Soil Loss Estimation in Assam Watershed Using RUSLE Model and GIS

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
Issue 05, Volume 5 (May 2018) www.ijirae.com
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SOIL LOSS ESTIMATION IN GIS FRAMEWORK:
A CASE STUDY IN CHAMPABATI WATERSHED
INAMUL HUSSAIN
Civil Engineering Department, (Watershed Management & Flood Control)
Assam Engineering College, Jalukbari, Guwahati-13, India
inamulaeccivil@gmail.com
Dr. UTPAL KUMAR MISRA
Associate Professor, Civil Engineering department
Assam Engineering College, Jalukbari, Guwahati-13, India
Manuscript History
Number: IJIRAE/RS/Vol.05/Issue05/MYAE10090
Received: 07, May 2018
Received: 11, May 2018
Final Correction: 15, May 2018
Final Accepted: 19, May 2018
Published: May 2018
Citation: HUSSAIN & MISRA (2018). SOIL LOSS ESTIMATION IN GIS FRAMEWORK: A CASE STUDY IN
CHAMPABATI WATERSHED. IJIRAE::International Journal of Innovative Research in Advanced Engineering,
Volume V, 187-196. doi://10.26562/IJIRAE.2018.MYAE10090
Editor: Dr.A.Arul L.S, Chief Editor, IJIRAE, AM Publications, India
Copyright: ©2018 This is an open access article distributed under the terms of the Creative Commons Attribution
License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited
Abstract— RUSLE (Revised Universal Soil Loss Equation) is widely used to predict average annual rate of soil
erosion. RUSLE parameters were assessed using Satellite Remote Sensing (RS) and GIS with a view to model soil
erosion in CHAMPABATI watershed in Assam state. Assessment of soil erosion is useful in planning and
conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to
estimate soil erosion under a wide range of conditions. The parameters of RUSLE model were estimated using
remote sensing data and the erosion probability zones were deter-mined using GIS. The five major input
parameters used in the study are Rainfall Erosivity Factor (R), Slope Length and Steepness Factor (LS), Soil
Erodibility Factor (K), Cover and Management Factor (C) and Support Practice Factor (P). The R factor had been
determined from monthly TRMM rainfall data of study area. The soil survey data from www.fao.org was used to
develop the K factor and DEM of study area was used to generate topographic factor (LS). The value of P & C factor
was obtained from land use land cover map & LANDSAT image respectively. Estimating C factor in this study
involves the use of Normalized Difference Vegetation Index (NDVI), an indicator which shows vegetation cover,
using the regression equation in Spatial Analyst tool of GIS Software. After generation of input parameters,
analysis was performed for estimation of soil erosion using RUSLE model by spatial information analysis
approach. The quantitative soil loss (t/ha/year) ranges were estimated and classified the watershed into different
levels of soil erosion severity and also soil erosion index map was developed. The average annual soil losses of the
study Watershed were then grouped into different severity classes based on the criteria of soil erosion risk
classification suggested by FAO (2006). The estimated average annual soil loss for the study area is 5.8044 million
t. yr-1.
Keywords— RUSLE; GIS; CHAMPABATI; TRMM; FAO;
I. INTRODUCTION
Soil erosion is regarded as the major and most widespread form of soil degradation. The erosion of soil is a
naturally occurring process on all land over geological time.

_________________________________________________________________________________________________
Soil erosion is a three phase phenomena consisting of the detachments of individual soil particles from the
soil mass and their transport by erosive agents, such as running water and wind. When sufficient energy is no
longer available with erosive agents to transport the particles then the third phase is called a „deposition
takes place. The potential for soil erosion varies from watershed to watershed depending on the configuration
of the watershed (topography, shape), the soil characteristics, the local climatic conditions and the land use
and management practices implemented on the watershed (Arora K. 2003 and Suresh R. 2000).
