Journal of Water Resource and Protection, 2013, 5, 1247-1261
Published Online December 2013 (
Open Access JWARP
Spatial Estimation of Soil Erosion Risk Using RUSLE
Approach, RS, and GIS Techniques: A Case Study of
Kufranja Watershed, Northern Jordan
Yahya Farhan, Dalal Zregat, Ibrahim Farhan
Department of Geography, Faculty of Arts, The University of Jordan, Amman, Jordan
Received September 1, 2013; revised October 1, 2013; accepted October 28, 2013
Copyright © 2013 Yahya Farhan et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Wadi Kufranja catchment (126.3 km2), northern Jordan, was selected to estimate annual soil loss using the Revised Uni-
versal Soil Loss Equation (RUSLE), remote sensing (RS), and geographic information system (GIS). RUSLE factors (R,
K, LS, C and P) were computed and presented by raster layers in a GIS environment, then multiplied together to predict
soil erosion rates, and to generate soil erosion risk categories and soil erosion severity maps. The estimated potential
average annual soil loss is 10 ton·ha1·year1 for the catchment, and the potential erosion rates from recognized erosion
classes ranged from 0.0 to 1850 ton·ha1·year1. About 42.1% (5317.23 ha) of the catchment area was predicted to have
moderate risk of erosion, with soil loss between 5 - 25 ton·ha1·year1. Risk of erosion is severe to extreme over 31.2%
(3940.56 ha) of the catchment, where calculated soil loss is 25 - 50 and >50 ton·ha1·year1. Apart from the gentle
slopes of the alluvial fan (Krayma town and surroundings), the lower and the middle reaches of the watershed suffer
from severe to extreme erosion risk. High terrain, slope steepness, removal of vegetation, and poor conservation prac-
tices are the most prominent causes of soil erosion. This investigation demonstrates that remote sensing (RS) and GIS
technologies are effective tools in modeling erosion, thus enabling extraction of significant information for implement-
ing soil conservation plans in the north Jordan highlands.
Keywords: Jordan; Soil Erosion; Risk Mapping; Severity; RUSLE; Wadi Kufranja
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 [1]. Soil erosion, a grad-
ual process, removes soil particles by runoff, thus causing
soil to deteriorate [2]. The accumulation of 10 to 15 cen-
timeters of soil behind newly constructed walls in a sin-
gle season indicates the severity of the problem. Erosion
of the topsoil leads to declining soil productivity, thus
restricting the area of potential future agriculture. Mod-
ern soil conservation and agricultural reorganization pro-
vide a wide choice of remedial measures to reduce soil
erosion rates in the country [3], and are considered im-
perative for the country’s future wellbeing. Eroded soil
materials are deposited over wadi floors and agricultural
lands, irrigation canals, even on roads, and more serious-
ly in reservoirs.
Several qualitative and quantitative investigations have
tackled soil erosion in Jordan. The Natural Resources
Authority [4] reported that heavy rain caused severe ero-
sion, and the resultant deposition filled the King Ab-
dullah Canal (East Ghor canal), which took three months
to clear at a cost of US$4.5 million. In the Southern Ghor
region, the Ministry of Agriculture estimated an ero-
sional loss of 1100 hectares of arable land out of 5400
hectares in the southern Ghor region.
Soil erosion losses for all catchments in Jordan are es-
timated at 1.328 million ton·year1, which amounts to a
loss of 0.14 cm over the entire area [5]. The soil erosion
map of the FAO et al. [6] shows most of Jordan within
10 - 50 ton·ha1·year1 due to water erosion. Part of the
highland falls within 50 - 200, and >200 ton·ha1·year1.
Harza [7] estimated the total sediment inflow to King
Talal Reservoir at about 1.7 Mm3·year1, while Lara [8]
gave a total sediment volume of 3.84 Mm3·year1. Field
measurement of soil erosion (1989-1995) by runoff in
Jordan (Table 1) has been assessed over small plots [9,
10] with sub-humid Mediterranean [11], semi arid [12],
and arid climates [13].
Soil erosion risk mapping has evaluated soil erosion
susceptibility qualitatively for three Jordanian watersheds
[14-16]. Airphoto interpretation of geomorphic units has
been used, overlain by a system of soil erosion suscepti-
bility classes [17,18] arrived at classes from slight to ex-
tremely high.
