Road runoff and sediment sampling for

determining road sediment yield at the watershed

scale

Christopher G. Surfleet, Arne E. Skaugset III, and Matthew W. Meadows

Abstract: In this study, we demonstrate that watershed-scale estimates of road sediment production are improved if field

measurements of road runoff and sediment production are used in the analysis. We used several techniques to spatially extrapolate measurements of road runoff and sampled sediment: comprehensive road runoff measurements, runoff estimates

derived from the Distributed Hydrology Soil Vegetation Model (DHSVM), and adjustment of the road erosion models

WARSEM and SEDMODL2.The sediment yield for the Oak Creek, Oregon, road network based on measured road runoff

and sediment was 6.5 tons/year. When DHSVM was used to simulate road runoff, the estimated sediment from roads was

similar, 6.9 tons/years. The road sediment production estimated by SEDMODL2 and WARSEM, adjusted with field-measured road runoff and sediment, was 28% and 34% less, respectively, than using the models with the default parameters.

When applied to a road network in commercial forest land with frequent road use, the sediment yield estimated by SEDMODL2 and WARSEM without adjustment from field measurements was 480% and 610% higher, respectively, than with

adjustments. We found that measuring runoff and sediment from one large storm event (≥1 year recurrence) provided a statistically significant relationship with the annual sediment yield.

Résumé : Dans cette étude, nous démontrons que les estimations de production de sédiments associés aux chemins à l’échelle du bassin versant sont meilleures si des mesures de l’eau de ruissellement des chemins et de production de sédiments

prises sur le terrain sont utilisées dans l’analyse. Nous avons utilisé plusieurs méthodes pour extrapoler dans l’espace les

mesures de l’eau de ruissellement des chemins et des échantillons de sédiments : mesures détaillées de l’eau de ruissellement des chemins, estimations du ruissellement dérivées du modèle DHSVM (« Distributed Hydrology Soil Vegetation Model ») et calibration des modèles d’érosion des chemins WARSEM et SEDMODL2. La production de sédiments associés au

réseau routier d’Oak Creek, Oregon, basée sur des mesures de l’eau de ruissellement des chemins et des sédiments, était de

6,5 tons/an. Lorsque le modèle DHSVM était utilisé pour simuler le ruissellement, la quantité de sédiments associés aux

chemins était semblable, soit 6,9 tons/an. La production de sédiments estimée par les modèles SEDMODL2 et WARSEM,

calibrés avec les mesures de ruissellement et de sédiments prises sur le terrain, était respectivement 28 % et 34 % moins élevée que lorsque ces modèles étaient utilisés avec les paramètres implicites. Lorsqu’elle est appliquée à un réseau routier

dans un territoire de forêt commerciale où les chemins sont fréquemment utilisés, l’estimation de la production de sédiments

avec les modèles SEDMODL2 et WARSEM sans calibration avec les mesures prises sur le terrain était respectivement 480

% et 610 % plus élevée comparativement aux résultats obtenus après calibration. Nous avons observé qu’il y avait une relation statistiquement significative entre les mesures de ruissellement et des sédiments associés à une grosse tempête (période

de récurrence plus longue qu’un an) et la production annuelle de sédiments.

[Traduit par la Rédaction]

Introduction vesting practices, and monitor reductions in sediment production. The implementation of forest policies has in many cases

A large amount of the sediment attributed to timber har- required landowners to accurately monitor reductions in erovest activities is due to surface erosion from roads and road sion and the subsequent improvement of aquatic habitat over

building (e.g., Reid 1981; Ketcheson and Megahan 1996). In- time.

creases in sediment production from timber harvest activities, Assessing sediment production from the erosion of forest

which include forest roads, can degrade water quality and roads is complex because of the many influences on the proaquatic habitat (e.g., Spence et al. 1996; Haskell 2000; Gu- duction and transport of erosion. The assessment becomes

cinski et al. 2001). Concerns regarding the impacts of sedi- more complex in efforts to estimate sediment production

ment due to timber harvest have resulted in regulations that from forest roads at a watershed scale. Watershed-scale

require landowners to repair erosion sites, use improved har- amounts of sediment inputs from forest roads are commonly
estimated from road erosion models. Common road surface

erosion models used in forest watersheds are SEDMODL2,

the Washington Road Surface Erosion Model (WARSEM),

and WEPP:Road. These models have been widely used for a

variety of objectives. For example, SEDMODL2 has been

used as an evaluation tool in a model to decrease culvert

spacing to assess sediment contributions (Damian 2001) as

well as incorporated into haul routing evaluations (Krogstad

and Schiess 2000) and road design and alignment evaluations

(Akay and Sessions 2005). Both WARSEM and SEDMODL2 have been used in regulatory planning and decision

making (LaPlante 2005) and monitoring of aquatic habitat

conservation (Raines et al. 2005). WEPP:Road has been

used by the US Forest Service and US Bureau of Land Management for assessment and planning (e.g., USDA Forest

Service LTBMU 2006).

Models used to analyze road surface erosion can provide

estimates of sediment production at a watershed scale or data

that can help the user make those estimates. A crucial factor

in the use of road erosion models is the accuracy of the

model results. The assumption is that the models give a reasonable interpretation of the magnitude and spatial pattern of

road sediment production. However, a lack of regional calibrations of model algorithms can lead to inaccurate model results (Sugden and Woods 2007). Further, without sitespecific data on the hydrologic response of roads, these models cannot be expected to provide accurate results for the variety of hydrologic responses that roads exhibit.

Overland flow is necessary to create surface erosion and

transport detached soil from a road tread. Some road treads,

the running surface of a road, or ditches draining a road can

have infiltration capacities that exceed rainfall intensities and

thus do not promote the overland flow necessary for surface

erosion. Road cutslopes, the steepened slope adjacent to

roads created from removing hillslope material while building roads, can intercept hillslope water, which can increase

road runoff. However, in many areas, road cutslopes do not

intercept hillslope water (e.g., Sugden and Woods 2007).

The quantity of water running off roads is not necessarily a

function of the road dimensions and associated precipitation

(Luce 2002). Since surface erosion on roads requires surface

runoff and the hydrologic responses of roads are not easily

predicted, measurement of road runoff is an important approach in assessing road erosion.

The physical attributes of roads may not provide a reliable

predictor of road hydrologic response; however, many physical attributes of roads do correlate with sediment loss. For

example, roads with a native surface texture can have as

much as 15–20 times the sediment production than a road

with a competent rock surface (e.g., Reid 1981; Bilby et al.

