### EFFECTIVENESS OF BMPS IN REDUCING SEDIMENT FROM UNPAVED ROADS IN THE STILLWATER CREEK, OKLAHOMA WATERSHED 1

Donald J. Turton, Michael D. Smolen, and Elaine Stebler 2

ABSTRACT: Erosion from rural unpaved roads is thought to be an important source of sediment in sedimentimpaired streams in Oklahoma and other locations. However, no direct measurements of sediment yields fromrural unpaved roads were previously available for Oklahoma. Four rural unpaved road segments in the Stillwater Creek Watershed were instrumented in a paired watershed design to measure sediment yields to streams before and after the installation of Best Management Practices (BMPs). One segment of each pair remained under current management to serve as a control. The second segment received BMPs after a 1-year calibration period. One BMP consisted of widening the ditches, re-shaping ditches and cutslopes, putting a proper crown on the road surface, and vegetating disturbed areas with grass. The other BMP consisted of creating a proper crown on the road bed, applying a geo-synthetic fabric to the road bed and surfacing with 127 mm of crusher run gravel containing 12-15% fines to serve as a binder. Road segment sediment yields for individual storms varied, depending on factors such as rainfall amount and intensity. During the pre-BMP year, storm sediment yields ranged from 0 to 4.3 Mg on one pair of segments and from 0 to 2.8 Mg on the other. The storm sediment yields and annual yields were in the same order of magnitude as sediment yields from unpaved rural or forest roads reported in other studies. Sediment yields were significantly reduced on both segments by the installation of BMPs, approximately 80% on one segment pair and 20% on the other. The average sediment yield (across the four segments) for the pre-BMP year was 138 Mg ⁄

ha or 120 Mg ⁄

km of road. By extrapolating these average yields across the 479 km of unpaved roads in the Stillwater Creek Watershed and comparing it to estimated sediment yields for other land uses obtained from other sources, we conclude that unpaved roads may contribute up to 35% of the total sediment load to Stillwater Creek.

(KEY TERMS: Best Management Practices; nonpoint source pollution; sediment; erosion; road erosion; unpaved roads; low-volume roads; best management practice effectiveness.)

Turton, Donald J., Michael D. Smolen, and Elaine Stebler, 2009. Effectiveness of BMPs in Reducing Sediment From Unpaved Roads in the Stillwater Creek, Oklahoma Watershed. Journal of the American Water Resources
Association (JAWRA) 45(6):1343-1351. DOI: 10.1111⁄ j.1752-1688.2009.00367.x

INTRODUCTION

Erosion on unpaved rural roads may be a significant source of stream sediment. Roads usually occupy a small percentage of a watershed’s area, but have been reported to contribute a disproportionately large percentage of a stream’s sediment load. Nagle (2001) developed a sediment budget for an agricultural watershed in the Dominican Republic and found that

1 Paper No. JAWRA-08-0186-P of the Journal of the American Water Resources Association (JAWRA). Received October 5, 2008; accepted July 16, 2009. a 2009 American Water Resources Association. Discussions are open until six months from print publication

. 2 Respectively, Associate Professor and Senior Research Specialist (Turton and Stebler), Department of Natural Resource Ecology and Management, Oklahoma State University, 008 C Ag Hall, Stillwater, Oklahoma 74078; and Professor (Smolen), Department of Biosystems and Agricultural Engineering, Oklahoma State University, Oklahoma 74078 (E-Mail⁄Turton: don.turton@okstate.edu).

J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATION 1343 JAWRA JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

Vol. 45, No. 6 AMERICAN WATER RESOURCES ASSOCIATION December 2009roads accounted for 30% of the total despite occupying a small portion of the watershed. In the grassland savannah of Kenya, Dunne (1979) determined that roads and foot paths were responsible for 25-50% of the total basin sediment yields. Nelson and Booth (2002) developed a sediment budget for Issaquah Creek in western Washington. Roads occupied 2.6% of the watershed area, but contributed 15% of the total sediment yield. Some states have recognized the importance of unpaved road erosion and have created rural roads programs to address the issue (Vermont Local Roads Program, 1999; Choctawatchee, Pea andYellow Rivers Management Authority, 2000; Pennsylvania State University Center for Dirt and GravelRoads, 2009). However, only a few studies have actually measured rates of erosion or sediment yields from rural unpaved roads in the United States (U.S.). Rates of unpaved road erosion vary greatly depending on soils, climate, road design, traffic, topography, and other factors. Welsh et al. (2006)measured erosion rates from 20 unpaved road segments in the Upper South Platte River Basin of Colorado over a 5-year period. The mean annual sediment production ranged from 4 to 67 Mg ⁄

