Building and Environment 43 (2008) 687–695

Estimating sediment yield from a forest road network by using a

sediment prediction model and GIS techniques

Abdullah E. Akay, Orhan Erdas, Mahmut Reis, Alaaddin Yuksel

Department of Forest Engineering, Faculty of Forestry, Kahramanmaras Sutcu Imam University, 46100 Kahramanmaras, Turkey

Received 4 May 2006; received in revised form 17 January 2007; accepted 26 January 2007

Abstract

In forest lands, the stream channels receive the highest amount of sediment during road construction activities due to removal of

vegetation cover from road surface, cut-slope, fill-slope, and ditch areas. Sediment delivered from a road section to streams causes serious

damages on water resources and aquatic life. Performing revegetation in cut-slope and fill-slope reduce sediment yield from a road

section; however, road surface and ditch continue to deliver considerable amount of sediment to the streams. Therefore, accurate

prediction of sediment yield from existing road network can be very critical. In order to estimate average sediment yield, several sediment

prediction models have been developed based on empirical relationships between various road erosion factors. In this study, average

annual sediment yield from a road network to streams in a forest watershed was estimated by using the methodology of a GIS-based

sediment prediction model, the SEDMODL model. GIS techniques were used to provide required data layers such as topography,

streams, roads, geology, and average precipitation. The results indicated that the SEDMODL model integrated with GIS techniques can

assist road managers to estimate total sediment yield quickly and effectively. Besides, critical road sections with high sediment yield

potential can be identified and the efficiencies of various sediment control measures can be evaluated for these sections.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Forest road network; Erosion factors; Sediment model; GIS

1. Introduction

A road network in forest lands provides easy access to

forest resources for extraction, regeneration, protection,

and recreation activities [1]. Traditionally, the planning of

low-volume road networks highly depends on economical

and social considerations [2]. In recent years, forest road

construction and maintenance activities have become

controversial, because of increasing public concerns about

short- and long-term effects of forest roads on environment

and the value that society now places on roadless

wilderness [3,4]. In the last decade, considerable increase

of traffic volume on forest roads, along with the expansion

of the road networks has led to some negative consequences such as unsafe trafficking, air and noise pollution,

and flora and fauna degradation [5–7].

Suitable forest road network is crucial for sustainable

management of forest resources; however, inadequately

constructed and maintained forest roads can cause more

environmental impacts than any of these activities [8].

Studies have indicated that forest roads produce the

highest amount of sediment yield to streams from forest

lands [9–11]. Road construction removes the forest

vegetation, disturbs forest floor, and damages soil structure,

which dramatically increases the sediment yield [12,13].

Sediment delivered to streams from road sections leads to

number of dramatic effects on water quality (i.e. increased

water temperature and reduced oxygen) and aquatic life (i.e.

siltation of spawning beds and aquatic insect habitat).

Therefore, forest road managers should consider not only

the total road cost but also environmental impacts caused by

the road construction and use [14].

To reduce the potential sediment yield from a road

section, cut-slope and fill-slope areas are immediately

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doi:10.1016/j.buildenv.2007.01.047

Corresponding author. Tel.: +90 344 223 7923x453;

fax: +90 344 221 7244.

E-mail addresses: akay@ksu.edu.tr (A.E. Akay), erdas@ksu.edu.tr

(O. Erdas), mreis@ksu.edu.tr (M. Reis), ayuksel@ksu.edu.tr (A. Yuksel).
revegetated following road construction activities. However, road surface and ditch areas still continue to deliver

sediment to the streams as long as the road is used. The

amount of sediment produced from road surface highly

depends on traffic density, road surface type, road

dimensions, and road gradient. The ditches receive the

sediment yield from the cut-slope areas, depending on road

section length, ground slope, and vegetation and rock

cover density. Inslope roads with ditch keep the runoff

water away from the fill-slope which may cause much

smaller sediment yield than road surface and cut-slope

areas.