Recently, the Universal Soil Loss Equation (USLE)
model in conjunction with RS and GIS technology has
been used to predict the annual soil loss in an area of the
Balqa district, central Jordan [19] and the southern part
of the Yarmouk valley in northern Jordan [20]. Estimated
average soil loss was 78 ton·ha1·year1 (Balqa District),
and 5 to >25 ton·ha1·year1 (Yarmouk valley). Erosivity
factor (R) and soil erodibility factor (K) were calculated
using both USLE and RUSLE models, then an estimate
of soil loss in three north Jordan locations was obtained
using the RUSLE model. Average soil loss ranged from
3.4 ton·ha1·year1 to 13 ton·ha1·year1 [21].
This study attempts to employ the revised version of
the Universal Soil Loss Equation (RUSLE), combined
with RS and GIS technologies to: 1) estimate the poten-
tial soil loss from areas within the Wadi Kufranja catch-
ment, 2) produce soil erosion risk and soil erosion se-
verity maps, and 3) identify areas of critical soil erosion
conditions which require urgent need for appropriate con-
servation measures and land management.
2. Study Area
Wadi Kufranja catchment constitutes the present study
area. It locates in the northern highlands of Jordan, and
lies between 32˚14ʹN to 32˚22ʹN and 35˚21ʹE to 35˚47ʹE
(Figure 1). The watershed area is 126.3 km2 (12,630 ha),
with elevations 1173 m asl (above sea-level) to ~329 m
bsl (below sea-level) over a distance of only 23 km. The
upper wadi consists of maturely dissected terrain, with
broad valley forms and smooth interfluves. In the middle
and lower parts, rejuvenation resulted in a narrow, in-
cised, steepsided gorge. Large and small arcuate scars
and hummocky topography indicate past landside epi-
sodes, probably of Pliocene and Quaternary age (<5 Ma)
[22,23]. The geology is dominated by Upper Cretaceous
marly clay and marly limestone of the Ajlun series,
which closely influences the basin soils. Vertisolic (cra-
cking soil), typic xerochrepts, and lithic soils cover the
largest area in the watershed, while other types com-
prise alluvial (wadi infill) soils, variable types on slopes,
and soils of the alluvial fan at the lower part of Wadi Ku-
franja, west of Krayma town.
About 10% of the watershed is bare rock, and trunca-
tion of upper soil horizons is widespread; fully developed
soil profiles are rare. Erosion exposes more loosely struc-
Table 1. Soil erosion rates estimated for three locations in
the country.
Area Climate Splash erosion
Runoff erosion
Al SaltSub-humid
Mediterranean 3.24 - 21.42 0.581 - 2.382
Muwaqar Semi arid 2.59 - 16.3 1.05
Azraq Arid 2.8 - 7.39 0.14
Source: [11-13].
Figure 1. The study area.
tured subsoil, which accelerates further erosion [1,24].
Climate is classified as “dry Mediterranean” in the upper
catchment and “semi-arid and arid” in the lower parts.
Mean annual rainfall ranges from 630.5 mm at Ajlune
town to 267.8 mm at Wadi Kufranja station (east of
Krayma) close to the Ghor. Most meteorological stations
in the watershed record 30 - 50 rain days per year [25].
Severe storms with maximum daily intensity of 2.1 - 5
mm·hr1 are common [26]. Severe soil erosion is there-
fore predictable. However, 95% of the precipitation falls
from November to March (70% in Dec-Feb). Winter
monthly temperatures of 3˚C - 5˚C are recorded in higher
parts of the watershed; summer months average 22˚C -
25˚C. In Krayma (in the Ghor, the Jordan Rift-floor) the
average annual temperature is 24˚C, with summer months
reaching 40+˚C. Frost-days number 5 - 15 per year [25].
Land-cover types vary from natural vegetation (forests)
mixed with crop-land. Four scattered associations of for-
ests are distinguished throughout the watershed: broad-
leaved forest of Quercus coccifera (Kermes oak), broad-
leaved forest of Quercus aegilops (Decideous oak, or
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Ithaburensis), coniferous forest of Pinus halepensis (Ale-
ppo pine), and mixed forest of oak and Olea europaea
(Wild olive). These forests have suffered severe anthro-
pogenic stresses. There is overgrazing especially by goats,
which graze the stubble of wheat, barley, and field crops
(tomatoes, lentils, chick-peas), vines and olives. Collec-
tion of fuel and charcoal-wood is an added stressor [27].
The worst effect of the cultivation pattern and land cover
changes is that the soil surface is bare during the moist
winter months. Low rainfall interception by vegetation
allows destructive splash erosion. Once soil particles are
displaced, nothing hinders their displacement downslope.
