Application of USLE Model & GIS in Estimation of Soil Erosion for Tandula Reservoir Ishtiyaq Ahmad 1, Dr. M. K. Verma 2 1 Ph.D. Research Scholar, Dept. of Civil Engg. NIT Raipur (C.G.) - India 2 Prof. & Head, Dept. of Civil Engg. NIT Raipur (C.G.) - India Abstract Assessment of soil erosion is expensive and intensively long exercise. A number of parametric models have been developed to forecast soil erosion at drainage basins, yet Universal Soil Loss Equation, popularly known as USLE model is most widely used empirical formula for estimating annual soil loss from agricultural basins. With the advance of Remote Sensing technique it becomes possible to measure hydrologic parameters on spatial scales while Geographic Information System integrates the spatial analytical functionality for spatially distributed data. In the present paper the application of USLE model and GIS, for soil loss estimation has been presented for the Tandula reservoir catchment area on Tanudula River at Balod Tehsil of Durg district of Chhattisgarh State, India. The result obtained from USLE model has been compared with existing model, Nayak and Khosla;s method, it is observed that USLE with GIS give better result as compared to other two methods. Keywords GIS, ILWIS, Map, Soil Erosion, USLE. I. INTRODUCTION This document is template. We ask that authors follow some simple guidelines. In essence, we ask you to make your paper look exactly like this document. The easiest way to do this is simply to download the template, and replace(copy-paste) the content with your own material. The soil has been defined by the International Soil Science Society as: The soil is a limited and irreplaceable resource and the growing degradation and loss of soil means that the expanding population in many parts of the world is pressing this resource to its limits. In its absence the biospheric environments man will collapse with devastating results for humanity [2]. Recent observations in India have brought in to light the alarming fact that reservoir sedimentation, resulting from degradation of the watersheds is on manifold rise compared to the rate that was assumed at the time the projects were designed [6]. This leads to watershed deterioration which renders fertile lands barren, reduction in storage capacity of the dams and hence reduction in their operational life [1]. The main factors causing soil erosion are climate, soil, vegetation, topography and man [5]. 570 Of these, vegetation and to some extent soil and the topography may be controlled. The climatic factors and also the topographic and soil factors are beyond the power of man to control. Scientific management of soil, water and vegetation resources on watershed basis is very important to arrest erosion and rapid siltation in rivers, lakes and estuaries [9]. Because land management practices create a variety of conditions that influence the magnitude of surface erosion, land managers frequently want to predict the amount of soil loss by surface erosion. Several models are available for predicting erosion. USLE model and ILWIS3.0 GIS has been used for determining the quantity of soil erosion. The ancillary data on landuse/land cover was interpreted from IRS 1C, LISS-3 digital data of the catchment area. ILWIS 3.0 GIS package has been used as the core of the spatial database and analysis. II. STUDY AREA TANDULA RESERVOIR The Tandula complex project is one of the major river projects of Chattisgarh State. The Tandula reservoir is situated in Balod Tahsil of Durg district at about 5 km. from the Balod city. This reservoir has been developed by constructing a dam on the confluence of Sukha nala and Tandula River. The construction of the dam was started in the year 1910 and completed in 1921 [3]. The total catchment area of the reservoir is about 302.7788 sq. mile (787.44 sq. km.). The length of the dam is 14,500 ft. The water-spread area of the reservoir at full supply level is 11,392 acre. The Low Supply Level (LSL) and Full Tank Level (FTL) of the reservoir are 1051.33 ft and 1089.33 ft respectively. The Highest Flood Level (HFL) and the Top Bund Level (TBL) have been designed as 1093.88 and 1099.83 ft respectively. The average monsoon rainfall adopted for the project is 54.99 inch. The total capacity of the reservoir is 312.285 million cubic meter, while the live and dead capacities of the reservoir are 302.316 million cubic meter and 9.969 million cubic meter respectively. The length of the main canal and distributaries are 110 km. and the total length of minors is 880 km. The reservoir has been designed to irrigate about 68,219 hectare of kharif crop.