Several sediment prediction models have been developed

to help road managers in estimating average sediment from

a road network to the streams. The Forest Road Sediment

Assessment Methodology (FROSAM) model was developed as a practical approach for estimating sediment yield

from forest roads as well as predicting and measuring the

capabilities of road improvement methods [15]. In the

model, the source areas for sediment yield include road

surface, ditches, cut-slope, and fill-slope. The erosion

factors considered in the model were cover, gravel, traffic,

and percent of sediment delivery.

The Water Erosion Prediction Project (WEPP): Road

Interface model was developed to estimate sediment yields,

considering the specific conditions for soil, climate, ground

cover, road surface, ditch, and topography [16]. The

WEPP: Road Interface model assumes that sediment is

produced from three overland flow elements including road

surface, fill-slope, and a forested buffer area. Sediment

yield is predicted based on road gradient, road width,

surface type, road design (insloped or outsloped), and

traffic density.

The X-DRAIN (Cross Drain Spacing and Sediment

Yield Program) model was developed as a stand-alone and

network interface to access the sediment yield data

generated by the WEPP model for more than 130,000

combinations of topography, soil types, and climates [17].

The user controls over the erosion factors such as climate,

soil, side-slope, and stream distance.

The model presented cannot identify the road sections

with high sediment yield potential. They do not have

capability to evaluate the effects of prevention techniques

on total sediment yield from a road network to the streams.

In this study, the methodology of a GIS-based sediment

prediction model, the SEDMODL (Road Sediment Delivery Model) model was used to estimate average annual

sediment yield from a road network to streams in a forest

watershed [18]. The formulas used in the model are based

on empirical relationships between road erosion factors

such as road use, parent material, road surfacing, road

surface slope, cut-slope vegetative cover, and distance to

streams [11,19–21].

In the SEDMODL model, GIS tools can be used to

generate some of the required data such as topography

(10 m DEM optimal), streams, roads, and other optional

layers (soil, local geology, precipitation, etc.). The accuracy

of the GIS data layers directly affects the performance of

the model in predicting sediment yield. After estimating

average sediment yield from the road network, SEDMODL can assist road managers to locate the road

sections with high sediment potential and to evaluate

various sediment control measures for these sections.

SEDMODL was successfully used in recent forest road

design studies to estimate the sediment yield from an

optimal forest road alignment [22].

2. Material and methods

2.1. Study area

The study area was the Baskonus Research and

Application Forest of Kahramanmaras Sutcu Imam

University, which is located approximately 45 km west of

the city of Kahramanmaras in the Mediterranean region of

Turkey. The research forest is about 458 ha in which

374.5 ha is forest land and 83.5 ha is open areas. The

dominant tree species in the forest are Pinus brutia, Pinus

nigra, Cedrus libani, and Abies cilicica. The average ground

elevation and side-slope were 1165 m and 73%, respectively. In the research forest, there are county-maintained

roads and secondary forest roads, with the lengths of

approximately 7250 and 3155 m, respectively. The stream

network in the research forest consists of two types of

streams including medium and small types.

2.2. Mathematical formulations

The sediment yield is typically produced from four

overland flow components including road surface, cutslope, fill-slope, and ditch (Fig. 1). Since the road sections

in the research forest are insloped with a ditch, surface

water stay away from the fill-slope area. Based on the field

observations and calculations, sediment yield from a

revegetated fill-slope produces very small amount of

sediment which can be ignored in the sediment prediction.

In the formulation, the sediment yield from road surface

and ditch were combined into one unit and called tread

sediment. The road tread width includes both running

surface width and ditch width. Therefore, total sediment

delivered from each road section was predicted based on

two components: road tread and cut-slope [18]:

Total Sedimentðt=yearÞ1⁄4ðTS þ CSÞAf, (1)

where TS is tread sediment, CS is cut-slope sediment, and

Af is road age factor. Tread sediment varies based on road

dimensions including length (Lr) and width (W) and

various erosion factors including geologic erosion rate

(GEr), road tread surfacing (Sf), traffic (Tf), road grade

(Gf), precipitation (Pf), and sediment delivery factors (Df):

TS 1⁄4 LrWrGErSfTfGfPfDf. (2)

Cut-slope sediment is a function of geologic erosion rate,

cut-slope factor (CSf), cut-slope height (CSh), road length,

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688 A.E. Akay et al. / Building and Environment 43 (2008) 687–695
and sediment delivery factor:

CS 1⁄4 GErCSfCShLrDf. (3)

The majority of sediment yield from a new road is

produced during the first two years until cut-slope, fillslope, and ditch areas are properly covered by vegetations.