1.1 SOIL EROSION TYPES:
Types of erosion:
a. Sheet Erosion:
Sheet erosion is more or less is the removal of a uniform thin layer or „sheet of soil by flowing water from a
given width of sloping land. The amount of soil removed by this type of erosion is small, but as it flows
down the slope, it increases in size and develops into rill erosion. (Arora K. 2003 and Suresh R. 2000)
b. Rill and Gully Erosion:
With rill erosion the erosive effect of flowing water suddenly increases at a location where a confluence of
surface water occurs. Due to low infiltration rates and the occurrence of rainfall, the excess water collects very
slowly over the land surface and into the rill. As this gathering of water continues the depth of water together
with the velocity, kinetic energy, and the soil particle carrying capacity of the water increases. Then the rill
erosion develops into gully erosion. (Arora K. 2003 and Suresh R. 2000)
c. Stream Bank Erosion:
The removal of soil from the stream of the stream bank occurs due to either water flowing over the sides of the
stream from overland runoff or the water flowing in the stream and scouring the banks. Stream bank erosion is a
continuous process in perennial streams and is caused by the souring and undercutting of the soil below the
water surface caused by wave action during normal stream flow events.
d. River Erosion:
This type of erosion occurs particularly in rivers in which permanent water flow takes place, usually with varying
rate. River erosion is likely to be more effective in the water courses of smaller catchment area and those
having less favourable conditions for draining discharge.
II. CHAMPABATI RIVER SYSTEM- LOCATION AND PHYSIOGRAPHY:
Latitude = N 26° 15' 44.352"
Longitude= E 90° 28' 39.54"
Champabati River flows west to Aai River. It is a combination of three rivers-“Bhur River”, “Mora Bhur River”,
“Lopani” and “Dhol pani” which are flowing out from Bhutan hills. Among them “Bhur River” is biggest. Bhur River
is originated from a place called Gurungdando and flows 14 km south east to Bhur Village and entered into Assam
near post no.267 and flows south for 2km and takes a small river called “Patiakhola” in its left. Then the river
turned into a narrow sandy form and flows 13 km through Manas National Park and arrives at Shantipur. From
Shantipur the river flows 4km in south west direction and entered into Bengtal Sanctuary taking Khungrung River
at its right in Hantupara. After coming through the sanctuary for 2km,a tributary flows out to south taking the
name of Baahbari River.1Km from that another tributary flows out to south. Then the Bhur River flows another 1
km to reach the Saalbari Bhurpar which is in south of Baahbari forest. After 2.5 km from that, the river takes two
tributary from phoolkumari River in its both sides. From this place the river flows 2 km to meet with the Dholpani
river which is west to centre Ranikhata and it finally takes the name of Champabati River. Dholpani is
Champabati’s one of the most prominent tributary. The Dholpani River is initiated in Bhutan and it flows 9 km
south and reach near to Assam and west to Bhur River. Then it flows in Assam Bhutan border and flows 3 km in
south west and enters into Assam. Near the border Dholapani takes Arne Khola River in its right. Then the river
flows 4km south and takes Tiniabadhi river in its right and turns to south east. The river flows through Chirang
sanctuary for 12 km and takes Champabati in its right. Then the river flows for 1km and takes Kalikhola or
Potolsonol River in its right and flows south west and flows for 1km and reach Charaguri. Then the Dholpani River
turns into a sandy river and flows for 7km and then takes Lopani River in its right.
This Lopani River is also a big river. Lopani flows west to Dholpani. Kharpani river enters into Assam and flows
south east for 1km and takes kofole khola river to its left and forms Lopani River. Then the river flows through
Chirang forest for 9 km and takes a tributary from Jhora Beel to its right and flows for 12 km before it meets the
Dholpani river. Dholpani and Lopani combined themselves to take the name of Champabati River.

