IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erd

Estimation of Soil Erosion Based on USLE and GIS
in Gardez Basin of Paktya Province, Afghanistan
ABDUL MALIK DAWLATZAI
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: abduldawlatzai@gmail.com

MACHITO MIHARA*
Faculty of Regional Environmental Science, Tokyo University of Agriculture, Tokyo, Japan
Email: m-mihara@nodai.ac.jp
Received 13 December 2016

Accepted 26 June 2017(*Corresponding Author)

Abstract Soil erosion is a serious problem in Afghanistan, which has been accelerated by
improper land management day by day and a growing problem especially in agricultural land.
The land in the country is facing continuous soil loss and sediment accumulation due to theirregular topography, deforestation and desertification. It does not only reduce agricultural andlivestock production, but also decrease the water availability for irrigation purpose. This study
focused on the estimation of the rate of soil loss and soil erosion risk using Universal Soil Loss
Equation (USLE) and ArcGIS for the Gardez basin Paktya Province, Afghanistan. All factors
used in USLE (R, K, L, S and C) were calculated for the study area using local data. The bestequation for estimating the R value from the annual rainfall was discussed on the basis of theobserved annual rainfall with the installed rain gauge. Also, all factors were presented by rasterlayers in ArcGIS platform then multiplied together to predict soil loss (A). The results indicatedthat the annual soil loss within Gardez Basin ranges from 0 to greater than 100 t ha-1 y-1. The
value was divided into five (5) risk classes. The result showed that slight class of soil loss
having a range of soil loss between 0 to 5 t ha-1 y-1, moderate class having rates between 5 to 10
t ha-1 y-1, high class having rates between 10 to 50 t ha-1 y-1, severe class rates between 50 to 100t ha-1 y-1 and very severe class rates greater than 100 t ha-1 y-1, covering 64.31%, 13.95%,
19.76%, 1.76% and 0.22% of the Gardez Basin area, respectively. Most of the agricultural lands
are slight to high soil loss categories. However, high soil erosion is found in the barren land,
rangeland and rainfed agricultural land. The soil erosion risk is extremely higher on steep slope
and foothills. Based on the mean soil erosion value of different land use classes, target land use
for conserving strategies was discussed for planning soil conservation practices.
Keywords USLE, ArcGIS, Gardez Basin, soil loss, Afghanistan

INTRODUCTION
Erosion is one of the major ecological problems which threaten our national reserves as well as thewhole world. It also reduces soil fertility significantly and crop yields. Afghanistan is located in thesouth and central Asia which is under high soil erosion effect mostly due to deforestation, arid andsemi-arid climates and irregular topography. Soil erosion has resulted in prolonged and great impact onsocial and economic development in the region, in fact, recent environmental assessments indicatedthat decades of war and continuous drought have caused widespread environmental degradation
throughout the country.
The Universal Soil Loss Equation (USLE) was developed by Wischmeier and Smith in 1978. It
has been the most commonly used model for predicting soil erosion loss. The USLE and its modified
versions such as RUSLE (Renard et al., 1997) and MUSLE (Williams, 1975) have been widely used inⒸ ISERD

