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ScienceDirect ScienceDirect

Aquatic Procedia 4 ( 2015 ) 1429 – 1436

Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: 10.1016/j.aqpro.2015.02.185

INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015)

Estimation of Soil loss using USLE model for Kulhan Watershed, Chattisgarh- A case study

C.P. Devatha

^a*

,VaibhavDeshpande

^b

, M.S. Renukaprasad

^c

aDepartment of Civil Engineering, NITK, Surathkal, Karnataka, India- 575025

bDepartment of Civil Engineering,Rungta College of Engineering and Technology, Bhilai - 490024 (CG), India

cDepartment of Civil Engineering, NITK, Surathkal, Karnataka, India- 575025

Abstract

Soil erosion is the common land degradation problem in the worldwide because of its economic use and environmental impacts. To estimate soil erosion and to establish soil erosion management plans, many computer models have been developed and used. Remote Sensing (RS) and Geographic Information System (GIS) being a well-known tool available for dealing with the major water resources problems, it is used in the present study. The objective of the present study is to estimate the annual soil loss using USLE model for Kulhan watershed of Shivnath basin, sub-basin of Mahanadi basin, Chhattisgarh using RS and GIS techniques. Land useland cover and topographical data for the study area was derived using GIS and carried out geographical data analysis. Data collection includes for the study were annual rainfall data, digital elevation model (DEM) from shuttle radar topographic mission (SRTM), land-use classification map and soil series maps were obtained from Chhattisgarh Council of Science and Technology (CCOST), Raipur, Chhattisgarh.

The five major input parameters used in the study are rainfall erosivity factor (R), Length slope factor (LS), soil erodability factor (K), vegetation cover factor (C) and erosion control factor (P). The rainfall erosivity factor had been determined from annual rainfall data of study area. The soil survey data was used to develop the soil erodability factor and DEM of study area was used to generate topographic factor (LS). The value of cover management factor and support practice factor were obtained from land use land cover map. After generation of input parameters, analysis was performed for estimation of soil erosion using USLE 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 watershed is classified according to Indian condition as suggested by Singh et, al.(2002) into different erosion classes such as (>5) slight, (5-10) moderate, (10-20) high, (20-40) very high, (40-80) sever, (>80) very severe. It was found that average annual soil erosion for

* Corresponding author. Tel.: +919483032757; fax: +0-000-000-0000 . E-mail address: [email protected]

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study area is 0.1783t/ha/yr and 83.97% of total area is under slight erosion risk class and only 0.45% of total area is under very severe class which is near the bank of main stream line.

Peer-review under responsibility of organizing committee of ICWRCOE 2015.

Keywords:RS and GIS; Soil Loss; Soil erosion; USLE; Land Use Land Cover map.

1. Introduction

Shifting cultivation on the hill slopes, non-adoption of soil conservation techniques and over exploitation of land or crop production due to population stress, leads to enormous soil erosion. Soil erosion by water is one of the most important land degradation problem and a critical environmental hazard in modern time, worldwide. It is one of the most serious problems as it removes soil rich nutrients and increases natural level of sedimentation in rivers and reservoirs reducing their storage capacity and about 5334 m tons of soil is being detached annually due to various reasons in India. Narayana and Babu (1983) suggested that sheet erosion is the most serious issues among India’s soil erosion problems. Soil erosion has been recognized as a hazard of significant concern, yet the number of studies on this problem is very limited in India. A proper assessment of the erosion problem is greatly dependent on its spatial, economic, environmental and agricultural context. A good soil loss management is therefore, needed to reduce land degradation and low water quality due to siltation and sedimentation.

Soil erosion modelling is able to consider many of the complex interactions that influence rates of erosion by simulating erosion processes in the watershed. Various parametric models such as empirical (statistical), conceptual (semi empirical), physical process based (deterministic) models are available to compute soil loss. Most of these models need information related with soil type, land use, landform, climate and topography to estimate soil loss.

They are designed for specific set of conditions in particular area.

Universal Soil Loss Equation(USLE) is most widely used model. USLE was developed by the United States Agricultural Research Service. Wischmeier(1978)was designed USLE to predict soil loss from sheet and rill erosion in specific conditions from agriculture fields. USLE estimates soil loss based on the product of rainfall erosivity (R), erodability of the soil (K), slope length in meter (L), slope in percent (S), cover management parameter (C), and support practice parameter (P). Roo and Jetten (1999) explained that the main reason for using a GIS is that the erosion process varies spatially and thus, grid cells must be used so that this spatial variation can be taken into account.

