enoch loam, 0-8 percent slopes 0.37 EnD Enoch loam, 8-25 percent slopes 0.37 EuA Euclid silt loam, occasionally flooded 0.37 FaB Fairpoint loam, 0-8 percent slopes 0.43 FaD Fairpoint loam, 8-25 percent slopes 0.43 FaE Fairpoint loam, 25 35 percent slopes 0.43 FbB Fairpoint gravelly clay loam, 0-8 percent slopes 0.28 FbD Fairpoint gravelly clay loam, 8-25 percent slopes 0.28 FbF Fairpoint channery silty clay loam, 25-70 percent slopes 0.28 FcA Fitchville silt loam, 0-2 percent slopes 0.37 FcB Fairpoint silty clay loam, 0-8 percent slopes 0.43 FcD Fairpoint silty clay loam, 8-25 percent slopes 0.43 FcE Fairpoint silty clay loam, 25-40 percent slopes 0.43 FdB Fairpoint loam, 0-8 percent slopes 0.43 FdD Fairpoint loam, 8-25 percent slopes 0.43 FdE Fairpoint loam, 25-35 percent slopes 0.43
109 Table A.1 (continued)
FeB Farmerstown loam, 0-8 percent loam 0.43 FeC Farmerstown loam, 8-25 percent slopes 0.43 FfB Fairpoint silty clay loam, 0-8 percent slopes 0.43 FfD Fairpoint silty clay loam, 8-25 percent slopes 0.43 FhA Fitchville silt loam, 0-2 percent slopes 0.37 FhB Fitchville silt loam, 2-6 percent slopes 0.37 FtA Fitchville silt loam, 0-3 percent slopes 0.37 GbB Gilpin silt loam 2-8 percent slopes 0.32 GcC Gilpin silt loam, 8-15 percent slopes 0.32 GcD Gilpin silt loam, 15-25 percent slopes 0.32 GcE Gilpin silt loam, 25-35 percent slopes 0.32 GcF Gilpin silt loam, 35-70 percent slopes 0.32 GdB Germano sandy loam, 2-6 percent slopes 0.32 GdC Germano sandy loam, 6-15 percent slopes 0.32 GdC2 Germano sandy loam, 6-15 percent slopes, eroded 0.32 GdD Gilpin silt loam, 15-25 percent slopes 0.32 GdE Gilpin and Dekalb very stony soils, 12-35 percent slopes 0.17 GdF Gilpin silt loam, 35-70 percent slopes 0.32 GdG Gilpin and Dekalb very stony soils, 35-70 percent slopes 0.17 GeD2 Germano fine sandy loam, 15-25 percent slopes, eroded 0.32 GfA Glenford silt loam, 0-2 percent slopes 0.37 GfB Glenford silt loam, 2-6 percent slopes 0.37 GfC2 Glenford silt loam, 6-15 percent slopes, eroded 0.37 GgB Guernsey silt loam, 3-8 percent slopes 0.43 GgC Guernsey silt loam, 8-15 percent slopes 0.43 GgD Guernsey silt loam, 15-25 percent slopes 0.43 GgD2 Guernsey silt loam, 15-25 percent slopes, eroded 0.43 GhB Gilpin silt loam, 2-6 percent slopes 0.32 GhC Gilpin silt loam, 6-15 percent slopes 0.32 GhD Gilpin silt loam, 15-25 percent slopes 0.32 GkB2 Gilpin-Upshur complex, 2-6 percent slopes, moderately eroded 0.32 GkC Gilpin silt loam, 8-15 percent slopes 0.32 GkC2 Gilpin silt loam, 8-15 percent slopes, eroded 0.32 GkD Gilpin-Upshur complex, 12-18 percent slopes 0.32 GkD2 Gilpin-Upshur complex, 12-18 percent slopes, moderately eroded 0.32 GkE2 Gilpin-Upshur complex, 18-35 percent slopes, moderately eroded 0.32 GkE3 Gilpin-Upshur complex, 18-35 percent slopes, severely eroded 0.32 GkF2 Gilpin-Upshur complex, 35-70 percent slopes, eroded 0.32
110 Table A.1 (continued)
GkG Gilpin-Upshur complex, 35-70 percent slopes 0.32 GkG3 Gilpin-Upshur complex, 35-70 percent slopes, severely eroded 0.32 GlE Gilpin-Upshur complex, steep, benched 0.32 GlG Gilpin-Upshur complex, very steep, benched 0.32 GnA Glenford silt loam, 0-2 percent slopes 0.37 GnB Glenford silt loam, 2-6 percent slopes 0.37 GnC Glenford silt loam, 6-15 percent slopes 0.37 Gilpin-Westmoreland silt loams, 2-6 percent slopes, moderately GoB2 0.32 eroded GoC2 Gilpin-Coshocton complex, 6-15 percent slopes, eroded 0.32 GoD2 Gilpin-Coshocton complex, 15-25 percent slopes, eroded 0.32 GoD3 Gilpin-Coshocton complex, 15-25 percent slopes, severely eroded 0.32 Gilpin-Westmoreland silt loams, 18-35 percent slopes, moderately GoE2 0.32 eroded Gilpin-Westmoreland silt loams, 18-35 percent slopes, severely GoE3 0.32 eroded Gilpin-Westmoreland silt loams, 35-70 percent slopes, moderately GoG2 0.32 eroded GpA Glenford silt loam, occasionally flooded 0.37 GpD2 Gilpin-Lowell complex, 15-25 percent slopes, eroded 0.43 GrC Guernsey silt loam, 8-15 percent slopes 0.43 GrC2 Guernsey silt loam, 8-15 percent slopes, eroded 0.43 GrD2 Guernsey silt loam, 15-25 percent slopes, eroded 0.43 Guernsey-Upshur complex, 18-35 percent slopes, moderately GrE2 0.43 eroded GsC Glenford silt loam, 6-15 percent slopes 0.43 GsG Guernsey-Upshur complex, 18-70 percent slopes, landslip 0.43 GtC Guernsey silt loam, 6-15 percent slopes 0.43 GtC2 Guernsey silt loam, 6-15 percent slopes, eroded 0.43 GtD Guernsey silt loam, 15-25 percent slopes 0.43 GtD2 Guernsey silt loam, 15-25 percent slopes, eroded 0.43 GuB Guernsey silt loam, 1-6 percent slopes 0.43 GuC Guernsey silt loam, 6-15 percent slopes 0.43 GuD Guernsey silt loam, 15-25 percent slopes 0.43 GuD2 Guernsey silt loam, 15-25 percent slopes, eroded 0.43 GuE Guernsey silty clay loam, 25-40 percent slopes 0.43 GuG Guernsey-Upshur complex, very steep, benched 0.43 GvC Guernsey silt loam, 8-15 percent slopes 0.43 GvD2 Guernsey-Upshur silty loams, 15-25 percent slopes, eroded 0.43
111 Table A.1 (continued)
GwC2 Guernsey silty clay loam, 8-15 percent slopes, eroded 0.43 GwD2 Guernsey silty clay loam, 15-25 percent slopes, eroded 0.43 Guernsey-Westmore silt loams, 18-35 percent slopes, moderately GwE2 0.43 eroded Guernsey-Westmore silt loams, 18-35 percent slopes, severely GwE3 0.43 eroded Guernsey-Westmore silt loams, 35-70 percent slopes, moderately GwG2 0.43 eroded GxD Guernsey-Upshur complex, 15-25 percent slopes 0.43 Gy Gullied land, Gilpin-Upshur material 0.00 HaD Hazleton channery sandy loam, 15-25 percent slopes 0.17 HaE Hazleton channery sandy loam, 25-35 percent slopes 0.17 HaF Hazleton channery sandy loam, 35-70 percent slopes 0.17 HbE Hazleton channery loam, 25-40 percent slopes 0.17 He Hartshorn silt loam 0.32 HeC Hazleton channery loam, 8-15 percent slopes 0.17 HeD Hazleton channery loam, 15-25 percent slopes 0.17 HeE Hazleton channery loam, 25-40 percent slopes 0.17 HeF Hazleton channery sandy loam, 25-70 percent slopes, very bouldery 0.15 Hf Hartshorn silt loam, occasionally flooded 0.32 HfF Hazleton channery loam, 25-70 percent slopes, stony 0.15 Ho Holton silt loam, occasionally flooded 0.37 Hr Hartshorn silt loam, wet variant 0.37 JmA Jimtown loam, 0-2 percent slopes 0.32 KaB Kanawha loam, 2-6 percent slopes 0.32 KcB Keene silt loam, 1-8 percent slopes 0.43 KdB Keene silt loam, 2-6 percent slopes 0.43 KeB Keene silt loam, 2-6 percent slopes 0.43 KeC Keene silt loam, 6-15 percent slopes 0.43 KeC2 Keene silt loam, 6-15 percent slopes, eroded 0.43 KfB Keene silt loam, 2-6 percent slopes 0.43 KfC Keene silt loam, 8-15 percent slopes 0.43 KlD2 Keene-Latham silt loam, 12-18 percent slopes, moderately eroded 0.43 Lc Lindsde silt loam, occasionally flooded 0.32 Ld Lindside silt loam, frequently flooded 0.32 LdE2 Latham-Keene silt loams, 18-35 percent slopes, moderately eroded 0.43 Le Lobdell silt loam, occasionally flooded 0.37 LeB Lowell silt loam, 3-8 percent slopes 0.37
