Abstract: Acceleratedrate of soil erosion is a serious and continuous endemic environmental problemin the western part of West Bengal. The present study is carried out in upperKangsabati watershed with an area of 276.
19 km2. It is fact that thesurface runoff of seasonal rainfall is more in this area due to its undulatingterrain characteristics. Average annual soil loss has been estimated based onthe five parameters defined in the Revised Universal Soil Loss Equation (RUSLE)and with the help of Geographical Information technology. Overlay of fiveparameters, i.
e. rainfall–runoff erosivity factor (R), soil erodibility factor(K), slope length and steepness factor (LS), cover and management factor (C)and support and conservations practices factor (P) has been done in GISplatform. Predicted average annual soil loss of the basin has been classifiedinto four categories. High rate of soil erosion (>15 t ha-1 year -1)was found along the north eastern part of watershed. On the other side lowamount of soil erosion (<1 t ha-1 year -1) was foundalong the hilly tract of dense forest cover and plantation areas. Keywords: RUSLE,Soil Erosion, GIS, Watershed, Terrain, Hilly TractIntroductionOneof the most serious and continuous environmental problem is soil erosion andland degradation particularly in third world country where agriculture is themain economic activity.
More than 50% ofthe total area of India is affected by land degradation resulting from soilerosion (Sehgal and Abrol 1994). It is now established fact that each year morethan 75 billion tons of soil is removed from agricultural land due to erosion(Pandey et al. 2009). Broadly acceptable to the geomorphologists, erosion isthe progressive removal of soil or rock particles from the parent mass by afluid agent (Strahler 1964). The forms of soil erosion are mainly sheet, rilland gully erosion.
But this type of erosion widely varied in different spatialand temporal scale depending on morpho-climatic and pedo-geomorphic factors(Ghosh and Maji 2011). The amount of soil erosion is measured on the basis oftwo models i.e. physical based and empirical based model.
GIS and remote sensing (RS) provide spatialinput data to the empirical model and predict the potential soil erosion rate. Byusing different type of models many scholar have worked on soil erosionthroughout the world. The most commonly used empirical model is Universal SoilLoss Equation (USLE) developed by Wischmeier and Smith in 1965 for measuringsheet and rill erosion (Ganasri and Ramesh 2016). Revised universal Soil LossEquation (RUSLE) uses the same empirical principles as USLE; however itincludes numerous improvements, such as monthly factors, incorporationof the influence of profile convexity/concavity using segmentation of irregularslopes, improved empirical equations for the computation of LS factor (Fosterand Wischmeier1974, Renard et al. 1991). In this study LISS III image has been used forgenerating C factor by using Normalized Difference Vegetation Index (NDVI).
Thepresent study is an attempt to focus on the estimation of soil erosion in theupper Kangsabati watershed by RUSLE model.Location and description of thestudy areaTheupper Kangsabati (also known as Kasai) watershed is located in Puruliyadistrict in the state of West Bengal, India (Fig. 1).It is extended between 23°13’26″N to 23°28’33″N latitude and 85°17’18″E to86°11’56” E longitude. The catchment area of the study site is about 276.19 km2.
It originates near Jhalda in Chotanagpur plateau of Puruliya district.Regionally the study area is a part of Chotanagpur Gnessic Complex (CGC), inwhich rock formation belongs to Archaean age, which is the oldest rockformation of the district (Halder and Saha, 2015). The soils in the study area are mainly fineloamy, coarse loamy and loamy skeletal (NBSS&LUP, 2008). The average annual rainfall in this watershedis 1393 mm and annual mean temperature is 25.
6°C with mean summer and meanwinter temperature are 29.0°C and 21.3°C respectively (Saini et al. 1999).Topographically the area is characterized by undulating rugged hilly terrain. Materials and methodsInthis study, GIS plays a major role to prepare different types of thematic map andestimation of soil erosion rate.