ha, varying mainly because of differences in summer
rainfall. Ramos-Scharro  ́ n and MacDonald (2007) measured a sediment yield of 194 Mg ⁄

km ⁄

year from an unpaved rural road segment during a 1-year study in the U.S. Virgin Islands. Concentrations of sediment in runoff from unpaved roads tend to be very high. In a study of unpaved road erosion in Guanella Pass, Colorado, Stevens (2000) measured sediment concentrations ranging from 34 to 38,800 mg ⁄

l, with a median of 1,510 mg ⁄

l. Ziegler et al. (2000) measured sediment concentrations of 100,000 mg ⁄

l and greater in runoff from unpaved roads in rural areas
of Thailand. Forest roads are similar to many unpaved ruralroads in that they are constructed from native materials and are typically unpaved. Sugden and Woods (2007) measured annual sediment yields ranging from 0 to 96.9 Mg ⁄

ha from 20 forest road plots in western Montana. Fransen, et al. (2001) measured sediment yields of 30-380 Mg ⁄

km ⁄

year from established roads and rates ranging from 266 to 7,600 Mg ⁄

km ⁄

year from newly constructed roads at
various locations across New Zealand. Reid and Dunne (1984) measured erosion rates ranging from 3.4 to 36 Mg ⁄

km ⁄

year and as high as 440 Mg ⁄

km ⁄

year on heavily used forest roads in western Washington. In
the Ouachita Mountains of southeastern Oklahoma and central Arkansas, sediment yields ranging from 18 to 172 Mg ⁄

ha ⁄

year (19-114 Mg ⁄

km ⁄

year) have been reported for forest road segments of various ages and slopes (Miller et al., 1985; Vowell, 1985; Turton and Vowell, 2000). There are approximately 117,000 km of unpaved county roads in Oklahoma. Approximately 81% are classified as gravel roads and the remaining 19% are classified as graded or unimproved roads (Association of County Commissioners of Oklahoma, August 4,2008, personal communication). In many cases, counties are required to maintain roads along section linesto provide access to agricultural land even when traffic volume is very low. Many of these roads are poorly designed and are poorly maintained due to restricted county budgets. Miller (2001) surveyed changes in road cross-sections to estimate erosion following the installation of road improvements on rural unpaved roads in southern Oklahoma, but did not quantify sediment yields.The objectives of this study were to measure sediment yields from typical low-volume unpaved road segments in north-central Oklahoma and determinethe reduction in sediment yields following the installation of Best Management Practices (BMPs).

METHODOLOGY

Study Sites

Two pairs of unpaved road segments in the Stillwater Creek, Oklahoma Watershed were chosen for runoff and sediment yield measurements as affected by the installation of road BMPs. Stillwater Creek is a tributary of the Cimarron River and has a drainage
area of 715 km 2 . The watershed is located approximately 70 km north of Oklahoma City and 96 km
west of Tulsa. The road sites were not chosen randomly, but in consultation with the Payne County, Oklahoma Commissioners, who were cooperators in the demonstration aspects of the project. The road segments on the sites represent two typical design
flaws and maintenance issues found in most of the low-traffic volume unpaved roads in the watershed.Site 1 (19th Street 0.5 km west of Vasser Rd.) consisted of two consecutive road segments on opposite sides of a common ridge. The road segments were named 1B (received BMPs) and 1C (control). The road was composed of native soil over shale⁄sandstone bedrock. Site 1 was chosen because like many nongraveled roads in the region, it had a narrow right-of-way, was incised below the surrounding land, lacked bar ditches to provide road bed drainage, was poorly crowned, and was susceptible to deep rutting
when the surface was wet. As a result the road was re-graded frequently. Site 2 (32nd Street just west of Country Club Rd.) consisted of two consecutive road segments that T URTON , S MOLEN