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various scales and regions.
Soil erosion has received scanty attention in Afghanistan. However, the study conducted by
Sahaar, (2013) using combined Revised Universal Soil Loss Equation (RUSLE) model and Geographic
Information System (GIS), the annual soil loss of Kabul Rivers was estimated 19 t acr -1 y-1, (4748 t km2 -1
y ). The excessive sedimentation clogs stream channels and increase costs for maintaining water
passage structures. Similarly, the annual soil loss of the Lower Harirud watershed in Heart province
used RUSLE model and GIS ranges from 0.025 to 778 Mg ha-1 y-1, which is 3.6 times greater thanmaximum tolerable soil erosion (Ehsan, 2015). Field study conducted by US Military Agricultural
Development Team, (2011) in the Dawlatzai village of Paktya Province, soil erosion depended on area
ranges between 500 t/ha to 1200 t/ha.
Estimation of soil erosion is economically and environmentally very important in Paktya province,
Afghanistan. Soil erosion does not only reduced soil fertility and water quality but also severeinterrupts irrigation network. Poor vegetation cover, steep slopes, deforestation and high intensity
rainfall in short time are the main factors influencing soil erosion in Paktya province. Therefore, toevaluate the impact of these factors on sustainable agriculture and environment, to quantify the extent
of soil erosion it needs for appropriate and applicable erosion model. For this reason, the USLE model
has been widely used worldwide approximately more than four decades to predict soil erosion.
Recently, there are many types of research conducted regarding the USLE model in conjunction with
GIS technology which has been used to predict the annual soil loss. The objective of this study is toevaluate soil erosion risk using ArcGIS technique and empirical Universal Soil Loss Equation in
Gardez basin of Paktya Province, Afghanistan.
MATERIALS AND METHODS
Study Area
The study was conducted in Gardez Basin located east part of the country and also it is the capital of
Paktya Province, Afghanistan. The basin covers approximately 48,104 ha (481.04 km2) as shown in
Fig 1. It is geographically positioned between latitude N 33° 46′ 0″- N 33° 28′ 0″ and longitude E
69° 26′ 30″- E 69° 26′ 30″. It topographically ranges in slope between 0 to 65 degrees with an elevationof approximately 3663 m above sea level. The rainfall data was obtained from the daily automatic rain
gauge installed in the study area for one-year duration (July 15, 2015 to July 14, 2016), the annual
rainfall is 354.6 mm y-1 and exhibits a dry climatic condition with a minimum and maximum
temperature of -11 °C and 41 °C, respectively.

Fig. 1 Map of study area in Paktya Province, AfghanistanⒸ ISERD

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Methods
The USLE model was developed by Wischmeier and Smith (1978), as an equation representing themain factors controlling soil erosion, namely climate, soil characteristics, topography and land covermanagement. The expression is shown in Eq. 1.
A= R×K×L×S×C×P(1)

Where A is computed annual soil loss per unit area (t ha-1 y-1), R is runoff erosivity factor (MJ mm
ha y-1), K is soil erodibility factor (t ha hr ha-1 MJ-1 mm-1), L is slope length factor, S is slope
steepness factor, C is cover management factor and P is supported practice factor.
In the present study, annual soil loss rates and scale were computed based on USLE in GIS
platform and different data sources were referred to analyze the estimation of soil loss in the study
area. A digital elevation model (DEM) with 30 m resolution was obtained from Aster Global Digital
Elevation Model (Available online: https://asterweb.jpl.nasa.gov/gdem.asp), the elevation range is from2205 m to 3663 m as shown in Fig. 2. The DEM was used to estimate slope gradient, flow direction,basin area, flow length and flow accumulation for the study area using ArcGIS 10.3.1. The slope lengthand slope steepness (LS) factor required by USLE was calculated. The land cover classification map
developed by Food Agriculture Organization (FAO, 2016), was used for the analysis of crop
management factor (C-value). Soil classification map developed by United States Department ofAgriculture, Soil Conservation Services (USDA-SCS, 2001) was used for analyzing the soil erodibility
factor (K-value). Analysis of rainfall erosivity factor (R-value) was derived from area automatic rain
gauge data in Gardez city and mean annual rainfall data in surrounding areas.
-1

Fig. 2 DEM map of Gardez BasinRESULTS AND DISCUSSION
Rainfall Erosivity Factor (R)Rainfall erosivity is defined as the aggressiveness of the rain to cause erosion. Rain has a direct impact
on the surface of the soil. The kinetic energy of the raindrops destroys the soil aggregates, making
them susceptible to transfer by runoff water. The R factor was computed using the Eqs. 2 and 3
developed by Wischmeier and Smith (1978).
KE = 11.87 + 8.73 log I

(2)
-1

-2

-1

Where I is the rainfall intensity (mm h ) and KE is the kinetic energy (Jm mm ).
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R

