Spat. Inf. Res. (2017) 25:399–409 DOI 10.1007/s41324-017-0107-5

Application of RUSLE model for soil loss estimation of Jaipanda watershed, West Bengal Subodh Chandra Pal1 • Manisa Shit2

Received: 16 December 2016 / Revised: 3 May 2017 / Accepted: 3 May 2017 / Published online: 12 May 2017  Korean Spatial Information Society 2017

Abstract Soil erosion is a major environmental problem due to both natural and anthropogenic factors and continuous erosion leads to land degradation. In the present study, soil loss in Jaipanda watershed of West Bengal, India is estimated employing revised universal soil loss equation (RUSLE) model and adopting integrated analysis in GIS. The factors used in RUSLE are namely the rainfall and runoff erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support practice factor related to slope direction (P). These factors were computed from different data which have been obtained from meteorological station, soil maps, topographic maps, digital elevation model and satellite image. The five RUSLE factors were represented by raster layers in a GIS framework and then multiplied together to estimate the soil erosion rate in the study area. The study provided a reliable prediction of soil erosion rates and erosion potential zones within the watershed. The average soil loss amount for this watershed is about 0.59 tons/ha/year. Keywords RUSLE  GIS  DEM  Jaipanda  Watershed

& Subodh Chandra Pal [email protected] Manisa Shit [email protected] 1

Department of Geography, The University of Burdwan, Burdwan, West Bengal 713104, India

2

Department of Geography, Jamini Roy College, Beliatore, Bankura, West Bengal 722203, India

1 Introduction Soil erosion is very complex and continuous process which refers to the detachment and transportation of topsoil elsewhere by natural agents like water, wind and others. Soil erosion which is worldwide problem may be called ‘‘creeping death’’ of soil. Soil loss is a complex and very dynamic process [1, 2]. It varies from place to place based on runoff, soil types, rainfall, topography and vegetal cover and so on [3, 4]. Significant hard works have been done on the development of soil erosion models [5, 6]. In India, particularly in the sub-humid and semi-arid region water induced erosional activity of top soil is a major problem. The falling of rain drops and runoff produce the soil erosion but it make worse by the numbers of anthropogenic factors like deforestation, overgrazing and unscientific agricultural practices. It was found that about 5334 million tonnes of soil are being removed per year in India [7–9]. The one-inch surface soil which may be formed in thousands years can be lost in just 1 year. Soil erosion can effect in the soil textures, soil nutrients loss as well as productivity decline, silting of reservoirs and flooding in lowlands. Soil loss beyond 1 ton/ hector/year is considered as irretrievable in a time length of 50–100 years [2, 10]. As it has adverse effect in the watershed, the implementation of effective soil conservation measures may be required. Different models have been developed to estimate the spatial and quantitative information of soil erosion. Soil erosion models were classified into three groups viz. Empirical, Conceptual (partly empirical/mixed) and Physically-based [6, 11–13]. On the other hand few researchers classified the models into two groups, such as physically based models and empirical models [2, 14]. Another researcher mentioned that USLE and its modifications fall into empirical models [13]. Universal soil loss equation (USLE), the modified universal

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soil loss equation (MUSLE), and the revised universal soil loss equation (RUSLE) are commonly used methods to predict soil erosion especially in a particular watershed due to their minimal data requirements and ease of application [2, 14–16]. The USLE is a simple empirical model; However, RUSLE which is the revised version of USLE not only provides the estimated soil loss at the plot scale, but also depicts the spatial distribution of soil erosion [17–19]. Remote sensing and GIS has become very useful tools for estimating of soil loss using RUSLE model. Several scholars have found that GIS and Remote Sensing are the significant and effective tools in the assessment of soil erosion through different models [2, 6, 10, 13, 20–27].Soil loss estimation and its spatial distribution is one of the key factors for successful erosion assessment. In this study, RUSLE model was used in a GIS environment to estimate soil erosion in the Jaipanda watershed. It has been observed that the population pressure and the anthropogenic activities are major causes behind the soil erosion. As the land use pattern is getting changed year after year the top soil of the earth surface getting deteriorated by the precipitation as well as by the anthropogenic activities. Rill, Gully and Ravine erosion are also playing crucial role for soil erosion. In the present study it has been observed that the local farmer or cultivators are facing lot of trouble for producing something from their own. The top soil and fertile soil is eroding year after year by the storm rainfall. So it is the need of hour to quantify the erosion rate geospatially for finding out the suitable remedial measures which will be taken care of therein.