, AND S TEBLER

JAWRA 1344 J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATIONdrain to the same stream. The road segments were named 2B (received BMPs) and 2C (control). Site 2 was approximately 13 km east and 1.5 km south of Site 1. The road was originally constructed from native soil with gravel in a narrow track down the
center of the road surface. Site 2 was chosenbecause like many roads in the area, it lacked a sufficient crown to allow the road bed to drain, and although the right of way was wide enough, theditch capacities were too small to handle flows without eroding the ditch and the road bed. This road also required frequent re-grading to re-shape the ditches and remove small ruts from the part of the road not covered by gravel. Mean annual precipitation for Payne County where the two sites were located is 942 mm (1971- 2000). Mean monthly precipitation is highest in May (145 mm) and June (109 mm) and lowest in January (31 mm) and February (41 mm) (Oklahoma Climatological Survey, 2008). The majority of the annual precipitation is produced by a few large
intense thunderstorms.

Measurements

The study was designed as a paired watershed study, where one road segment of each pair remained under existing conditions and maintenance (1C and2C) to serve as a control, and the neighboring segment was treated with road BMPs (1B and 2B).Maintenance was not carried out on a regular schedule. The road surfaces were re-graded to eliminate ruts after major rain events. Ditches were pulled onan ‘‘as-needed’’ basis determined by the grader operator. Sediment yield, rainfall, and discharge data were collected for 1 year before BMPs were installed and 1 year after the BMPs were installed. The watershed of each segment included approximately half of the road surface (from the crown to the bar ditch), the bar ditch and the associated cutslope (Figure 1). The upslope divides were defined by the hillcrest of
the road. Road segment lengths, slopes, and areas were determined using standard land surveying techniques
and submeter resolution GPS. Soil textures were determined by taking samples from random locations on the road segment surface. A composite sample from each segment was sifted through a 2 mm sieve to remove rock and analyzed for texture by settling tube analysis (Gee and Bauder, 1986). Each site contained a siphoning tipping bucket rain gage, a plastic rain can to serve as a backup,
and two sediment collection stations. Each station was equipped with a sediment collection trough, a 0.46 m H-Flume, and a pumping sampler to sample suspended load not captured in the trough (Figure 2).
The sediment collection stations were connected to
the bar ditches and located at the end of each road segment close to where runoff entered a stream or
other natural outlet. The troughs were constructed of treated plywood and were approximately 2.4 m long, 0.6 m deep, and0.4 m wide. Seven plastic baskets lined with landscape fabric to trap coarse sediment were placedinside the troughs. After each storm, the baskets containing sediment were individually weighed in the field. Subsamples of sediment were pulled from eachbasket, oven dried, and the moisture content determined. The moisture content was used to determine the dry weight of sediment in each basket. Sediment FIGURE 2. Sediment Measuring System at Road Segment 2C. Thesame equipment was installed at each of the road segments. However, only one recording rain gage was installed at each site.FIGURE 1. Road Segment Boundaries and Areas for Road Segment 2B. Boundaries for all of the road segments were defined by the road crown, bar ditch, and cutslope boundaries. E FFECTIVENESS OF BMP S IN R EDUCING S EDIMENT F ROM U NPAVED R OADS IN THE S TILLWATER C REEK , O KLAHOMA WATERSHED

J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATION 1345 JAWRAcollected in the trough baskets was called bulk
sediment.Water stage in the flumes was measured by pressure transducers installed in the stilling wells. The transducers were capable of measuring stage to thenearest 3 mm. Measurements of stage and precipitation were collected by data loggers every 5 minutes. The data loggers also triggered the pumping samplers, based on the following criteria. If the stage rose above 21 mm, a sample was taken. When the stage was above the minimum, samples were taken every 5 minutes if the stage rose or fell 15 mm during that interval, or every 30 minutes if the stage remained constant. This allowed samples to be taken throughout a storm to account for changes in flow and sediment concentration while conserving available bottles. The sampler intakes were located in the wall of the flumenear the flume outlet where velocity, and hence, mixing are greatest. The quantity of sediment that left the system at flow rates below the 21 mm minimum stagewas assumed (and later confirmed by our measurements) to be negligible when compared to the sediment yields when flows were higher. Additional details about the equipment used in this project are available
in Turton et al. (2006).Suspended sediment concentrations were determined by evaporation of a weighed sample (Guy, 1969). Samples were shaken and the entire contents were poured into preweighed aluminum tins. The evaporation method was chosen because preliminary grab samples of road ditch water indicated that sediment concentrations would be too high to make filtering practicable. Sediment concentrations were calculated in parts per million (ppm) and converted directly to mg ⁄