Singh et al. (2002) carried out study on prioritization of annual soil loss of Bata river basin using RS and GIS techniques. Assessment of soil erosion and analysis of soil loss based on sub-watershed within a watershed has been done by using Morgan model (1984). The model result shows that the average annual soil loss in the watershed is 17.22 ton/ha. Mohan et al. (2002) carried out analysis using ILWIS GIS for computation of soil loss estimation.

USLE factors were computed and the annual and seasonal soil erosion rates were estimated for the study area. Venu et al. (2012) estimated sediment yield at the outlet of Indravathi catchment using USLE model. From the model output, it is observed that average soil erosion rate and sediment yield at the outlet of the catchment (Million tons per year) and predicted data is verified with the observed data. It is indicated that the area of high soil erosion can be accounted for in terms of steep unstable terrain. Jasrotia and Singh (2006)computed runoff and soil erosion in the catchment area along the NH-1A between Udhampur and Kud covering an area of approximately 181 km² using RS data and GIS technique. Different thematic layers, for example lithology, a landuse and landcover map, geomorphology, a slope map, and a soil-texture map, were generated from these input data. By the use of the US Soil Conservation Service curve number method, estimated runoff potential and it was classified into five levels as very low, low, moderate, high, and very high. Annual spatial soil loss estimation was computed using the Morgan- Morgan-Finney mathematical model in conjunction with RS data and GIS techniques. Greater soil erosion was found to occur in the northwestern part of the catchment area. When average soil loss from the catchment area was calculated, it was found that a maximum average soil loss of more than 20 t ha^-1 occurred in 31 km² of the catchment area. Haan et al.,(1994) showed that increased slope length and steepness produces higher overland flow velocities and correspondingly higher erosion. Zhang et al. (2012) explained that USLE and Revised USLE (RUSLE) are often

Peer-review under responsibility of organizing committee of ICWRCOE 2015

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used to estimate soil erosion at regional landscape scale, but a major limitation is the difficulty in extracting the LS factor and also developed a LS-Tool in Microsoft’s.NET environment using C with a user friendly interface. By comparing the values of LS factor obtained from the three methodologies namely UCA, FCL and LS-Tool, it was found that LS Tool has efficient tool to determine LS value for large area of extension.

Lee and Heo (2011) were used rainfall erosivity (R) factors a primary input parameter for soil erosion, sediment yield and water quality modeling. At least 20 years of continuous high resolution rainfall data for the study area are required to compute original rainfall erosivity in (R)USLE. The purpose of the study was to evaluate models for estimating rainfall erosivity based on annual precipitation and to identify the most applicable model for Korea. 20 years of actual rainfall erosivity data for 33 Korean weather stations has been calculated by high resolution precipitation data. Correlation analyses between actual rainfall erosivity and rainfall parameters were investigated to identify appropriate estimators of rainfall erosivity. They found that 31 of the 33 stations indicated a strong positive relationship (r>0.5) between annual rainfall erosivity and annual precipitation with a 99% confidence level. Ozcan et al. (2008) pointed out some limitations of the USLE as a prediction model, because this equation predicts the total soil loss of rill erosion and inter-rill erosion without differentiating these components. Furthermore, it does not include soil loss as a result of gully formation and it is not estimates sediment deposition in specific areas. An additional limitation of the USLE is that it requires long-term data to develop the parameters for climate (R factor) and erodibility (K) factor for locations and soils outside the original dataset.

The tillage of agriculture lands, which breaks up soil into finer particles, is one of the primary factors. Due to mechanized agricultural equipment that allows for deep plowing, this severely increases the amount of soil that is available for transport by soil erosion. In the present study soil erosion hazard mapping was carried out by using GIS base modelling approach integrated with satellite remote sensing derived parameters. The aim of this study was, to determine spatial distribution of soil loss and to analyze the effect of land use, slope exposition and terrace farming on soil erosion and also evaluate the application of GIS and USLE to determine soil loss.

2. Study area

The study area considered is Kulhan watershed of Kharun river basin, sub basin of Shivnath basin, Chhattisgarh state with a location of 21ô34’20”- 21ô24’0.5”N and 81ô38’32.81”- 81ô55’43.82”, location map is shown in figure 1.