2 Description of study area This study area is semielliptical shape and occupies the Indpur, Onda, Taldangra and Simlapal C.D. block of Bankura district (Fig. 1). It is bounded by latitudes 22530 2600 to 23100 2700 and longitudes 86510 2100 to 87120 4900 encompassing an area of 385.715 km2. According to the watershed atlas of India, the present study area belongs to 2A2C5 code. The maximum altitude is 166 m., demarcated in the north western part and the minimum elevation is about 45 m., observed in the south eastern part of the watershed. In the upper part, major portion of the study area is covered by pink granite/biotite granite gneiss and sand, silt and clay (unoxidised or occasionally oxidised) are concentrated towards the lower part and laterite is scattered throughout the study area. The climate is extreme with maximum temperature up to 42 C and minimum temperature down to 6 C. The annual rainfall of the study area varies between 1055 and 1070.3 mm. The maximum amount of rainfall (about 80.73%) occurs during the monsoon season from June to September. According to the field

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survey the soil texture mainly concentrates here i.e. Fine Loamy, Fine Loamy-Coarse Loamy, Fine Loamy-Sandy, Gravelly loam and Fine. Single and double cropping systems of paddy cultivation are practiced. In the present study soil erosion provides the detachment and transportation of the top soil due to the water action therefore this study is taken care of in this study area. 2.1 Data used Survey of India (SOI) topographical sheets (73 I/12, 73 I/16, 73 J/13, 73 M/4 and 73 N/1) on 1:50,000 scales have been used as a base map to study the watershed. Cartosat data, SRTM data, soil texture data, geological map (1:250,000 scale) published by Geological Survey of India was also used. Except these, station wise rainfall data (2011), Landsat TM satellite data (Table 1) have been used for this work where band 1–5 used for this investigation which is in the following. 2.2 Objectives The objective of this study is: •

To estimate the rate of soil erosion and spatial distribution of soil loss in the Jaipanda watershed using a GIS based RUSLE model.

3 Materials and methods The revised universal soil loss equation (RUSLE) method was used in the present study to estimate soil erosion in the Jaipanda watershed. RUSLE is an erosion model (Fig. 2) that estimates soil erosion caused by raindrop impact and associated overland flow. The factors of RUSLE represent the effect of precipitation, soil, topography, land cover, and support practices on soil erosion. The factors used in RUSLE were obtained from the meteorological station, soil surveys and soil data, DEM and satellite data. These factors represents as five thematic layers which overlaid in GIS framework to compute the spatially distributed average annual soil erosion map for the Jaipanda watershed. The average annual soil loss was found in RUSLE by multiplying the factors indicated in the following equation [18]: A ¼ R  K  LS  C  P where, A—is the average annual soil loss in tons per hector per year (t/ha/year), R—is the rainfall and runoff erosivity factor, K—is the soil erodibility factor, LS—is the slope length and steepness factor, C—is the cover and management factor, and P—is the support practice factor related to slope direction.

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Fig. 1 Location map of the Jaipanda watershed

4 Results and discussion 4.1 R factor The rainfall and runoff erosivity index factor expresses the erosivity which occurs from rainfall and runoff. From longterm annual rainfall records, the average value or R factor is estimated. If the intensity and amount of rainfall increases, the value of R also increases. R factor for a given

location is expressed in MJ mm ha-1 year-1 [17]. There are a variety of equations that have been developed to determine the relationship between rainfall intensity and energy. These relationships combine the intensity of storms and empirical relationships relating the intensity to energy and then combining the energy and intensity to produce the R value [28]. The R factor is usually expressed as a raster layer produced by natural neighbor interpolation techniques in ArcGIS. The natural neighbor interpolation

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Table 1 Details of the satellite data used in this study Satellite

Sensor

Path/row

Bands

Date of acquisition

Spatial resolution

LANDSAT

TM

139/044

1, 2, 3, 4, 5, 7

02/04/2011

Band 1–5, 7 (30 m) and band 6 (120 m)

Fig. 2 Model of soil loss estimation

Climate

Soil

Rainfall

Soil texture

Obtained from station wise Rainfall data

R Factor

Satellite Imagery, DEM, SOI Topographical Maps

Slope

Calculated using soil texture

K Factor

Modeled using topographic data & DEM

NDVI

Obtained from Satellite data

LS Factor

C Factor

Slope

Calculated using up & down slope

P Factor

Integration in GIS

Soil Loss = R×K×LS×C×P

technique has been used to quantify the actual rate of rainfall and runoff erosivity in the said region. The average annual rainfall data collected from three nearby meteorological stations i.e. Taldangra, Onda and Khatra was used for preparation the rainfall distribution map of the entire watershed. As the study area and its surrounding region receive more rain than one meteorological station, therefore the interpolation technique was used to get the map. In this study, empirical equation that estimates R from annual total rainfall was used [29]. It is given as, R ¼ 38:5 þ 0:35  Pr where R is rainfall and runoff erosivity factor and Pr is the average annual precipitation (mm). The rainfall erosivity map of the Jaipanda watershed is presented in Fig. 3. The