Consequently, environmental degradation including soil
erosion is widespread throughout Wadi Kufranja water-
shed [24]. Five types of land use/cover exist in the wa-
tershed; residential and commercial areas (7.52%), forest
(18.53%), mixed agricultural (51.23%), open rangeland
(13.30%) and bare soil 9.42% [28]. The objective of this
study is to estimate soil erosion loss and severity for
Wadi Kufranja, which in turn can be used as a represen-
tative example for other comparable watersheds charac-
terized by similar rainfall, topography, soil, and land use
in the northern highlands of Jordan.
3. Materials and Methods
Soil Loss Estimation Method
Many accurate soil erosion models were developed over
the last four decades to assess soil erosion risk at differ-
ent levels: single slope, river/wadi catchment, regional
and global scales [29,30]. Among these, the Univeral
Soil Loss Equation (USLE) [31,32], European Soil Ero-
sion Model (EUROSEM) [33], Soil Erosion Model for
the Mediterranean Region (SEMMED) [34], Water Ero-
sion Prediction Model (WEPP) [35,36], the Soil and
Water Assessment Tool (SWAT) [37], and the Chemi-
cals, Runoff, and Erosion from Agricultural Management
Systems (CREAMS) [38]. The USLE model has been
widely used worldwide over the last 40 years to estimate
soil erosion risk. The requirements of the model, in terms
of intensive data and computation, reinforce the elabora-
tion of more accurate and less demanding ones. The Re-
vised Universal Soil Loss Equation (RUSLE) is consid-
ered the alternative improved version of the proto USLE
model [39-41]. Although RUSLE adopted the same em-
pirical principles and the fundamental structures as the
USLE model, substantial modifications of the USLE
were carried out which cover all aspects of the model to
assist with computations and wider applications espe-
cially in developing countries. New term values, correc-
tions, factor algorithms, slope morphology, and elabo-
rated approaches for calculating the parameters of the
model were introduced. It accommodates more accurate
methods to estimate of rainfall erosivity (R), soil erodi-
bility (K), slope length and steepness (LS), land cover
management (C), and conservation practice (P) factors
[42]. Information on factors leading to soil erosion can
be utilized as a guide for formulating appropriate soil
conservation and land management plans. The Revised
Universal Soil Loss Equation (RUSLE) is frequently us-
ed to estimate the magnitude of soil erosion loss from
watershed areas, the spatial distribution of soil erosion
severity, and delimiting sites vulnerable to soil erosion
for both agricultural and forested watersheds [30,42-48].
Finally, the RUSLE model has several advantages: 1) it
is easy to implement and understand from a functional
perspective [32], 2) is compatible with the Geographic
Information System (GIS), and 3) the data requirements
to implement the model are not too complex or unattain-
able especially in a developing country [49]. However,
the watershed heterogeneity (i.e. variations in soil, land
use/cover, topography) and spatial rainfall variability led
to a prominent variation in the predicted soil loss, there-
fore, the RUSLE model is normally executed in conjunc-
tion with a raster-based GIS, to predict erosion potential
on a cell by cell basis [49]. Hence, watersheds need to be
discretized into small homogenous units. In the present
study, grid cells of 30 m × 30 m size were determined
before making the computation of the physical charac-
teristics of these cells such as: slope, land use, and soil
type all of which affect soil erosion processes in different
cells of the watershed. Such a procedure is essential to
create a uniform spatial analysis environment for GIS
modeling [30,42].
The RUSLE model was developed as an equation rep-
resenting the main factors controlling soil erosion, name-
ly climate, soil characteristics, topography, and land co-
ver management. The equation is expressed as:
 
A = computed annual soil loss per unit area
R = runoff erosivity factor (rainfall and snowmelt) in
[MJ mm·ha1·hr1·year1].
K = soil erodibility factor (soil loss per erosion index
unit for a specified soil measured on a standard plot, 22.1
m long, with uniform 9% (5.16˚) slope, in continuous
tilled fallow) [ton·ha·hr·ha1·MJ1·mm1].
L = slope length factor (ratio of soil loss from the field
slope length to soil loss from standard 22.1 m slope un-
der identical conditions) (dimensionless).
S = slope steepness factor (ratio of soil loss from the
field slope to that from the standard slope under identical
conditions) (dimensionless).