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It flows to south east for 3 km and join Bhur River to its right in Centre ranikhata and then it flows as Champabati
River. After flowing for2km in south from centre Ranikhata a tributary is emerged and takes the name of
Demdema River and flows to west for 2km and again join Champabati. Then the Champabati flows for another
2km and takes Raampati River to its left. Then the river flows for 3km south and takes Morabhur River to its left.
Fig 1: Location Map of the Study area
In Saalbari Champabati divides into two parts after crossing 31(c) National Highway. The main part flows to
south east and the other past flows in the south west in Tarang River’s path. The main Champabati river slows
south and takes a zig zag path and after flowing for 5km it takes Sukti river in its left in Tirimari. From Tirimari
the river flow for 3km in south east and takes Shakati River in its left. From that the river flows for 1.5 km and
takes kakormari Dokong River in its left and flows another 1 km before it crossed railway line 2km from Basugaon
Railway station. Then the river flows for 10 km in south east direction in zigzag path and then follows a straight
path before reaching Tilakgaon. Here it takes Duramari river in its left.
Then near Bidyapur it takes Kujia River in its left. Then west to Lathuri Tila the river flows for 2.5 km and reach
Naaldoba. Here the river again divided itself into 2 parts. The main part flows south east and near bamuni Tila it
flows for 5km and takes Ghoga River to its left. Then eventually the two parts meet again and flows for another 1
km before crossing 31St National Highway. From here it flows for 3km and opposite to Jalikura it takes Tunia
River in its left. Then the river flows to south west. Then it flows for 3km and takes Garojhara in its right. Then it
flows for 2km and it reach Chapar town. From Chapar the river flows for 1km and joins Brahmaputra.

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2.1 LAND USE/LAND COVER OF STUDY AREA:
Fig 2: Land use land cover map
TABLE 1: APPROXIMATE AREA UNDER EACH LAND USE TYPE
SL NO LAND USE TYPE AREA ( ) AREA (%)
1 BUIILT-UP AREA/SETTLEMENT 130.595 10.392
2 DENSE FOREST 288.599 22.965
3 NATURAL VEGETATION/PLANTATION 413.096 32.872
4 AGRICULTURE LAND 374.959 29.836
5 WATER BODIES 49.444 3.935
1. REVISED UNIVERSAL SOIL LOSS EQUATION (RULE):
The Revised Universal Soil Loss Equation (RUSLE) is a revision and update of the widely used Universal Soil Loss
Equation (USLE) developed by Renard et al. (1997). RUSLE retains the factors of the USLE to calculate annual
sheet and rill erosion from a hill slope; however, changes have been made for each factor. RUSLE was developed
to incorporate new research since the earlier USLE publication in 1978 (Wischmeier and Smith, 1978). The
basic form of the RUSLE equation has remained the same, but modifications in several of the factors have
changed. Both USLE and RUSLE compute the average annual erosion expected on field slopes and is shown in
equation (1) (Renard et al., 1997).
A = R×K×LS×C×P…........................................ (1)
Where,
A = Average Annual Soil Loss Rate (t ha-1 year-1)
R = Rainfall Erosivity Factor (MJ mm ha-1 h-1 year-1)
K = Soil Erodibility Factor (t ha h ha-1 MJ-1mm-1)
LS = Slope Length and Steepness Factor
C = Cover and Management Factor
P = Support Practice Factor
3.1 R FACTOR:
Among the factors used within RUSLE, rainfall erosivity is of high importance as precipitation is the driving
force of erosion and has a direct impact on the detachment of soil particles, the breakdown of aggregates and
the transport of eroded particles via runoff. Rainfall erosivity is the kinetic energy of raindrop's impact and
the rate of associated runoff (Wischmeier & Smith, 1978).

_________________________________________________________________________________________________
Wischmeier and Smith (1958) derived the rainfall and runoff erosivity factor from research data from many
sources. The rainfall runoff erosivity factor (R) derived by Wischmeier appears to meet these requirements better
than any of the many other rainfall parameters. Wischmeier and Smith (1978) developed a relation for the
calculation of rainfall Erosivity factor (R) from monthly rainfall data.