112 Table A.1 (continued)
LeC Lowell silt loam, 8-15 percent slopes 0.37 LeD Lowell silt loam, 15-25 percent slopes 0.37 LeE Lowell silt loam, 25-40 percent slopes 0.37 LeF Lowell silt loam, 40-70 percent slopes 0.37 Lh Lindside silt loam 0.32 Lk Lindside silt loam, occasionally flooded 0.32 Lm Lobdell loam, channery substratum, occasionally flooded 0.37 Lo Lobdell silt loam, occasionally flooded 0.37 LoB Lowell silt loam, 3-8 percent slopes 0.37 LoC Lowell-Westmoreland silt loams, 8-15 percent slopes 0.37 LoD Lowell-Westmoreland silt loams, 15-25 percent slopes 0.37 LoD2 Lowell-Westmoreland silt loams, 15-25 percent slopes, eroded 0.37 LoE Lowell-Westmoreland silt loams, 25-40 percent slopes 0.37 LoF Lowell-Westmoreland silt loams, 40-70 percent slopes 0.37 LpC Lowell silt loam, 8-15 percent slopes 0.37 LpC2 Lowell silt loam, 8-15 percent slopes, eroded 0.37 LpD Lowell silt loam, 15-25 percent slopes 0.37 LpD2 Lowell silt loam, 15-25 percent slopes, eroded 0.37 LpE2 Lowell silty clay loam, 25-40 percent slopes, eroded 0.37 LpF Lowell-Westmoreland silt loams, benched, 30-70 percent slopes 0.37 LrE2 Lowell silty clay loam, 25-40 percent slopes, eroded 0.37 LrF Lowell-Westmoreland silt loams, 40-70 percent slopes 0.37 LtE2 Lowell-Elba silty clay loams, 25-40 percent slopes, eroded 0.37 LtF2 Lowell-Elba silty clay loams, 40-70 percent slopes, eroded 0.37 LuE Lowell-Upshur complex, 25-40 percent slopes 0.37 LuF Lowell-Gilpin silt loams, 35-70 percent slopes 0.37 LvD2 Loudonville silt loam, 15-20 percent slopes, eroded 0.37 LvE Lowell-Upshur silty clay loams, 25-40 percent slopes 0.37 LvE2 Lowell-Upshur silty clay loams, 25-40 percent slopes, eroded 0.37 LvF2 Lowell-Upshur silty clay loams, 40-70 percent slopes, eroded 0.37 LwC Lowell-Westmoreland complex, 8-15 percent slopes 0.37 LwD Lowell-Westmoreland complex, 15-25 percent slopes 0.37 LwE Lowell-Westmoreland complex, 25-40 percent slopes 0.37 LwF Lowell-Westmoreland complex, 40-70 percent slopes 0.37 LxE Lowell-Gilpin complex, 25-40 percent slopes 0.37 LxE2 Lowell-Gilpin complex, 25-40 percent slopes, eroded 0.37 LxF Lowell-Gilpin complex, 40-70 percent slopes 0.37 LyC Lowell-Westmoreland silt loams, 8-15 percent slopes 0.37 LyD Lowell-Westmoreland silt loams, 15-25 percent slopes 0.37 113 Table A.1 (continued)
LyE Lowell-Westmoreland silt loams, 25-40 percent slopes 0.37 LyF Lowell-Westmoreland silt loams, 40-70 percent slopes 0.37 Ma Made land 0.00 MaB Markland silt loam, 2-6 percent slopes 0.49 MaC Markland silt loam, 6-15 percent slopes 0.49 MaD2 Markland silt loam, 15-35 percent slopes, eroded 0.49 McA McGary silt loam, 0-3 percent slopes 0.49 McD2 Markland-Glenford complex, 15-35 percent slopes, eroded 0.43 Md Melvin silt loam, ponded 0.43 MdA McGary silt loam, 0-3 percent slopes 0.49 Me Melvin silt loam, ponded 0.43 MeB Mentor silt loam, 2-8 percent slopes 0.37 MeC Mentor silt loam, 8-15 percent slopes 0.37 MeD Mentor silt loam, 15-25 percent slopes 0.37 MfB Mentor-Urban land complex, 2-8 percent slopes 0.37 Mg Melvin silt loam, frequently flooded 0.43 MgB Mentor silt loam, 2-6 percent slopes 0.37 Mh Melvin silt loam, ponded 0.43 MnA Mentor silt loam, 0-2 percent slopes 0.37 MnB Morristown clay loam, 0-8 percent slopes 0.43 MnC Mentor silt loam, 6-15 percent slopes 0.37 MnD Morristown clay loam, 8-25 percent slopes 0.43 MoB Morristown stony clay loam, 0-8 percent slopes 0.32 MoC Mentor silt loam, 8-15 percent slopes 0.37 MoD Morristown stony clay loam, 8-25 percent slopes 0.32 MoE Morristown stony clay loam, 25-40 percent slopes 0.32 MoF Morristown very stony clay loam, 40-70 percent slopes 0.32 MrB Morristown shaly silty clay loam, 1-15 percent slopes 0.32 MrD Morristown shaly silty clay loam, 15-25 percent slopes 0.32 MrF Morristown channery silty clay loam, 25-70 percent slopes 0.32 MsB Morristown silty clay loam, 1-8 percent slopes 0.43 MsC Morristown silty clay loam, 8-15 percent slopes 0.43 MsD Morristown silty clay loam, 15-25 percent slopes 0.43 MsE Morristown silty clay loam, 25-50 percent slopes 0.43 MtF Morristown channery silty clay loam, 25-70 percent slopes 0.32 Nd Newark silt loam, occasionally flooded 0.43 Ne Newark silt loam, occasionally flooded 0.43
114 Table A.1 (continued) Nf Newark silt loam, frequently flooded 0.43 Ng Newark silt loam, frequently flooded 0.43 Nm Newark silt loam, ponded 0.43 Nn Newark variant silt loam, frequently flooded 0.37 No Nolin variant silt loam, occasionally flooded 0.37 Np Nolin silt loam, frequently flooded 0.43 OmB Omulga silt loam, 1-6 percent slopes 0.43 OmC Omulga silt loam, 6-15 percent slopes 0.43 Or Orville silt loam, occasionally flooded 0.37 OtC Otwell silt loam, 8-15 percent slopes 0.55 Pg Pits, gravel 0.00 RaB Rawson silt loam, 2-6 percent slopes 0.32 RcC Richland loam, 8-15 percent slopes 0.37 RcD Richland loam, 15-25 percent slopes 0.37 RcE Richland moderately stony loam, 25-40 percent slopes 0.28 RdD Rigley channery loam, 15-25 percent slopes 0.17 RfC Rigley loam, 8-15 percent slopes 0.24 RgC Rigley sandy loam, 6-15 percent slopes 0.24 RgD Rigley sandy loam, 15-25 percent slopes 0.24 RgE Rigley sandy loam, 25-35 percent slopes 0.24 RhB Richland silt loam, 3-8 percent slopes 0.37 RhE Rigley-Coshocton complex, 25-40 percent slopes 0.17 RoF Rodman gravelly sandy loam, 25-70 percent slopes 0.15 Sa Sarahsville silty clay loam, frequently flooded 0.43 Sb Sarahsville silty clay, frequently flooded 0.32 Se Sebring silt loam 0.37 SeB Sees silty clay loam, 2-6 percent slopes 0.37 SsD Sees-Woolper silt loams, 12-18 percent slopes 0.37 SsE Sees-Woolper silt loams, 18-35 percent slopes 0.37 Tf Tioga fine sandy loam, occasionally flooded 0.37 Tk Tioga fine sandy loam, occasionally flooded 0.37 Tm Tioga fine sandy loam, frequently flooded 0.37 Ub Udorthents, loamy-Rock outcrop complex 0.00 Uc Udorthents-Pits complex 0.00 Ud Udorthents-Urban land complex 0.00 Uf Udorthents, loamy, hilly 0.00 Ug Udorthents, loamy 0.00 Uk Udorthents-Pits complex 0.00