C = cover-management factor (ratio of soil loss from a
specified area with specified cover and management to
that from the same area in tilled continuous fallow) (di-
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P = support practice factor (ratio of soil loss with a
support practice—contour tillage, strip-cropping, terrac-
ing—to soil loss with row tillage parallel to the slope (di-
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 Irriga-
tion, and the soil data was acquired from 1995 national
soil survey maps and reports [50] along with 115 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.
4. Calculation of RUSLE Factors
4.1. Rainfall Erosivity (R)
R factor is the quantitative expression of the erosivity of
local average annual precipitation and runoff causing soil
erosion. It is a measure of the erosive force of a specific
rainfall. R-value is greatly affected by the volume, inten-
sity, duration and pattern of rainfall whether for single
storms or a series of storms, and by the amount and rate
of the resulting runoff. Differences in the R factor reflect
differences in precipitation patterns between regions.
Areas with low slope degree have low erosivity R values
which imply that flat areas would increase the water
ponding on the surface, thus protecting soil particles
from being eroded by rain drops. Large numbers of R
factor indicate more erosive weather conditions. R values
can be obtained from isoerodent maps, tables or calcu-
lated from historic data [41]. A period of 20 - 25 year is
recommended for computing the average R [32]. Rainfall
data of 30 years average for five weather stations distrib-
uted over the watershed were used to calculate R values
based on the equation elaborated by Eltaif et al. [51] who
expanded the equations of RUSLE and USLE developed
by Renard and Freimund [40]. The elaborated equation
has been tested in northern parts of Jordan using pluvi-
ometric data from 18 stations. The achieved mean annual
erosivity index (R), and mean annual precipitation (mm)
were found to be highly correlated (r = 0.99). The equa-
tion of Eltaif et al. [51] used to compute R values is the
0.0048 p
R23.61 e
where p is the mean annual precipitation.
In terms of GIS layers, each weather station was rep-
resented by a point. The Inverse Distance Weighted
(IDW) interpolation method in GIS was used to generate
a raster map for R factor.
Table 2 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 (85.4 - 486.9).
4.2. Soil Erodability Factor (K)
Soil erodability factor (K) expresses the soil susceptibil-
ity to detachment and transport of soil particles (grains or
crumbs), under an amount and rate of runoff for a spe-
cific rainfall, measured under standard plot. The K factor
is rated on a scale from 0 to 1, where 0 refers to soils
with least susceptibility to erosion and 1refers to soils
which are highly susceptible to erosion by water. Gener-
ally, soils become of low erodibility if the silt content is
low, regardless of corresponding high content in the sand
and clay fractions [29]. The factor was computed using
the following equation [32,41]:
1.14 8
27.6610 120.00432
0.0033 3
 
Kma b
c, (2)
K = Soil erodibility factor (ton·hr1·ha1·MJ·mm).
m = (Silt% + Sand%) × (100 clay%).
a = % organic matter.
b = structure code: 1) very structured or particulate, 2)
fairly structured, 3) slightly structured, and 4) solid.
c = profile permeability code: 1) rapid, 2) moderated
to rapid, 3) moderate, 4) moderate to slow, 5) slow, 6)
very slow.
The K factor is influenced by intrinsic soil properties
related to soil profile parameters such as: percent silt
(0.002 - 0.1 mm), percent sand (0.1 - 2 mm), percent
organic matter, soil structure and permeability [43]. Soil
types in the study area were identified from the National
Soil Map and Land Use Project [50], and the associated
reports. Then 115 soil samples were collected from the
field representing the different soil types over the water-
shed. Location of these samples was controlled by GPS.
Applying the Equation (2) to generate GIS layer, it was
important to generate digital maps of soil properties used
in the equation. These were: sand, silt, clay and organic
matter, and permeability classes. The maps were gener-
ated using the inverse distance weighted (IDW) interpo-
lation method on point data (vector layers) for the soil
samples analyzed. The map of K factor was checked by
computing values obtained from the equation with those
obtained from a soil erodibility Nomograph [52]. Results
were nearly identical and variations were obtained to the
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Table 2. Rainfall erosivity (R) values.
Station P (mm) (R) (MJ·mm·ha1·hr1·year1)
W. Kufranja 267.8 85.4
Ras Muneif 562.7 351.6
Kufranja 593.2 407.1
Ajlun 630.5 486.9
Anjara 600 420.6
third digital. Therefore the map was adopted to apply it
in the RUSLE model. The classes of soil structure of b
variable (Equation (2)) were identified using soil texture
as described by Edmonds et al. [53].