1989;). Rutting of the road surface from vehicle traffic creates depressions where road runoff is concentrated, generating greater sediment loss (Foltz and Burroughs 1990; Toman

and Skaugset 2011). As the slope of a road increases, the

force of road runoff for detachment of surface material increases, producing greater sediment loss (e.g., Megahan and

Ketcheson 1996; Luce and Black 1999; Sugden and Woods

2007). Vegetation cover of road cutslopes provides significant protection against sediment loss (Wischmeier and Smith

1960; Luce and Black 1999). External factors such as the

amount of heavy vehicle traffic or intensity of precipitation

further affect road sediment loss (Wischmeier and Smith

1960; Reid 1981; Bilby et al. 1989). Road inventories and

field methods that identify physical factors such as these,

that cause greater risk of sediment loss are important. However, the runoff of a road determines whether erosion will occur.

We propose that watershed-scale estimates of road sediment production will be improved if field-measured road runoff and sediment production are used in the analysis. Field

measurements must be obtained by means of an appropriate

sampling design to allow inference to watershed-scale estimates. Several different approaches could be used for the

spatial extrapolation of road runoff and sediment production

observed in the field. One approach would be to measure

road runoff at all road locations in a watershed and then sample road sediment production at representative sites. Sediment

production would be extrapolated to roads from the runoff

and sediment load relationship developed for the watershed.

However, measuring the runoff from all roads in a watershed

is unrealistic, particularly for large watersheds. Another approach would be to use a hydrologic model that estimates

road runoff in combination with the sediment–runoff relationship to provide spatial extrapolation of road sediment

contribution. Yet another approach would be to adjust the parameters used in existing road erosion models such as SEDMODL2 and WARSEM based on field-sampled road runoff

and sediment measurements and then use these adjusted

models to spatially extrapolate sediment production estimates

to the roads in the watershed.

This paper presents the results of road runoff and suspended sediment sampling from forest roads for Oak Creek

watershed, Oregon. The road network in Oak Creek represents roads with low levels of use; the use is primarily light

vehicles with infrequent heavy truck and equipment use. We

used road runoff – sediment loss relationships in conjunction

with road runoff measurements to estimate watershed-scale

sediment losses. We also applied a hydrologic model and

two road erosion models to also estimate watershed-scale

sediment losses. We then applied our Oak Creek approach at

the South Fork of the Albion River, California, a watershed

with a road network used for commercial forest management,

a road network representing a high level of heavy truck and

equipment use. We additionally tested if grab suspended samples alone could provide a reasonable prediction of the magnitude or relative amount of road erosion from road

segments.

Study sites

This study was conducted at Oak Creek, Oregon, with additional research at the South Fork of the Albion River, California. Oak Creek is located within the Oregon Coast Range

approximately 4.5 km northwest of Corvallis, Oregon. Oak

Creek is part of the McDonald/Dunn Forest owned and managed by the College of Forestry, Oregon State University.

Average annual precipitation measured at Oak Creek from

water years 2003–2007 was 970 mm/year with a range of

830–1110 mm/year. The bedrock underlying the watershed,

the Siletz River Volcanics, is a basalt formation (Knezevich

1975). Soils in the watershed are classified as silty clay loam

with some areas of silty loam (Knezevich 1975). Minor tim­
ber harvest operations and low levels of heavy equipment use

occur on the low-traffic roads in Oak Creek.

The South Fork of the Albion River is in a coastal watershed in western Mendocino County, California, approximately 200 km north of San Francisco, California. Rainfall

is seasonal in this region, with most of the rain (approximately 1200–1400 mm/year) occurring between October and

May. The South Fork of the Albion River is characterized by

the Coastal Belt of the Franciscan Complex. The highly

sheared rocks of the Coastal Belt are composed of structurally deformed, massive, hard greywacke sandstone and shale

interbedded with small amounts of limestone and pebble conglomerate. Soils in the Coastal Belt are of the Inceptisol soil

order with primarily silt loam and clay loam textures (Rittiman and Thorson 1993). The roads in the area of the South

Fork of the Albion River are used for timber harvest operations. Road traffic throughout the road network varies from

heavy to none depending on forest harvest timing and proximity.

Methods

Road hydrology, turbidity, and suspended sediment

measurements for Oak Creek

From 2001 to 2007, all road drainage structures (114 culverts and road ditches) in Oak Creek were instrumented for

collecting stage measurements to be used in calculating discharge based on a rating curve developed for the Oak Creek

culverts. Stage measurements for the 23 stream-crossing culverts were recorded continuously by Tru-Track capacitance

rods installed in trapezoidal flumes in the adjoining road

ditches. Stage measurements for the 91 road drainage culverts

were recorded at the inlets of the culverts by Tru-Track capacitance rods or crest gages that recorded peak stage measurements. Crest gages and capacitance rods were randomly

placed among the 91 cross-drain culverts.

During the 2006 and 2007 water years, we used a turbidity

threshold sampling (TTS) approach (Lewis 1996) to collect

measurements of suspended sediment, turbidity, and road

runoff at 17 road segments in the Oak Creek watershed.

From the populations of road drainage culverts, we chose

road segments for measurement based on a spatially balanced

sample (Stevens and Olsen 2004). Oak Creek has relatively

similar road design and level of road use across the watershed. Spatial variations in road response due to climate or

hillslope hydrologic conditions were present, making a sample balanced across the watershed important.

The TTS monitoring stations had to be located at the outlets of culverts. This was to ensure that adequate water depth

was available for submersion of the turbidity instrument. Because of this, no stream-crossing culverts could be used. The

discharge from the outlet of stream-crossing culverts was not

exclusively road runoff. The TTS monitoring station equipment consisted of a metal flume fitted with a Druck PDCR

1830 pressure transducer for discharge calculation, an OBS-3

turbidimeter from D&A Instruments (now Campbell Scientific), a Campbell CR10-X data logger, and an ISCO 3700

automated water sampler for collecting suspended sediment

samples. Turbidity and stage were measured continuously;

suspended sediment samples were taken based on changes in

incremental turbidity and discharge thresholds (Lewis and

Eads 2001). Monitoring took place for 1–2 months at each

road segment during winter precipitation. The equipment

was then moved to the next sites in the sample. In all, we

monitored 17 road segments over the course of two winters.