115 Table A.1 (continued)
UmC Upshur silt loam, 8-15 percent slopes 0.43 UnD3 Upshur silty clay loam, 12-18 percent slopes, severely eroded 0.37 Up Udorthents-Pits complex 0.00 UpB Upshur silt loam, 3-8 percent slopes 0.43 UpC Upshur silty clay loam, 6-15 percent slopes 0.43 UpC2 Upshur silty clay loam, 6-15 percent slopes, eroded 0.43 UpD2 Upshur silty clay loam, 15-25 percent slopes, eroded 0.43 UrC Upshur silty clay loam, 6-15 percent slopes 0.37 UrC2 Upshur silty clay loam, 6-15 percent slopes, eroded 0.37 UrC3 Upshur silty clay loam, 6-15 percent slopes, severely eroded 0.32 UrD Upshur silty clay loam, 15-25 percent slopes 0.37 UrD3 Upshur silty clay loam, 15-25 percent slopes, severely eroded 0.37 UrE3 Upshur silty clay, 25-40 percent slopes, severely eroded 0.32 UsB Urban land-Glenford complex, 2-8 percent slopes 0.37 VaD2 Vandalia silty clay loam, 15-25 percent slopes, eroded 0.37 VaE2 Vandalia silty clay loam, 25-40 percent slopes, eroded 0.37 VcC2 Vandalia-Guernsey silty clay loams, 8-15 percent slopes, eroded 0.37 VcD2 Vandalia-Guernsey silty clay loams, 15-25 percent slopes, eroded 0.37 VcE2 Vandalia-Guernsey silty clay loams, 25-35 percent slopes, eroded 0.37 VtC Vincent silt loam, 6-15 percent slopes 0.43 VwB Vincent silty clay loam, 2-6 percent slopes 0.43 W Water 0.00 WaA Watertown sandy loam, 0-2 percent slopes 0.17 WaB Watertown sandy loam, 2-6 percent slopes 0.17 WaC Watertown sandy loam, 6-15 percent slopes 0.17 WaD Watertown sandy loam, 15-25 percent slopes 0.17 WaF Watertown sandy loam, 25-70 percent slopes 0.17 WbC Watertown sandy loam, 6-15 percent slopes 0.17 WeC Wellston silt loam, 6-15 percent slopes 0.37 WhB Wellston silt loam, 3-8 percent slopes 0.37 WhB2 Wellston silt loam, 3-8 percent slopes, eroded 0.37 WhC Wellston silt loam, 8-15 percent slopes 0.37 WhC2 Wellston silt loam, 8-15 percent slopes, eroded 0.37 WhD Westmoreland silt loam, 15-25 percent slopes 0.37 WhD2 Westmoreland silt loam, 15-25 percent slopes, eroded 0.37 WhE Westmoreland silt loam, 25-35 percent slopes 0.37 WhF Westmoreland silt loam, 40-60 percent slopes 0.37 WjC Wellston silt loam, 8-15 percent slopes 0.37
116 Table A.1 (continued)
WkB Westmore silt loam, 3-8 percent slopes 0.37 WkC2 Westmore silt loam, 8-15 percent slopes, eroded 0.37 WkE Westmoreland silt loam, 25-40 percent slopes 0.37 WmB Westmoreland silt loam, 3-8 percent slopes 0.37 WmC Westmoreland silt loam, 8-15 percent slopes 0.37 WmC2 Westmoreland silt loam, 8-15 percent slopes, eroded 0.43 WmD Westmoreland silt loam, 15-25 percent slopes 0.37 WmE Westmoreland silt loam, 25-40 percent slopes 0.37 WmF Westmoreland silt loam, 40-70 percent slopes 0.37 WnA Wheeling silt loam, 0-2 percent slopes 0.37 WnB Wheeling silt loam, 2-6 percent slopes 0.37 WnD Westmoreland-Guernsey silt loams, 15-25 percent slopes, eroded 0.37 WnE Westmoreland-Dekalb complex, 25-40 percent slopes 0.37 WnF Westmoreland-Berks complex, 40-70 percent slopes 0.37 WoB Woodsfield silt loam, 1-6 percent slopes 0.43 WoC Westmoreland-Upshur complex, 8-15 percent slopes 0.37 WoD Westmoreland-Upshur complex, 15-25 percent slopes 0.37 WrC Westmoreland-Urban land complex, 6-15 percent slopes 0.37 WrD Westmoreland-Urban land complex, 15-25 percent slopes 0.37 WrF Westmoreland-Berks complex, 40-70 percent slopes 0.37 WsD Westmoreland-Upshur complex, 15-25 percent slopes 0.37 WtB Woodsfield silt loam, 1-8 percent slopes 0.43 WtC Westmoreland silt loam, 8-15 percent slopes 0.37 WtC2 Westmoreland silt loam, 8-15 percent slopes, eroded 0.37 WtD Westmoreland-Coshocton silt loams, 15-25 percent slopes 0.37 WtD2 Westmoreland-Coshocton silt loams, 15-25 percent slopes, eroded 0.43 WtE Westmoreland silt loam, 25-40 percent slopes 0.37 WuC Woodsfield silt loam, 6-15 percent slopes 0.43 WuC2 Woodsfield silt loam, 6-15 percent slopes, eroded 0.37 WuD2 Westmoreland-Guernsey silt loams, 15-25 percent slopes, eroded 0.37 WuE2 Westmoreland-Guernsey silt loams, 25-40 percent slopes, eroded 0.37 WxB Woolper silt loam, 2-6 percent slopes 0.37 WyC Woolper and Sees silt loams, 6-12 percent slopes 0.37 ZaB Zanesville silt loam, 1-6 percent slopes 0.43 ZaC Zanesville silt loam, 6-15 percent slopes 0.43 ZnB Zanesville silt loam, 3-8 percent slopes 0.43 ZnB2 Zanesville silt loam, 3-8 percent slopes, eroded 0.43 ZnC Zanesville silt loam, 8-15 percent slopes 0.43
117 Table A.1 (continued)
ZnC2 Zanesville silt loam, 8-15 percent slopes, eroded 0.43 ZnD2 Zanesville silt loam, 12-18 percent slopes, eroded 0.43 ZoB Zanesville-Woodsfield silt loams, 2-6 percent slopes 0.43 ZoB2 Zanesville-Woodsfield silt loams, 2-6 percent slopes, eroded 0.43 ZoC2 Zanesville-Woodsfield silt loams, 6-12 percent slopes, eroded 0.43 ZoD2 Zanesville-Woodsfield silt loams, 12-18 percent slopes, eroded 0.43 Zp Zipp silty clay loam, frequently flooded 0.28 Zs Zipp silty clay loam, ponded 0.28 118
Figure A.1 Soil Composition in Watersheds
119
Figure A.1 (continued)
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139
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140
Figure A.1 (continued) 141 Appendix B C++ Program for Computing LS Factor (Van Remortel, Maichle, Hickey, 2004)