4.3. Slope Length and Steepness Factor (LS)
The (LS) factor expresses the effect of local topography
on soil erosion rate, combining effects of slope length (L)
and slope steepness (S). Thus, LS is the predicted ratio of
soil loss per unit area from a field slope from a 22.1 m
long, 9% (5.16˚) slope under otherwise identical condi-
tions. The Digital Elevation Model (DEM) with a resolu-
tion of 30 m was used to calculate L and S parameters.
The DEM (Figure 2) was provided by the Royal Jorda-
nian Geographic Centre (RJGC). The following equation
was adopted to compute the LS factor [54]:
 
 
LS rm+1A rasinbrb
where: A(r) = upslope contributing area per unit contour
width; b(r) is = slope; m = 0.6; n = 1.3 are parameters, ao
= 22.1 m = 72.6 ft is the length; b = 0.09 = 9% = 5.16
degree is the slope of the standard USLE plot.
The spatial analyst toolkit of the GIS software was
used 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 inter-
face to generate the map of LS factor based on the equa-
tion using the flow accumulation grid as follows:
PowFlowAccresolution22.1, 0.6
PowSinslopegradient0.017450.09, 1.3
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. [55] 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
4.4. Crop Management Factor
The crop management factor (C) expresses the effect of
cropping and management practices on the soil erosion
rate [41], and is considered the second major factor (after
topography) controlling soil erosion. It expresses the pro-
tection of soil by cover-type and density. C is thus a rela-
tion between erosion on bare soil and erosion observed
under a cropping system. 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 to 9/10 under root and tuber crops [21,56].
An increase in the cover factor indicates a decrease in ex-
posed soil, and thus an increase in potential soil loss.
Mapping was undertaken using an on-screen digitizing
procedure to produce land use/cover map, which trans-
ferred to crop management factor C and support practice
factor P layers as a factors in RUSLE. Digitizing was
carried out to generate polygons by inclosing areas
(classes), with specific boundaries. After that field survey
was performed to verify and correct results of land use/
cover maps. The Look Up Tool in Arc GIS was used to
reclassify the land use/cover map according to its C val-
ues (Table 3), which were assigned based on Wisch-
meier and Smith [32] and previous studies undertaken in
northen Jordan [21,57].
The normalized difference vegetation index (NDVI)
(by computing the ratio
Band 4Band3Band 4 Band 3was derived from
LANDSAT ETM + image (2009) and used to calculate
the spectral ground-based data, which shows the highest
correlation with the above-ground biomass [58]. The re-
lationship between C and NDVI was determined (Fig-
ure 3) as C = (0.7388 × NDVI + 0.4948), where the C
value in each land cell can be specified.
4.5. Conservation Practice Factor (P)
Conservation practice factor (P) in the RUSLE model
expresses the effect of conservation practices that reduce
the amount and rate of water runoff, which reduce ero-
sion. It is the ratio of soil loss with a specific support
practice on croplands to the corresponding loss with
slope-parallel tillage [32,59]. It includes different types
of agricultural management practices such as: strip crop-
ping, contouring and terracing. A “P factor” map was de-
rived from the land use/cover-type maps, and each value
of P was assigned to each land use/cover type and slope
(Table 4) [32]. The Look Up Tool in Arc GIS was used
to reclassify the land use/cover and slope length maps
according to its P value.
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Figure 2. The digital elevation model.
Table 3. C factor values for different land cover-types. Table 4. Support practices factor (P).
Landuse/Cover NDVI-Values C-Values
Bare soil 0.01 0.50
Forest area 0.6 0.05
Mixed agricultural area 0.4 0.20
Open Rangeland area 0.2 0.35
Land Use Type Slope - % P Factor
0 - 5 0.10
5 - 10 0.12
10 - 20 0.14
20 - 30 0.19
30 - 50 0.25
50 - 100 0.33
Other land All 1.00
erosion risk and severity for Wadi Kufranja. The rainfall
erosivity factor (R) for five weather stations was found to
be in the range of 85.5 and 487 MJ·mm·ha1·hr1·year1
(Figure 4). The distribution of R values assumed to be
varied and consistent with annual precipitation across
the watershed. The highest R values (413 - 487
MJ·mm·ha1·hr1·year1) prevail in the upper catchment
(humid areas of Kufanja-Ajlune-Anjara), and the lowest
(85.5 - 164 MJ·mm·ha1·hr1·year1) occurs in the arid
Krayma area. The map of K values for the entire catch-
ment (Figure 5) shows a maximum value of 0.063
ton·ha·hr1·MJ1·mm1 in the middle and upper catchment,
especially where vertisols and typic xerochrepts soils are
dominant, and were landslide compexes characterized the
lay marly and the marly limestone units exist. The
Figure 3. Correlation between C-factor values and NDVI
5. Results and Discussion
The data layers (maps) extracted for K, LS, R, C, and P
factors of the RUSLE model were integrated within the
raster calculator option of the Arc GIS spatial analyst in
order to quantify, evaluate, and generate the maps of soil c
Figure 4. Spatial distribution of rainfall erosivity (R) factor.