Watershed covers four blocks such as Raipur, Abhanpur, Arang and Tandula. The Kulhan watershed comprises about 935km² in size consist of various topographic features.

Fig. 1. Study area map.

The study area is characterized by a gentle undulating and flat terrain. Maximum slope in percentage in study area is 10.49%. The total Geographical area of Kulhan watershed is 935km² out of which agriculture occupies

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723.33km², which is about 77.44% of total area, 4% area occupies by water bodies, 8% by wasteland, 10% by built up area and 0.014% by tree-clad area. The average rainfall in the study is 1219mm. In study area context the classification and distribution of soil is adopted as per the soil orders in US soil taxonomy and there Indian equivalents. Out of 12 orders in US soil taxonomy only four orders are viz. Vertisol, Ultisol, Inceptisols and Alfisol are found in Chhattisgarh.

3. Materials and Methods 3.1. Data source

For Kulhan watershed G 64- 10, 11, 12, 14, 15, 16 numbers of toposheets with 1:50000 are used and these are collected from Survey of India, Raipur. SRTM DEM of 90m resolution is used in the present study. For the present study land use land cover map and Soil map were obtained from CCOST, Raipur. Land use land cover features have been precisely captured through on-screen visual interpretation and digitally on fused very high resolution and medium to coarse resolution (LISS IV, LISS III and Landsat) satellite imagery. Soil map is geographical representation of showing diversity of soil type and soil properties in the area of interest.

3.2. Method

In the present study GIS analysis is carried out by Arc GIS software and using RS data. Kulhan watershed is delineated from SRTM DEM 90m resolution raster data and the drainage for Kulhan watershed were developed using toposheets. USLE requires five parameters such as R factor, K factor, LS factor, C factor and P factor, these are obtained from meteorological data, field measurement data as well as from remote sensing data. The rainfall erosivity factor determined from annual rainfall data of study area by interpolation technique through GIS approach.

The soil survey data was used to develop the soil erodability factor by extracting texture and organic matter content in soil and LS factor is generated by DEM of study area. Land use and land cover information has been obtained from Landsat satellite imagery of 30 m resolution of the study area and values of cover management factor and support practice factor were obtained from land use land cover map. Methodology adopted in the study is shown in figure 2.

Fig. 2. Procedural Flow Chart of the study

4. Results and Discussion

USLE analysis includes R factor, K factor, LS factor, C factor and P factor values which are determined using following approaches and maps are generated using obtained R, K, LS, C and P values and finally these values are integrated to generate final soil erosion map.

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4.1. Rainfall erosivity (R) factor

R factor is an erosion index for the given storm period in MJ.mm/(ha.hr.year). R factor is determined for selected rain gauge stations of study area over a period of 25 years from equation 1 and these values are interpolated spatially through GIS technique and R factor map is generated is shown in figure 3.

0.5

R uP (1)

Where, P= mean annual rainfall in mm and R= rainfall erosivity factor in MJ/ha.mm/h. Mean annual rainfall data of 32 rain gauge stations of the study area were used in the study. The rainfall erosivity value shows that R value is higher where rainfall intensity is found to be more and vice versa.

4.2. Soil erodability (K) factor

It is depends upon organic matter, soil texture and soil structure. The total volume of potassium dichromate used to oxidize the organic matter in the soil andthe volume of organic matter present in the oven dried sample is determined (IS: 2720, part-22). Soil erodability (K) of the study area was calculated using the relationship between soil texture class and organic matter content proposed by Stone and Hillborn (2000) and also Wischmeire et al.

(1971). The K factor map for study area is shown in figure 4. The soil erodability factor values assigned to different texture classes using GIS technique.

Fig. 3. R factor map Fig. 4. K factor map Fig. 5. LS factor map

By the standard soil class as suggested by USDA in soil survey manual and standard texture class through laboratory experiments it was found that study area have five major types of soil namely clay loam, gravelly sandy clay loam, sandy clay loam and sandy loam. According to soil survey manual soil erodibility factor represents both susceptibility of soil to erosion and rate of runoff, as measured under the standard unit plot condition. The value of K factor is affected by infiltration capacity and structural stability of the soil. The K value varies from1.0 to 0.01with highest values for soils with high content of silt or very fine sand. Soil of study area has moderate K factor value, about 0.33 to 0.14, Therefore soil particles are moderately susceptible to detachment and the produce moderate runoff.