/*RUSLE_LS_4_PC.AML /* /*Calculates LS Factor using DEM data according to RUSLE-based criteria. /* /*Code prepared by: Rick D. Van Remortel, Lockheed Martin Environmental /*Services, Las Vegas, NV, latest draft dated Dec 2003. Other primary /*contibutors are: Robert J. Hickey, Central Washington University, /*Ellensburg, WA; Mathew E. Hamilton and Robert W. Maichle, Lockheed /*Martin Environmental Services, Las Vegas, NV. /* /*RUSLE Version 4 /*Corrects computational order of operations for S-constituent elements from /*previous versions, which results in a more accurate LS factor estimate for RUSLE. /* /*RUSLE Version 3 (May 2002; revised Aug 2003 to correct rounding problem in final /*LS grid) increased speed by inverting order of slope-length re-initialization code; /*adjusted slope angle code to get more consistent results on assignment of minimum /*slope gradients; adjusted cell length code to make more generic and solve ESRI's /*ArcInfo 7 error with "in" function and resultant portability to ArcInfo 8 on PC. /* /*RUSLE Version 2 added more caveats about watershed catchment configuration of /*input DEM, and modified number of nodata check grids that were produced in the /*initial RUSLE Version 1. /* /*Original USLE-based AML code written by Robert Hickey, USLE Version 1 documented by /*Hickey et al. (1994) and USLE Version 2 by Hickey (2000). The USLE Version 2 code /*was modified by Rick Van Remortel and Matt Hamilton, Lockheed Martin Environmental /*Services, Las Vegas, NV, with a RUSLE focus, to change a few of the assumptions /*about filled sinks and flat areas, and to address the handling of any residual /*nodata strips near the watershed boundary, allow assignment of separate slope /*cutoff factors for different slope ranges, and utilize LS-calculation algorithms /*in accordance with numerous RUSLE improvements documented in McCool et al. (1997) /*as Chapter 4 within the RUSLE Handbook (Renard et al. (1997). A journal article /*describing the RUSLE-based AML has been published with the following citation: /*Van Remortel, R.D., M.E. Hamilton, and R.J. Hickey. 2001. Estimating /*the LS factor for RUSLE through iterative slope length processing of digital /*elevation data within ArcInfo Grid. Cartography Vol. 30, No. 1, Pg. 27-35. /* /*Tested on: ArcInfo Workstation 8.2 on WinXP 142 /* /*Notes for the user: /* /*Steeper, longer slopes produce higher overland flow velocities, but soil loss /*is much more sensitive to changes in S than to changes in L. The RUSLE effects /*of irregular and segmented slope shapes are not addressed within the AML. /* /*LS calculation algorithms are based on the RUSLE research of McCool et al. (1997) /*which corrects slope length for horizontal projection; useful in GIS where slope /*lengths are measured off grid cells or maps (x,y) instead of in the field (x,y,z). /* /*The AML calculates slope length from high points (e.g., ridgetops) towards low points /*such as the watershed pour point or other outlet. An administratively-defined /*watershed (e.g., HUC) may not be suitable unless it's also a hydrologically defined /*catchment area. The ideal input for generating an LS-factor grid is a DEM dataset /*(e.g., NED) of suitable extent that has been either clipped or enlarged to encompass /*the zone of interest plus any additional relevant catchment area. To avoid any /*scale-induced edge effects, the mapextent should be slightly larger than the area of /*interest. Make sure DEM elevation units are the same as horizontal distance units /*(the default is meters). /* /*The output from the L and S calculations should be closely examined to ensure /*that the calculations are being applied properly and that there are no significant /*format problems with the input DEM data. If processing difficulties occur with the /*use of a floating-point format, truncating or rounding to an integer format may be /*advisable as many DEM product suppliers will not attest to the significance of /*decimal digits in their data sets. The presence of horizontal or vertical stippling, /*corn-rowing, or edge-matching anomalies in the DEM can yield erratic or discontinuous /*slope length features. There are smoothing algorithms available that may correct /*some of the DEM irregularities but will also result in unwanted smoothing /*or generalization of other DEM elevation cells that did not require any such /*correction. If utilized, DEM-enhancement algorithms should be well-documented and /*applied with caution to avoid gross over-extension of slope lengths. /* /*Define slope angle (theta) in degrees (inverse tangent of %slope gradient). The /*slope cutoff factor (a value between 0 and 1) is the relative change in slope /*angle that will cause the slope length cumulation to end and start over with the /*next downslope cell; a high factor value will cause the slope length cumulation /*to end more easily than a small factor value, i.e., a smaller slope differential /*between cells is required to end cumulation when using a factor of 0.7 versus /*using a factor of 0.5 (the opposite of what one would initially think). This /*is a very important consideration for the initial settings, so use care. /* /*The routine periodically uses 1-cell buffer grid to avoid nodata around edges; 143 /*this will often be sufficient to prevent edge errors for many accurately clipped /*input DEMs; however, adding a buffer of about 10 cells to the input watershed DEM /*is recommended to ensure that possible "trapped pools" or strips of nodata /*cells near the outer border of the watershed can later be clipped out of the /*LS-factor grid using the actual watershed boundary. /* /*********************************************************************** ***
&echo &off
/*define a root prefix name (4 characters or less) for study area. &type &sv sa = [response 'Enter a study area root prefix name, 4 characters or less']
/*identify the workspace containing DEM and study area boundary grids. &type &sv ws = [response 'Enter full path to workspace holding DEM and boundary grids'] &if [exists %ws% -workspace] &then &goto skipto11 &if ^ [exists %ws% -workspace] &then &do &type &type NOTE: Wrong path identified! &sv ws = [response 'Re-enter full path to workspace holding DEM and boundary grids'] &end &label skipto11
/*specify input dem elevation grid name. &type &sv dem_input = [response 'Enter name of the input DEM grid']
/*specify watershed boundary grid for clipping final LS grid. &type &sv wshed = [response 'Enter name of study area boundary grid']