Figure 5. Spatial distribution of soil erodibility factor (K).
minimum K values is of 0.048 ton·ha·hr1·MJ1·mm1 in
the lower catchment and associated with soils materials
constituting the infill wadis/tributaries. The LS factor va-
lues in the watershed vary from low (0.0) to high (405.0).
High LS values are associated with steep slopes greater
than 15˚ - 20˚ and 20˚ - 30˚ slope category in the middle
and upper reaches of the wadi. The low LS factor values
associated with the alluvial fan surface and wadi/major
tributary beds (Figure 6).
The magnitude and the spatial distribution of crop
management factor C show values between 0.01and 0.2
(Figure 7). The highest (poor land cover management)
almost coincide with the lowest NDVI values, (0.22 -
.05), since forest protects soils against erosion, while 0
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Figure 6. Spatial distribution of (LS) factor.
Figure 7. Spatial distribution of crop management (C) factor.
the open rangeland exposed to plowing has a high C-
value (0.35). Similarly, the mixed rain-fed areas have a
C-value of (0.2). The model showed logical results after
applying the assumed C values for each land-cover class,
with a trend of increasing erosion with low vegetation
By considering land use/cover-type and support factor,
three classes of P factor were recognized (Figure 8). P
factor ranges from 0.19 to 1.0, the higher values in areas
east of Krayma with no conservation practices (forest,
natural vegetation), and other major settlements in the ca-
tchment. By contrast, maximum P values correspond to
crop-land with relatively poor conservation practices in
the upper and middle catchment.
P values decrease towards the upper catchment, where
n flat land units slope length decreases. This explained i
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Figure 8. Spatial distribution of crop management (C) factor.
lower P values in the irrigated lands compared to open
rangeland, since irrigated farms mainly occupy flat/un-
dulating lands. Average annual soil loss of 10
ton·ha1·year1 was estimated for the whole catchment,
and the final soil loss map compiled using the RUSLE
model indicates a minimum of 0.0 to a maximum of 1865
ton·ha1·year1 (Figure 9).
Generally, if the estimated(A)value is high, it means a
higher rate of sediment yield, while a lower value de-
notes a lower rate of sediment yield [47]. The Wadi Ku-
franja watershed was classified into five soil erosion risk
categories (Figure 10). The area and proportion of soil
erosion risk classes are illustrated in Table 5.
Potential soil erosion risk (Table 5) and severity (Ta-
ble 6) increase from the upper to the lower reaches of the
catchment. It is obvious that surface erosion can vary
spatially due to rainfall variability, topographic and mor-
phological changes, different soil types and characteris-
tics, and human-induced disturbances. However, soil ero-
sion is very severe between Krayma and Kufranja towns,
and accounts for 31.2% of the total watershed soil loss.
The distrbution of risk classes and soil severity zones
(Figures 10 and 11) show that 26.7% of the watershed
has minimal soil loss, 36.5% is low, 5.6% and 7.9% is
moderate and severe, while extreme soil erosion occupies
23.3% of the watershed. The highest soil loss values are
clearly correlated with slope steepness. The upper and
lower reaches of the wadi is dominated by moderate and
steep slope categories: 10˚ - 15˚, 15˚ - 20˚ and 20˚ - 30˚.
The first category makes up a minor portion of the total
watershed. Gentle slopes are restricted only in the lower
parts of the catchment which comprise the alluvial fan
Table 5. Area and proportion of each soil erosion risk class.