¦ EI

(3)

max

1000

R is rainfall erosivity factor in MJ m km-2 h-1 y-1 and EI is the total storm energy in Jm-2 mm-1. The
R value was calculated from energy-intensity relationships. Daily rainfall data was recorded using
automatic rain gauge located in the study area for the duration of one year, (July 15, 2015 to July 14,2016). Using Eq. 2 and 3, the data in Table 1 was obtained.
Table 1 Calculation of the erosivity factor
Max. intensity(mm hr-1)
3.95
3.72
3.57
3.98
21.27
8.20
11.11
15.88
Total

Total energy(J m-2)402.06343.16
272.62
270.63292.55294.06341.36277.45Rainfall erosivity(MJ m km-2 hr-1 y-1)
1.59
1.28
0.97
1.08
6.22
2.41
3.79
4.41
21.75

Rainfall erosivity(MJ mm ha-1 hr-1 y-1)
15.9
12.8
9.7
10.8
62.2
24.1
37.9
44.1
217.5

Using the data obtained from the automatic rain gauge installed in the study area for one-year
duration, the R-factor value was calculated as 21.75 MJ m km-2 hr-1 y-1 (217.5 MJ mm ha-1 hr-1 y-1). The
original method for calculating the R value for a storm event requires rainfall amount in mm, intensity
in mm h-1 and the maximum 30 minutes intensity in mm h-1. Due to lack of adequate metrological data
and long-term rainfall intensity data in some countries such as Afghanistan, it is hard to apply Eq. 2
and 3. It is therefore necessary to interpolate between available data hence, the attention should be paid
to investigate new methods and equations to calculate the erosivity factor using annual rainfall. R
factor based on annual precipitation were computed using various equations shown in Table 2.
Table 2 List of equations used to investigate correlation
No

Reference

1
2
3
4
5
6
7
8
9
10
11
12

Morgan, 1974
Foster et al., 1981
Cooper, 2011
Eltaif et al., 2010
Deumlich et al., 2006
Renard and Fremund, 1994
Yu and Roswell, 1996
Parveen and Kumar, 2012
Singh et al., 1981
Arnoldus, 1980
Renard and Freimund, 1993
Roose, 1975

EquationⒸ ISERD142

R = 2.28P - 8,838
R = (0.27P75)/100
R = 9.17P0.20
R = 23.61e(0.0048P)
R = 12.98 + 0.0783P
R = 0.04830P1.510
R = 0.0438P1.61
R = 29 + 0.363P
R = 79 + 0.363P
R = 0.03P1.9
R= 0.07397F1.847/17.02
R = 0.5P

IJERD – International Journal of Environmental and Rural Development (2017) 8-1

However, the erosivity index calculated using Eq. 4 by Singh et al., 1981 showed the best fit wasachieved between R value calculated with USLE and the mean annual rainfall for the Gardez Basin.
The results based on Eq. 4 was summarized in Table 3.
R = 79 + 0.363P(4)

Where P is the mean annual precipitation (mm) and R is the erosivity factor (MJ mm ha-1 hr-1 y-1).
In terms of ArcGIS layers, each weather station was represented by a point. The Inverse Distance
Weighted (IWD) interpolation method in ArcGIS was used to create a raster map for R factor.
However, rainfall erosivity (R) was calculated using rainfall data from six rainfall stations across the
Gardez Basin. The high erosivity was found in the northeast and low erosivity was found in thesouthwest part of Basin. The R-factor varied from 157.58 to 199.72 MJ mm ha-1 hr-1 y-1 as shown in
Fig. 3.
Table 3 Rainfall erosivity, (R value)
No

StationRainfall (mm y-1)

R value (MJ mm ha-1 hr-1 y-1)

1Tera Garden (Gardez city)

333

199.882

Rhoni Baba farm (Zarmat District)

216

157.41

3

Khost (Province)

