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International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 10, October 2018, pp. 681–697, Article ID: IJCIET_09_10_071

Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=10

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

©IAEME Publication Scopus Indexed

SPATIAL DISTRIBUTION OF SOIL EROSION

RISK USING RUSLE, RS AND GIS TECHNIQUES

Ramzi Ameen Almaaitah, Ayu Wazira Azhari, Mohd Asri Ab Rahim,

Fahmi Muhammad Ridwan

School of Environmental Engineering, Universiti Malaysia Perlis,

Pauh Putra Campus, 02600 Arau, Perlis, Malaysia

ABSTRACT

This investigation is intended to estimate the annual soil loss in Wadi Bin

Hammad watershed, and to examine the spatial patterns of soil loss and intensity, as

an essential procedure for proper planning of conservation measures. To achieve

these objectives, the revised universal soil loss equation (RUSLE) model has been

applied in a geographical information system framework. After computing the RUSLE

parameters (R, K, LS, C and P) soil erosion risk and intensity maps were generated,

then integrated with physical factors (terrain units, elevation, slope, and land

uses/cover) to explore the influence of these factors on the spatial patterns of soil

erosion loss. The estimated potential annual average soil loss is 40.4 ton ha-1year-1,

and the potential erosion rates from calculated class ranges from 0.0 to 100 ton ha-

1year-1. Soil erosion risk assessment indicates that 14.63 % of the catchment is prone

to high to extreme soil losses higher than 75 ton ha-1year-1. The lower and middle

parts of the catchment suffer from high, severe, to extreme soil erosion. While 57.83 %

of the basin still undergoes very low , low and moderate levels of soil loss of less than

75 ton ha-1year-1. The present results provide a vital database necessary to control

soil erosion in order to ensure sustainable agriculture in the southern highlands

region of Jordan.

Key words: Erodibility, Erosivity, Terrain units, Land use change, Landsat ETM ,Gis,

Rs, Jordan, Wadi Bin Hmmad.

Cite this Article: Ramzi Ameen Almaaitah, Ayu Wazira Azhari, Mohd Asri Ab

Rahim, Fahmi Muhammad Ridwan, Spatial Distribution of Soil Erosion Risk Using

Rusle, RS and GIS Techniques, International Journal of Civil Engineering and

Technology (IJCIET) 9(10), 2018, pp. 681–697.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=10

1. INTRODUCTION

Jordan is currently suffering from serious soil erosion. This is by no means a new problem for

the country but one that has intensified recently as human population pressures on the land

increase( Beaumont et all ,1969) . Soil erosion, a gradual process, removes soil particles by

runoff, thus causing soil to deteriorate (Al-Kaisi,.2000). The accumulation of 10 to 15

centimeters of soil behind newly constructed walls in a single season indicates the severity of

Ramzi Ameen Almaaitah, Ayu Wazira Azhari, Mohd Asri Ab Rahim, Fahmi Muhammad Ridwan


the problem. Erosion of the topsoil leads to declining soil productivity, thus restricting the

area of potential future agriculture. Modern soil conservation and agricultural reorganization

provide a wide choice of remedial measures to reduce soil erosion rates in the country

(Battikhi and Arabiat ,1983) and are considered imperative for the country‘s future wellbeing.

Eroded soil materials are deposited over wadi floors and agricultural lands, irrigation canals,

even on roads, and more seriously in reservoirs.

High rainfall intensities are a recurrent phenomenon in the southern highlands. In March

1966 for example, a severe storm was recorded in the Ras En Naqb area, southern Jordan. The

average 4 h rainfall intensity was 16 mm h-1 (Central Water Authority 1966; Schick 1971).

Following that storm, a small farm fence was exposed 15–20 cm due to water erosion. Again

in 1991/1992, the annual rainfall doubled, resulting in excessive soil slumping and shallow

landslides and mudflows. Sheet and gully erosion affected the valley on both land units of

side slopes and farming areas. During the last four decades, southern Jordan was exposed to

several severe storms, with high maximum rainfall intensities in 24 h ranging between 25 and

65 mm (Aqaba Region Authority 1987), which caused serious soil erosion. Rapid population

growth since the 1950s has necessitated continuous expansion of rainfed mixed cultivation to

secure food production. The expansion of farming was carried out at the expense of forest and

rangelands.

Consequently, recent land use/cover changes represent a major cause of accelerating soil

erosion in the highland catchments (Beaumont and Atkinson 1969; Atkinson and Beaumont

1971; Khresat et al. 2008; Alkharabsheh et al. 2013). Several studies and reports on soil

erosion were carried out in Jordan at local, regional and national scales. Soil erosion loss due

to water erosion has been estimated for the surface water catchments east of the rift to be

1.328 million tons year-1 which means, 0.14 cm of the top soil is eroded annually (McDonald

Partners and Hunting Technical Services LTD 1965; Shamoot and Hussini 1969). FAO et al.