The mean monsoon rainfall in the area is about 50.9 inch. An index map showing Tandula reservoir has been presented in Fig. 1. Fig. 1 Index map of Mahanadi Basin showing Ravishankar Sagar Reservoir R R 79 0. 363 (2) a X a 50 0. 389 (3) s X s B. Soil Erodibility Factor (K) The factor quantifies the cohesive character of a soil type and its resistance to dislodging and transport (particle size and density dependent) due to raindrop impact and overland flow sheer forces. The soil erodibility factor, Wischmeier and Smith [9] is a function of complex interaction of a substantial number of its physical and chemical properties. C. The Topographic Factor (L & S) Steeper slopes produce higher overland flow velocities. Longer slopes accumulate runoff from larger areas and also result in higher flow velocities. Thus, both result in increased erosion potential, but in a non-linear manner. Degree of slope factor (S) is the ratio of soil loss on actual gradient to that from 9% slope under identical conditions. Different works have evaluated the exponent for variation of soil loss with percent slope, such as 1.49, 1.35 [10]. The equation for estimating the slope gradient and slope length factor is: III. UNIVERSAL SOIL LOSS EQUATION MODEL Effective control of soil erosion requires an ability to predict the amount of soil loss, which would occur under alternate management strategies and practices. The model with the greatest acceptance and use is the Universal Soil Loss Equation (USLE), developed by Agriculture Research Services (ARS) scientists Wischmeier and D. Smith [2]. While newer methods are now becoming available, most are still founded upon principles introduced by the USLE. The USLE states that the field soil loss in tons per hectare, A, is the product of six causative factors [7]: A R K L S C P (1) A. Rainfall Erosivity index (R) Please check with your editor on whether to submit your manuscript by hard copy or electronically for review. If hard copy, submit photocopies. The rainfall erosivity index implies a numerical evaluation of rainfall pattern, which describes its capacity to erode soil from an unprotected field. In India, simple relationship between erosivity index (R) and annual or seasonal rainfall (X) has been developed by Singh et al, 1981 after analyzing the data collected from 45 stations distributed in different rainfall zones throughout the country [2]. The relationship can be expressed by the following equations. 571 S L 22.13 0.5 2 (4) 0.065 0.045 G 0.0065 G D. Cropping Management Factor (C) This factor is the ratio of soil loss from land cropped under specified conditions to corresponding loss under tilled, continuous fallow conditions. It measures the combined effect of vegetation cover and management variables. E. Conservation Practice Factor (P) The conservation practice factor P, in the Universal Soil Loss Equation is the ratio of soil support practice to the corresponding loss with up and down slope culture. Practices induced in this term are contouring, strip cropping (alternate crops on a given slope established on the contour), and terracing. The value of P ranges from 1.0 for up and down cultivation to 0.25 for contour strip cropping of gentle slope. IV. APPLICATION OF ILWIS 3.0 (THE GIS TOOL) All the factors required for soil erosion estimation as given in the equation 1 were calculated using ILWIS 3.0 GIS software and stored as thematic maps in raster format. These maps were then multiplied together to generate the soil erosion map using Map Calculation operation in 015.