Continuous stage or crest gage measurements continued at

the inlet of a culvert when the TTS equipment was removed.

We developed relationships between turbidity and suspended sediment according to storm or groups of storms during which four or more sediment samples were taken

following the approach discussed in Lewis (1996). We used

the continuous turbidity measurements with these relationships to calculate the storm sediment loads. We also developed sediment rating curves for each culvert to calculate the

suspended sediment load for the remainder of winter storms

without TTS monitoring.

For the culverts where we did TTS monitoring, we determined storm runoff volumes and peak flows for the 2006

and 2007 water years. We used linear regression to determine

relationships between storm runoff volumes or peak flows

and storm sediment load. We also used linear regression to

determine relationships between storm peak flow and sediment load for culverts at which only crest gages had been

placed. We calculated annual suspended sediment load for

each road by summing the storm suspended sediment loads

for each water year and then averaging the annual sediment

loads for the two water years.

Using measured road runoff and simulated runoff to

estimate watershed-scale road sediment

The sediment measured in the runoff from Oak Creek

roads was suspended sediment (fine sediment). As a percentage of total sediment load in Oak Creek, sediment that

quickly settled (coarse sediment) varied from 10% to 90%

with a median value of 50% (Amann 2004). To obtain a representation of total sediment production from road surface

erosion, we increased the suspended sediment load estimates

generated from field measurements by 50%.

We also simulated the road runoff to Oak Creek with the

Distributed Hydrology Soil Vegetation Model (DHSVM)

(DHSVM 2009) for the 2006 and 2007 water years. We obtained meteorological inputs to DHSVM from the meteorological station and precipitation gauges in the watershed.

When meteorological inputs were missing from the Oak

Creek station, we used measurements from the Corvallis

Agrimet weather station, Corvallis, Oregon (maintained by

the US Bureau of Reclamation). Soil inputs for DHSVM

consisted of soil textures from the Benton County Soil Survey (Knezevich 1975). We calibrated DHSVM based on

comparison with runoff results for the entire watershed

(630 ha), two smaller watersheds (55 and 4 ha), and two

road ditch observations (<1 ha). We used a systematic manual calibration approach similar to the technique from Whitaker et al. (2003). The calibration time period selected

(October 2005 through summer 2006) showed the greatest

variety of stream flow magnitudes and the largest events of

the time period analyzed. We evaluated model fit to stream

hydrographs quantitatively according to (i) volume error for

predicted versus observed runoff, (ii) model efficiency (Nash

and Sutcliffe 1970), and (iii) coefficient of determination (D!)

(Whitaker et al. 2003). Because it proved impossible to get

good quantitative fits for the model output at the wide variety
of scales used for the calibration, particularly the road runoff

locations, we also qualitatively evaluated the general fit of

model output to measured values. We used only the one calibrated parameter set for the road runoff simulation from

DHSVM. We used the relationship between storm runoff volume and sediment load to calculate storm sediment loads for

the DHSVM simulated road runoff to Oak Creek for the

2006 and 2007 water years.

Road erosion model use

We combined the sampled road runoff and sediment load

measurements with two road erosion models to spatially extrapolate the sediment estimates to the watershed scale. The

models were SEDMODL2, a GIS-based road erosion delivery model (NCASI 2002), and the Washington Road Surface

Erosion Model (WARSEM), a database road erosion delivery

model. Both models use the same calculations to estimate

road surface erosion; the difference between the models is

data input to the models. SEDMODL2 is an Arcinfo-based

tool where many of the road attributes are developed from

analysis of a digital elevation model. WARSEM is a spreadsheet model; we populated WARSEM with measurements

from a road inventory. Detailed descriptions of SEDMODL2

and WARSEM can be found in technical documents provided

from web pages maintained by the National Council of Air

and Stream Improvement (NCASI 2002) and the Washington

Department of Resources (2007), respectively.

In SEDMODL2 and WARSEM, total sediment delivered

(in tons per year) from each road segment is calculated by

the road tread plus the sediment delivered by the cutslope

times a road age factor; both models incorporate a road age

factor that increases the sediment delivery estimate if the

road was built in the last 2 years: tread delivered sediment =

geologic erosion factor × tread surfacing factor × traffic factor × segment length × road width × road slope factor ×

rainfall factor × delivery factor; and cutslope delivered sediment = geologic erosion factor × cutslope cover factor ×

segment length × cutslope height × rainfall factor × delivery

factor.

We used annual suspended sediment load estimates from

the 17 roads measured in the Oak Creek watershed to calculate a combined geologic erosion factor, precipitation factor,

and traffic factor within WARSEM and SEDMODL2. The

geologic erosion factor represents the erosion rate based on

the geology of the watershed; direct measurement of erosion

from roads replaces this factor. The precipitation factor represents the hydrology affecting road erosion; the direct observation of road runoff replaces this factor. The traffic factor

represents the amount of hauling and vehicle use on the road

tread. We selected both inactive roads and roads on which recent hauling had occurred, thus capturing the range of traffic

effects on road erosion for this watershed. This strategy allowed us to replace the traffic factor from the WARSEM

and SEDMODL2 calculations with the road hydrology and

sediment measurements for a watershed-scale estimate of

road sediment production. The geologic erosion, precipitation, and traffic factors are multiplied in the model analysis;

thus, they can be represented as one number.

For each of the road sites measured for runoff and suspended sediment, we adjusted a variable that represented the

combined geologic erosion, precipitation, and traffic factors

within SEDMODL2 and WARSEM until the model results

equaled the annual sediment load determined for each road

observed in the field. We then calculated a mean factor of

geologic erosion, precipitation, and traffic for all 17 of the

observed roads. We used the mean value of the geologic erosion, precipitation, and traffic factors in WARSEM and SEDMODL2 to calculate the sediment contribution for the Oak

Creek watershed for the 2006 and 2007 water years. Using

this approach, we adjusted the modeled sediment contribution by the percentage of coarse sediment to provide an estimate of total sediment production.

Road hydrology and suspended sediment measurements

for the South Fork of the Albion River, California

In the watershed of the South Fork of the Albion River, we

measured runoff and erosion yield for 22 road segments during the 2007 water year. We used a stratified random sample

to select road segments from the population of road segments

that deliver sediment to watercourses. We sampled six strata

based on slope position (upper or lower half of the slope) and

log hauling traffic (hauled or not hauled that year). We also

sampled the main haul road exiting the watershed (both

hauled and not hauled segments).