l. When sediment concentrations were very high (above 16,000 ppm) a correction factor (Guy, 1969) was applied to account for the volume of the sample that was occupied by the sediment particles. The suspended sediment yield of each storm was calculated by multiplying together the discharge ofeach time interval, the interval time and the suspended sediment concentration associated with the interval. Interval suspended sediment loads were summed to obtain the total storm suspended load. The total yield for each storm was equal to the sum of the suspended sediment yield and the dry weight
of the bulk sediment.

Road Best Management Practices

Oklahoma does not currently have a generally accepted BMP manual for unpaved rural roads. We used erosion control and road construction practices recommended by the Local Technology Assistance Program (LTAP) located in the Center for Local Government Technology at Oklahoma State University. The LTAP roads program provides training to county road crews in the proper design, construction, and
maintenance of roads. The BMPs used were tailored
to fit the conditions at each site. Because the roadway at Site 1 was incised 2-3 mbelow the surrounding land, properly sloping the cutbanks and widening the ditches would have required extensive excavation and extending the construction beyond the narrow right-of-way (10 m either side ofcenter). Considering the limited budget and capabilities of the county road crew, more cost-effective techniques that would also disturb the least amount of otherwise stable soil were employed. The existing road bed was graded to achieve a 13-19 mm per meter crown, and nonwoven geo-synthetic fabric (3.66 m wide, 141.7 g) was installed. The fabric was covered with 127 mm of crusher run limestone (3⁄ 4 minus) containing approximately 12-15% fines. Three quarters minus is rock that has been crushed such that it passes through a 19 mm (3⁄ 4 inches) sieve.The limestone crusher run compacts to a dense, uniform roadbed material and the geo-synthetic fabric acts as a separator between it and the soil. These practices, however, did not increase the capacity of
the bar ditches. The major problem at Site 2 was poor drainage because of an inadequate road crown and unstable ditches. The BMPs consisted of creating a 13-19 mm per meter crown on the road surface and trapezoidal ditches with a 1:4 fore slope, 1.2-1.8 m flat bottom, and 1:3 back slope. Vegetation was removed from the right-of-way as necessary to allow for the re-grading. To re-establish vegetation as quickly as possible, the disturbed area of the right-of-way was seeded with native Bermuda grass ( Cynodon dactylon ) and covered with a biodegradable mesh ⁄straw erosion control mat. The same type of crusher run limestone used on Site 1 was spread to a thickness of 51 mm over the
surface of the road.

RESULTS

Road segment lengths ranged from 0.16 to 0.26 km. Mean segment slope ranged from 6.6 to8.3% (Table 1). Road segment slope profiles were typically S-shaped, flat at the top and bottom and steep in the middle. The steep sections were 11% on three segments and 15% on another. Due to the distance between them (13 km), the two sites had different storm and annual precipitation totals (Table 2). The precipitation on Site 1 in the pre- and post-BMP T URTON , S MOLEN

, AND S TEBLER

JAWRA 1346 J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATIONyears was approximately 25% below the mean annual amount. Precipitation on Site 2 for both the pre- and post-BMP years was about 4% below the mean annual amount (Table 2). Individual storm rainfalls for storms that produced measurable flow on both road segments at either site ranged from 3 to 116 mm. Average storm rainfall intensities over both sites ranged from <1 to 31 mm ⁄

hour and maximum 30-minute intensities ranged from 2 to 108 mm ⁄

hour.Frequency–depth–duration analysis based on Hershfield (1961) showed a 2-year 6-hour and a 10-year 1-hour storm occurred at Site 2 and 5-year 6-hour, 5-year 12-hour and 10-year 30-minute stormsoccurred at Site 1. One other large event was equivalent to the 2-year 1-hour storm. The remaining events on both sites were less than the 1-year storm
for all durations between 30 minutes and 24 hours.The Revised Universal Soil Loss Equation (RUSLE) rainfall energy-intensity factor (R-factor) was calculated for each storm and totaled for each year (Dissmeyer and Foster, 1984). Annual R-factors for the pre-BMP year were below the annual mean on Site 1 (