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4.3. Topographic Factor (LS)

LS factor is the combination of Length factor and Steepness factor. The influence of terrain on erosion is represented by length slope factor which reflects the fact that erosion increases with slope angle and slope length.

SRTM DEM of 90m resolution is used to calculate LS factor, steeper the slope more will be the loss.Length factor (L) according to Desmet & Govers (1996) and slope factor (S) according to Smith & Wischmeire (1978) were calculated.

In this study DEM is used to calculate LS factor. For study area maximum slope is observed to be 10.49%, so this area is considered as moderate sloping area as from slope classes and LS factor map is generated and shown in figure 5. It was found that the minimum slope is at main stream channel nearly about 0-0.75%. According to slope map it was observed that slope at the study area is moderate. Analysis of the topographic factor is very important in USLE application, since this parameter characterizes surface runoff speed and therefore it is an indicator of soil erosion risk in watershed.

4.4. Cover management factor (C)

It is the ratio of soil loss from an area with specified cover and management to that from an identical area in tilled continuous fellow. Vegetation cover and management data is determined with the help of land use land cover map obtained from Landsat imagery. Table 1 shows C factor value obtained from past researches and this C factor values are assigned to each individual grid of watershed and obtained C factor map shown in figure6. The total geographical area of Kulhan watershed is 934 km² out of which agriculture occupies about 77.44% of total area.

Cfactor map shows that study area consists of high percentage vegetation cover which will restrict soil erosion in greater extent.

Table 1.C factor value

Land cover classes C factor

agricultural land 0.34

built-up land 0.2

tree clad area 0.001

waste land 0.4

water bodies 0

4.5. Support practice (P) factor

It is the ratio of soil loss with specific support practice to the corresponding loss with up and slope tillage. The factor is an expression of the effect of specific conservation practice in soil such as contouring, strip cropping, terracing and subsurface drainage. These practices affect erosion by modifying the flow pattern, grade or direction of the surface runoff and by reducing the amount and rate of runoff. The P value for the study area ranging from 0.1 to 1 and P factor map is obtained from LULC map is showing in figure7.Support practice factor values are obtained from various research works and assign to land cover classes and adopted value of P for different land use is shown in table 2. Erosion loss is more with high P factor.

Table 2. P factor value for study area.

Land cover classes P factor

agricultural land 0.4

built-up land 1

tree clad area 0.1

waste land 1

water bodies 0.5

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Fig. 6. C factor map Fig. 7. P factor map Fig. 8. Soil erosion map.

4.6. USLE model result

The average annual soil erosion potential (A) is computed by multiplying the developed raster data from each USLE analysis (A= R K L S C P). The average annual R factor values vary from 90.54 to 180.112 MJ.mm ha^-1 h^-1, with a mean value of 135.326 MJ.mm ha^-1 h^-1. The combined spatial distribution of LS factor is derived using the DEM of the study area. LS factor varies from 0.11 to 68.215 and C value in the study area varies from 0 to 0.4 and support practice factor P value is observe in between 0.1 to 1. The final USLE map displays the average annual soil erosion potential (A) of the Kulhan watershed is shown in figure 8.Various researches indicate that LS factor play very important role in USLE while finding the erosion rate of soil and also LS factor is the most difficult factor in USLE model. When natural vegetation is cleared and when farmland is ploughed, the exposed topsoil is often blown away by wind or washed away by rain. Results shows that the study area has gentle slope so the erosion loss is obtained with low rate and it is within acceptable limit.

The highest value of estimated soil erosion potential is 556 ton/ha/yr. The mean annual soil loss for the entire watershed area is 0.1783 ton/ha/year and also it is observed that maximum soil loss is occurred on the main stream, because of high length and steepness factor value (68.16) as well as slope value ranging from 0 to 10.49%. Total soil loss is calculated to be 16556 ton/year is moved from the watershed. Soil erosion map is reclassified according to erosion risk classes suggested by Singh et al. (2002) for Indian conditions and output map of soil erosion index is generated. According to erosion risk classes it is observed that 78433.12 ha area is under slight class whereas only 425.78 ha. area is under very severe class as shown in table 3.

Table3.Soil loss classification according to area.