We collected road hydrologic measurements in low-cost

circular flumes as described in Samani and Herrera (1996).

The flumes were placed at locations where runoff left the

road, either in the road ditch or at the outlet of a dip or erosion control feature. Stage was measured in the flumes by

Odyssey water level recorders or crest gages. We collected

suspended sediment samples at the flumes by hand during

four winter storms. We calculated storm sediment load for

each of the storms from sediment rating curves developed

for individual road locations or aggregates of roads within

sampled strata. We developed relationships between storm

runoff volume and peak flow to storm sediment load for the

different road strata and used these relationships to calculate

the sediment loads for the storms without rating curve information.

The annual sediment load for each road segment was the

sum of storm sediment loads. To represent total erosion yield

for each road, we increased the total annual suspended sediment load by 67%, the percentage of coarse particles from

roads determined in an adjacent study (Barrett and Tomberlin

2007). We back-calculated geologic, traffic, and precipitation

factors from the annual sediment loads measured and used

within WARSEM and SEDMODL2 to spatially extrapolate

the road erosion measurements to the watershed.

Calculation of sample size

We used the following power analysis to estimate the number of road sites at Oak Creek and the South Fork of the Albion River that might be monitored to reduce errors for

watershed-scale sediment load estimates:

2 2 1⁄21 Number of road sites 1⁄4 ðZa=2Þ S2

=e

where (Za/2)2 is the Z statistic squared for a standard normal

distribution with a two-tailed probability of a/2, S2 is the variance of sediment load estimates, and e2 is the acceptable error of the answer squared.
 

 

Grab water samples for estimation of magnitude of road Fig. 1. Relationship between suspended sediment load for the culsediment load verts with TTS measurements at Oak Creek and (a) storm runoff

volume and (b) storm peak flow for the 2006 and 2007 water years.

Estimating storm sediment load from grab water samples
 

We used TTS measurements from the 17 Oak Creek road

segments to determine if samples for suspended sediment

concentration (SSC) alone could be related to storm sediment

load. We used the SSC samples to represent random grab
 

samples. To attempt to replicate the random collection timing, we randomly selected four, three, and two TTS samples
 

per storm. We used linear regression to determine if there
 

was a relationship between the mean of the randomly selected SSCs for each storm and storm sediment load. We
 

also tested additional variables of storm peak flow and storm
 

runoff volume to see if they improved the relationship.
 

We used linear regression to determine if there was a relationship between storm event sediment yield and the annual

(a)

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0 1×106 2×106 3×106 4×106 

Storm sediment load (tons) 

Storm volume (L)
 

sediment yield from the road segments in Oak Creek during

two large winter storms (the largest annual events), one in the

2006 water year and the other in the 2007 water year.

Results

Relationship between storm volume or peak flow and

storm sediment load

For road drainage locations measured with TTS within

Oak Creek for the 2006 and 2007 water years, the ratio of
 

storm sediment load (tons) and storm runoff volume (litres)

was 5.18 × 10–9 with 95% confidence intervals of 2.328 ×

10–9 and 8.027 × 10–9 (p < 0.001, adjusted r2 = 0.25)

(Fig. 1a; eq. 2). The ratio of storm sediment load (tons) and

storm peak flow (litres per second) was 0.0006 with 95%

confidence intervals of 0.00029 and 0.00092 (p < 0.001, adjusted r2 = 0.27) (Fig. 1b; eq. 3). The resulting model ex­

(b)

0 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

Storm sediment load (tons) 

plained only some of the variability in these relationships, as

shown by low adjusted r2 values of 0.25 and 0.27:

1⁄22 Storm sediment load ðtonsÞ

1⁄4 5:18 x 10-9 x storm runoff volume ðLÞ - 0:0004

1⁄23 Storm sediment load ðtonsÞ

1⁄4 0:0006 x storm peak flow ðL=sÞ - 0:0002

Variability was high for the storm peak flow and runoff volume relationships with storm sediment loads in Oak Creek,

which is common for sediment–runoff relationships. However, there were chronic soil failures at one road site used in

this relationship. As a result, this road had much higher

storm sediment loads than other roads (see outlier point in

Figs. 1a and 1b). If that one road is removed from the analysis, the relationships between storm volume or peak flow and

storm sediment load remain statistically significant and the

adjusted r2 values increase to 0.5 and 0.52, respectively. We

chose not to remove the road from the sediment–runoff relationships used to predict sediment load from Oak Creek

roads. The sediment measurements from this road represent

a statistical outlier; however, problematic high-erosion sites

occur on road networks in watersheds. A relationship that

considers these high sediment delivery occurrences provides

a more realistic estimate of the total sediment load for roads

in a watershed.

0 10 20 30 40 

Storm peak flow (L/s) 

Road sediment estimates for Oak Creek

From the observed road runoff and the relationship of observed storm volume and storm suspended sediment load, adjusted to include coarse sediment, the watershed-scale

estimate of sediment production from roads in the Oak Creek

watershed was 6.2 tons/year with 95% confidence intervals of

4.5 and 7.8 tons/year. From the relationship of storm volume

and storm suspended sediment load, with DHSVM simulated

storm volume adjusted to include coarse sediment, the estimate of sediment production was 6.5 tons/year with 95%

confidence intervals of 4.7 and 8.2 tons/year (Fig. 2).

For the 17 road segments in our Oak Creek study, the

average product of the geologic erosion, precipitation, and

traffic factors derived from field-measured sediment load

was 2.7; the value suggested from the technical documentation (NCASI 2002) is 8. With the 2.7 factor derived from

field observations, SEDMODL2 estimated sediment delivery

from roads of the Oak Creek watershed, with an adjusted increase for coarse sediment, at 9.1 tons/year and 95% confidence intervals of 8.1 and 10.0 tons/year (Fig. 2). With this

same factor, WARSEM estimated sediment delivery, with an

adjusted increase for coarse sediment, at 8.8 tons/year and
Fig. 2. Comparison of total annual road sediment load for Oak

Creek with SEDMODL2, WARSEM, observed road runoff, and

DHSVM simulated road runoff for the 2006 and 2007 water years.

Error bars can only be presented for models with field measurements where a variance for the inputs to the model could be calculated.

14 

Fig. 3. Road sediment load from SEDMODL2 and WARSEM for

the South Fork of the Albion River for the 2007 water year. Error

bars can only be presented for models with field measurements

where a variance for the inputs to the model could be calculated.