For thepresent study different type of data are collected from different sources.These are mainly numerical data, thematic maps etc, which help to analysis theresearch work. Those data are mainly, rainfall data of five years (2012-2016)from India Meteorological Department (IMD), soil data from National Bureau ofSoil Survey and Land Use Planning (NBSS & LUP, 2010), topographical sheets(73I/3, 73I/4, and 73E/15, scale of 1:50000) from Survey of India (SOI),Shuttle Radar Topography Mission (SRTM) 30 m resolution and IRS P6 LISS IIIsatellite image. ArcGIS 10.
3 and ERDAS Imagine 14.0 were used for creation ofdigital database, data integration and analysis. After correction the DEM inGIS platform, it was used to prepare the slope map. Later DEM and slope mapwere used to prepare LS factor.
IRS P6 LISS III data along with topographicalsheets were used to prepare detailed landuse landcover (LULC) map.Soil erosion estimation model RUSLETheRevised Universal Soil Loss Equation (RUSLE) model was adopted to estimate theannual soil loss and the equation (Eq. 1) is as:A= R × K × L × S × C × P (1) Where,A is the average annual soil loss per unit area expressed in tones/ha/year (tha-1 year -1); R is the rainfall–runoff erosivity factor(MJ mm ha-1 h-1); K is soil erodibility factor (t ha h MJ-1mm-1); L is the slope length factor; S is the slope steepnessfactor; C is the cover and management factor and P is the support andconservation practices factor. Flowchart (Fig.
2) showing the methodology adoptedin this research study.Rainfall erosivity factor (R)Rainfallerosivity factor is the annual total value of the erosion index (EI30) for aparticular location (Sarkar et al. 2005). Rainfall intensity represents the principalfactor of kinetic energy and to estimate the rainfall erosivity (Balasubramaniet al. 2015).
If the intensity and rainfall increases the value of R factoralso increases. In this study, Singh et al. (1981) established empiricalequation (Eq. 2) has been used for estimating annual rainfall erosivity. Thelinear relationship of erosion index is:Ra= 79 + 0.
363 × P (2) Where,Ra is the average annual rainfall erosivity factor (MJ mm ha-1 h-1)and P is the rainfall (mm). In this study, 5 years (2012 – 2016) average annualrainfall data from India Meteorological Department (IMD) has been used forcalculating R factor (Table 1). The average annual rainfall data collectedfrom four rain-gauge stations namely Jhalda, Jaipur, Arsha and Baghmandi locatedin upper Kangsabati watershed.
Spatial distribution of R factor has beenobtained using Inverse Distance Weighted (IDW) interpolation techniques. Soilerodibility factor (K)Soilerodibility factor is a measure of potential erodibility of soil and it dependson the inherent properties of the soil. The K factor is related to theintegrated effects of rainfall, runoff and infiltration on soil loss,accounting for the influences of soil properties on soil loss during stormsaction on uplands areas (Renard et al. 1997). Soil erodibility factor map hasbeen derived based on different soil types, texture, organic matter andpermeability. On the basis of the district level pedological map derived fromNational Bureau of Soil Survey and Land Use Planning (ICAR, 2008), K values ofdifferent soil type in the study area have been estimated.
Thirteen types ofsoil classes (Table 2) in the study area havebeen identified and values are imputed to respective classes of soil. Topographicfactor (LS)Topographicfactor includes slope length factor (L) and slope steepness factor (S) mainlyreflect the effect of surface topography on erosion by water action (Yildirim2012; Shit et al. 2015; Sarkar et al. 2005).
Slope length (L) and slopesteepness (S) have been derived from SRTM DEM (30 m resolution) in ArcGIS 10.3platform. Slope length factor (L) has been calculated on the basis of followingformula (Eq. 3) given by McCool et al. (1987) is: (3) Where,L is the slope length factor; ? is the slope length in meter; m is the variableslope-length exponent.
22.13 is the RUSLE unit plot length in meter.Theslope steepness factor (S) is evaluated based on the relationship (Eqs. 4.
1,4.2) given by McCool et al. (1987) for slope length longer than 4 meter: (4.1) (4.2)Where,S is the slope steepness factor which is dimensionless and is the slope angle in degree.TheLS factor is calculated by multiplying L and S factor together (Moore andBurch, 1986) in raster calculator in ArcGIS platform with the help of followingequation (Eq.