R = 1.735 ……………….….…….(2)
Where,
R = Rainfall Erosivity Factor (MJ mm ha h-1 year-1)
Pi= Monthly Rainfall in mm
P = Annual Rainfall in mm
Fig 3: TRMM Raingauge Station Point Fig 4: R Factor Map
For interpolation, 24 station points (Figure 3) were selected inside and outside (near watershed) the watershed
boundary. The TRMM monthly precipitation data was extracted for 24 stations points by using spatial analyst tool
(extract multi values to points) in GIS and R factor was calculated for those stations. The extracted monthly TRMM
station point precipitation data were processed in MS Excel and calculated the R factor for the 24 stations points
by using the Wischmeier and Smith (1978) equation (2). After calculating average 20 years of rainfall for each
station point, the R factor was converted in to raster surface using IDW (Inverse Distance weighted) interpolation
methods in ArcGIS software to get a spatially distributed R factor map of the watershed area. The IDW
interpolation method was selected because rainfall erosivity sample points are weighted during interpolation
such that the influence of rainfall erosivity is most significant at the measured point and decreases as distance
increases away from the point. The IDW interpolation method is based on the assumption that the estimated value
of a point is influenced more by nearby known points than those farther away (Weber and Englund 1992, 1994).
The R factor value found in the range of 3911.74 to 9470.42 MJ mm ha-1 h-1y-1 for the study area. The spatial
distributions of Rainfall erosivity factor (R) is represented in Fig 4.
3.2 K FACTOR:
Soils vary in their susceptibility to erosion. The soil erodibility factor K is a measure of erodibility for a standard
condition. The soil erodibility factor K represents both susceptibility of soil to erosion and the amount and rate
of runoff, as measured under the standard unit plot of 22.1 m long with a slope of 9% percent (Renard et al.,
1997) condition. As such soil erodibility is best estimated by carrying out direct measurements on field plots
(Kinnell, 2010).
To estimate the soil erodibility factor (K) the following equation (3) is proposed by Romkens et al. (1997)

_________________________________________________________________________________________________
………………. (3)
Where
……………………………….. 3(a)
Where
Geometric mean particle diameter
Primary particle size fraction in percent
Arithmetic mean of the particle size limits of that size
The study area soil map (Fig 5) is extracted from the DSMW map using ArcGIS. There are 4 soil groups found in
the study area namely Orthic Acrisols, Eutic Cambisols, Dystric Cambisols and Dystric Regosols. The percentage of
silt, clay, sand and organic matter for each soil group was calculated. Using equation (3) the erodibility factor K
found for each soil in the range of 0.095 to 0.331. To prepare K factor map, at different latitude and longitude
within the watershed, K values were assigned for each soil type. After that, interpolation of the calculated K
values is done by Kriging method for the whole watershed using ArcGIS (mathematical K value in excel file
converted to shape file by using GIS) software. The spatial distribution of soil erodibility factor (K) is represented
in Fig 6.
Fig 5: Soil Map Fig 6: K Factor Map
3.3 LS FACTOR:
a. L FACTOR:
Wischmeier and Smith (1978) defined the L-factor as the ratio of soil lost from a horizontal slope length to the
corresponding loss from the slope length of a unit plot (22.13 m). According to this definition, slope length is the
distance from the point of origin of overland flow to the point where either the slope gradient decreases enough
for deposition to start, or runoff waters are streamed into a channel. According to this simple definition, the L-
factor can be represented as:
…………………………….. (4)
Where
= (Flow Accumulation× Cell Size)
m = Slope exponent
m is related to the ratio β of the rill to interill erosion:
………………… 4 (a)
Where,

_________________________________________________________________________________________________
………………………4(b)
θ = Slope angle in degrees.