(1979) and Battikhi and Arabiat (1983) reported that part of the highlands of Jordan was

classified within soil loss categories of 50–200 tons ha-1 year-1 and [200 tons ha-1 year-1.Al-

Ansari and Knutsson (2012) reported recently that W. Alarab Dam (northern highlands of

Jordan) will be filled with sediments within 38 years. Consequently, the predicted sediment

yield and the estimated high soil erosion rate, will seriously endanger the future of dams

under construction such as Wadi Kufranja Dam in the northern highlands, and the proposed

dam on Wadi Kerak, (Ministry of Water and Irrigation2010, 2011). and the proposed dam on

Wadi Bin Hammad in the southern highlands (Ministry of Water and Irrigation2016). Land

degradation is not a recent problem for Jordan, it was active prehistorically and historically in

the highlands of central and southern Jordan (Cordova 1999, 2000).

A variety of approaches and models were developed to assess soil erosion by water and to

predict soil erosion risk and intensity. Each approach or model has its own characteristics and

purpose of application. Available quantitative and semi-quantitative models for predicting soil

erosion at a basin scale, were reviewed and evaluated in details (de Vente and Poesen 2005;

Broadman 2006). The dominant model utilized worldwide and selected for the present

investigation is the RUSLE model (Angima et al. 2003; Hoyos 2005; Lim et al. 2005; Yue-

Qing et al. 2008; Hlaing et al. 2008; Kouli et al. 2009; Wu and Wang 2011; Abu Hammad

2011; Ozsoy et al. 2012; Prasannakumar et al. 2012; Krishna Bahadur 2012; Kumar and

Kushwaha 2013; Chatterjee et al. 2014; Xu et al. 2014). Recently, the RUSLE model has been

employed in combination with sediment delivery ratio (SDR) to assess the life expectancy of

dams in semi-arid watersheds, Turkey (Saygin et al. 2014). The selected RUSLE model, is an

empirical one and characterized by several benefits: easy to implement and familiar from a

functional perspective, compatible with geographic information system (GIS); the data

required to apply within the model are not overly complex and are accessible. Moreover, the

Spatial Distribution of Soil Erosion Risk Using Rusle, RS and GIS Techniques


approach makes soil erosion estimation and observation of its spatial patterns feasible at a

reasonable cost. It provides better accuracy for catchment and regional scales (Wischmeier

and Smith 1978; Millward and Mersey 1999; Krishna Bahadur 2009; Prasannakumar et al.

2011). The universal soil loss equation (USLE) and the revised universal soil loss equation

(RUSLE) (Wischmeier and Smith 1978; Renard et al. 1997) were adopted to predict potential

soil loss caused by water erosion in the Jordan northern highlands (Al-Zitawi 2006; Farhan et

al. 2013).

2. STUDY AREA

Wadi Bin Hammad catchment constitutes the present study area. It located to the Northwest

of Karak governorate, Jordan, between longitudes (41″ 44′ 350 - 8″ 13′ 35

0) Eastward and

between latitudes (310 13′ 27″ -310 20′ 49″) Northward. Its area is about (136, 13) km2. It

has borders with the Mawjab Valley basin to the East, the Jarrah Valley basin to the North,

Karak Valley basin to the South, and the Dead Sea to the West. Figure (1) illustrates the

geographical location of the study area.

Figure 1 Illustrates the geographical location of the study area.

Elevation varies from 1400 m above mean sea level in the upper catchment close to

Rakeen town, decreasing towards the west to 1000 m at Kerak city, then dropping to -410 m

below mean sea level close to the Dead Sea. The catchment exhibits a typical highland/rift

(Ghor) topography. Consequently, climatic variation is prominent across the Wadi bin

Hammad watershed.

Climate is classified as ‗‗dry Mediterranean‘‘ in the upper catchment ( Rakeen and Qaser

areas) and arid in the lower part (Ghor Mazra‘a) close to the Dead Sea. Mean annual rainfall

ranges from 325 mm at Rakeen town to 77.5 mm at Ghor Mazra‘a west of Kerak.