Area (ha.) Class

78433.12 Slight

8232.81 Moderate

41.37 High

1438.63 Very high

731.44 Severe

425.78 Very severe

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5. Conclusion

It is observed that the soil erosion for Kulhan watershed is very less (0.1783 ton/ha/year) because slope of the study area is gentle undulating about 10.49% and most of the area (78%) is occupied by agricultural land. It is found that 83.97% of total area is under slight erosion risk class and only 0.45% of total area under very high severe class and this is observed near the bank of mainstream line of the watershed. The erosion risk in the study area is within the acceptable limit and this result will be helpful for decision making process also. With the use high resolution satellite data it is possible to achieve more accurate results. Due to the lack of field data, validation of developed model is not been carried out in this study. The study proves that soil erosion USLE model in combination with GIS is an efficient tool to handle large volume data needed for watershed soil loss studies.

Acknowledgement

The authors are thankful to CCOST, Survey of India (SOI), Raipur, Chhattisgarh. for the data provided by the concerned departments.

References

Chandramohan T. and Dilip G. Durbude, 2002. Application of GIS for the demarcation of potential soil erosion zones using universal soil loss equation, Journal of Indian Society of Remote Sensing (Photonirvachak), vol. 30 (4), pp. 181-190.

Desmet& Govers,1996.A GIS Procedure for Automatically Calculating the USLE LS Factor on Topographically Complex Landscape Units, Journal of Soil and Water Conservation, vol. 51(5), pp.427-433.

De Roo, A.P.J., Jetten, V.G., 1999. Calibrating and validating the LISEM model for two data sets from the Netherlands and South Africa. Catena vol. 37, pp. 477-493.

Haan, C.T., B.J. Barfield, and J.C. Hayes,1994. Design Hydrology and Sedimentology for Small Catchments. Academic Press, San Diego, California, USA.588 pp.

Hongming Zhang, QinkeYang, RuiLi, QingruiLiu, DemieMoore, PengHe, CoenRitsema, J., VioletteGeissen, 2012. Extension of a GIS procedure for calculating the RUSLE equation LS factor, J. Computers & Geosciences, vol. 52, pp.177–188.

Jasrotia, A. and Singh, R., 2006. Modeling Runoff and Soil Erosion in a Catchment Area, using the GIS, in the Himalayan Region, India.

Environmental Geology, vol.51, pp. 29-37.

Joon-Hak Lee, Jun-HaengHeo, 2011. Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea, Journal of Hydrology, vol. 409, pp.30–48.

Morgan, R.P.C., Morgan, D.D.V., and Finney, H.J., 1984. A predictive model for the assessment of soil erosion risk,J.Agric. Engg. Res. vol. 30, pp. 245-253.

Narayana D. and Babu R., 1983. Closure to Estimation of soil erosion in India, J. Irrig. Drain Eng., vol. 111(4), pp. 408-410.

Ozcan, A.U., Erpul, G., Basaran, M., Erdogan, H.E., 2008. Use of USLE/GIS technology integrated with geo-statistics to assess soil erosion risk in different land uses of Indagi Mountain Pass–Çankiri, Turkey. Environmental Geology, vol. 53, pp. 1731–1741.

Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder (1997).Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE).Agriculture Handbook No. 703.US department of Agriculture, Agricultural Research Service, Washington, District of Columbia, USA.404 pp.

Singh et al., 2002.Prioritization of Bata river basin using RS and GIS techniques, Indian journal of soil conservation, vol. 30 (3), pp. 200-205.

Singh, G., Ram Babu, Narain, P., Bhushan, L.S. and Abrol, I.P., 1992. Soil erosion rates in India, J. Soil & Water Conservation, vol. 47 (1), pp.

97-99.

Sreenivasalu Vemu U. B. P., 2012. Sediment yield estimation and prioritization of watershed using RS and GIS, Intenational archives of the photogrammetry, Remote sensing and spatial information science, vol.B8.

Stone, R. P., and D. Hilborn, 2000.Universal Soil Loss Equation (USLE), Ontario Ministry of Agriculture, Food and Rural Affairs Factsheet.

Wischmeier, W.H., and D.D. Smith,1978.Predicting Rainfall Erosion Losses - A Guide to Conservation Planning. Agriculture Handbook No.

537.Agriculture Science and Education Administration, Washington, District of ColumUSA.

Wischmeier, W.H., Johnson, C.B., Cross, B.V., 1971. A soil erodibility nomograph for farmland and construction sites.Journal of Soil Water Conservation. Vol. 26, pp. 189–193.