The m ranges between 0 and 1, and approaches 0 when the ratio of rill to interill erosion is close to 0.
b. S FACTOR:
S is the slope steepness represents the effect of slope steepness on erosion. Soil loss increases more rapidly with
slope steepness than it does with slope length. It is the ratio of soil loss from the field gradient to that from a 9
percent slope under otherwise identical conditions. The relation of soil loss to gradient is influenced by density of
vegetative cover and soil particle size. McCool et al. (1987) proposed to calculate steepness factor
……………………… 5(a)
…..………………….. 5(b)
Where
S= Dimensionless steepness Factor
= Slope angle in degree
The LS factor value for study area varies from 0 to 14.755 which are represented in Fig 7.
3.4 C FACTOR:
The C-factor represents the effect of soil-disturbing activities, plants, crop sequence and productivity level, soil
cover and subsurface bio-mass on soil erosion .The C factor ranges from 0 in water bodies to slightly greater than
1 in barren lands. The Normalized Difference Vegetation Index (NDVI), an indicator of the vegetation vigor and
health is used to generate the C-factor value for the study area
Fig 7: LS Factor Map Fig 8: C Factor Map
The radiance was converted into reflectance and NDVI was calculated using the reflectance data. SVI measures the
vegetation cover (VC) amount in term of vegetation condition. The spectral NDVI formula is given as
………………….. (6)
Red and NIR stand for the spectral reflectance measurements acquired in the red (visible) and near-infrared
regions, respectively. Van der knijff (1999), after performing a lot of experimentations came to a nonlinear
relationship between C and NDVI which seemed to be adequate.
…………………….. (7)

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α and β are unit less parameters that determine the shape of the curve relating to NDVI and the C factor. Van der
Knijff et al. (2000) found that this scaling approach gave better results than assuming a linear relationship, and the
values of 2 and 1 were selected for the parameters α and β, respectively. To determine the C factor, the equation
(7) proposed by Van der Knijff et al was used in raster calculator in spatial analyst toolbar and the value found for
the C factor in the range of 0.6987 to 1.8596. The C factor map distribution shown in the Fig 8.
3.4 P FACTOR:
From all the six RUSLE input factors (Renard et al., 1997) , values for the support practice P-factor are
considered as the most uncertain (Haan et al.,1994;Morgan; Nearing,2011).The P-factor reflects the impact of
support practices on the average annual erosion rate. It is the ratio of soil loss with contouring and/or strip
cropping to that with straight row farming up-and-down slope. In other words, the P factor accounts for control
practices that reduce the erosion potential of runoff by their influence on drainage patterns, runoff
concentration, runoff velocity and hydraulic forces exerted by the runoff on the soil surface (Renard et al., 1991).
It is an expression of the overall effects of supporting conservation practices – such as contour farming,
strip cropping, terracing, and subsurface drainage – on soil loss at a particular site, as those practices principally
affect water erosion by modifying the flow pattern, grade, or direction of surface runoff and by reducing the
volume and rate of runoff (Renard et al., 1997). In present study, no relative information concerning such as strip
cropping, terracing and contour cultivation practices are available. Assuming that no preventive measures are
taken, the P factor was assigned a value equal to 1 throughout the watershed area.
[[
3.5 SOIL LOSS IN CHAMPABATI WATERSHED:
After multiplication of the five factors as per RUSLE formula (Eq. 1) we got the average annual soil loss of the
study area and are shown in Fig 9. From Fig 9 it can be observed that the annual soil loss of the area ranges
between 0 and 37947.6 t.ha-1yr-1. The mean value of soil loss is 46.188344t.ha-1yr-1. The maximum value 37947.6
is the value of soil loss of only one pixel; it does not signify any overall soil loss scenario of the study area. The
calculation of soil loss of that pixel is shown below –
Pixel Size = 30 m X 30 m
Area of one pixel = 900 m2 = 0.09 ha
Therefore the soil loss in that pixel = 37947.6 X 0.09 = 3415.284 t.yr-1
The total soil loss of the study watershed = Watershed Area X Mean Value
= 125669.5 ha X 46.188344 t.ha-1yr-1.