Rainfall is concentrated in winter (October–March) during the cold season. Severe storms

with maximum daily intensity of 2.1–6.66 mm h-1 are common in the highland region

(Farhan 1999, 2002). Serious soil erosion is therefore predictable. Repetitive heavy rainstorms

are considered the main triggering factor for extreme soil erosion and floods in the Kerak and

Wadi Musa—Petra areas. The average maximum and minimum temperatures are 17 and 2C0

in the Rakeen and Qaser areas , respectively, while the average maximum temperature in

Ghor Mazra‘a is 32C0 with summer months reaching 40C

0. In the Rakeen and Qaser areas

north of Kerak, part of the precipitation falls as snow. Several days of freezing temperatures

(below 0.0 C0) are recorded between November and February. Geomorphologically,

progressive river incision and continuous rejuvenation of Wadi Bin Hammad draining the rift,

associated with recurrent lowering of the base level (the Dead Sea), and uplifting of the scarp



zone during late Tertiary and Quaternary tectonics produced irregular slope segments (150–

350) separated by rocky benches. The wadi profile also display prominent irregularities which

probably represent some forms of rejuvenation points.

When major breaks of slopes combined with major long profile irregularities, four or five

rejuvenation phases can be identified (Farhan 1982). Rejuvenation phases have resulted in

deeply dissected topography, dense incised drainage and over steepened slopes which

encourage slope instability and soil erosion. Clay loam, silty clay, silty clay loam and silty

loam soils dominated most of the catchment (Ministry of Agriculture Jordan 1995) and are

characterized by very low permeability. Thus, runoff erosion is expected to be high.

The vegetation cover in the southern highlands occurs under more arid conditions

compared with northern highlands. Here, lower rainfall and greater marginality are

characteristic. Population densities are lower, and nomads from the eastern Jordanian desert

occasionally visit the southern highlands with their herds of camels, sheep and goats

(Atkinson and Beaumont 1971). Anthropogenic factors accelerating soil erosion are: long and

continuous human interference with land resources, deforestation, overgrazing in the past and

present, farming practices, and poor conservation measures.

3. MATERIALS AND METHODS

3.1. RUSLE Model and Soil Erosion Calculation

The revised universal soil loss equation (RUSLE) has been employed for this investigation

(Renard et al. 1997). The model is considered the updated version of the proto USLE model

(Wischmeier and Smith 1978). With the RUSLE model the average annual rate of soil loss

can be estimated and the spatial distribution of the soil erosion risk map can be established. It

is the most appropriate model that can be utilized to predict soil erosion loss based on the

available data in Jordan generally and Wadi Bin Hammad specifically.

The RUSLE model represents how rainfall, topography, soil and land use affect rill and

sheet soil erosion caused by raindrop impact and surface runoff (Renard et al. 1997). It has

been recognized as the most widely used empirical model to assess soil erosion loss, to

estimate soil erosion risk and to guide soil conservation plans in order to control soil erosion

(Millward and Mersey 1999; Angima et al. 2003; Prasannakumar et al. 2012). With the

RUSLE model, it is possible to predict the average annual soil loss for any number of

scenarios in relation to cropping systems, land management techniques, and erosion control

practices.

Coupled with GIS environment, soil erosion loss is predicted on a cell-by-cell basis

(Millward and Mersey 1999). Thus, grid cells of 30 m 9 30 m size were determined before the

calculation of the physical characteristics of these cells such as: slope, land use and soil type

all of which affect soil erosion processes in different cells of the catchment. Such a procedure

is essential to create a uniform spatial analysis environment for GIS modeling (Krishna

Bahadur 2009; Prasannakumar et al. 2011). The average annual soil loss (A) in tons per

hectare per year was quantified using RUSLE, expressed by the following equation (Renard et

al. 1997):

(1)

Where: R = is the rainfall erosivity expressed in MJ mm ha-1

h-1

yr-1

K= is the susceptibility of soils to erosion, expressed in ton acre-1

, U.S Units (ton ha h ha-

1 MJ

-1 mm

-1 SI metric units).

L = is the length of slope (dimensionless factor),

S = is magnitude of the slope (dimensionless factor),



C = is the cover and crop management (dimensionless factor), (values are ranging between 0

and 1.5)

P = is the conservation practices (dimensionless values ranging between 0 and 1).

A= is the average soil loss for the period of time represented generally at 1 year expressed in

ton ha-1

yr-1

.

Each factor is calculated on the cell bases in order to recognize the spatial patterns of soil

loss. Such a procedure enables the model to isolate small areas with a high erosion risk in the

catchment, and to identify the role of individual RUSLE factors in the existing erosion

potential (Millward and Mersey 1999). Through multiplying factor map layers in a GIS, the

spatial distribution of soil erosion loss and severity maps/tables are generated. An assessment

of the spatial relationships between soil erosion and environmental factors (i.e., terrain units,

elevation, slope, and land use/cover types) for the W. Bin Hammad catchment was also

established. In the present study, annual soil loss rates and severity were computed based on

RUSLE in GIS environment using Arc GIS 10.1 and ERDAS Imagine 8.5, and the associated