140 

120 

0 

SEDMODL2 WARSEM
 

SEDMODL2 

WARSEM 

Runoff 100 Sediment Yield (tons/year)

Sediment yield (tons/year)

12 

10 

8 

6 

4 

2 

80 

60 

40 

20 

0 

w/o Field Field Measured DHSVM 

adjustment adjusted runoff simulated 

runoff 

95% confidence intervals of 8.0 and 9.7 tons/year (Fig. 2).

With the factor of 8 suggested by the technical documentation (NCASI 2002), SEDMODL2 and WARSEM estimated

average annual road sediment delivery at 12.7 and 13.3 tons/

year, respectively. A confidence interval is not available from

the models using default parameters; only a single answer is

provided.

Road sediment estimates for the South Fork of the Albion

River

For all road sites monitored in the watershed of the South

Fork of the Albion River, there was no statistically significant

relationship between road runoff volume or peak flow and

storm sediment load. However, when the roads were categorized into the six sampling strata, we observed relationships

between storm volume or peak flow and storm sediment

load. The relationships were not statistically significant because of the low number of measurements within each of the

six sample strata. For the 22 road segments measured, the

average product of the geologic erosion, precipitation, and

traffic factors for use in SEDMODL2 and WARSEM was

1.16. The values for the models suggested by the technical

documentation (NCASI 2002) varied between 11.2 and 56

depending on road traffic class. With the 1.16 factor calculated from the observed road sediment measurements, SEDMODL2 estimated sediment delivery for the 2007 water

year, with an adjusted increase for coarse sediment, at 14.3

tons with 95% confidence intervals of 0.05 and 57.7 tons

(Fig. 3). With the 1.16 factor, WARSEM estimated fine sediment delivery, with an adjusted increase for coarse sediment,

at 18.0 tons with 95% confidence intervals of 0.06 and 69.8

tons. When the factors varied between 11.2 and 56, as suggested by the technical documentation (without field measurements; NCASI 2002), SEDMODL2 and WARSEM

w/o field measurement adjustment 

Field measurement adjusted 

estimated average annual road sediment delivery for roads of

the South Fork of the Albion River watershed at 103.2 and

129.7 tons/year, respectively.

Sample size estimate for watershed-scale road sediment

measurement from road runoff

Tables 1 and 2 present estimates, based on field measurements taken at Oak Creek and at the South Fork of the Albion River, of the numbers of road sites to be monitored for

runoff and sediment measurements for estimation of storm

sediment loads with confidence level and error. These results

represent not only a consideration of the confidence in the

results (a) to not have a type I error (a false positive of a

tested hypothesis) but also consider the error (b) to avoid a

type II error (a false negative of a tested hypothesis). Comparing the sample sizes calculated from estimated road sediment loads from Oak Creek and South Fork of the Albion

River, much fewer samples are needed in a watershed like

Oak Creek, with uniform low traffic roads, using a TTS approach for road runoff and sediment load estimation.

Grab suspended sediment samples for estimation of the

magnitude of road sediment load

The ratio of storm sediment load (tons) and the mean of

four randomly selected SSC samples (milligrams per litre)

from individual storms was 0.0001 with 95% confidence intervals of 0.00005 and 0.00015 (p = 0.01, adjusted r2 =

0.19) (Fig. 4; eq. 4). The resulting model explained little of

the variability in the relationship, as shown by a low adjusted

r2 of 0.19. There was no statistically significant relationship

between the mean of either three or two randomly selected

suspended samples from a storm and the storm sediment

load at the 95% confidence level with p values of 0.07 and

0.84, respectively:
 

 

 

 

Table 1. Number of road sites to be sampled, based on error and confidence level, by the turbidity threshold sampling and road sampling scheme used at Oak Creek.

Number of road sites by confidence level (a)

Error (b) 90% confidence 95% confidence 99% confidence

1% 13 18 21

5% 1 1 1

Table 2. Number of road sites to be sampled, based on error and confidence level, by the lowtechnology approach of sediment sampling used at the South Fork of the Albion River.

Number of road sites by confidence level (a)

Error (b) 90% confidence 95% confidence 99% confidence

1% 10620 15000 20600

5% 425 600 825

10% 110 150 210

20% 26 37 52

Fig. 4. Relationship between the mean of four randomly selected

suspended sediment samples and suspended sediment load for

storms at Oak Creek for the 2006 and 2007 water years.

0.06 

0.05 

0.04 

0.03 

0.02 

0.01
 

0
 

Storm sediment load (tons)
 

0 50 100 150 

Mean of four suspended sediment 

samples (mg/L) 

1⁄24 Storm sediment load ðtonsÞ

1⁄4 0:0001 x mean of four SSC samples ðmg=LÞ - 0:0003

Multiple linear regression showed that including the peak

flow value in a model with four randomly selected sediment

samples explained more of the variability for the storm sediment load estimate. We found a statistically significant relationship for the estimate of the natural log of the storm

sediment load by combining the mean of four randomly suspended sediment samples and the storm peak flow (p < 0.02,

adjusted r2 = 0.47) (eq. 5). Evidence was not sufficient to

show that including storm volume (instead of peak flows)

provided a better model for the sediment load estimates than

using only four randomly selected sediment samples (p <

0.001):

1⁄25 Storm sediment load ðtonsÞ

1⁄4 0:0001 x mean of four SSC samples ðmg=LÞ

þ 0:0006 x peak flow ðL=sÞ - 0:0032

We observed a statistically significant relationship between

road storm sediment load and the total annual road sediment

load for the 2006 (Fig. 5a; eq. 6) and 2007 (Fig. 5b; eq. 7)

water years (WY) (p < 0.0001 for both relationships, r2 =

0.86 and 0.77, respectively):

1⁄26 2006 WY annual sediment load ðtonsÞ

1⁄4 9:27 x large storm sediment load ðtonsÞ - 0:004

1⁄27 2007 WY annual sediment load ðtonsÞ

1⁄4 7:94 x large storm sediment load ðtonsÞ - 0:006

Discussion

Sediment yield estimates for roads

The road sediment yield for the 2006 and 2007 water years

varied according to the technique used to quantify it. When

modeled by SEDMODL2 and WARSEM, estimates of total

annual road sediment production for Oak Creek were 40%

and 51% higher, respectively, without adjustment from field

measurements than with adjustments. At the South Fork of

the Albion River, the sediment yields estimated by SEDMODL2 and WARSEM without adjustment from field measurements were 480% and 610% higher, respectively, than

with adjustments.