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Land Use Planning

values of R factor also vary according to rainfall distribution. The values of R factor vary between 58.6 and 59.2 MJ mm ha-1 year-1 (2011) for the Jaipanda watershed as the highest values being in the lower part and the lowest values in the middle part of the watershed. 4.2 K factor The soil erodibility factor (K) indicates inherent erodibility of the soil or surface material. It represents resistance of soil material to the impact of raindrops on the soil surface. The K factor is related to the integrated effects of rainfall, runoff and infiltration on soil loss, accounting for the influences of soil properties on soil loss during storm actions on upland areas [18]. The K factor represents both vulnerability of soil to

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Fig. 3 R factor of the Jaipanda watershed

erosion and the amount or rate of runoff. The erodibility of a particular soil is determined by the soil texture, organic matter, structure and permeability of that soil. The organic matter of soil reduces erodibility of that particular soil. The K factor was computed for each soil type based on different soil properties using the equation given by Wischmeier and Smith [17].

calculate the LS factor in GIS environment. The LS factor is calculated by multiplying the L and S factors together [30].

K ¼ 2:1  10  6  M1:14  ð12OMÞ þ 0:0325  ðP2Þ þ 0:025  ðS3Þ

The Raster Calculator in the Spatial Analyst extension of Arc Map was used to calculate the LS grid. The Raster Calculator expression of the equation above was:

LS ¼ ðFlow Accumulation grid  cell size=22:13Þ0:4  ðSin½Slope grid  0:01745=0:0896Þ1:4 1:4

where K = soil erodibility (ton ha-1 unit of R); M = (% silt ? % very fine sand) (100 - % clay); OM = percentage of organic matter; P = permeability class; and S = structure class The value of K-factor was found to be ranging between 0 and 0.43 for study area (Fig. 4). As results revealed, the lowest K factor value ranges between 0 to 0.07 ton ha-1 unit of R which was scattered in the southeastern part of this watershed. The highest value of K factor corresponded to southern part of the Jaipanda watershed and ranged between 0.35 to 0.43 ton ha-1 unit of R.

where Pow is the Power in ArcGIS environment, Flow Accumulation is the grid layer of flow accumulation expressed as the number of grid cells, and cell size is the length of a cell side. The L and S factors were figured out from a DEM of the study area. As results revealed that the LS factor value ranges from 0 to 47.93 which is shown in the Fig. 5.

4.3 LS factor

4.4 C factor

LS factor is a dimensionless factor, function of both slope length factor (L) and the steepness factor (S). It is also known as the topographic factor. It can be estimated through field measurement or from a digital elevation model (DEM). The following equation was used to

Cover and management factor is a dimensionless factor and important consideration for RUSLE model. C factor is the ratio of soil loss from areas with protective cover to the corresponding loss from the areas without it [31]. Vegetation cover is the one of most important biophysical

LS ¼ Powð½Flow Accumulation grid  10=22:13; 0:4Þ  PowðSin½Slope grid  0:01745Þ=0:0896; 1:4Þ  1:4

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Fig. 4 K Factor of the Jaipanda watershed

Fig. 5 LS factor of the Jaipanda watershed

indicator to soil erosion which can be estimated using vegetation indices derived from satellite images. The Normalized Difference Vegetation Index (NDVI), one of

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the dimensionless vegetation indices and a radiometric measure that indicate the concentration of green vegetation. Here Landsat TM data of the study area with spatial

Application of RUSLE model for soil loss estimation of Jaipanda watershed, West Bengal

resolution of 30 m was used for generation of NDVI image (Fig. 6). The formula can be expressed as [32]: NDVI ¼ ðNIRRedÞ=ðNIR þ RedÞ where NIR means Near Infrared band and Red means Red band. NDVI values range between -1.0 and ?1.0 where positive values are for green vegetation and low values for other common surface materials like water bodies which expresses as negative NDVI values and bare soil represents closest to zero. NDVI values varied from -0.21 to 0.62 of the study area. After the production of the NDVI image, the following formula was used to generate a C factor map from NDVI values. C factor ¼ 1:021:21  NDVI C factor is computed using empirical equations that contain field measurements of ground cover. [17, 18]. The NDVI has been used widely in remote sensing studies since its development [33]. The values of C factor for Jaipanda watershed ranges between 0.36 and 2.03 (Fig. 7). 4.5 P factor The support practice factor is a dimensionless factor which is important parameter for the RUSLE model. It is the soil loss ratio in a particular support practice to the corresponding soil loss with up-and-down slope tillage [34].