GIS packages. Land use/cover information for the watershed was obtained from LANDSAT

ETM+ 2009, and revised and updated using Google Earth pro (2011). Rainfall data for

calculation of rainfall erosivity (R) was obtained from the Ministry of Water and Irrigation,

and the soil data was acquired from 1995 national soil survey maps and reports ( Ministry of

Agriculture.,1995) along with 24 field soil samples for analyzing soil properties. NDVI values

were generated and mapped from a LANDSAT image, and used to determine the C factor and

to verify land use/cover information. A three dimensional DEM (Fig. 2) for the study area

based on digital topographic maps (scale 1:50,000, with 20-m intervals) provided by the

Royal Jordanian Geographic Centre (RJGC), was used to calculate L and S factors.

Figure 2 The digital elevation model

3.2. Calculation of RUSLE Parameters

3.2.1. Rainfall Erosivity Factor (R)

The R factor is often calculated as an average of EI-values measured over 20–25 years to

accommodate cyclical rainfall patterns (Angima et al. 2003). It is a measure of the erosivity of

local average annual precipitation and runoff causing soil erosion. Thus, the R value is greatly

affected by the volume, intensity, duration and pattern of rainfall whether for single or a series

of storms, and by the amount and rate of the resulting runoff. R values are also influenced by

slope steepness. Areas with low slope degree have low erosivity. R values also indicate that

flat areas would increase the water ponding on the surface, hence protecting soil particles



from being eroded by rain drops. R values can be obtained from isoerodent maps, tables, or

calculated from historical data (Renard et al. 1997).

Rainfall data of 18 years average for five weather stations distributed over or close to the

watershed were used to calculate R values based on the equation elaborated recently by Eltaif

et al. (2010); they expanded the original equations of RUSLE and USLE developed by

Renard and Freimund (1994).

Table 1 Rainfall erosivity (R) values

Stations P (mm) R (MJ mm ha-1 h-1 year-1)

Alrrabah 350 245.39

Rakeen 290 203.32

Almazraah 85 59.59

Alkarak 310 217.34

Alsafy 90 63.1

The achieved mean annual erosivity index (R), and the mean annual precipitation (mm) in

the elaborated equation were found to be in high correlation (r2 = 0.99). Using the

pluviometric data, the rainfall erosivity factor (R) in MJ mm ha-1 h-1 year-1 was calculated

according to the Eltaif et al. (2010) equation:

(2) Where:

p is the mean annual precipitation.

Each weather station was represented by a point in the GIS, and the available inverse

distance weighted (IDW) interpolation method was employed to generate a raster map for the

R factor. Table 1 illustrates the computed rainfall erosivity (R) values using data from five

weather stations across the Wadi Kufranja watershed (and two additional stations close to the

upper and lower parts of the watershed). The R values in this study were in the range (<40 -

>160).

3.2.2. Soil Erodibility Factor (K)

Soil erodibility factor (K) is defined as the rate of soil susceptibility to detachment and

transport of soil particles under an amount and rate of runoff for a specific storm event,

measured under standard plot. It is a function of inherent soil properties related to soil profile

parameters (El-Swaify and Dangler 1976) such as: percent silt (0.002– 0.01 mm), percent

sand (0.1–2 mm), percent organic matter in the sample, soil structure, and permeability. The

K factor rated on a scale from 0 to 1, with 0 indicating soil with least susceptibility to erosion,

and 1 refers to soils which are highly susceptible to erosion by water. The K factor was

computed using the following equation:

(3)

Where:

K is the soil erodibility factor (ton ha h ha-1 - MJ-1 mm-1), m is particle size parameter

(% silt? % very fine sand) 9 (100 - % clay), a is the organic matter content (%), b is soil

structure code used in soil classification, and c is the soil permeability class.Fine particles are

resistant to detachment because of their cohesiveness, while large particles are resistant to

transport because of the greater force required to entrain them. Subsequently, soils with high

silt content are highly erodible, since the least resistant particles are silts and fine sands

(Pradhan et al. 2012).

The K factor was evaluated and determined following the soil erodibility: Nomograph

method. (Wischmeier and Smith,1971,1978) combined with soil properties such as sand, clay,



silt, very fine sand, organic matter content in soil, structure type,and soil permeability which

were obtained from the National Soil Map and Land Use project along with the associated

reports (Ministry of Agriculture 1995). Thirteen different soil types existed in the study area.

Applying Eq. (2) and GIS, a digital map of soil properties was generated using the inverse

distance weighted (IDW) interpolation method. Afterwards, a vector soil map was converted

into raster format using the spatial analyst tool in Arc GIS.Then, the value field of the soil

layer was reclassified by respective values of the K factor, using the reclassifying tool of

spatial analyst extension in ArcGIS, and consequently, the raster layer of K factor was

implemented. Considering different intrinsic properties of soils (i.e., texture, organic matter

and permeability), K values were attained (Pradhan et al. 2012) and a soil erodibility map was

developed.