The map was assigned the minimum value of 10 tones/ha/year and slicing operation was applied to generate classified map of various erosion class such as 0-10, 10-20, 10-20, 20-40, 40-80, 80-120, >120 etc. Total soil loss from the catchment area was estimated by collecting histogram of the erosion map. The histogram provides total number of pixels falling in each erosion intensity, for example, 288267 pixels in <10.0 tones/ha/year; 10366 pixels in 10-20 tones/ha/year etc. The number of pixels was then multiplied with the corresponding mid value of erosion intensity (i.e. 2.5, 7.5, 12.5 tones/ha/year etc.) to get the total soil loss. Similarly, the geographical areas under different categories of erosion were then calculated by collecting histogram of the classified map. A flow chart describing briefly the procedure for estimation of soil erosion from a catchment area is given in Fig. 2. SPATIAL DATA NON-SPATIAL DATA Raingauge Station Contour Map Landuse/Land cover Soil Map Map Theissen Polygon Map Rainfall Map 'R' Factor Map DEM Map Slope Map 'LS' Factor Map Cultivated land Map 'P' Factor Map 'K' Factor Map 'C' Factor Map A = R x K x L x S x C x P Soil Erosion Map Soil Erodibility Factor Cropping Mang. Factor Conservation Practise Factor Rainfall Data Fig. 2. Procedure for Estimation of Soil Erosion by USLE & GIS V. DATA PROCESSING & PARAMETER ESTIMATION A. Base Map A base map has been generated by digitizing the Survey Of India (SOI) toposheet as reference map for all other purposes. The watershed boundary was marked on the basis of the contours and the drainage lines available on the SOI topographic map. The drainage lines were also marked on the base map of the basin as shown in the Fig. 3. Fig. 3. Base Map of Tandula Reservoir The digital base map was stored as thematic layer in GIS for further analysis. The basin boundary is digitized and stored as basin map in vector format. The map was then polygonised and converted into raster format assigning the pixel size of IRS 1C LISS-III satellite data, i.e., 24 m. Total geographical area of the reservoir catchment is 787.443 sq. km. including 43.227sq. km. water spread area. Therefore, the land surface considered for estimation of soil erosion remains 744.21 sq. km. only. B. Rainfall The seasonal rainfall data recoded at Tandula dam site (Amadabad Raingauge stations) were obtained from Water Resources Department of Tandula Circle and Land Records office located at Durg. In Tandula dam site only one raingauge station so here is not necessaries for make Thiessen polygon map. The Rainfall Factors corresponding to rainfall values were calculated using the equation 3. 572
S.No. C. Soils International Journal of Emerging Technology and Advanced Engineering Total Five categories of soils fall in the study area, namely clay loam, fine sandy loam, loam, silty clay and silty clay loam. The organic matter (O.M.) contents in these soils are reported about 2%. The Fig. 4 shows the soil map and value of K factor and the geographic area under each soil class has been given in Table I. TABLE I Distribution of Soil Class and K Value in Tandula Catchment Soil Class No. of pixels (Sq. km.) (%) K-Factor 1 Silty Clay 15167 37.917500 4.82 0.25 2 Clay Loam 17079 42.697500 5.42 0.28 3 Fine Sandy Loam 48173 120.43250 15.29 0.35 4 LoamyFine sand 12477 31.192500 3.96 0.24 5 Sandy Loam 18312 45.780000 5.81 0.27 6 Sandy Clay 82980 207.45000 26.34 0.14 7 Sandy Clay Loam 94642 236.60500 30.05 0.35 8 Loam 8856 22.140000 2.81 0.38 9 Water body 17291 43.227500 5.49 0.00 Total 314977 787. 44250 100 The soil map digitized was converted into raster format first and then the map was attributed to the table containing K factor values to generate the kfactor map. The kfactor map is a raster map showing K value for each and every pixel. D. Topography Contour lines and spot heights given in the Survey of India topographic maps is the only source of information on topography of the study area. To create a Digital Elevation Model (DEM) map, interpolation of the segment map combine was done via the operation InterpolSeg in ILWIS 3.0 GIS. Contour Interpolation: Contour interpolation first rasterizes contour lines in the segment map. This results in values for all pixels that are located on the segments. Then values have to be calculated for pixels that fall in between the segments. For each undefined pixel, the distance is calculated towards the two nearest contour lines. The distances are calculated forwards and backwards, until no more changes occur. Then a linear interpolation is performed using the two distance values. This returns the value for an undefined pixel. Slope Calculation: The DEM map thus generated is a raster map showing the elevation or height above mean sea level of each pixel in the study area. Fig. 5 shows the DEM map of Tandula catchment. Fig. 4. Soil Map of Tandula Catchment 573 Fig. 5. DEM Classified Relief Map of Tandula Catchment