= 5.8044 million t. yr-1
The average annual soil losses of the study Watershed were then grouped into different severity classes based on
the criteria of soil erosion risk classification suggested by FAO (2006). The details of severity classes and the
spatial distribution of the same in the study area are shown in Table 2 and Fig 10 respectively.
TABLE 2: SOIL LOSS SEVERITY CLASSES WITH LOSS RATE AND AREA COVERED
TABLE 3: TOTAL SOIL LOSS IN EACH SUB WATERSHED
SUB
WATERSHED
SOIL LOSS RANGE
(t/ha/yr)
MEAN SOIL LOSS
(t/ha/yr)
TOTAL SOIL LOSS
(million×t/yr)
PERCENTAGE OF SOIL
LOSS IN EACH S-W
SW1 0-17042.4 28.41 0.426 7.34 %
SW2 0-33433.5 60.32 0.878 15.12 %
SW3 0-7905.56 32.26 0.710 12.23 %
SW4 0-37947.6 69.23 1.847 31.83 %
SW5 0-24016.2 40.08 0.164 2.82 %
SW6 0-9471.09 36.78 0.557 9.59 %
SW7 0-26521.5 43.33 1.213 23.85 %
Severity Class Soil Loss ( t.ha-1.yr-1) Area (km2) Area (%)
Slight <30 1064.880 84.73
Moderate 30-80 24.7796 1.971
Severe 80-150 143.8669 11.44
Extremely Severe >150 23.16876 1.843

_________________________________________________________________________________________________
Fig 9: Spatially Distributed Soil Loss Map Fig 10: Soil Erosion Severity Class Map
Fig 11: Sub Watershed of Champabati
IV. CONCLUSIONS
RUSLE is often used to estimate average annual soil loss from an area. RUSLE model in GIS environment is a
relatively simple soil erosion assessment method. ArcGIS 10.1 software was used to generate the spatial
distribution of the RUSLE factors. The four factor layers (R, K, LS, and C) were all converted into grids using a 30-
m data set of the CHAMPABATI watershed in the same reference system. Subsequently, these grids were
multiplied in the GIS as described by the RUSLE function. Thus, the annual soil loss was estimated on a pixel-by-
pixel basis, and the spatial distribution of the soil erosion in the studied CHAMPABATI was obtained. To adopt
the RUSLE, large sets of data starting from rainfall, soil, slope, crop, and land management are needed in detail. In
developing countries all the necessary data are often not available or require ample time, money, and effort to
prepare such data sets. RUSLE is a straightforward and empirically based model that has the ability to predict
long term average annual rate of soil erosion on slopes using data on rainfall pattern, soil type, topography, crop
system and management practices. Based on this analysis, the amount of soil loss in the CHAMPABATI varies 0 to
37941.6 t ha-1 yr-1. The average annual soil loss of the CHAMPABATI has been found out to be 46.18834 t/ ha/
yr. It also reveals that the potential soil loss is typically greater along the steeper slope and poor vegetation cover
area. The Range land and dense forest of the CHAMPABATI are shown to be the least vulnerable to soil erosion.

_________________________________________________________________________________________________
4.1 RECOMMENDATIONS FOR CONSERVATION AND MANAGEMENT PRACTICE:
The following important points are recommended for the study area for conservation and management practice to
reduce the soil erosion.
 While implementing soil conservation and management activities, priorities and serious attention should
be given to areas which were identified as extensive soil loss areas.
 Proper land use planning and a proper Environmental Impact Assessment (EIA) should be done for the
watershed before any developmental project being conducted in the area.
 Different soil protective methods like mulching, strip cropping, terracing, contour plowing, multiple
cropping and other indigenous means of soil conservation techniques should adopt by local communities
for immediate soil conservation measures in their cultivated lands.
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