3.2.3. Slope Length and Steepness Factor (LS)

The effect of terrain factor on soil erosion rates is expressed by the combined effect of slope

length (L), slope steepness (S), and slope morphology on rill, inter-rill erosion and sediment

production. As slope length increases (L), the total soil erosion loss per unit increases, as a

result of progressive accumulation of runoff in downslope.

As the slope steepness increases, the soil erosion also increases as a result of increasing

the velocity and erosivity of runoff (Wischmeier and Smith 1978). Rill erosion is mainly

caused by surface runoff and increase in a downslope direction because the runoff increases in

this direction. Interrill erosion is the result of raindrop impact on soil surface and is

considered uniform along a slope (Pradhan et al.2012). The (L) parameter expresses the ratio

of rill erosion (initiated by flow) to inter-rill erosion (raindrop impact) to find the loss of soil

in relation to the standard plot length of 22.1 m. Renard et al. (1997) define slope length as

the horizontal distance traversed from the origin of overland flow to the point where

deposition occurs, or runoff concentrates into a defined channel. The slope steepness

parameter (S) relates to the effect of the slope gradient on erosion in comparison to the

standard plot steepness of 5.16. The effect of slope steepness is greater on soil erosion loss

compared to slope length. Therefore, (LS) is the predicted ratio of soil loss per unit area from

a field slope from a 22.1 m long, 5.16 slope under otherwise identical conditions. The Digital

Elevation Model (DEM) drawn from 20-m contour interval from 1:50,000 topographic sheets,

was employed to derive the LS factor.

The following equation adopted from Mitasova et al. (1996) was used to calculate the LS

factor:

[

⁄ ]

[

⁄ ]

(4)

Where:

A(r) upslope contributing area per unit contour width,

b(r) slope. m = 0.6; n = 1.3 are parameters;

ao = 22.1, m = 72.6 ft is the slope length,

bo = 0.09 = 9, % = 5.16o is the slope of the standard USLE plot.

The spatial analyst toolkit of the Arc GIS was employed to generate raster layers of slope

gradient (degrees), and from the hydrology toolkit, the flow direction and then the flow

accumulation were calculated. The output layers were then used in the GIS raster calculator

interface to generate the map of LS based on the equation using the flow accumulation grid as

follows:

LS = Pow (( flow Acc) × resolution / 22,1; 0,6) × Pow



((Sin (slope gradient) × 0.01745) / 0,09 , 1:3) (5)

As the slope length L increases, the total soil loss and soil erosion per unit increase; as a

result of progressive accumulation of runoff in the down slope. As the slope steepness

increases, the soil erosion also increases as a result of increasing the velocity and erosivity of

runoff. However, (Zhang,et,al,2013) developed more accurate method to calculate the LS

factor to estimate soil erosion at regional landscape scale. Breakes in slope were identified

from DEM and utilized to locate channel networks, convergence flow areas, and soil erosion

and deposition areas.

3.2.4. Cover and Management Factor (C)

The cover and management factor (C) represents the effect of cropping and management

practices on the runoff and soil erosion rate (AlZitawi 2006; Roose 1996). and is considered

the second major factor (after topography) controlling soil erosion. The (C) factor combines

plant cover, the level of its production, and the associated cropping techniques. It varies from

1 on bare soil to 1/1000 under forest, 1/100 under grasslands and cover plants, and 1–9/10

under root and tuber crops . A land use/cover map was produced for 2016 using LANDSAT

ETM image (2010, 30 m resolution) using a supervised classification method. Subsequently,

a field survey was performed to verify and correct the results of classification. The Look Up

Tool in Arc GIS was employed to reclassify the land use/cover map according to its C values,

which were derived based on Wischmeier and Smith (1978) and previous investigations

carried out in similar environments in northern Jordan (Al-Zitawi 2006).

3.2.5. Support Practice Factor (P)

The conservation practice factor (P) in the RUSLE model is the ratio of soil loss using a

specific support practice to the corresponding soil loss after up and down cultivation (Renard

et al. 1997). It is a measure of the effect of conservation practices that reduce the amount and

rate of water runoff, which reduces erosion. It includes different types of agricultural

management practices such as: strip cropping, contouring and terracing. Unfortunately,

agricultural practice across Wadi Bin Hammad catchment consist of upslope and down slope

tillage with poor conservation measures. The only support practice that exists in the study area

is poor stone terrace especially where rainfed mixed farming including olive farming is

practiced. Stone terraces influenced rill and sheet erosion by breaking the hill slope length

into a slope segment of shorter distances, thus decreasing runoff and the resultant soil erosion.