S.No International Journal of Emerging Technology and Advanced Engineering The slope length was taken same as the pixel size, i.e. 24 m for the calculation of LS factor. LS factor map was calculated using the equation 4 by putting the value of the slope length and the degree of slope. The slope factor map was stored as LS factor in raster format shown in Fig. 6. The land use classes include Agriculture, barren/grazing lands, open forest and shrubs/bushes. The land use map thus classified from satellite data is shown in the following Fig. 7 and spatial distribution of all the five land use classes is given in the Table II. TABLE II LAND USE DISTRIBUTION IN TANDULA CATCHMENT AND C FACTOR Land Use Class No. of pixels (Sq. km.) (%) K-Factor 1 Agriculture 137872 344.690000 43.78 0.28 2 Barren land 4486 11.235000 1.42 0.75 3 Forest 135528 338.830000 43.03 0.05 4 Mining activity 2411 6.027500 0.77 1.00 5 Shrubs land 13064 32.670000 4.15 0.15 6 Settlement 2195 5.497500 0.70 0.25 7 Water body 19393 48.492500 6.16 0.00 Total 314949 787.442500 100.00 Fig. 6. LS Factor Map of Tandula Catchment F. Conservation Practice Factor P The classified slope map slope class was attributed to P factor values to create a raster map of conservation practices factor. The non-agricultural lands (forest, shrubs/bushes and barren/grazing land) were assigned value 1.0 for P factor. After assigning the P factor values for agricultural and non-agricultural lands, the output map was named as pfactor. The pfactor map thus created is given in Fig. 8. Fig. 7. Land Use/Land Cover Map of Tandula Catchment E. Land Use Indian Remote Sensing Satellite 1-C, LISS-III, Path 102- Row 58 covers the entire catchment area. The digital image processing of LISS-III data of the year 2001-02 was carried out to prepare the land use map of the Tandula catchment. 574 Fig. 8. P Factor Map of Tandula Catchment
S.No International Journal of Emerging Technology and Advanced Engineering G. Estimation of Soil Erosion The various factors responsible for soil erosion, i.e. R, K, L, S, C and P were brought in the form of raster maps as stated earlier. The estimated soil loss map USLE was further classified into defined group of erosion intensities to create the classified expected soil loss map usleclfy and the same has been presented in Table III. The maximum intensity of estimated soil loss has been estimated to be 710 tons/ha/year. The map shown in Fig. 9 gives the distribution of various categories of estimated soil loss over the catchment area. TABLE III AREA UNDER DIFFERENCE CATEGORIES SOIL EROSION CLASS IN TANDULA CATCHMENT Land Use Class No. of pixels (Sq. km.) (%) K-Factor 1 Slight 0 to 10 288267 720.667 43.78 2 Moderate 10 to 20 10366 25.915 1.42 3 High 20 to 40 7283 18.207 43.03 4 Very high 40 to 80 4582 11.455 0.77 5 Severe 80 to 120 2350 5.875 4.15 6 Very severe > 120 2129 5.3225 0.70 Total 314977 787.443 100.00 VI. RESULTS & DISCUSSION The sedimentation value of Tandula Reservoir estimated by T.R. Nayak using Satellite Remote Sensing [8] and by Khosla s Method [4] has been used to compare the result obtained from Universal Soil Loss Equation. Soil Erosion by USLE: The quantity of actual soil erosion calculated by USLE model comes out to be 490615 tones/year. This value can be converted in terms of volume by dividing the same with the specific gravity of the sediment load, i.e. 1.05 tones/m3. Thus, the soil erosion from the Tandula catchment will be 467252 m3/year. Sedimentation Yield of Tandula Reservoir: A brief account of estimation of sedimentation yield for Tandula Reservoir [8] is as follows; the sedimentation in grass capacity has been reported as 792.637 Mcu.ft. If we assume a constant rate of sedimentation over the period of 80 years, it comes out to be 28.056 ha-m/year. Thus sedimentation yield is calculated as 294588 tons/year, assuming sediment density 1050 Kg/m3. Khosla s Method: Khosla analysed the data from various reservoirs in India and abroad and observed that the annual rate of sediment deposition decreased with the age of reservoirs. He plotted curve between the annual sediment deposited and the catchment area and suggested the following empirical formula. 0.323 Q s (5) 0.28 A By applying the Khosla s formula, the annual sediment deposit for Tandula catchment was found to be 396286.479 tons/year. Thus, the estimated soil loss from the Tandula catchment using USLE, compared with Sediment yield of Tandula Reservoir [8] and Khosla s Method is as follows: Method Used By USLE By Nayak Khosla s Method Soil Loss in tons/year 490615 294588 396286.479 Fig. 9. Estimated Erosion Class Map of Tandula Catchment 575 VII. CONCLUSIONS The soil erosion can be controlled effectively if it is predicted accurately under alternate management strategies and practices. The Universal Soil Loss Equation model has been accepted and used most widely all over the world to predict the soil erosion from a watershed.