According to the empirical observation, sediment yield is

affected by time following construction [23]. The model

considered the effects of the road age by multiplying the

total sediment by the road age factor. It was assumed that

the road age factor within the first year is 10, while it was 2

after two years or more [18].

The values for the erosion factors were obtained from

user input values or from model-supplied tables generated

based on the previously conducted studies. The following

section describes the erosion factors and their associated

values used in the equations.

2.2.1. Geological erosion rate

The sediment yield potential from a specific road

segment highly depends on sub-soil properties and geology

[23,24]. For example, the silt-dominated soils are the most

erodible soils, followed by clay-dominated and graveldominated soils [25]. The erosion rates for basalt, andesite,

granite, and sedimentary rocks are higher for Mesozoic

and older rocks [18]. After determining the geologic

information of lithology and geologic age, the geological

erosion rates for common parent materials were obtained

from Table 1. The erosion rates listed in this table were

generated based on previously conducted studies

[11,19,24,26–28]. In these studies, the local erosion factors

such as surfacing, traffic, slope, and precipitation were

factored out in determining the values of erosion rates.

2.2.2. Tread surfacing factor

The quality of the road surfacing material directly affects

the sediment yield from the road tread surface [29]. After

determining the road segments with high sediment yield

potential, road managers can choose a better road

surfacing type to reduce sediment yield. Based on the

previous studies, tread surfacing factors for common road

surface types are shown in Table 2 [21,30–32].

2.2.3. Traffic factor

The sediment yield from a road tread surface is also

affected by road use, as well as road surface material [19].

The effect of road use on sediment yield is represented by

traffic factor. Reid and Dunne [11] reported that traffic

factor was to be the single most essential factor influencing

sediment generation. There is an opposite relationship

between traffic factor and surfacing factor [18]. The most

heavily used roads with high traffic factor have high quality

surfacing with low surfacing factor, while rarely used roads

with low traffic factor have low quality surfacing with high

surfacing factor. Therefore, these two factors tend to even

each other out in estimating sediment yield from a road

segment. Traffic factors for various road classes are

indicated in Table 3 where the values are estimated based

on previously performed field measurements and road

erosion inventories [11,21,28]. To determine average

sediment yield from a forest land, the road class should

be selected from the table by considering long-term traffic

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Fig. 1. A sample forest road template.

A.E. Akay et al. / Building and Environment 43 (2008) 687–695 689
use, including log-truck, residential, recreational, and

administrative traffic [18]. To determine sediment yield

from a specific timber sale, the road class should be selected

by considering the traffic use during that sale.

2.2.4. Road grade factor

A road grade factor was assigned to each road segment

based on the road gradient classes. Luce and Black [23] and

Reinig et al. [33] suggested road grade factor of 0.2, 1.0,

and 2.5 for road grades less than 5%, between 5% and

10%, and more than 10%, respectively. The road grade

affects the type and the amount of road surface material to

be used in base course. In forest roads, pitrun materials are

commonly used on grades less than or equal to 10%, while

gravel materials are used on grades more than 10% [34].

Increasing road grade from 5% to over 10% increases the

road grade factor from 1 to 2.5 (i.e. 2.5 times), while using

gravel materials instead of pitrun material on road grade of

more than 10% reduces the surfacing factor from 0.50 to

0.20 (i.e. 2.5 times).

2.2.5. Precipitation factor

The amount of sediment yield potential from a road

segment can vary with annual regional precipitation.