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Renard and Forster [35] explained that support practice basically concern about the soil erosion through altering the flow pattern and gradients. This factor indicates the rate of soil loss according to the various cultivated lands on the earth. The value of P factor depends upon the measures of soil management which is associated with the slope of the area. P values range from 0 to 1, whereby the value 0 indicates very good manmade erosion resistance facility and the value 1 represents absence of manmade erosion resistance facility. In the present study the value of P factor ranged between 0 and 0.43 where the lower part of the study area was concentrated with lower values and the upper part of the study area was concentrated with the higher values (Fig. 8). Lower values were also concentrated along the side of the streams. In the study area there were some agricultural support practices, such as field bunding and contour bunding due to presence of undulating upper part and plain land in the lower part. 4.6 Estimation of soil erosion This study has demonstrated the capabilities of using Remote Sensing and GIS for quantifying as well as representing of different erosional potential zones, especially in diverse geological setup. This gives more realistic for erosional potential zones map of an area which may be used for the future management studies or to control the degradation caused by soil erosion. The annual average soil

Fig. 6 NDVI of the Jaipanda watershed

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Fig. 8 P factor of the Jaipanda watershed

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Application of RUSLE model for soil loss estimation of Jaipanda watershed, West Bengal

loss in the Jaipanda watershed was estimated by computing the five RUSLE factors. The annual average soil loss ranged from 0 to 1.54 tons /ha/year in the entire study area. Now, we have calculated the Erosion Vulnerability Units (EVU) of the entire basin based on the soil. Erosion values obtained across the various land use/ land covers classes of the basin. There were five EVU for this region to understand the geo-spatial pattern of soil erosion and its vulnerability, such as very low (\0.15 tons / ha/year), low (0.15–0.30 tons /ha/year), medium (0.30–0.68 tons /ha/year) high (0.68–1.15 tons /ha/year) and very high (1.15–1.54 tons /ha/year). As seen from the Fig. 9, the majority of the study area experiences soil erosion between 0.15 and 0.30 tons /ha/year (Low EVU), especially where the slope is low. However, excessive soil erosion is observed in very few parts of the study area where the soil erosion is more than 1 tons/ha/year (i.e., High and Very High EVU). This amount of soil loss depends upon the above mentioned factors which are controlled by the environmental conditions, so the amount of soil loss and its severity varies in different environmental settings. It depends upon climatic condition, soil texture, vegetation cover, slope or surface topography etc. This study area is very similar like Jhargram sub-division of Paschim Medinipur district, West Bengal, India as par as the climate and topography is concerned but the rate of erosion is very low in comparison with the Jhargram subdivision between 0 and 13.28 ton/ha/year [36] it may be

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that the Interfluvial setting of the Jhargram sub-division could be the possible reason behind this higher rate of erosion.

5 Conclusions Though the creation of database through the used methods is time consuming, tedious and is difficult to handle but GIS modeling can be used as a successful and helpful methods for calculating and mapping the soil losses. The above study revealed that soil erosion risk is low to very low in more than half of the study area (70.78%) with soil erosion rate below 0.304 tons/ha/year. Some part (25.91%) of the watershed is under slight to moderate soil loss zone with the soil erosion rate ranges between 0.30 to 0.68 tons/ ha/year. Only 3.31% of the study area is under high or very high soil loss risk where the soil erosion rate is above 0.68 tons/ha/year. The very high and high erosion risks are mainly seen in north western parts and very few part of the river course of the Jaipanda where slope values are generally higher. From this study, mainly three conclusions can be drawn: Firstly high amount of soil loss is noticed in the upper part due to presence of exposed soil surface and higher slope. Secondly, presence of large patches of open scrub and agricultural fallow land in upper part which causes moderate soil loss. Thirdly, low soil loss is noticed in forest cover area. The average annual soil loss map will

Fig. 9 Annual average soil loss of the Jaipanda watershed

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absolutely be helpful in identification of main concern areas for implementation of soil conservation measures and effective checking of soil loss.

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