WARSEM and SEDMODL2 provide estimates interpreted

as long-term averages of erosion from roads. Average erosion

over many years, with both wet and dry years, is assumed in

the WARSEM and SEDMODL2 estimates. For this study,

the field-measured sediment yield was for two water years

for Oak Creek and one water year for the South Fork of the

Albion River. The South Fork of the Albion River received

32 inches of rain, well below the average annual rainfall of

50 inches. At Oak Creek, annual rainfall for the 2006 and

2007 water years was about average, but several large storms

(>1 year recurrence interval) occurred during the 2006 water

year. The large storms of 2006 used for the Oak Creek analysis probably make the comparison of results between the

modeled and measured sediment estimates reasonable. However, the year of low precipitation for the South Fork of the

Albion River partly explains why WARSEM and SED­
Fig. 5. Relationship between storm sediment load and total annual

sediment load for road suspended sediment Oak Creek for (a) one

storm in the 2006 water year and (b) one storm in the 2007 water

year.

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0 

(a)

2007 water year 2006 water year


sediment load (tons) sediment load (tons)
 

0 0.01 0.02 0.03 

Storm sediment load (tons) 

0.25 

0.20 

0.15 

0.10 

0.05 

0 

(b)

0	 0.01 0.02 0.03 

Storm sediment load (tons) 

MODL2, without adjustments for field measurements, overestimated the sediment yield. However, the difference in estimated sediment yields is so large that we concluded that

model inputs in addition to low rainfall contributed to errors.

Road hydrologic response and sediment yield

The relationship between storm peak flow or runoff volume and storm sediment load demonstrated that road runoff

measurements can be used for estimating sediment production from roads. Forest road runoff is highly variable. Some

roads exhibit infiltration excess overland flow from the road

surface, some roads intercept significant amounts of hillslope

water, while other roads do not, and many roads have road

treads with high infiltration capacity that have little to no

runoff. Almost all road surface erosion models rely heavily

on measurements of the physical attributes of a road. Physical attributes such as road surface type, surface ruts, traffic

on the road, gradient of the road, length of the road segment,

or vegetation cover on road cut- and fill-slopes are related to

road erosion (e.g., Reid 1981; Bilby et al. 1989; Luce and

Black 1999). Yet the amount of runoff on the road influences

how these physical attributes affect erosion. If a road with

high erosion potential, as predicted by its physical features,

has no runoff, then no erosion will occur. Similarly, a road

with low erosion potential yet very high runoff could have

considerable erosion. Given the uncertainties associated with

using the physical attributes of a road to estimate hydrologic

response, developing techniques for predicting or observing

road hydrologic response used in conjunction with inventories of road physical features would (i) enable more accurate

estimates of road sediment yield and (ii) provide more accurate indications of the magnitude of sediment delivery from

individual road locations.

In this study, we used the hydrologic model DHSVM to

estimate road hydrologic response for the Oak Creek roads.

We estimated sediment yield by means of the storm volume

to sediment load relationship for the simulated road runoff

from DHSVM. The 95% confidence interval of total annual

road sediment load estimated from DHSVM simulated road

runoff was 4.7–8.2 tons/year. This result was closer to the

sediment estimate produced from the actual road runoff

measurements than the estimates produced from SEDMODL2 and WARSEM adjusted by field measurements.

At Oak Creek, DHSVM performed poorly at simulating

site-specific effects from roads, but at the watershed scale,

DHSVM simulated the hydrologic response from roads well

(Surfleet et al. 2010). This result suggests that a hydrologic

model that estimates road response in conjunction with a

road runoff and sediment sampling scheme could be useful

in quantifying the watershed-scale production of road-associated sediment. However, the watershed size must be large

enough for the hydrologic model to accurately quantify the

volume of road runoff. Given the uncertainties seen in

DHSVM predictions of individual road locations (Surfleet et

al. 2010), DHSVM may not provide accurate quantification

of the magnitude of sediment production for individual road

sites.

The relationships determined at Oak Creek between sediment load and storm volume or peak flow were also observed at the South Fork of the Albion River. However, we

had to develop the relationships by the road sample stratification class because of variability in road design and hydrologic response throughout the watershed. At the South Fork

of the Albion River, these relationships were not statistically

significant because of the few data points used. We also observed the linear relationship between road runoff and sediment load, but again, it had no statistical significance. In

watersheds with a variety of road designs, road uses, geology,

soils, and precipitation, these variations must be considered

in efforts to determine the relationships between road runoff

and sediment load.

Reducing uncertainty for estimates of road sediment

production

At Oak Creek, the TTS approach for measuring road sediment and turbidity, combined with continuous measurement

of road runoff, provided estimates of sediment yield with

low variance. At the South Fork of the Albion River, we did

not monitor road runoff continuously throughout the winter

and we collected road sediment measurements at varying

times throughout a few storm events. This approach provided

a sediment yield estimate with high variance. Achieving

comparable precision in results, at a 95% confidence interval

within a 1% margin of error, would require 18 roads to be

sampled via the Oak Creek TTS approach but 15 000 roads

with the approach used at the South Fork of the Albion

River. Continuous measurements of road runoff throughout a

winter, along with sampling techniques that provide accurate
measurement of sediment and turbidity, would provide considerable reduction in uncertainty of estimates of road sediment production.

The sediment load estimated by SEDMODL2, adjusted by

field measurements, for the South Fork of the Albion River

resulted in a wide 95% confidence interval (0.05–57.7 tons/

year), a range of close to 700% of the estimate of 14.3 tons/

year. Most sediment budget calculations accept an order of

magnitude margin of error; a range of 700% would be within

this range. However, to discern long-term changes in sediment delivery trends from forest roads, a range of 700% in

the confidence interval is probably too high. The number of

roads suggested for measurement to achieve a 95% confidence interval within a 10% margin of error, as calculated

from results at the South Fork of the Albion River, was 150.

Because an average annual sediment yield is desired, roads

do not have to be measured in the same year. The effort

could be spread over several consecutive years to represent

the road sediment contributions for that time frame. This

strategy would provide a greater number of road sediment

delivery locations for monitoring, spanning many storm

events, and thus reduce uncertainty in the watershed’s road

sediment estimates.