According to WFPB [15], exceeding the average annual

precipitation of 1200 mm can increase the effects of erosion

factors on sediment yield estimation about two times. In

the methodology of SEDMODL, the precipitation factor

was computed based on the average annual precipitation

(Pavr in mm) in the basin [26]:

Pf 1⁄4 Pavr

1524 0:8

. (4)

The average annual precipitation can be estimated by

using an interpolation method based on a meteorology

station with known elevation and average precipitation. In

this study, following formula was used to estimate the

average annual precipitation in the research forest [35]:

Ph 1⁄4 P0 

54h

100 (5)

where Ph is the estimated average annual precipitation at a

study site (mm), P0 the annual precipitation at the nearest

meteorology station (mm), h the elevation difference

between meteorology station and study site (m), 54 the

increase in annual precipitation for every 100 m increment

in elevation.

2.2.6. Delivery factor

Estimating percent of sediment yield from a road

segment to a near by stream is the most difficult part in

sediment prediction models [15]. The road sections with a

long distance to the streams tend to have a low percentage

of sediment yield since most of the sediment from the road

section traps in the forest land and cannot reach the

streams [36]. In the methodology of SEDMODL model,

the erosion delivery factor for each road stage was

estimated based on the distance from the middle point of

the nearest stream to the middle point of the road stage

[21]. The model assumed that a road segment at stream

crossing directly delivers sediment to streams with a

delivery factor of 100%. A road segment within 30 and

60 m of a stream resulted in a delivery factor of 35% and

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Table 2

Road tread surfacing factors for various common road surface types

Surface type Surfacing factor

Asphalt 0.03

Gravel 0.20

Pitrun 0.50

Grassed native 0.50

Native surface 1.00

Native with ruts 2.00

Table 1

Geologic erosion rates based on lithology and geologic age (in t/ha/year)

Lithology Geologic age of formationa

Quaternary Tertiary Mesozoic Paleozoic Precambrian

Metamorphic – 37 37 37 37

Schist – 148 148 148 148

Basalt 37 37 74 74 74

Andesite 37 37 74 74 74

Ash 124 124 124 124 124

Tuff 124 124 74 74 74

Gabbro – 25 25 25 25

Granite – 49 74 74 74

Intrusive – 37 37 37 37

Hard Sediment – 37 37 74 74

Gravelly Sediment 37 37 – – –

Soft Sediment 74 74 – – –

Fine-Grained Soft Sediment 148 148 – – –

a

Some of the lithology/ages categories with no occurrence do not have erosion rates (i.e. Soft Sediment and Mesozoic category does not occur).

690 A.E. Akay et al. / Building and Environment 43 (2008) 687–695
10%, respectively. These roads were located closely parallel

to streams and indirectly deliver sediment to streams. The

road segments that were located further than 60 m did not

deliver sediment to streams since sediment was infiltrated

into the forest floor. A delivery factor for road segments

with no sediment delivery to streams was 0. The sediment

delivery factors for each segment were determined based on

the sediment delivery zones generated by using ‘‘buffer’’

tool in ArcView GIS 3.2 (ESRI, Redlands, CA, USA).

2.2.7. Cut-slope cover factor

The cut-slope cover factor can be defined as the percent

of non-erodible cover on road tread, cut-slope, and fillslope areas [18]. The cut-slope cover factor as percent of

vegetative or rock cover on cut-slopes are included into the

sediment yield estimations based on the local conditions on

the watershed [21]. In order to control road segments with

high sediment yield potentials, road managers may

consider improving rock cover on the cut-slope areas as a

quick prevention method. Improving vegetation cover on

these cut-slopes can be also considered as a long-term

solution. The cut-slope cover factors are listed in Table 4,

depending on percentages of vegetation or rock cover on

the cut-slope areas [21].