) 35%) and above the annual mean on Site 2 (36%). The annual R-factors were 56 and 187% above the mean on Site 1 and Site 2, respectively, for the post-BMP year (Table 2). Most of the rainfall energy was delivered by the larger events mentioned above. Storm sediment yields were linearly regressed with total storm rainfall, maximum 5- and 30-minute intensities and storm R-factor for each segment and year. As rainfall amounts, intensities, and energy increased, sediment yields increased. The rainfall parameters explained from as little as 11% to as muchas 78% of the variation in sediment yields. Flowweighted mean sediment concentrations for each storm across both sites ranged from 223 to 108,000 mg ⁄

l and were in the same orders of magnitude (10 3 to 10 5 mg ⁄

l) as reported in road runoff studies by Stevens (2000) and Ziegler et al. (2000). Thepre-BMP year sediment yields from the four road segments ranged from 6 to 20 Mg (Table 3). The sediment yield from one large storm (68 mm) near theend of the first year was not measured on road segment 2B because construction on the BMPs was underway when the rain occurred. We estimated the sediment yield for that storm using the pre-BMP relationship between segments 2C and 2B. This allowed comparison of annualized sediment yield between sites without sacrificing measurements on the other three segments. The estimated value was not used in the Analysis of variance (ANOVA) and Analysis of covariance (ANCOVA) analysis. Post-BMP annual sediment yields from the two control segments were 6.1 and 24.8 Mg. Post-BMP annual sediment yields from the two BMP-treated segments were 11.5 and 5.7 Mg, respectively (Table 3). Sediment yields from the two BMP-treated segments were considerablylower in the post-BMP year despite the fact that rainfall energy was greater than in the pre-BMP year. Sediment yields from all segments in the pre-BMP year and from the control segments in the post-BMP TABLE 2. Measured Rainfall and Calculated Rainfall Erosivity (R-factors) and Deviations From Annual Means.

Period Measured

Annual Precipitation (mm) Precipitation

Percent
Deviation

From Mean Calculated

Annual
Rainfall Erosivity

MJ (mm⁄ha⁄hour) Erosivity

Percent
Deviation

From Mean

Before BMPs

Site 1 722

) 24 2,018

) 35
Site 2 908

) 4 4,250 +36
After BMPs

Site 1 700

) 26 4,860 +56
Site 2 908

) 4 8,940 +187

Note: Rainfall is based on the period 1971-2000, Oklahoma Climatological Survey, 2008. TABLE 1. Physical Characteristics of the Four Unpaved Road Segments Used in the Study.

Segment 1C 1B 2B 2C

Length (m) 158 203 215 264 Mean slope (%) 8.3 7.8 6.6 6.7 Highest slope (%) 15.3 10.6 11.0 10.6 Road area (ha) 0.055 0.074 0.192 0.109 Drainage area (ha) 0.119 0.157 0.243 (0.294) 0.200 Soil texture Clay

loam Sandy clay loam Loamy
sand Sandy

loam

Note: The road area includes the road surface, ditch, and cut banks on which erosion occurred. The drainage area includes the road area plus surrounding well-vegetated land that drained into the ditches. Value in parenthesis represents the drainage area after

the installation of BMPs. TABLE 3. Annual Sediment Yields and Totals for the Four Road Segments.

Road Segment Year 1, Pre-BMPs Year 2, Post-BMPs

Mg Mg ⁄ha Mg ⁄km Mg Mg ⁄ha Mg ⁄km

1C 6.0 109 76 6.1 111 77

1B 20.1 272 198 11.5 155 113
2C 11.2 103 85 24.8 227 188

2B 13.1* 68 122 5.7 30 53

Station totals 50.0

- - 48.1

- - Area-weighted

means - 138 120

- 112 114

Note: Area-specific yields are based on the road area (Table 1). *Includes an estimate of 5.5 Mg for the storm of May 14, 2005. The value was estimated from the pre-BMP regression relationship developed between the two Site 2 road segments. E FFECTIVENESS OF BMP S IN R EDUCING S EDIMENT F ROM U NPAVED R OADS IN THE S TILLWATER C REEK , O KLAHOMA WATERSHED

J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATION 1347 JAWRAyear were in the same orders of magnitude reported for unpaved rural or forest road runoff (Miller et al.