The RUSLE calculation of (P) factor depends on the spacing between terraces. The maximum

efficiency of terraces (as reflected by P values) is achieved whenever the spacing between

successive terraces is 33.5 m or less. An increase in the spacing above this value would leads

to a gradual increase in the value, indicating a lower efficiency for terraces in reducing runoff

and erosion (Wischmeier and Smith 1978; Renard et al. 1997; Foster et al. 2002). Visual

photo interpretation of air photos (1:25,000 and 1:10,000) and field observations were used to

recognize stone terraces and rural tarmac roads in order to assess the support practice factor

(P). Olive farming was expanded over the last three decades in the study area, and the spacing

between terraces was found to be 30–35 m. Hence, the (P) factor was assigned a value of 0.55

by the RUSLE (Wischmeier and Smith 1978; Renard et al. 1997) and 0.6 for the rural tarmac

roads. Since, a small area has conservation practice, and large areas lack any conservation

measures, the (P) factor value was assumed equal to one as suggested by Wischmeier and

Smith (1978). A uniform value of 0.8 for the (P) factor was assigned for the whole watershed

as recommended by other researchers who carried out similar research in the Mediterranean

environment (Mhangara et al. 2012; Ozsoy et al. 2012; Farhan et al. 2013, 2014; Abu

Hammad 2011; Karydas et al. 2009; Irvem et al. 2007).

The ArcGIS geoprocessing lookup tool was employed to reclassify the land use/cover and

slope length maps according to its (P) value.



4. RESULTS AND DISCUSSIONS

The RUSLE layers derived for R, K, LS, C and P factors were integrated within the raster

calculator option of the ArcGIS ver. 9.3 spatial analyst in order to quantify and generate soil

erosion risk and severity maps for the Wadi Bin Hammad watershed. The influence of

environmental factors on spatial distribution of soil erosion loss (terrain units, elevation,

slope, and land use/cover) were analyzed and evaluated. The average annual rainfall erosivity

factor (R) for five weather stations was found to be in the range of 245.39 and 63.1 MJ mm

ha-1 h-1 year-1 (Table 1; Fig. 3). The distribution of R values was assumed to vary

consistently with annual precipitation across the catchment .The highest value of the annual R

factor was observed between Rakeen and Alqaser towns ( > 160 MJ mm ha-1 h-1 year-1) at

the middle and the upper parts of the catchment, and the lowest values were observed in the

lower catchment and arid rift (Mazra village, < 40 MJ mm ha-1 h-1 year-1). The values

gradually increased towards the eastern and southern parts of the catchment.

Figure 3 Rainfall erosivity (R) factor

K values vary from 0.9 to 4.20. Silty loamy soils have a higher proportion of silt and fine

sand, making them more susceptible to erosion. All soils of the basin are with less than 3.5 %

organic matter and considered to be erodible; thus high values of soil erodibility indicated its

higher susceptibility to erosion. The soil erodibility factor ranges from 0.2 to 3.5. K factor

values are higher in the middle and north parts of the catchment (2.71 – 4.20 ton ha h ha-1

MJ-1 mm-1) which were strongly affected by the faults and the dense branching faults and

joints. The catchment here, is also dominated by the Upper Cretaceous marly–clay and marly–

limestone weak rocks, very steep slope categories (150–250, 250–350 and>350) and

influenced by old landslide complexes, and repetitive shallow landslides.

Figure 4 Soil erodibility factor (K)



Generally, as the slope length (L) and slope steepness (S) increase, total soil erosion per

unit area also increases due to the acceleration of overland flow velocity and erosivity of

runoff in the downslope direction (Renard et al.1997; Onori et al. 2006; Prasannakumar et al.

2011; Ozsoy et al. 2012). The LS factor in the present investigation varies from 0.32 to 1.65

(Fig. 5). The spatial distribution of the LS factor values is closely associated with slope

categories (150–250, 250–350 and>350) and high elevation exceeding 1080 m. The lowest

values of the LS factor were mainly concentrated: (1) in the upper part of the catchment

where the remnants of the Miocene–Pliocene erosion surface exist (Quennell 1958; Beheiry

1969) with slight to moderate slopes (<150), and (2) along the alluvial fan surface of Ghor

Mazraa‘ (<50) across the rift zone. High elevations (1080 m) and steep hillside slopes along

the main course of Wadi Bin Hammad are characterized by the greatest LS values (Fig. 5).

Figure 5 LS factor

The C factor values in the Wadi Bin Hammad catchment varied between 0.0 and 0.9. The

scattered forest areas show values between 0.05 and 0.10 (Table 3).