/*identify DEM units, ensure vertical & horizontal are same. &type &sv demunits = [response 'Enter DEM measurement units, meters or feet '] &if [null %demunits%] &then &sv demunits = meters &if %demunits% eq meters or %demunits% eq feet &then 144 &goto skipto12 &if %demunits% ne meters or %demunits% ne feet &then &do &type &type NOTE: Wrong DEM vertical/horizontal units! &sv demunits = [response 'Re-enter DEM measurement units, meters or feet '] &if [null %demunits%] &then &sv demunits = meters &end &label skipto12
/*set slope cutoff factors for ending/beginning slope length cumulation; use /*different factors for lt or ge 5 percent slope gradients. &type &sv scf_lt5 = [response 'Enter slope cutoff factor for slopes < 5% : suggested = .7'] &if [null %scf_lt5%] &then &sv scf_lt5 = .7 &if %scf_lt5% lt 1.1 &then &goto skipto13 &if %scf_lt5% ge 1.1 &then &do &type &type NOTE: Erroneous factor value! &sv scf_lt5 = [response 'Re-enter slope cutoff factor for slopes < 5% : suggested = .7'] &if [null %scf_lt5%] &then &sv scf_lt5 = .7 &end &label skipto13 &type &sv scf_ge5 = [response 'Enter slope cutoff factor for slopes >= 5% : suggested = .5'] &if [null %scf_ge5%] &then &sv scf_ge5 = .5 &if %scf_ge5% lt 1.1 &then &goto skipto14 &if %scf_ge5% ge 1.1 &then &do &type &type NOTE: Erroneous factor value! &sv scf_ge5 = [response 'Re-enter slope cutoff factor for slopes >= 5% : suggested = .5'] &if [null %scf_ge5%] &then &sv scf_ge5 = .5 &end &label skipto14
145 w %ws% &if ^ [exists ls_rusle -workspace] &then cw ls_rusle w ls_rusle
&wat runspecs.log &type %sa% &type %ws% &type %dem_input% &type %wshed% &type %demunits% &type %scf_lt5% &type %scf_ge5% &wat &off grid setwindow ..\%dem_input% setcell ..\%dem_input%
/*create filled dem grid using Hickey's alternative to the Grid fill command; this /*one uses a sliding 1-cell donut annulus applied to an individual sink cell /*to adopt the minimum value of its octagonal neighbors, thus filling the sink. &if [exists dem_fill -grid] &then kill dem_fill all &if [exists dem_fill2 -grid] &then kill dem_fill2 all dem_fill = ..\%dem_input% finished = scalar(0) &do &until [show scalar finished] eq 1 finished = scalar(1) rename dem_fill dem_fill2 if (focalflow(dem_fill2) eq 255) { dem_fill = focalmin (dem_fill2, annulus, 1, 1) test_grid = 0 } else { dem_fill = dem_fill2 test_grid = 1 } endif kill dem_fill2 all /*test for no more sinks filled docell finished {= test_grid end 146 kill test_grid all &end
/*create inflow and outflow direction grids which assign possible inflow or /*outflow direction values within a cell's immediate octagonal neighborhood; /*these grids may legitimately include a few cells with values corresponding to /*other than the primary orthogonal or diagonal directions. &if [exists flowdir_in -grid] &then kill flowdir_in all flowdir_in = focalflow (dem_fill) /*create outflow direction grid &if [exists flowdir_out -grid] &then kill flowdir_out all flowdir_out = flowdirection (dem_fill)
&describe dem_fill /*reset window to include a 1-cell buffer around input DEM boundary. setwindow [calc [show scalar $$wx0] - [show scalar $$cellsize]] ~ [calc [show scalar $$wy0] - [show scalar $$cellsize]] ~ [calc [show scalar $$wx1] + [show scalar $$cellsize]] ~ [calc [show scalar $$wy1] + [show scalar $$cellsize]] /*create 1-cell buffer dem to change nodata (nd) on edge cells to a value &if [exists dem_fill_b -grid] &then kill dem_fill_b all dem_fill_b = con (isnull(dem_fill), focalmin(dem_fill), dem_fill) kill dem_fill all
/*set cell length for orthogonal and diagonal flow directions. &sv cell = [show scalar $$cellsize] &sv cellorth = (1.00 * %cell%) &sv celldiag = (1.4142 * %cellorth%)
/*calculate downslope angle in degrees for each cell; amended previous code to reset /*groups of "flat" cells (0.0-degree slope by default, where flowdir_out ^= octagonal /*direction) to a value >0.00 and <0.57 (inv. tan of 1% gradient); suggested value /*is 0.1; new assumption is that all cells, even essentially flat areas such as dry /*lakes, have slope > 0.00 degrees; this ensures that all cells remain connected to /*the flow network, and therefore are assigned a slope angle and final LS factor /*value, however small it might be; the () below prevents problems that occur with /*using whole numbers. &if [exists down_slp_ang -grid] &then kill down_slp_ang all if (flowdir_out eq 64) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(0, -1)) div %cellorth%) else if (flowdir_out eq 128) 147 down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(1, -1)) div %celldiag%) else if (flowdir_out eq 1) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(1, 0)) div %cellorth%) else if (flowdir_out eq 2) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(1, 1)) div %celldiag%) else if (flowdir_out eq 4) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(0, 1)) div %cellorth%) else if (flowdir_out eq 8) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(-1, 1)) div %celldiag%) else if (flowdir_out eq 16) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(-1, 0)) div %cellorth%) else if (flowdir_out eq 32) down_slp_ang = deg * atan((dem_fill_b - dem_fill_b(-1, -1)) div %celldiag%) else down_slp_ang = 0.1 endif &if [exists down_slp_ang2 -grid] &then kill down_slp_ang2 all down_slp_ang2 = con (down_slp_ang eq 0, 0.1, down_slp_ang) kill down_slp_ang all rename down_slp_ang2 down_slp_ang
/*reset window to normal extent and clip downslope grid, rename as original name. setwindow ..\%dem_input% &if [exists down_slp_ang2 -grid] &then kill down_slp_ang2 all down_slp_ang2 = down_slp_ang kill down_slp_ang rename down_slp_ang2 down_slp_ang
/*calculate cell slope length considering orthogonal & diagonal outflow dir. &if [exists slp_lgth_cell -grid] &then kill slp_lgth_cell all if (flowdir_out eq 2) slp_lgth_cell = %celldiag% else if (flowdir_out eq 8) slp_lgth_cell = %celldiag% else if (flowdir_out eq 32) slp_lgth_cell = %celldiag% else if (flowdir_out eq 128) slp_lgth_cell = %celldiag% else slp_lgth_cell = %cellorth% endif
148 /*reset window to buffer extent, create outflow dir grid w/ buffer cells eq 0. setwindow dem_fill_b &if [exists flowdir_out_b -grid] &then kill flowdir_out_b all flowdir_out_b = con (isnull(flowdir_out), 0, flowdir_out) kill flowdir_out all