, 1985; Vowell, 1985; Turton and Vowell, 2000; Fransen
et al. , 2001; Ramos-Scharro  ́ n and MacDonald, 2007). Road segment sediment yields were calculated for all storms that produced measurable runoff from bothpaired segments. Thirty-three pre-BMP and 20 postBMP storms produced measurable runoff on Site 1, while 31 pre-BMP and 22 post-BMP storms produced
measurable runoff on Site 2. Individual storm sediment yields varied, depending on factors such as rainfall amount and intensity. Several small storms generated low sediment yields of <10 kg at both sites. The largest single storm sediment yield recorded at either site (Site 2; 5,702 kg) was generated by a 10- year 1-hour storm. The three largest storms recorded on Site 1 and Site 2 during the pre- and post-BMP years generated between 40 and 72% of the total yearly sediment yields. The relationship between pairs of BMP-treated and control road segments was analyzed storm by storm using a procedure described by the USEPA (1993) and applied by Claussen et al . (1996). Singlestorm sediment yields were found to be log-normally distributed (Kolmogorov–Smirnoff test). An F-test showed that the length of the calibration period was sufficient to detect a 10% change in sediment yield ( p < 0.01). Linear regression was applied to the logtransformed data to determine the relationships between road segments in the pre- and post-BMP years (Figures 3 and 4). ANOVA was used to test the significance of the regressions. The regressions forboth sites and the pre- and post-BMP years were significant (F-test) at p < 0.001. ANCOVA analysis was used to detect changes in slopes and intercepts between the pre- and post-BMP years. On Site 1 the post-BMP slope (1.36) was significantly ( p < 0.05)greater than the pre-BMP slope (0.788), but the postBMP intercept (

) 1.01) was significantly ( p < 0.005) less than the pre-BMP intercept (0.97). On Site 2 the post-BMP slope (1.24) was not significantly ( p < 0.05)different than the pre-BMP slope (1.07), but the postBMP intercept (

) 1.48) was significantly ( p < 0.005) less than pre-BMP intercept (

) 0.17). To estimate the change in sediment yields at eachsite due to the installation of BMPs, we applied postBMP sediment yields from the control segments to the pre-BMP regression equations to estimate the sediment yields that would have occurred had the
BMPs not been installed. The differences between measured and predicted values were calculated and summed (Figure 5). The measured and predicted

FIGURE 3. Storm Sediment Yield Regressions for the Pre- (calibration) and Post-BMP (treatment) Periods for Site 1 Road Segments. FIGURE 5. Road Segment Storm Sediment Yields of Road Segments Predicted by the Measured Minus the Calibration Equation Values During the Post-BMP Year for Sites 1 and 2. Negative values represent a reduction in sediment yield as a result of BMP installation. Storms are graphed in order of occurrence. FIGURE 4. Storm Sediment Yield Regressions for the Pre- (calibration) and Post-BMP (treatment) Periods for Site 2 Road Segments. T URTON , S MOLEN

, AND S TEBLER

JAWRA 1348 J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATIONsediment yields for the post-BMP year at Site 1 were 11.5 and 14.4 Mg, respectively (20% reduction) and 5.7 and 29 Mg, respectively (80% reduction) at Site 2.

DISCUSSION

Best Management Practices Effectiveness

The ANOVA ⁄

ANCOVA analysis demonstrated overall reductions in sediment yields of 20 and 80% for Sites 1 and 2, respectively. However, on Site 1,
this reduction was not consistent across storm sizes (Figure 3). The slope of the post-BMP regression was significantly greater than the pre-BMP slope, but the post-BMP regression intercept was significantly lower than the pre-BMP intercept. This indicates that asstorms became larger, the BMPs became less effective. This result may be explained by short-comings of the BMP installation and the magnitude of the firstfew storms that occurred immediately after construction. Four of the first five storms that occurred in the first month following BMP installation produced sediment yields greater than the yields predicted from the pre-BMP regression equation (Figure 5). Two of these storms produced large rainfalls (30 and 36 mm) with high maximum 30-min intensities (28 and