Erosion risk
Numeric Range
Minimal 0 - 5 26.7 3372.21
Low 5 - 15 36.5 4609.95
Moderate 15 -25 5.6 707.28
Severe 25 - 50 7.9 997.77
Extreme >50 23.3 2942.79
Table 6. Soil erosion severity zones.
severity zone
Numeric range
Slight 0 - 5 26.7 3372.21
Moderate 15 - 25 42.1 5317.23
Very Severe >25 31.2 3940.56
around Krayma, and the interfluve and col areas across
the watershed. The second slope category comprises more
than 75% of the area of the upper reaches. Slopes greater
than 20˚ - 30˚ and more create a distinctive pattern and
are restricted to steep wadi side slopes. They emphasize
the gorge-like nature along the main wadi course and ma-
jor tributaries, thus the wadi floor is of difficult accessi-
bility. Kufranja and Krayma areas are considered as high-
ly erodible with potential erosion is more than 25 and
>50 ton·ha1·year1.
The results of the present investigation in the Wadi Ku-
ranja watershed are comparable with similar studies f
Open Access JWARP
Figure 9. Spatial distribution of minimum and maximum soil loss (ton·ha1·year1).
elsewhere in central and northen Jordan [19,20,52], where
similar terrain and climatic conditions, and historic land
mismanagement prevail. Al-Alawi and Abujamous [19]
reported an estimated average soil loss of 78 ton·ha1·year1
for a part of the Belqa district before installation of soil
conservation structures. After 20 years of this construc-
tion and tree-planting, the predicted average soil loss de-
creased dramatically to an average of 33 ton·ha1·year1.
Such encouraging results, therefore, emphasize the ne-
cessity for well-executed research on soil erosion and im-
proved conservation methods.
The present results are also in consistent with those
obtained from other Mediterranean watersheds of similar
envionmental characteristics investigated elsewhere us-
ing the RUSLE model. The mean annual soil loss for
nine watersheds in northwestern Crete, Greece [46] is
predicted. The range is found from 77.174 ton·ha1·year1
and 205.467 ton·ha1·year 1. It has been reported that
77.5% of the Yialias area Cyprus is classified as low po-
tential erosion risk (20 ton·ha1·year1), 17.5% as moder-
ate potential risk (100 ton·ha1·year1), and only 5% as
high risk [60,61]. On the contrary, Bonilla, et al. [62] re-
ported lower figures from Central Chile. Under current
conditions, low soil erosion rates were achieved and
range from <0.10 ton·ha1·year1 to 6.1 - 8.0 ton·ha1·year1,
where 89% of the country is predicted to have low ero-
sion rates, and no areas are affected by high soil loss
compared to other Mediterranian countries. Similarly,
Demirci and Karaburun [63] provided low figures re-
garding soil loss prediction for a watershed located in
northwestern Turkey. The annual soil loss rate range
from 1 ton·ha1·year1 to >10 ton·ha1·year1. By contrast,
in South Africa, Mhangara, et al. [29] reported that the
Keiskamma catchment is exposed to excessive rates of
soil loss due to high soil erodibility, steep slopes, poor
conservation practices, and low vegetation cover 0.65%
of the catchment shows very low to moderate levels of
soil loss (<25 ton·ha1·year1), and 35% of the catchment
is prone to high to extremely high soil loss (>25
ton·ha1·year1). More comparable results on soil erosion
potential with central and northern Jordan, are obtained
for Foupana river watershed in Algarave (Mediterranean
Portugal) [64], and Nisava river watershed (Southern-
east Serbia), not far from the Mediterranean [65]. Poten-
tial erosion across Foupana watershed was estimated be-
tween 76 to 99 ton·ha1·year1. The rest of the area has a
low to moderate risk of erosion (14 to 60 ton·ha1·year1).
The average annual soil loss for Nisava river watershed
was estimated at 27 ton·ha1·year1, thus classified under
very high erosion rate category. Severe erosion rate (40 -
80 ton·ha1·year1) was observed at 14.2% of the water-
shed, whereas very severe erosion rate (>80 ton·ha1·year1)
described about 7.8% of the watershed. Soil erosion rates
are found to be high over most parts of the catchment;
consequently, implementation of effective soil conserva-
tion measures is required to reduce soil erosion.
The upper part of the watershed is forested with mixed
rainfed agriculture. Population growth, overgrazing,
ropland expansion, and human activities have result in c
Open Access JWARP
Figure 10. Spatial distribution of erosion risk categories.
Figure 11. Spatial distribution of soil erosion severity zones.
widespread removal of vegetation cover. Between 1978
and 2010, prominent changes in land cover/land use were
taken place across Wadi Kufranja [28]. Among these
changes was a dramatic increase of cultivated lands from
9.98% of the catchment area in 1978 to 51.23% in 2010),
due to high rates of population growth. The residential
areas also increase from 1.5% of the catchment area in
1978 to 7.52% in 2010. Therefore, forested areas around
the settlements have been decreased or removed com-
pletely. On the contrary, small scattered areas of range-
land and bare land where transformed to forest, thus for-
est land increased from 13.38% of the catchment area in
1978 to 18.53% in 2010. Due to land cover/land use
changes and climatic change in Jordan over the last sev-
eral decades, soil erosion rates across Wadi Kufranja, and
other parts of northern Jordan have been changed [66].