2.2.8. Cut-slope height

Increasing the cut-slope height increases the amount of

sediment yield from cut-slope area to ditch area through

soil creep, sheet wash, and slumping [23]. In the SEDMODL model, cut-slope height was determined based on

hillside gradient class over the length of a road segment

that draines to the stream. Four gradient classes were

specified during the field measurements and cut-slope

height was assigned for each gradient class based on the

average cut-slope heights measured in road erosion

inventories [18]. The mean cut-slope heights for gradient

classes of 0–15%, 15–30%, 30–60%, and over 60% were

estimated as 0.75, 1.5, 3.0, and 7.5 m, respectively. Therefore, the forest lands with steep hillside gradient deliver

more sediment yield to the streams than a forest land with

a even hillside gradient [36]. The hillside gradient data over

the research forest was generated based on 10 m DEM and

cut-slope height was determined for each road segment.

3. Results and discussion

GIS data layers for road and stream networks were

generated based on a 1:25,000 scale topographic map by

using Ilwis 3.2 Academic (ITC, Enschede, Netherlands).

There were two county-maintained and four secondary

forest road sections in the research forest. Each road

section consisted of various numbers of road segments. In

sediment prediction method, the sediment yield from each

road segment was estimated and then cumulative sediment

was computed for each road section. The road specifications of the road sections and their associated average

annual sediment yields are listed in Table 5. The results

indicated that the highest amount of sediment yield to the

streams was produced by Section V, followed by Sections

II, VI, I, and IV. There was no sediment delivery from

Section III since the distance between all the road segments

in Section III and the nearest stream was more than 60 m.

The average annual sediment delivery from a square meter

of the road sections was also computed by factoring out the

road dimensions (i.e. length and width). The amount of

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Table 3

Road traffic factors for various road classes

Road classes Road descriptions Traffic factor

Highway Main highway 120

Main Haul Heavily used by log truck traffic throughout the year; usually the main access road in

a watershed that is being actively logged

120

County Road Wide, county-maintained road that receives heavy residential and/or log truck use 50

Primary Road Receives heavy to moderate use by log trucks throughout all or most of the year.

Usually roads branching off main haul road that head up tributaries or that access

large portions of the watershed

10

Secondary Road Receives light log truck use during the year. May occasionally be heavily used to

access a timber sale. Receives car/pickup or recreational use

2.0

Spur Road Short road used to access a logging unit. Used to haul logs for a brief time while unit

is logged. On the average receives little use

1.0

Abandoned/blocked Road is blocked by a tank trap, boulders, etc. or is no longer used by traffic 0.1

Table 4

The cut-slope cover factors as a function of vegetation or rock cover rates

Vegetation or rock cover (%) Cover factor

100 0.1023

90 0.1500

80 0.2003

70 0.2540

60 0.3116

50 0.3742

40 0.4435

30 0.5222

20 0.6155

10 0.7700

0 1.0000

A.E. Akay et al. / Building and Environment 43 (2008) 687–695 691
sediment yield (t/m2

) to the streams was 0.0037, 0.0199,

0.0004, 0.0636, and 0.0051 for Sections I, II, IV, V, and VI,

respectively. Therefore, the highest amount of sediment

yield in tons per square meter was also produced by Section

V, followed by Sections II, VI, I, and IV.

Geology data coverage for the research forest was

generated based on a 1:100,000 scale geologic map of

Kahramanmaras. Then, this coverage was used to determine geologic erosion rates for the road segment in t/ha/

year. The results indicated that available geologic age and

lithology combinations in the research forest were Mesozoic/Hard Sediment, Tertiary/Hard Sediment, and Tertiary/Metamorphic. The estimated annual geologic erosion

rate for all of these classes was 37 t/ha (Table 1). Therefore,

the geologic erosion rate in tons per square meter (0.0037 t)

was the least effective erosion factor on sediment yield

from each road segments.

The secondary roads with gravel surfacing had about

seven times greater surfacing factor than that of the county

roads with asphalt surfacing. However, the traffic factor

for the county road was 25 times greater than that of the

secondary road. The cumulative effects of surfacing and

trafficking on the potential sediment yield from a unit road

length were 1.5 (i.e. 0.03  50) and 0.4 (i.e. 0.2  2) for

county and secondary roads, respectively. Therefore,

trafficking factor was more effective than surfacing factor

on sediment yield from the road network in the research

forest. Foltz and Burroughs [32] also reported that

sediment yield from a low quality surfacing was 4–17

times greater than that of good quality surfacing, while the

section with log-truck traffic produced 2–25 times greater

sediment yield than the section with no traffic.