/*create initial cumulative slope length grid and do bitwise compare of flowdir_in /*with flowdir_out to find normally flowing cells, set these to nodata, then /*calculate high points (includes filled sinks) to 1/2 cell length. &if [exists slp_lgth_cum -grid] &then kill slp_lgth_cum all if ((flowdir_in && 64) and (flowdir_out_b(0, -1) eq 4)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 128) and (flowdir_out_b(1, -1) eq 8)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 1) and (flowdir_out_b(1, 0) eq 16)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 2) and (flowdir_out_b(1, 1) eq 32)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 4) and (flowdir_out_b(0, 1) eq 64)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 8) and (flowdir_out_b(-1, 1) eq 128)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 16) and (flowdir_out_b(-1, 0) eq 1)) slp_lgth_cum = setnull(1 eq 1) else if ((flowdir_in && 32) and (flowdir_out_b(-1, -1) eq 2)) slp_lgth_cum = setnull(1 eq 1) else slp_lgth_cum = 0.5 * slp_lgth_cell endif
/*set beginning slope length points (high points and filled sinks) to be added back /*in later after slope lengths for all other cells have been determined for each /*iteration; beginning points will have a value of 1/2 their cell slope length; /*a beginning point is a cell that has no points flowing into it or if the only /*cells flowing into it are of equal elevation; amended previous code to change /*assumption that "flat" high points get a value of zero cell slope length to /*1/2-cell slope length; the new assumption is that the minimum cumulative /*slope length is 1/2 cell slope length even for filled sinks and "flat" high /*points, thereby ensuring the LS factor value for every cell > 0.00. &if [exists slp_lgth_beg -grid] &then kill slp_lgth_beg all slp_lgth_beg = con (isnull(slp_lgth_cum), %cell%, slp_lgth_cum)
149 /*assign slope-end factor where slope length cumulation is ended; amended previous /*code to use RUSLE guidelines suggesting that a slope break of 5% (2.8624 deg angle) /*separates two different erosion/deposition regimes for gentle and steep slopes; /*this is also a convenient break to address concentration dependency issues, where /*the effects of relative changes in slope are inordinately amplified at lower gradients; /*for slope gradients of < 5%, use a higher factor than for >= 5%; this makes it easier /*on shallower slopes to end erosion and begin deposition; i.e., a higher cutoff factor /*means that less slope reduction is needed to end cumulation. &if [exists slp_end_fac -grid] &then kill slp_end_fac all if (down_slp_ang lt 2.8624) slp_end_fac = %scf_lt5% else if (down_slp_ang ge 2.8624) slp_end_fac = %scf_ge5% endif
/*remove any residual directional grids if present from a previous run. &if [exists fromcell_n -grid] &then kill fromcell_n all &if [exists fromcell_ne -grid] &then kill fromcell_ne all &if [exists fromcell_e -grid] &then kill fromcell_e all &if [exists fromcell_se -grid] &then kill fromcell_se all &if [exists fromcell_s -grid] &then kill fromcell_s all &if [exists fromcell_sw -grid] &then kill fromcell_sw all &if [exists fromcell_w -grid] &then kill fromcell_w all &if [exists fromcell_nw -grid] &then kill fromcell_nw all
/*amended previous code to set up additional nodata tests that create a series of /*nodata grids to track progress of run; reset window to normal extent, use filled /*dem grid to mask testing of buffer cells. setwindow ..\%dem_input% setmask ..\%dem_input% ndcell = scalar(1) /*amended previous code to set iterative nodata cell count grids to zero. &if [exists slp_lgth_nd2 -grid] &then kill slp_lgth_nd2 all slp_lgth_nd2 = 0 &sv warn = .FALSE. 150
/*begin iterative loop to calculate cumulative slope length for every cell. &sv finished = .FALSE. &sv n = 1 &do &until %finished%
/*keep copy of previous iterations's max cumulation grid to check progress. &if [exists slp_lgth_prev -grid] &then kill slp_lgth_prev all copy slp_lgth_cum slp_lgth_prev
&sv counter = 0 &do counter = 1 &to 8 /*set variables for the if that follows. &select %counter% &when 1 &do &sv fromcell_dir = fromcell_n &sv dirfrom = 4 &sv dirpossto = 64 &sv cellcol = 0 &sv cellrow = -1 &end &when 2 &do &sv fromcell_dir = fromcell_ne &sv dirfrom = 8 &sv dirpossto = 128 &sv cellcol = 1 &sv cellrow = -1 &end &when 3 &do &sv fromcell_dir = fromcell_e &sv dirfrom = 16 &sv dirpossto = 1 &sv cellcol = 1 &sv cellrow = 0 &end &when 4 &do &sv fromcell_dir = fromcell_se &sv dirfrom = 32 &sv dirpossto = 2 &sv cellcol = 1 151 &sv cellrow = 1 &end &when 5 &do &sv fromcell_dir = fromcell_s &sv dirfrom = 64 &sv dirpossto = 4 &sv cellcol = 0 &sv cellrow = 1 &end &when 6 &do &sv fromcell_dir = fromcell_sw &sv dirfrom = 128 &sv dirpossto = 8 &sv cellcol = -1 &sv cellrow = 1 &end &when 7 &do &sv fromcell_dir = fromcell_w &sv dirfrom = 1 &sv dirpossto = 16 &sv cellcol = -1 &sv cellrow = 0 &end &when 8 &do &sv fromcell_dir = fromcell_nw &sv dirfrom = 2 &sv dirpossto = 32 &sv cellcol = -1 &sv cellrow = -1 &end &end
/*test flow source cell for nodata using n-notation, control downslope cell /*advance. First test inflow and outflow direction grids for possible flow /*source cell. if (not(flowdir_in && %dirpossto%)) %fromcell_dir% = 0 else if (flowdir_out_b(%cellcol%, %cellrow%) <> %dirfrom%) %fromcell_dir% = 0 /*then test current cell with respect to source cell slope-end factor cutoff /*criteria; if met, set to 0 to start cumulation at and below the cell. 152 else if (down_slp_ang lt (down_slp_ang(%cellcol%, %cellrow%) * slp_end_fac)) %fromcell_dir% = 0 else if (down_slp_ang ge (down_slp_ang(%cellcol%, %cellrow%) * slp_end_fac)) %fromcell_dir% = slp_lgth_prev(%cellcol%, %cellrow%) + ~ slp_lgth_cell(%cellcol%, %cellrow%) else if (isnull(slp_lgth_prev(%cellcol%, %cellrow%))) %fromcell_dir% = setnull(1 eq 1) else %fromcell_dir% = 0 endif &end
/*select max cumulative slope length in fromcell dir grids, else beg. cell value. &if [exists slp_lgth_cum -grid] &then kill slp_lgth_cum all slp_lgth_cum = max(fromcell_n, fromcell_ne, fromcell_e, fromcell_se, ~ fromcell_s, fromcell_sw, fromcell_w, fromcell_nw, slp_lgth_beg)