In India also many researchers have applied the model and different model parameters (factors) have been estimated for Indian conditions. The estimated soil erosion from Tandula catchment estimated using USLE has been compared with two empirical formulae, namely Sediment yield of Tandula Reservoir (Satellite Remote Sensing study by T.R.Nayak) and Khosla s Method and also with the Sedimentation analysis using Remote Sensing data. APPENDIX: LIST OF SYMBOLS A : soil loss in tons per hectare R : rainfall erosivity index K : Soil erodibility factor L : length of slope factor S : degree of slope factor C : cropping-management factor P : conservation practice factor Ra : annual erosivity index Xa : average annual rainfall (mm) Rs : seasonal erosivity index Xs : average seasonal rainfall (mm) : length of the field in meters G : Slope gradient in percent Qs : annual rate of sediment deposition REFERENCES [1] V. K. Choubey, Assessment of Sediment Distribution Pattern in the Tungabhadra Reservoir Using Satellite Imagery, Journal of the Indian Society of Remote Sensing, Vol. 22, No. 2, 1994, pp. 103-111. [2] M. K. Choudhary and T. R. Nayak, Estimation of Soil Erosion in Sagar Lake Catchment of Central India, Watershed hydrology: proceedings of the International Conference on Water and Environment (WE-2003), December 15-18, Bhopal, India, 1994, pp. 387 [3] S. Jain, P. Agrawal and V. Singh, Mahanadi, Subernarekha and Brahmani Basins. Hydrology and Water resources of India, Water Science and Technology Library, Vol. 57, No. 3, 2007, pp. 597-639. [4] A. N. Khosla, Silting of Reservoirs, CBI & P, New Delhi. 1953. [5] R.P.C. Morgan, Soil Erosion and Conservation, Longman Group Limited, U.K., 1986, pp. 63-74. [6] Narayana, V. V. Dhruva and R. Babu, Estimation of Soil Erosion in India Journal of Irrigation and Drainage Engineering, ASCE, Vol. 109, No. 4, 1983, pp. 419-433. [7] J. P. Nema, B. Verma and A. P. Patel, Predicting Universal Soil Loss Parameters, Indian Journal of Soil Conservation, Vol. 6, No. 2, 1978, pp. 75-79. [8] T. R. Nayak and R. K. Jaiswal, Rainfall-Runoff Modelling using Satellite Data and GIS for Beas River in Madhya Pradesh, Journal of Institution of Engineers India, Vol. 84 (May), 2003, pp. 47-50. [9] W. H. Wischmeier and D. D. Smith, Predicting Rainfall Erosion Losses- A Guide to Conservation Planning, Agriculture Handbook, No. 537, U.S. Dept. of Agriculture, Washington D.C., 1978. [10] G. W. Musgrave, The Quantitative Evaluation of Factors in Water Erosion: A First Approximation, Journal of Soil and Water Conservation, Vol. 2(3), 1947, pp. 133-138. 576