42 mm ⁄

hour, respectively). At that time some of the crusher run gravel was still loose and unconsolidated, especially along the sides of the road and in the barditches. This material was easily moved by runoff produced by the two large storms. Surface runoff cut new ditches down to bedrock then expanded laterally into the cutslope and road bed. Subsequent storms used the newly cut channels, but did not expand further. After the fifth storm, the measured sediment yield was always less than that predicted by the pre-BMP relationship (what would have occurred had the BMPs not been installed). The reduction in sediment yield was 50% for storms 6-21 (measured yield minuspredicted by pre-BMP relationship divided by the predicted). This indicates that once the site stabilized following construction and the ditches did not expand further, the sediment yields decreased. Because the post-BMP regression developed in the ANOVA ⁄ ANCOVA procedure uses all the data, it may actually be underestimating the reduction in sediment yield over the long run. However, we cannot rule out that future storms larger than those measured in the short post-BMP period would not cause additional lateral cutting into the road bed and cutslope and higher than predicted sediment yield.
The bar ditches on Site 1 were not re-constructed with sufficient capacity because the road was incised approximately 3 m below the surrounding land. Some of the incision may have been created when the roadwas originally built, but most is likely the result of erosion and improper road grading through time. There is no practical way of breaking up the drainage in ditches and routing sediment away from streams on incised roads. The installation of ditches with the proper width and stable shape would have required extensive
excavation and disturbance of road banks that were already stabilized with vegetation. The amount of excavation would have been too expensive for county road budgets and would also have extended the road prism beyond the legal right-of-way into private land.
Some other cost-effective method to stabilize ditches for unpaved roads in narrow cuts is needed. The crusher run gravel was effective in reducing erosion and sediment yield from the road surface at both sites. We observed no rutting in the road surface following rains for at least two years after the gravel was installed. As a result, the road surface required no re-grading during the period. The installation of the geo-synthetic fabric and gravel not only created a better road surface for traffic, it reduced road surface erosion and has saved the county maintenance costs.The road on Site 2 was not deeply incised and cutslopes were relatively small. There was sufficient space to construct the bar ditches with sufficientcapacity and stable side slopes. The slope of the postBMP regression was not significantly different than the pre-BMP slope, but the post-BMP regression intercept was significantly lower then the pre-BMP intercept. As a result, the measured storm sediment yields were consistently less than those estimated by the pre-BMP relationship throughout the post-BMPperiod for all storm amounts and intensities (Figure 5). Sediment yields were relatively high duringthe three large storms that occurred in the month following construction and installation of BMPs. The largest and most intense storm (84 mm; maximum 30 minutes intensity 108 mm ⁄

hour) recorded on Site 2 during the entire 2-year study occurred about 2 weeks after construction. At that time, the grass
was not well established. We observed rill erosion in the ditch in the steepest part (11% slope) of the road segment after this large event. After the sixth storm (about one month), and the grass had grown and thesite stabilized from construction, the reduction in sediment yield was 87% (measured yield minus yield predicted by the pre-BMP relationship).

Importance of Unpaved Road Sediment Yields to the Stillwater Creek Sediment Budget

Bar ditches carrying road runoff from unpaved rural roads in Oklahoma typically drain directly into E FFECTIVENESS OF BMP S IN R EDUCING S EDIMENT F ROM U NPAVED R OADS IN THE S TILLWATER C REEK , O KLAHOMA WATERSHED