/*test for the last iteration filling in all cells with data. &sv nodata = [show scalar ndcell] &if %nodata% eq 0 &then &sv finished = .TRUE. /*test for any residual nodata cells. &if [exists slp_lgth_nd -grid] &then kill slp_lgth_nd all if (isnull(slp_lgth_cum) and not isnull(flowdir_out_b)) slp_lgth_nd = 1 else slp_lgth_nd = 0 endif ndcell = scalar(0) docell ndcell }= slp_lgth_nd end
/*amended previous code to allow monitoring of whether nodata cells decrease with /*each iteration; if no more decrease after 2 iterations, end the iterative loop /*and proceed to creation of LS grid; in this event the likelihood is that there /*are one or more small nodata strips along outer boundary, probably within the /*10-cell buffer area of the input DEM and not within the actual study area. &if [exists nd_chg2 -grid] &then kill nd_chg2 all if (slp_lgth_nd eq slp_lgth_nd2) nd_chg2 = 0 else 153 nd_chg2 = 1 endif ndchg2 = scalar(0) docell ndchg2 }= nd_chg2 end &sv nd2 = [show scalar ndchg2] &if %nd2% eq 0 &then &do &sv finished = .TRUE. &sv warn = .TRUE. &end
/*remove temporary directional grids from the latest iteration. kill (!fromcell_n fromcell_ne fromcell_e fromcell_se fromcell_s fromcell_sw ~ fromcell_w fromcell_nw!) /*amended previous code to move nodata-test grid 1 notch to prepare for next loop. &if [exists slp_lgth_nd2 -grid] &then kill slp_lgth_nd2 all copy slp_lgth_nd slp_lgth_nd2 kill slp_lgth_nd all
&sv n = %n% + 1 &type This begins slope length iteration %n%
&end
/*change name of cumulation grid from final iteration to max, clip, rename back again. rename slp_lgth_cum slp_lgth_max /*resetting window to normal extent. setwindow ..\%dem_input% &if [exists slp_lgth_max2 -grid] &then kill slp_lgth_max2 all rename slp_lgth_max slp_lgth_max2 slp_lgth_max = slp_lgth_max2 kill slp_lgth_max2 all
/*convert slope length in meters to feet if necessary. &if [exists slp_lgth_ft -grid] &then kill slp_lgth_ft all &if %demunits% eq meters &then slp_lgth_ft = slp_lgth_max div 0.3048 &else slp_lgth_ft = slp_lgth_max
154 /*amended previous code to assign RUSLE slope length exponent (m) from rill/interrill /*ratio; assuption is that rangeland/woodland has low susceptibility; used guidelines /*in Table 4-5 in McCool et al. (1997) with minor extrapolation for end members. &if [exists m_slpexp -grid] &then kill m_slpexp all if (down_slp_ang le 0.1) m_slpexp = 0.01 else if ((down_slp_ang gt 0.1) and (down_slp_ang lt 0.2)) m_slpexp = 0.02 else if ((down_slp_ang ge 0.2) and (down_slp_ang lt 0.4)) m_slpexp = 0.04 else if ((down_slp_ang ge 0.4) and (down_slp_ang lt 0.85)) m_slpexp = 0.08 else if ((down_slp_ang ge 0.85) and (down_slp_ang lt 1.4)) m_slpexp = 0.14 else if ((down_slp_ang ge 1.4) and (down_slp_ang lt 2.0)) m_slpexp = 0.18 else if ((down_slp_ang ge 2.0) and (down_slp_ang lt 2.6)) m_slpexp = 0.22 else if ((down_slp_ang ge 2.6) and (down_slp_ang lt 3.1)) m_slpexp = 0.25 else if ((down_slp_ang ge 3.1) and (down_slp_ang lt 3.7)) m_slpexp = 0.28 else if ((down_slp_ang ge 3.7) and (down_slp_ang lt 5.2)) m_slpexp = 0.32 else if ((down_slp_ang ge 5.2) and (down_slp_ang lt 6.3)) m_slpexp = 0.35 else if ((down_slp_ang ge 6.3) and (down_slp_ang lt 7.4)) m_slpexp = 0.37 else if ((down_slp_ang ge 7.4) and (down_slp_ang lt 8.6)) m_slpexp = 0.40 else if ((down_slp_ang ge 8.6) and (down_slp_ang lt 10.3)) m_slpexp = 0.41 else if ((down_slp_ang ge 10.3) and (down_slp_ang lt 12.9)) m_slpexp = 0.44 else if ((down_slp_ang ge 12.9) and (down_slp_ang lt 15.7)) m_slpexp = 0.47 else if ((down_slp_ang ge 15.7) and (down_slp_ang lt 20.0)) m_slpexp = 0.49 else if ((down_slp_ang ge 20.0) and (down_slp_ang lt 25.8)) m_slpexp = 0.52 else if ((down_slp_ang ge 25.8) and (down_slp_ang lt 31.5)) m_slpexp = 0.54 else if ((down_slp_ang ge 31.5) and (down_slp_ang lt 37.2)) m_slpexp = 0.55 155 else if (down_slp_ang ge 37.2) m_slpexp = 0.56 endif
/*amended previous code to calculate L constituent by slopelength/72.6 to the /*mth power as defined by McCool et al. (1997). &if [exists %sa%_ruslel -grid] &then kill %sa%_ruslel all docell %sa%_ruslel = pow((slp_lgth_ft div 72.6), m_slpexp) end
/*amended previous USLE code to calculate S constituent using different algorithms /*for lt or ge sin of 9% slope as defined by McCool et al. (1997), where: /*radian = 57.2958 deg (factor = 6.2832); deg (theta) = inv tan of % gradient; /*(e.g., 0.09 slope gradient = 5.1428 deg angle = 0.0898 radians). /*NOTE: RDV 12/03 Fixed previous computational order-of-operations problem below &if [exists %sa%_rusles -grid] &then kill %sa%_rusles all %sa%_rusles = con (down_slp_ang ge 5.1428, 16.8 * (sin(down_slp_ang div deg)) - .50, ~ 10.8 * (sin(down_slp_ang div deg)) + .03)
/*multiply L and S constituents to produce LS-factor integer grid clipped to the /*watershed boundary, use .vat to perform statistical analysis as necessary; /*define grid value as * 100 to retain significant digits for future calculations. /*NOTE: RDV 8/03 Fixed previous rounding problem in integer function below setwindow ..\%wshed% setmask ..\%wshed% &if [exists %sa%_ruslels2 -grid] &then kill %sa%_ruslels2 all %sa%_ruslels2 = int (((%sa%_ruslel * %sa%_rusles) * 100) + .5) buildvat %sa%_ruslels2 q
/*define actual LS-factor attribute as "value/100" rounded to 2 decimal places. additem %sa%_ruslels2.vat %sa%_ruslels2.vat ls_factor 8 8 n 2 tables sel %sa%_ruslels2.vat calc ls_factor = value / 100 q w
156 &echo &off &return
157 Appendix C L Factor Maps
Figure C.1 Wills Creek Lake Watershed Slope Length Factor 158
Figure C.2 Senecaville Lake Watershed Slope Length Factor 159
Figure C.3 Salt Fork Lake Watershed Slope Length Factor 160
Figure C.4 Wills Creek Lake Contributing Area Slope Length Factor
161 Appendix D S Factor Maps
Figure D.1 Wills Creek Lake Watershed Slope Steepness Factor 162
Figure D.2 Senecaville Lake Watershed Slope Steepness Factor 163
Figure D.3 Salt Fork Lake Watershed Slope Steepness Factor 164
Figure D.4 Wills Creek Lake Contributing Area Slope Steepness Factor
165 Appendix E Watershed Land Cover Maps
Figure E.1 Wills Creek Lake Watershed Land Cover 166
Figure E.2 Senecaville Lake Watershed Land Cover 167
Figure E.3 Salt Fork Lake Watershed Land Cover 168
Figure E.4 Wills Creek Lake Contributing Area Land Cover