330

198.79

4

Sharana (Paktika Province)219

158.505

Urgoon (Paktika Province)

252

170.48

6

Logar (Province)

294

185.72

Fig. 3 Rainfall erosivity map of Gardez Basin

Fig. 4 Soil erodibility map of Gardez Basin

Soil Erodibility Factor (K)The soil erodibility factor indicates susceptibility of soil particles or surface materials to be detachedand transported by rainfall and runoff (Renard et al, 1997). Soil erodibility factor was obtained fromsoil classification map of the country which is presented by USDA-SCS, 2001 as shown in Fig. 4.
Based on the classification of soil and soil texture classes, the K factors (t ha hr ha-1 MJ-1 mm-1) are
shown in Table 4.

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Table 4 Soil classification and erodibility values
No

Soil classification

Soil texture

Order

K value1

Xerochrepts with Xerorthents

Silt loam

Xeric

0.048

2

Haplocambids with Torriorthents

Silt loam

Aridic0.040

3Torriorthents with Torrifluvents

Silt clay loam with cobbly loam

Aridic0.038

4Haplocambids with Torriorthents

Silt loam with fine sandAridic0.063

Fig. 5 Slope map of the Gardez Basin

Fig. 6 LS-factor map of Gardez Basin

Slope Length and Slope Steepness Factor (LS)The LS factor has been used in a single index, which expresses the ratio of soil loss as defined by
Wischmeier and Smith, 1978.
LS = (X/22.1)m (65.41 sin2 θ + 4.56 sin θ + 0.065)(5)

Where, X is slope length (m); θ is the angle of slope in degrees; and m is a constant dependent on
the value of the slope gradient: 0.5 if the slope angle is greater than 2.86 degree, 0.4 on slope of 1.72 to
2.85 degrees, 0.3 on slope of 0.57 to 1.72 degrees, and 0.2 on slopes less than 0.57 degrees. Erosion
increases as slope length and slope steepness increases. The slope length (L) and slope steepness (S)are combined in a single topographic index termed LS-factor was computed for the Gardez Basin byusing spatial analyst extension in ArcGIS software has used to generate raster layers of the slope. The
slope of Gardez Basin range values between 0 to 65 degree and was derived from the DEM as shownin Fig. 5.
First step, the elevation value was modified by filling the sinks in the grid. Second step Flow
direction was generated from the fill grid. Third step the flow accumulation was calculated andgenerated from the flow direction. Flow accumulation tool identifies how much surface flow
accumulates in each cell; cells with high accumulation values are usually stream or river channels andalso recognizes local topographic feature such as mountain peaks and ridgelines. Finally, raster
calculator function under Spatial Analyst tool was used to input the modified Eq. 5. To compute LS
factor. The values between 0 to 175.64 as shown in Fig. 6.
Crop Management Factor (C)Ⓒ ISERD

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The crop management factor (C factor) is the ratio of soil loss of a specific crop to the soil loss under
the condition of continuous fallow (Renard et al., 1997). It measures the effect of canopy and groundcover on the hydraulics of raindrop impact and runoff.
C factor is a relation between erosion on bare soil and erosion observed under a cropping system.
It varies from 1 on bare soil to 1/1000 under dense forest, 1/100 under grasslands and plants and 1 to
4/10 under root and tuber crops (Morgan, 2005). Based on the national land cover map published by
FAO-UN, 2016, the land cover classification of the Gardez basin has 11 classes. Therefore a crop
management factor (C factor) was assigned for each land use type from the literature reviewed asshown in Fig. 7.