To measure a greater number of roads at the South Fork of

the Albion River, throughout the winter, we moved the

flumes and the 12 capacitance rods that were measuring

road runoff. At Oak Creek, we moved the TTS system

throughout the winter, but road runoff measurement equipment remained in place. Moving the road runoff equipment

at the South Fork of the Albion River necessitated extrapolating hydrologic responses from one road to another based on

the relationship between sites, which introduced error, increasing the variance of the estimates. The most accurate

storm sediment load estimates are those generated from actual hydrologic measurements of road runoff for the storm

being estimated. To reduce uncertainty in road sediment estimates, hydrologic measurements throughout the entire winter

at selected road locations should be used. The hydrologic

measurements are necessary to allow the extrapolation of

sediment and discharge relationships to other storms. Continuous observation of discharge at road sites is preferable, but

crest gages that provide the peak flow can be useful for a

subset of road sites.

The number of sediment samples used to determine sediment rating curves influences the variance of sediment load

estimates. Using a turbidity and sediment sampling scheme

such as TTS ensures adequate samples for reduction in variance of sediment load estimates. Using grab samples, taken

by hand during storms, to develop sediment rating curve will

be less accurate and result in a higher variance for sediment

load relationships; this increases the uncertainty in sediment

yield estimates. Increasing the number of grab samples per

storm will reduce this uncertainty, but at the trade-off of a

considerable effort of labor.

Storm sampling for magnitude of road sediment

production

The road sediment load for the two large storms sampled

in 2006 and 2007, respectively, had a linear relationship

with the corresponding annual sediment yield from those

roads. This suggests that a road sediment sampling approach

with the objective of quantifying the sediment load for a single large storm would provide enough information to describe the magnitude of sediment delivery from roads in a

watershed for at least that year if not longer.

At Oak Creek, 50% of the annual sediment delivery came

from 13 road segments out of a total of 94 road segments.

From a management perspective, that result might be more

important than accurate quantification of watershed-scale

sediment delivery. An approach whereby one grab sample is

taken from each road site would not approximate the storm

sediment load magnitude from a site. At Oak Creek, we

found that at least four grab suspended sediment samples

had to be taken throughout a storm event to correlate with

the road’s storm sediment load. Admittedly, the relationship

between four suspended sediment samples and storm sediment load is weak (r2 = 0.19), but it is statistically different

from zero. This suggests that taking at least four suspended

sediment samples of road runoff from large storm events can

be used to determine the magnitude of road sediment contributions, if not the actual quantity.

Conclusions

This study demonstrated that field measurements of road

runoff and sediment load from roads improved estimates of

road sediment production at a watershed scale. Field measurements of road runoff and suspended sediment were used

to adjust the hydrologic, geologic, and traffic hazard factors

in the road erosion models WARSEM and SEDMODL2.

The adjusted WARSEM and SEDMODL2 models improved

watershed-scale estimates of road sediment delivery. This

demonstrated that not only is model calibration important

but the use of road runoff combined with physical attributes

of roads that influence erosion provides a more accurate approach to evaluation of road sediment production. The importance of road runoff for road sediment estimates was shown

in Oak Creek, a watershed with a uniform road design and

level of road use. The estimates of road sediment yield from

measured road runoff were similar but slightly lower than estimates from WARSEM and SEDMODL2 adjusted with field

measurements. When the sediment yield from roads was estimated with simulated road runoff from a hydrologic model,

DHSVM, the result was similar to the sediment yield estimated from observed road runoff.

A sampling approach in which sediment loads from roads

during a large storm were quantified provided a linear relationship with the annual sediment yield from those roads.

We found a statistically significant relationship with four

grab suspended samples and a road’s storm sediment load.

These findings suggest that a road sediment sampling approach that has the objective of quantifying the sediment

load for a single large storm with only grab suspended sediment samples could provide information that would help describe at least the magnitude of sediment delivery from

individual road locations in a watershed.

A tremendous amount of resources are spent on inventorying roads for prioritization of road sediment control or monitoring of road sediment production. Yet, road inventories

alone may not accurately predict the response that they intend

to measure. An approach where the runoff of roads is considered along with the physical features and conditions of the

roads will address the processes that affect road sediment
production. Indeed, the measurement of road runoff may be

the more important variable to measure for determining road

sediment production.

Acknowledgments

This work was made possible by grants from the National

Council of Air and Stream Improvement, Oregon Timber Tax

Fish and Wildlife funds, and Center for Wood Utilization

funds from the Department of Forest Engineering, Oregon

State University. Access to the South Fork of the Albion

River, equipment, and travel expenses were provided by the

Mendocino Redwood Company, LLC. The L.L. Stewart

Graduate Fellowship provided financial support for Christopher Surfleet. Kirk Vodopals provided field assistance and

was the liaison for support from the Mendocino Redwood

Company. We also thank Elizabeth Toman, Amy Simmons,

Nicolas Zegre, Kelly Kibler, Tim Royer, and Tim Otis, who

provided technical assistance and assisted with collection of

synoptic samples.

References

Akay, A., and Sessions, J. 2005. Applying the decision support

system, TRACER, to forest road design. West. J. Appl. For. 20:

184–191.

Amann, J. 2004. Sediment production from roads in the upper Oak

Creek watershed of the Oregon Coast Range. M.Sc. thesis, Forest

Engineering Department, Oregon State University, Corvallis, Ore.

Barrett, B., and Tomberlin, D. 2007. Sediment production on forest

road surfaces in California’s Redwood Region: results for

Hydrologic Year 2005–2006. In Proceedings of the Society of

American Foresters Annual Meeting, 2007. Society of American

Foresters, Portland, Ore.

Bilby, R.E., Sullivan, K., and Duncan, S.H. 1989. The generation and

fate of road-surface erosion in forested watersheds in southwestern

Washington. For. Sci. 35(2): 453–468.

Damian, F. 2001. Improving cross drain culvert spacing with GIS

interactive design tool. In Proceedings of the International

Mountain Logging and 11th Pacific Northwest Skyline Symposium, Seattle, Wash. pp. 201–206. Available from http://depts.

washington.edu/sky2001/ [accessed 16 September 2011].

DHSVM. 2009. Web site for the Distributive Hydrology Soil

Vegetation Model. Available from http://www.hydro.washington.

edu/Lettenmaier/Models/DHSVM/index.shtml [accessed 30 September 2009].