Accordingly, soil erosion becoming more serious on mo-
derate and steep slopes transformed into cultivated or
range land. Therefore, the expansion of cultivated cereals
increase the susceptibility of soils to erosion, and the cul-
tivated lands with poor conservation measure exhibit
higher rate of soil erosion and decline in soil fertility.
Open Access JWARP
Minimal vegetation cover dominated the steep and long
slopes in the middle and lower parts of the cathment.
Slopes over 15˚ are commonly cultivated, and plowing
with animals has been observed on steep slopes as 25˚
and more.
It is postulated elsewhere that the RUSLE parameters
can be altered significantly by human activities [29]. The
C and P factors can be improved to reduce the soil ero-
sion loss through afforestation and shifting community
environmental practice. The LS factor also can be modi-
fied by shortening the length and steepness of slopes by
the construction of contour walls and stone terraces. Con-
struction of soil conservation measures is vital to control
runoff and soil erosion across different agroecological
zones and under varouis land uses. Expected benefits of
enhancing soil and water conservation in the studied area
could be summarized in the following: control of upland
soil erosion; reduction in sediment load of Wadi Kufranja;
and reducing the peak flows of the wadi [67]. Also, the
construction of check dams along gullies is an essential
measure to minimize gully erosion. The integration of
trees in farmland and rangeland will act as appropriate
coverage and a protector for soil from rainfall energy,
and through stabilizing the soil structure against sheet
and gully erosion. Steep slopes may be suitable for graz-
ing and forest plantation. Moderate and slightly steep
slopes could be utilized for tree crops, and the wadi bot-
tom (accessible strips) for growing vegetables, and the
flat summits and gentle structural benches may be allo-
cated for cereals farming. The results of soil erosion risk,
severity, and land use/cover-type can assist decision-ma-
kers in identification of priority areas in urgent need of
conservation and land-management plans. The afore-
mentioned measures must be implemented through the
government and the participation of the farmers and vil-
lagers living across the watershed. However, the figures
obtained regarding soil loss and severity are disturbing,
considering that the Ministry of Water and Irrigation be-
gan construction of a dam west of Kufranja which will
collect storm-water runoff and base-flow. Annual sedi-
ment yield of the catchment contributing to the reservoir
has not been determined by the Ministry of Water and
Irrigation [60], and in light of the present results, the pre-
dicted large sediment yield will seriously threaten the life
of the reservoir behind this dam.
6. Conclusion
The present determination of RUSLE parameters for the
Wadi Kufranja basin has revealed the severity of soil ero-
sion. The computed soil erosion rate compares comforta-
bly with estimates reported for other areas in northern
Jordan and elsewhere, thus validating the RUSLE model.
The mean soil loss estimated for the watershed was 10
ton·ha1·year1, with the five erosion risk classes, ranging
from 0.0 to 1865 ton·ha1·year1. Areas of 53.1723 km2
(5317.23 hectares) and 39.4056 km2 (3940.56 hectares)
were classed as suffering moderate or very severe soil
erosion. These areas in Wadi Kufranja catchment, and
other similar areas in northern and central Jordan should
therefore be prioritized for conservation. Similarly, spa-
tial analysis denoted high soil erosion rates in the middle
and lower reaches of the catchment. Here, long and con-
tinuous human disturbance and deforestation, with the
combined effect of K, LS, and C factors, account for high
soil erosion loss across the study area. Further research
should focus on soil erosion parameters in the rainfed
highland region of the country. More data on rainfall and
its duration and intensity provided a basis for calculating
erosive of rainfall. Field measurements of rainfall erosion
(direct measurements and simulated rainfall) are highly
recommended, and the results should be compared against
soil loss figures obtained by the RUSLE and other pre-
dicting models. Finally, the present investigation has de-
monstrated that GIS and RS techniques are simple and
low-cost tools for modeling soil erosion, with the pur-
pose of assessing erosion potential and risk for the water-
sheds of northern Jordan.
7. Acknowledgements
This work is part of a research project funded by the
Faculty of Scientific Research, The University of Jordan,
Amman, Jordan.
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