Fig. 7 C factor map of Gardez Basin

Fig. 8 Soil loss map of Gardez Basin

Conservation Practice Factor (P)Factor P in USLE model expresses the effect of conservation practices that reduce the amount and rateof water runoff, which decrease erosion. It is the ratio of soil loss with specific support practice tocorresponding soil loss with upslope and downslope parallel tillage (Renard et al, 1997 andWischmeier and Smith, 1978). Currently, there are no support practices in the study area, hence P is
assigned value of 1 in the calculation.
Estimated Soil Loss
The data layers (maps) extracted for R, K, LS and C factors of the USLE model were multiplied within
the raster calculator of ArcGIS spatial analyst in order to generate the map of soil loss for Gardez Basin.
The final map presents the annual soil loss t ha-1 y-1 a pixel level. The soil loss values estimated for
Gardez basin ranges from 0 to > 100 t ha-1 y-1 which is shown in Fig. 8.
The annual soil loss map obtained was classified into 5 classes. The results presented in Table 5shows that about 64.31% of the study area is classified as slight erosion risk (0 - 5 t ha-1 y-1), 13.95% of
the area is classified as moderate soil erosion risk (5 - 10 t ha-1 y-1), 19.76% of the area is classified ashigh soil erosion risk (10 - 50 t ha-1 y-1), 1.76% of the area is classified as severe soil erosion risk (50 100 t ha-1 y-1) and 0.22% of the area is classified as very severe soil erosion risk (greater than 100 t ha-1y-1). The higher soil loss is due to high slope steepness, very poor vegetation and no conservation
practices which are the most prominent causes of soil erosion, severe and very severe soil erosion risk
classes mainly located in mountains/foothills.

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Table 5 Annual soil loss rate and risk categories
Soil loss (t ha-1 y-1)
0-5
5 - 10
10 - 50
50 - 100
> 100
Total

Risk categories
SlightModerateHigh
SevereVery severe

Area (ha)30,934
6,713
9,503
847
107
48,104

Area (%)
64.31
13.95
19.76
1.76
0.22
100.00(Morgan et al., 2004)

The land use map of country was developed by FAO-UN 1993 and subsequently updated in 2016
(FAO-UN). Land use classification map of Gardez basin consists of 11 classes as shown in Fig. 9, thatare explained as follows; irrigated agricultural land, rainfed agricultural land, rangeland,
rangeland/barren land, barren land, barren land/rangeland, forest and shrubs, fruit trees, vineyards, built
up and water bodies and marshland. Rangeland is the most scattered land covering over 59% of thetotal area, irrigated agricultural land covers 24.5%, forest and shrubs covers 2.1% and built up areas
covers 3.2% of the Gardez basin as shown in Table 6.

Fig. 9 land use map of Gardez Basin (FAO, 2016)
Table 6 Dominate land use/land cover in different mean annual soil loss rate
No
1
2
3
4
5
6
7
8
9
10
11

LULC
Rangeland
Rangeland/barren land
Forest and shrubs
Built-up
Irrigated agriculture land
Water bodies and marshland
Fruit trees
Vineyards
Rainfed agriculture land
Barren land
Barren land/rangeland

Area (ha)
28529
3731
1045
1687
11806
427
273
4
409
2
191

Area (%)
59.31
7.76
2.17
3.51
24.54
0.89
0.57
0.01
0.85
0.00
0.40

Mean soil erosion (t ha-1 y-1)
7.18
11.26
2.21
2.67
3.48
2.77
2.28
2.86
6.39
11.54
74.81

In order to identify average soil erosion rates for different land use classes of Gardez Basin, land
use/land cover map of the study area was intersected with classified soil erosion map. From Table 6, it
is clear that high levels of soil erosion classes are found on the fallow land, barren land/rangeland,barren land, rangeland/barren land, rangeland and rainfed agricultural land. The annual average soil
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erosion is lower in the forest/shrubs and fruit trees. Moreover, slight, moderate, high, severe and very
severe soil loss area which was obtained based on land use map approximately, 30,933.96, 6,713.24,
9,503.04, 846.89 and 106.87 ha shown at Table 7, respectively. In addition, irrigated and rainfed
agricultural lands area is about 12,215 ha of the total area and also most parts soil losses occur in slightto high soil loss categories. Since most of the agricultural lands are slight to high soil loss classes,
immediate attention of soil conservation practices is required. To suggest site specific sustainable land
use practices for controlling slight to high soil erosion risks, this result allows assessment of soil loss
quantitatively, identify the risk zones and draw appropriate planning measure for implementing
optimal land use management practices.
Table 7 Risk categories of Gardez Basin area (ha) based on land use/land cover
Risk categories
No
1
2
3
4
5
6
7
8
9
10
11