Table 2 C factor values for different land cover types

Land use/cover C values

Barren lands 0.48

Forest areas 0.10

Rainfed mixed/irrigated farming 0.17

Rangeland 0.34

While barren land, open rangeland exposed to ploughing, and residential areas show

values approaching 0.5. Rainfed mixed farming areas show C factor values varying between

0.15 and 0.2. In general C factor values increased in the lower parts of the watershed

including the rift floor and the Mazraa‘ alluvial fan. Here, barren land, rangeland and steep

slopes with expected higher water velocities predominante. C factor values also decreased

towards the upper part of the catchment where flat/undulating lands are utilized for rainfed

mixed farming (Fig. 6). The high C factor values indicate more vulnerability to soil erosion,

as soil surfaces are considered to be unprotected barren land and rangeland.



Figure 6 Cover and management (C) factor

Similarly, P factor values increased towards the lower catchment, where slope length and

steepness are greater (the P factor here ranges between 0.25 and 1.00 , Table 4). The highest

values represent areas with no conservation practices (natural land such as forests, grass land

etc.), and construction of conservation measures such as terraces, or farming practices (i.e.,

crop land with strip and contour cropping). The lower the P value, the more effective the

conservation practice is considered to be at reducing soil erosion. Field observations and air

photo interpretation indicate that most of mixed rain-fed farming areas are characterized by up

and down slope tillage without conservation support practices such as contouring or terracing

(Erdogan et al. 2007; Yue- Qing et al. 2008; Ozsoy et al. 2012).

Table 3 Support practices factor (P)

Support Practice P factor

Up and Down slope 1

Cross slope 0.75

Contour farming 0.50

Strip cropping, cross slope 0.37

Strip cropping, contour 0.25

4.1. Soil Erosion Loss Maps

The final estimation of the annual soil loss (A) was calculated through full integration of the

RUSLE model in the GIS environment, in order to calculate the soil loss for each individual

grid cell in one run. The soil loss rate was calculated from all layers of RUSLE factors

generated earlier. These were R, K, LS, C, and P factors (the spatial maps). Each layer was

organized in a grid format with a cell size of 30 m × 30 m. The layers were combined by

multiplying each cell of identical position from all existing surface information based on the

relationship defined by the RUSLE model. Thus, multiplication of all these cells found in

identical position from the different layers was made possible with Arc GIS ver. 9.3 Spatial

Analyst Tool and raster calculator, to generate the final soil erosion map. The soil loss map

was then classified into five categories for visual interpretation: very low, low, moderate, high

and severe. Values of estimated soil loss categories are listed in Table 5, and the spatial

distribution of soil losses across the catchment is illustrated in Fig. 8. The annual soil loss

values range between 0 and 100 ton ha-1 year-1, with a mean value of 40.4 ha-1 year-1. The

highest soil loss values are spatially correlated with rainfed mixed and irrigated farming,

barren land, rangeland, and steep slopes (i.e., 00–60, 60–150, 150–250 slope categories),

weak rocks and structures, and landslides. The estimated annual soil loss for Wadi Bin



Hammad (40.4 ton ha-1year-1) is higher than that of the watersheds and areas investigated in

northern Jordan (Al-Zitawi 2006; Farhan et al. 2013; Alkharabsheh et al. 2013).Although

higher precipitation is experienced in these areas (300–650 mm). Estimated average soil loss

for three locations in northern Jordan ranged from 3.4 to 13 ton ha-1 year-1. Higher rates of

soil erosion loss in W. Bin Hammad is largely attributed to poor vegetation cover or

protective land cover, and the abundance of old degraded landslide complexes, deep and

shallow landslides, the influence of dense subsidiary faults deviating from the Kerak-Al-feha

fault, and most importantly the remarkable increase in built-up areas and impervious road

network. It is obvious that the estimated average annual soil loss rate in W. Bin Hammad

exceeds the acceptable soil loss tolerances.

Table 4 Area and proportion of each soil loss category

Class Erosion (ton/hec/yr) Area (km2) %

V. Low 0 - 10 45.138 33.16

Low 11 - 20 33.571 24.67

Med 21 - 40 37.642 27.64

High 41 - 80 13.975 10.27

Severe 81 - 100 5.804 4.26

Figure 7 Spatial distribution of soil erosion losses Figure 8 Spatial distribution of soil erosion risk.

limits from 2 to 12 ton ha-1 year-1 for the Mediterranean environments (Nearing et al.