Foltz, R., and Burroughs, E.R. 1990. Sediment production from forest

roads with wheel ruts. In Proceedings: Watershed Planning and

Analysis in Action. Edited by R.E. Riggins et al. American Society

of Civil Engineers, New York. pp. 266–275.

Gucinski, H., Furniss, M., Ziemer, R., and Brookes, M. 2001. Forest

roads: a synthesis of scientific information. U.S. For. Serv. Gen.

Tech. Rep. PNW-GTR-509.

Haskell, D.G. 2000. Effects of forest roads on macroinvertebrate soil

fauna of the southern Appalachian Mountains. Conserv. Biol. 14

(1): 57–63. doi:10.1046/j.1523-1739.2000.99232.x.

Ketcheson, G., and Megahan, W. 1996. Sediment production and

downslope sediment transport from forest roads in granitic

watersheds. U.S. For. Serv. Res. Pap. INT-RP-486.

Knezevich, C.A. 1975. Soil survey of Benton County area, Oregon.

U.S. Department of Agriculture, Soil Conservation Service, in

cooperation with the Oregon Agricultural Experiment Station. U.S.

Government Printing Office, Washington, D.C.

Krogstad, F., and Schiess, P. 2000. Haul routing: an overlooked factor

in environmental driven road decommissioning. In Technologies

for New Millennium Forestry: Proceedings of the 23rd Annual

Meeting of the Council on Forest Engineering. Council of Forest

Engineering, Kelowna, B.C.

LaPlante, D. 2005. A geodatabase system for TMDL sediment

production analysis. In Proceedings of the 25th Annual ESRI User

Conference (Editors). ESRI, San Diego, Calif.

Lewis, J. 1996. Turbidity-controlled suspended sediment sampling for

runoff-event load estimation. Water Resour. Res. 32(7): 2299–

2310. doi:10.1029/96WR00991.

Lewis, J., and Eads, R. 2001. Turbidity threshold sampling for

suspended sediment load estimation. In Proceedings of the

Seventh Federal Interagency Sedimentation Conference, 25–29

March 2001. The Federal Subcommittee on Sedimentation, Reno,

Nev. pp. III-110–III-117.

Luce, C.H. 2002. Hydrological processes and pathways affected by

forest roads: what do we still need to learn? Hydrol. Process. 16

(14): 2901–2904. doi:10.1002/hyp.5061.

Luce, C.H., and Black, T.A. 1999. Sediment production from forest

roads in western Oregon. Water Resour. Res. 35(8): 2561–2570.

doi:10.1029/1999WR900135.

Megahan, W.F., and Ketcheson, G.L. 1996. Predicting downslope

travel of granitic sediments from forest roads in Idaho. Water

Resour. Bull. 32(2): 371–382. doi:10.1111/j.1752-1688.1996.

tb03459.x.

Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through

conceptual models part 1 — A discussion of principles. J. Hydrol.

(Amst.), 10(3): 282–290. doi:10.1016/0022-1694(70)90255-6.

NCASI. 2002. Technical documentation for SEDMODL version 2.0

road erosion/delivery model. Downloadable from the NCASI

website: http://www.ncasi.org/support/downloads/Detail.aspx?

id=5 [accessed 14 October 2009].

Raines, M., Conrad, R., Clark, J., Coe, D., Palmquist, R., and

Veldhuisen, C. 2005. Road sub-basin scale effectiveness monitoring design. Study plan. Department of Natural Resources,

Olympia, Wash.

Reid, L.M. 1981. Sediment production from gravel-surfaced forest

roads, Clearwater basin, Washington. FRI-UW-8108. Fisheries

Research Institute, University of Washington, Seattle.

Rittiman, C.A., and Thorson, T. 1993. Soil survey of Mendocino

County, California, western part. U.S. Department of Agriculture,

Natural Resources Conservation Service, in cooperation with the

California Department of Forestry, Soil Vegetation Survey;

Georgia-Pacific Corporation; the Regents of the University of

California (Agricultural Experiment Station); and the United States

Department of the Interior, Bureau of Land Management.

Published online at http://www.ca.nrcs.usda.gov/mlra02/wmendo/

Samani, Z., and Herrera, E. 1996. A low-cost water measuring

device. Guide M-226:PH-4–205. New Mexico State University

Cooperative Extension Service, Las Cruces, N.M.

Spence, B., Lomnicky, G., Hughes, R., and Novitzki, R. 1996. An

ecosystem approach to salmonid conservation. Report TR-4501­

96-6047. Mantech Environmental Technology, Inc., Corvallis,

Ore.

Stevens, D. , Jr, and Olsen, A. 2004. Spatially balanced sampling of

natural resources. J. Am. Stat. Assoc. 99(465): 262–278. doi:10.

1198/016214504000000250.

Sugden, B.D., and Woods, S.W. 2007. Sediment production from

forest roads in western Montana. J. Am. Water Resour. Assoc. 43

(1): 193–206. doi:10.1111/j.1752-1688.2007.00016.x.

Surfleet, C., Skaugset, A., and McDonnell, J. 2010. Uncertainty

assessment of forest road modeling with the Distributed

Hydrology Soil Vegetation Model (DHSVM). Can. J. For. Res.

40(7): 1397–1409. doi:10.1139/X10-079.
Toman, E.M., and Skaugset, A.E. 2011. Reducing sediment

production from forest roads during wet-weather hauling. J.

Transportation Res. Board, 1(2203): 13–19.

USDA Forest Service LTBMU. 2006. 2005/2006 Monitoring

program annual report. USDA Forest Service LTBMU, Lake

Tahoe, Calif.

Washington Department of Resources. 2007. Web citation for

Washington road surface erosion model (WARSEM): http://

www.dnr.wa.gov/BusinessPermits/Topics/ForestPracticesApplications/Pages/fp_warsem.aspx [accessed 16 September 2011].

Whitaker, A., Alila, Y., Beckers, J., and Toews, D. 2003. Application

of the distributed hydrology soil vegetation model to Redfish

Creek, British Columbia: model evaluation using internal catchment data. Hydrol. Precesses, 17(2): 199–224. doi:10.1002/hyp.

1119.

Wischmeier, W.H., and Smith, D.D. 1960. A universal soil loss

equation to guide conservation farm planning. Transactions of the

7th International Congress of Soil Science. Vol. 1. International

Society of Soil Science, Madison, Wis. pp. 418–425.