169 Appendix F C Factor with Percent Land Cover and Percent Erosion
Wills Creek Lake Watershed (50th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.1 Wills Creek Lake Watershed Cover-Management Factor (50th Percentile)
Senecaville Lake Watershed (50th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.2 Senecaville Lake Watershed Cover-Management Factor (50th Percentile) 170 Salt Fork Lake Watershed (50th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.3 Salt Fork Lake Watershed Cover-Management Factor (50th Percentile)
Wills Creek Lake Contributing Area (50th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.4 Wills Creek Lake Contributing Area Cover-Management Factor (50th
Percentile) 171 Wills Creek Lake Watershed (75th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.5 Wills Creek Lake Watershed Cover-Management Factor (75th Percentile)
Senecaville Lake Watershed (75th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.6 Senecaville Lake Watershed Cover-Management Factor (75th Percentile)
172 Salt Fork Lake Watershed (75th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.7 Salt Fork Lake Watershed Cover-Management Factor (75th Percentile)
Wills Creek Lake Contributing Area (75th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.8 Wills Creek Lake Contributing Area Cover-Management Factor (75th
Percentile) 173 Wills Creek Lake Watershed (90th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.9 Wills Creek Lake Watershed Cover-Management Factor (90th Percentile)
Senecaville Lake Watershed (90th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.10 Senecaville Lake Watershed Cover-Management Factor (90th Percentile) 174 Salt Fork Lake Watershed (90th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.11 Salt Fork Lake Watershed Cover-Management Factor (90th Percentile)
Wills Creek Lake Contributing Area (90th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.12 Wills Creek Lake Contributing Area Cover-Management Factor (90th
Percentile) 175 Wills Creek Lake Watershed (95th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.13 Wills Creek Lake Watershed Cover-Management Factor (95th Percentile)
Senecaville Lake Watershed (95th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.14 Senecaville Lake Watershed Cover-Management Factor (95th Percentile) 176 Salt Fork Lake Watershed (95th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.15 Salt Fork Lake Watershed Cover-Management Factor (95th Percentile)
Wills Creek Lake Contributing Area (95th Percentile) 100 90 80 70
60 50
Percent 40 30 Land Cover 20 Erosion 10 0
Figure F.16 Wills Creek Lake Contributing Area Cover-Management Factor (95th
Percentile) 177 Appendix G GIS Watershed Modeling Tutorial
Elevation/Watershed Delineation o Downloaded from USGS Seamless Server . 1/3” NED (file system raster) . NED1, NED2, NED3, NED4 o Steps to create watersheds . Project Raster NAD 1983 17N Bilinear Resampling . “Mosaic to new raster” (NED1, NED2, NED3, NED4) . “Fill” . “Flow Direction” . “Flow Accumulation” . “Pour point” Create point shapefile in ArcCatalog with same shapefile as NED ArcMap o Editor, start editing, create new feature, sketch, place crosshairs on mouth . “Snap Pour Point” . “Watershed” Input raster: Flow Direction Input Pour Point: Pour Point Output: Watershed R Factor o Download data from USGS Seamless Server . Choose window large enough to include 8 counties . Download “National Atlas Counties 2001” o County R-Factor from Ohio DNR o “Clip” layer to watershed boundary
178 K Factor o Download data from NRCS Soil Data Mart . Belmont . Coshocton . Guernsey . Harrison . Monroe . Muskingum . Noble . Tuscarawas o Open “physical properties” of soils in Microsoft Access . Export to excel . Delete all fields except “Map unit symbol” and “Kw” . Insert pivot table . Delete all redundant information o Join to corresponding layer in ArcMap . Use MUSYM field o “Merge” layers o “Clip” layer to watershed boundary o Multiply by factor of ten to omit decimal places if necessary . Note: for this study, the K factor was multiplies by 100
179 LS Factor o Downloaded from Van Remortel et al. o C ++ Instructions . ArcMap select “From raster” “Raster to ASCII” Choose the NED to be converted . Run C++ program Enter path and filename for NED in ASCII format Specify output location for files (no longer than four letters) Program asks if intermediate files should be produced during the computation process “Yes” Program asks if cells with no data should be fixed “Yes” After the program runs, 16 files with .dat suffix o Change suffix to .txt in order for ArcMap to recognize it . Open ArcMap and select “ASCII to Raster” under conversion toolbox Individually convert output files with suffixes o –rusle_l o –rusle_s o –rusle_ls2 Note: the LS factor has already been multiplied by 100 in order to increase accuracy
180 C Factor o Downloaded from seamless.usgs.gov/website/seamless/viewer.htm . NLCD 2006 (CONUS) Land Cover (.tif) . Projection USA Contiguous Albers Equal Area Conic USGS Version Datum D North American 1983 o C Factor from Ohio EPA o Steps for C Factor . Project Raster NAD 1983 17N Nearest Neighbor Spatial analyst reclassify raster o Remove all attributes without data Add new field for “C factor” in the attribute table enable editing Properties Field Primary Display Field = C_FACTOR Clip to watershed o Use input features for clipping geometry Multiply by factor of ten to omit decimal places if necessary o Note: for this study, the C factor was multiplied by 10,000 RUSLE Calculation o Using the “raster calculator” to multiply the R, K, LS, and factors o Divide the results by aggregate factor of 10 used in the RUSLE factors to see results in tons/acre/year . Note: for this study, the RUSLE calculation was divided by 100,000,000 o RUSLE projection NAD 1983 UTM Zone 17N Correlation Matrices o “Band Collection Statistics” (Spatial Analyst) . Input raster bands to be investigated . Check box displaying “Compute covariance and correlation matrices” ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
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