The road grade for each road segment was computed by

using a 10 m DEM (Fig. 2) which was generated based on a

1:25,000 scale contour map. The average road grade for

Sections IV and V was between 5% and 10% while for

Sections I, II, and VI it was more than 10%. Therefore,

effect of road gradient on the potential sediment yield from

a unit road length in Sections I, II, and VI was 2.5 times

greater than that in Sections IV and V. This result agreed

well with the findings of Luce and Black [23] where

increases in road gradient lead to increased sediment yield.

Akay and Sessions [22] estimated the average annual

sediment yield of an economically optimized road section

for two different surfacing scenarios. The results indicated

that the sediment yield from a road section with high

quality surfacing and steep road gradient (i.e. 410%) in

the first scenario was more than the sediment yield from a

road section with low quality surfacing and less steep road

gradient (i.e. o10%). Therefore, the effect of road grade

factor on sediment yield was more than that of surfacing

factor.

To determine the precipitation factor for each road

segment, a 100 m precipitation map was generated by using

interpolation method based on the DEM and meteorology

data from the station in Kahramanmaras. The elevation

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Table 5

Specification summary for road sections located in the research forest

Road sections Road classes Surface type Length (m) Width (m) Average grade (%) Sediment (t/year)

I Secondary Gravel 893.50 5 14 0.839

II Secondary Gravel 866.65 5 17 2.715

III Secondary Gravel 443.26 5 12 0.000

IV Secondary Gravel 951.43 5 6 0.258

V County Asphalt 3195.73 10 7 28.667

VI County Asphalt 4053.24 10 12 1.476

Fig. 2. The DEM (10 m) of the study area indicating road networks and

streams.

692 A.E. Akay et al. / Building and Environment 43 (2008) 687–695
and the average annual precipitation at the meteorology

station was about 566 m and 712 mm, respectively. Then,

precipitation factor for each road segment was computed

(Eq. (4)). The results indicated that the average precipitation factor for road sections varied from 0.64 (Section I) to

0.75 (Section VI) based on the average elevation along the

road sections. Therefore, the effect of precipitation factor

on sediment yield was not significant in the road network.

However, in seasons with intense rainfall events, a

considerable amount of sediment yield might occur,

especially in the road sections with low quality surfacing.

In order to prevent excessive sediment yield due to intense

rainfall, the surfacing quality of the roads should be

improved [28].

In the road network, the road segments with high

sediment delivery factor and segments with no delivery

were identified. For each road section, the number of road

segments under each sediment delivery category was

indicated in Table 6. Only 28 segments out of 130 segments

delivered sediment to the stream network in the research

forest. In Section V, six segments directly deliver sediment

to the streams at stream crossings, while the rest of the

segments were closely parallel to the streams and indirectly

deliver sediment to the streams. In Section III, the sediment

yield from each segment was infiltrated into the forest floor

before reaching the streams.

Based on the field measurements, the percentages of

vegetation and rock cover on cut-slopes for countymaintained roads and secondary roads were estimated as

80% and 60%, respectively. The associated average cutslope cover factors were 0.2003 and 0.3116, respectively.

Therefore, secondary roads had about 36% less cut-slope

cover capacity than that of county-maintained roads. Luce

and Black [23] also indicated that reduction in the amount

of cut-slope vegetation and rock cover material caused a

significant increase in sediment yield.

The cut-slope heights were estimated for each road

segment based on the hillside gradient data layer generated

based on the DEM. The results indicated that the average

cut-slope heights for the road segments that deliver

sediment to the streams in Sections I, II, IV, V, and VI

were 1.52, 3.43, 0.76, 1.42, and 4.57, respectively. Therefore, the effect of cut-slope height on sediment delivery was

very considerable in Sections II and VI. Luce and Black

[23] also suggested that high cut-slopes were the most

important source of sediment yield from the road segment

after two years or more.