J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATION 1349 JAWRAstreams, because the right-of-ways are limiting (10 m on either side of the road center) and many roads are deeply incised. The deep incision prevents installation of drainage relief structures such as wing ditches orcross drain culverts that reduce velocity and sediment transport capacity. In a related component ofthis study, roads, ditches, stream crossings, and outlets for all unpaved road segments in the Lake Carl Blackwell subbasin of Stillwater Creek were mapped using submeter resolution GPS and GIS (Turton and Stebler, 2006 unpublished data). The analysis showed that 80% of the total unpaved road area drained directly into streams. The remaining 20% drainedinto vegetated swales or riparian land a short distance from streams.The water quality impacts of these high concentrations of sediment and high sediment yields from unpaved road runoff may be significant, depending on the magnitude of the flow in the stream when the road runoff is produced. When streamflow is high, road sediment may be diluted, but when streamflowis low or the stream is relatively small, high sediment concentrations will occur downstream of road
ditch outlets. This condition is common in summer storms in Oklahoma when runoff may be producedfrom unpaved roads, but the surrounding land produces none. During such conditions, road sediments are added to streams, and the high concentrations and quantities of sediment may have a deleterious effect on residual pools and other habitat used as refuges by aquatic life during low flow periods. Regardless of the magnitude of streamflow, the high sediment yields from roads may be a large part of
the total load of sediment delivered to streams and
lakes. There are 479 km of unpaved roads in the StillwaterCreek Watershed that occupy about 1.3% of the watershed’s area (Storm et al. , 2003). Direct measurement of sediment load to Stillwater Creek from unpaved road erosion is unavailable, but Storm et al. (2003) developed an estimate of the sediment budget using the Soil and Water Analysis Tool (SWAT) to estimate upland erosion and the Water Erosion Prediction Project(WEPP) model to estimate road erosion. They estimated erosion from all sources to be 118,000 Mg ⁄

year and unpaved roads to be 12,700 Mg ⁄

year, or 10.7% of the total average annual load. The average sediment yield across the four road segments in the pre-BMP year was 120 Mg ⁄

km (Table 3). If we make a coarse extrapolation of this yield across the entire 479 km of unpaved roads in the Stillwater Creek Watershed, we would estimate that unpaved roads produced as much as 57,500 Mgof sediment during the pre-BMP year. If approximately 80%, or 45,600 Mg, were delivered directly to the streams in Stillwater Creek, it would raise the estimated erosion from all sources to 162,800 Mg, with sediment yields from roads accounting for about 35%. This is an alarming rate considering that roads only occupy 1.3% of the watershed area.
This estimate is consistent with several other studies that have also shown that unpaved roads occupying small portions (2-8%) of watersheds can account for 25-73% of a watershed’s annual sediment budget (Dunne, 1979; Dunne and Dietrich, 1982; Nagle, 2001).

SUMMARY AND CONCLUSIONS

Our study has shown that unpaved rural roads in the Stillwater Creek, Oklahoma Watershed yield large quantities of sediment delivered directly tostreams. During 1 year of the 2-year study, the average sediment yield across four typical road segments was 138 Mg ⁄

ha or 120 Mg ⁄

km. Using these average sediment yields, the total unpaved road distance in the watershed and estimates of sediment yields from other sources obtained by watershed modeling, we estimated that unpaved road erosion and sediment delivery may account for up to 35% of the sediment
load delivered to Stillwater Creek. We demonstrated that sediment yields from unpaved roads can be reduced as much as 80% byinstalling appropriate BMPs, even on a limited budget. Particularly effective were providing a stable surface with geotextile fabric and gravel, a proper crown for drainage of the roadway, and stable channels to remove water without eroding the road bed or the cutslopes. This work suggests that where roads are deeply incised and a proper bar ditch and stable drainage cannot be provided, surface BMPs may
become less effective as storm size increases. Reductions in road sediment yield and runoff may be especially effective in improving rural stream water quality because sediment from roads is added directly to streams where its impacts are the greatest.

ACKNOWLEDGMENTS

The authors thank USEPA Region 6, the Oklahoma Conservation Commission, the Oklahoma Agricultural Experiment Station, and the Oklahoma Cooperative Extension Service for funding this work through sec. 319 of the Clean Water Act. The Payne County, Oklahoma Commissioners provided manpower and supplies for the BMP installation and Doug Wright and Mike Hinkston at theLocal Technology Assistance Program in the Oklahoma State University Center for Local Government Technology provided design and consultation. We also thank graduate research assistants Cory Peranich, Leah Thomas, Andy Lyons, and Phillip Busteed T URTON , S MOLEN

, AND S TEBLER

JAWRA 1350 J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATIONfor their help in the field and the staff of the Oklahoma State University Kiamichi Forestry Research Station for their help in building and installing the field equipment. We also thank two reviewers who made excellent suggestions that have improved this manuscript.

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J OURNAL OF THE A MERICAN WATER R ESOURCES A SSOCIATION 1351 JAWRA

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