LULC
Rangeland
Rangeland/barren
land
Forest and shrubs
Built-up
Irrigated
agriculture land
Water bodies andmarshland
Fruit trees
Vineyards
Rainfed agriculture
land
Barren land
Barrenland/rangeland
Total

Slight0–5
t ha-1 y-1
16804.82

Moderate
5 – 10t ha-1 y-1
3920.66

High
10 – 50t ha-1 y-1
7215.25

Severe50 – 100t ha-1 y-1
570.58

Very severe> 100t ha-1 y-1
17.73

28529.04

2089.36

511.15893.20210.8026.49

3731.00

909.15

46.29

89.35

0.16

0.00

1044.95

1365.96215.94102.062.36

0.67

1687.00

8813.18

1888.96

1074.35

27.15

2.36

11806.00

384.00

35.48

7.00

0.30

0.21

427.00259.00

12.99

0.79

0.22

0.00

273.00

3.80

0.20

0.00

0.00

0.00

4.00

252.60

74.07

77.10

5.24

0.00

409.00

0.00

0.00

2.00

0.00

0.00

2.00

52.09

7.49

41.94

30.08

59.40

191.0030933.96

6713.249503.04

846.89106.8748103.99

Total

CONCLUSION
The present study indicates that using GIS technologies for soil loss mapping, based on the USLE
model provided satisfactory results. Different components of USLE model were used withmathematical equations; the rainfall erosivity R-factor calculated, using USLE method (rainfall in mm,
intensity in mm hr-1, maximum 30 minutes intensity in mm hr-1) from daily automatic rain gauge is217.5 MJ mm ha-1 hr-1 y-1 and the R-factor calculated based on annual rainfall amount using various
equations, range between 157.58 to 199.72 MJ mm ha-1 hr-1 y-1. However, the best fit was achieved
between the R value from USLE method and annual rainfall for Gardez Basin. Soil erodibility factor(K) which was obtained from soil classification map range between 0.038 to 0.063 t ha hr ha-1 MJ-1
mm-1. Slope length and slope steepness factor (LS) values obtained from DEM between 0 to 175.64.
Crop management factor (C) values were obtained from land cover classification map range between 0 to 1.
The final map presents the annual soil loss, the values range from 0 to greater than 100 t ha-1 y-1, andclassified into five (5) classes; includes that about 64.31% of the study area is slight erosion risk (0 - 5 t
ha-1 y-1), 13.95% of the area is moderate soil erosion risk (5 - 10 t ha-1 y-1), 19.76% of the area is high soil
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erosion risk (10 – 50 t ha-1 y-1), 1.76% of the area is classified as severe soil erosion risk (50 – 100 t ha-1y-1) and 0.22% of the area is classified as very severe soil erosion risk (greater than 100 t ha-1 y-1).
Most of the agricultural lands are classified slight to high soil loss. However, high soil erosion isfound in the barren land, rangeland and rainfed agricultural land. The soil erosion risk is extremely
higher on the steep slope and mountains/foothills. The land use map of the study area was prepared andthe average annual soil loss for different land use will be highly useful in recognizing the priority areas
for application of land use practices and soil conservation measures in Gardez Basin. The rainfed andirrigated agricultural lands require immediate attention for soil conservation practices. Based on the
result of this study, the estimated soil loss and proposed land use map could be an effective input for
the future planning and implementing soil conservation strategy in the eastern part of Afghanistan.
ACKNOWLEDGEMENTS
The authors are profoundly grateful to Department of Agriculture Engineering, Tokyo University ofAgriculture and Japan International Cooperation Agency (JICA).
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