The effects of improving tread surfacing material and

cut-slope cover capacity in secondary roads were evaluated. It was found that using asphalt surfacing type in

secondary roads reduced the annual sediment delivery by

about 74%, 63%, and 73% in Sections I, II, and IV,

respectively. However, improving cut-slope cover capacity

of secondary roads from 60% to 80% reduced the sediment

delivery by only 5%, 10%, and 5% in Sections I, II, and

IV, respectively. Therefore, the effect of road tread

surfacing factor on sediment yield was much higher than

that of cut-slope cover factor.

To investigate the most effective erosion factors in

sediment yield, the erosion factors whose average values

were more than 1.0 in the equations were determined for

each road section. In Section I, road grade factor was the

most influential factor, followed by traffic factor and cutslope height. In Section II, the most influential factor was

cut-slope height, followed by road grade factor and traffic

factor. In Section IV, only traffic factor had a considerable

effect on the sediment yield. Fig. 3 indicated the effects of

these three factors on sediment yield in percentage. In the

county-maintained road sections, traffic factor significantly

affected the amount of sediment yield produced from the

road segments.

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Table 6

The number of road segments within the road sections with a high sediment delivery potential

Road sections Number of segments Direct delivery Within 0–30 m Within 30–60 m No delivery

I 11 2 – – 9

II 15 1 3 4 7

III 3 – – – 3

IV 7 1 – – 6

V 47 6 2 6 33

VI 47 – – 3 44

Fig. 3. The effects of traffic, road grade, and cut-slope height on sediment

yield in percentage.

A.E. Akay et al. / Building and Environment 43 (2008) 687–695 693
The amount of sediment yield produced from road tread

surface and cut-slope area was also evaluated. The results

indicated that sediment yield from road tread surfacing was

7, 3, 6, 26, and 14 times more than sediment yield from the

cut-slope area in Sections I, II, V, and VI. There was a

significant difference between these two sediment sources in

Sections V and VI due to the high value of traffic factor

(i.e. 50). In Section IV, sediment yield from road tread

surface was not high due to low value of road grade factor.

4. Conclusions

The average annual sediment yield from a road network

to streams in a forest watershed was estimated based on the

methodology of the SEDMODL model, which was

relatively easy and flexible in sediment prediction. It is

capable of identifying the road segments with high

sediment yield potential. The success of various road

improvement techniques in reducing sediment yield can be

evaluated through a sensitivity analysis of the road erosion

factors. Besides, GIS technologies can be easily integrated

with the methodology in generating some of the main data

layers (such as DEM, streams, roads, soil, local geology,

and precipitation), which can assist road managers to

quickly and efficiently evaluate the sediment yields in large

forest lands. However, there are a number of limitations of

the model in predicting sediment yield. The model assumes

that all roads are in-slope with ditch. Besides, the model

may over-predict sediment yield if the GIS data layers

(stream, soil, etc.) are not accurate and road template

information (road length, cut-slope, etc.) is not complete.

The results from this study proved that the sediment

yield from road sections highly depends on several erosion

factors. Even though their effects on sediment yield vary

based on the road types (i.e. secondary or countymaintained roads), the most important attributes to

consider in predicting sediment yield are road use, gradient,

and cut-slope heights. In spite of the fact that these three

factors are very effective on sediment yield, their effects can

be dramatically reduced or even vanished by sediment

delivery factor based on the distance between the road

segments and the streams.

The accuracy of these results still needs to be validated in

a field study. However, the effects of the erosion factors on

the sediment yield were in good agreement with the results

derived from the previously conducted observational

studies. Therefore, this methodology can be used as a

decision support tool in predicting sediment yield and

identifying the problematic road segments with high

sediment yield potential.

Acknowledgments

This research was supported by the Research Fund of

the Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey. Project Number: 2004/4-16.

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