1990; Irvem et al. 2007; Trabucchi et al. 2012).Therefore, priority must be given to the

protection of woodlands and afforestation of bare lands, steep slopes, abundant shallow

landslide areas, and the construction of appropriate conservation measures to reduce erosivity

effects on soil loss. This investigation shows that 10.27 % (13.975km2), and 4.26 % (5.804

km2) of the catchment area are under extremely high and severe soil erosion loss. In such

areas, soil loss was calculated in the category 41–80, and 81-100 ton ha-1 year-1. Thus, there

is a priority for appropriate conservation measures to be adopted. The highest soil loss values

are clearly correlated with morphology and slope steepness. The lower part of the catchment

is characterized by the highest LS factor, C factor, P factor values, and the highest slope

categories (150–250, 250–350, and>350). By contrast, the upper part of the catchment is

considered a remnant of a planation surface, where flat and gentle slopes (0–60) are dominant.

Thus, soil erosion here and on the alluvial fan of Ghor Mazraa‘ to the west is minimal.

Soil loss categories (Table 5) and soil erosion risk levels increases from east to west, from

the upper to lower reaches of the catchment. It is clear that surface erosion can vary spatially

due to rainfall variability, morphological and topographic discontinuities, tectonic and

instability conditions, different soil types and characteristics, and human induced



disturbances. It can be concluded, however, that soil erosion is severe between Rakeen and

Bateer, and accounts for 33.9 % of the total watershed soil loss, with soil loss between 41-80

ton ha-1 year-1. 19.779 km and 14.53 % of the catchment area (the lower reaches including

the piedmont glacis slopes) have undergone very low and low soil erosion, where calculated

soil loss is >10 and 11–20 ton ha-1 year-1. High rates of soil erosion loss in the lower part of

the catchment could be attributed to several factors: the dominance of silty-loam soils (61.8 %

silt, 26 % clay, and 1.8 % organic matter), poor vegetation cover and lack of conservation

measures (Ministry of Agriculture Jordan 1995). The topographic factor (LS) and steep

rejuvenated topography appear to be the most significant environmental factor contributing to

high soil erosion rates in W. Bin Hammad. The construction of efficient conservation

measures (i.e., stone terraces and afforestation) should be adopted in the areas of high, very

high and extremely high erosion in order to reduce soil loss. The results of soil erosion loss

and soil erosion risk, land use/cover, and slope steepness, should assist decision makers in

identifying priority areas in urgent need of conservation and land management practices.

5. CONCLUSIONS

The present investigation illustrates the spatial patterns of soil erosion loss and soil erosion

risk, vulnerable terrain units towards soil erosion, weak jointed and fissured rocks, and

landslide zone where high rates of erosion occur within the watershed. Historical and present-

day human intervention, coupled with the absence of conservation measures, and improper

farming practice, have exercised a negative effect on soil erosion. Under the pressing need for

food production during the 1960s and 70s, and high population growth rate (&3 % annually),

farmers were obliged to cultivate marginal areas where the average annual rainfall is less than

250 mm. Such areas lie in the highland regions in southern and northern Jordan. The

transformation of rangeland to agricultural utilization accelerates soil erosion. Overgrazing,

together with frequent drought, gradually damaged the grazing capacity of the land. Since the

1960s soil erosion by water is reported to be a serious problem in the Jordan highlands. The

recorded high rates of soil loss recently are disturbing if they continue at the same rate in the

future. If this occurs, soils will no longer be useful for crop production in a country suffering

from food and water shortages. Despite the fact that several dams had been already built,

integrated watershed management including maintenance operations and reduction of siltation

rates, are still not up to the proper standards. High annual sediment yield originating in the

highland watersheds threaten the reservoirs already in existence over the highland region, and

the old rainwater harvesting systems constructed on the marginal areas. Moreover, the

estimated soil loss and sediment yield seriously endanger the future life of constructed dams

(i.e., W.Alarab Dam), or, the dams under construction (i.e., W.Kufranja Dam), and the

proposed dam on W.Kerak and W. Bin Hammad. The RUSLE model provides an efficient

tool for soil erosion loss and soil erosion risk estimation, and therefore, areas vulnerable to

soil erosion and landslides must be prioritized for conservation. The outputs of the present

study (maps and information) could be employed for immediate applications in soil

conservation planning and implementation. However, further research is highly recommended

on soil erosion factors in the rainfed highland regions of Jordan. More data on rainfall and its

duration and intensity provided the basis for calculating rainfall erosivity. In addition, direct

field measurements of soil erosion by water, or by simulated rainfall must be executed, and

the results should be compared by the RUSLE and other predictive models. The adopted

model can also be implemented locally by land developers on the Kerak governorate level,

where the data and software needed are available. The techniques adopted in this investigation

demonstrate that GIS, RS tools, and the RUSLE model are simple and low cost techniques for

modeling and assessing soil erosion risk in other comparable watersheds in the southern

Jordan highlands.



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