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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
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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
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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
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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),
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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
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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,
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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
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((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 identi- fied
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.
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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)
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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.
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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
Ramzi Ameen Almaaitah, Ayu Wazira Azhari, Mohd Asri Ab Rahim, Fahmi Muhammad Ridwan
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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
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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.
Ramzi Ameen Almaaitah, Ayu Wazira Azhari, Mohd Asri Ab Rahim, Fahmi Muhammad Ridwan
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