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Int.J.Curr.Microbiol.App.Sci (2020) 9(10): 2048-2051 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 10 (2020) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2020.910.250 MUSLE Model Approach for Soil Loss Studies in Tonshyal Micro Watershed G. Er. Shwetha*, S. S. Shirahatti and M. S. Shirahatti Faculty of Agricultural Engineering, College of Agriculture, Vijayapur, UAS Dharwad, India *Corresponding author ABSTRACT Keywords MUSLE; QGIS, Runoff Article Info Accepted: 17 September 2020 Available Online: 10 October 2020 Soil erosion depletes the productive layer of soil which is an ultimate resource for all agricultural activities. Since erosion rate varies with watershed characteristics, a study of soil and water dynamics at a watershed level can facilitate a scientific approach toward their conservation and management. The study involves obtaining peak runoff and input parameters (K,LS,C & P) of Modified Universal Soil Loss Equation (MUSLE) integrating GIS platform to estimate soil loss of Tonshyal micro watershed which lies between 16º 30’- 16º 45’ latitudes and 75º 30’- 75º 45’ longitudes with area of 269.42 ha, watershed code of 4D7A5E2. The base map and thematic maps were prepared using Survey of India toposheet No. 47 P/10 with scale of 1:50000 in a QGIS environment. The study area contains Very deep (>150 cm), Clay soils occurring on very gently sloping (1-3%), Clay soils occupy in entire micro-watershed. The calculated data and factors that were considered in the present study summarized and applied in MUSLE model for estimating annual soil loss. Introduction Soil loss is a process wherein soil features are disturbed influenced by variable parameters like rainfall, runoff. The process threatens natural resources and environment. Soil loss is deeply fallen because of biophysical and socioeconomic factors, e.g., pasturage, agriculture system, and impoverishment (Hoffman and Todd, 2000). In India, an area of about 175 M ha out of the total land area of 328 M ha, accounted nearly 53 percent of the total land area is prone to soil erosion (Upadhyay et al., 2012). It is estimated that about 5334 Million tons (16.4 t ha−1) of soil is detached annually in India out of which about 29 percent is carried away by river into the sea and 10 percent is deposited in reservoirs resulting in the considerable loss of the storage capacity. Thus quantification of soil loss is a significant issue for soil and water conservation practitioners and policy makers. According to Martin and Saha (2007) the most effective way of estimating soil loss is to integrated remote sensing and GIS. RS is a technique significantly provides the input parameters necessary in MUSLE model. In GIS platform database for the catchment 2048 Int.J.Curr.Microbiol.App.Sci (2020) 9(10): 2048-2051 which is very much useful for carrying out spatial analysis thereby can be created effectively (Shwetha et al., 2016) C = Cover and management factor and P = Supporting conservation practice factor The peak discharge (Qp) was calculated through the equation Materials and Methods Study Area Qp = Tonshyal micro-watershed is located in Vijayapur district and geographically lies between 16º30’ and 16º45’ N latitude and 75º 30’ and 75º 45’ E longitude with an area of 269.2 ha as delineated from Survey of India (SOI) toposheet No. 47 P/10. Figure 1 shows the location map of the study area. Data Used The Survey of India topographic map no 47 P/10 was used for the demarcation of the watershed boundary on 1:50000 scale. The rainfall data of Meteorology Department, RARS Vijayapur. The soil information was collected from the soil map prepared by Karnataka State Remote Sensing Application Centre (KSRSAC). Morphometric analysis was done to understand the characteristics of watershed and further study was done based on the results obtained (presented in Table 1). The thematic maps were generated keeping the toposheet map as a base. MUSLE model (0.208 × A × Q) (0.5 × D + 0.6 t c ) Where A = Basin size (km2) Q = Depth of runoff (mm) D = Duration of storm in hours, assumed as 24 hours, and tc = Concentration time in hours calculated through standard formulae Kirpich equation: t c = 0.0195 × L0.77 × S-0.385 Where A = Basin size (km2) S = Average channel slope (m m-1) L = Length of channel from divide to outlet (km) Peak discharge obtained using kirpich time of concentration, runoff volume along with four potential and actual soil erosion-controlling factors K, LS, C and P were integrated as in MUSLE to obtain soil loss. Results and Discussion A = 11.8 × (Q × Q p ) 0.56 × K × LS × C × P Where Thematic layers obtained by the digitization in QGIS platform is presented in Figure 2. A = Annual soil loss (t km-2 yr-1) Q = Runoff volume (mm3) Qp = Peak discharge in cubic metres per second (m3 s-1) K = Soil erodability factor L = Slope length factor S = Slope steepness factor Annual soil loss is estimated from the product of factors (Q, Qp, K, LS, C and P) which represents geo-environmental scenario of the study area. The values of soil loss obtained through the MUSLE model are presented in Table 2. 2049 Int.J.Curr.Microbiol.App.Sci (2020) 9(10): 2048-2051 Table.1 Linear aspects of drainage network Parameters Stream order II III 15 5 5 5 I 68 4.53 No. of streams Bifurcation ratio (Rb) Total IV 1 - 89 Table.2 Estimated soil loss using MUSLE model K L.S C P 0.12 2.2 0.655 0.55 MUSLE factors Time of concentration(min) 53.15 Peak discharge (m3s-1) 1.58 Fig.1 Location map of the study area Fig.2 Thematic layers of watershed Fig. 1. (a) Cadastral Map of Tonshyal Micro- Fig.1. (b) Base Map of Tonshyal Micro-Watershed Watershed Fig.1. (c) Contour Map of Tonshyal Micro- Fig.1. (d) DEM Extracted- Map of Tonshyal Micro- Watershed Watershed Fig.1. (e) Drainage Map of Tonshyal Micro- Fig.1. (f) Satellite view Map of Tonshyal Micro- Watershed Watershed 2050 Soil loss (t ha-1yr-1) 1.773 Int.J.Curr.Microbiol.App.Sci (2020) 9(10): 2048-2051 In conclusion the soil erosion can be controlled effectively if it is predicted accurately with different available management strategies. Thus application of MUSLE is appropriate as it produces reasonable estimate of the soil loss from a watershed. Integrating the model with remote sensing and GIS technology for obtaining the input parameters makes the work more accurate. References Bhattarai R, Duttta D (2007) Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resource Management 21(10):1635– 1647 Hoffman M.T. and Todd S., 2000, A national review of land degradation in South Africa: The influence of biophysical and socio-economic factors, Journal of Southern African Studies, 26 (4): 743758 Martin, D. and Saha, S.K. 2007. Integrated approach of using remote sensing and GIS to study watershed prioritization and productivity, Journal of the Indian Society of Remote Sensing, 35(1), 2130. Shwetha G, Maheshwara Babu, Srinivas Reddy G. V., Aruna K. T. and Shanwad U. K., 2016, Soil Loss Estimation through Mathematical Models Using GIS and RS Techniques, International Journal of Agricultural Science and Research, 6(3):251258(NAAS: 3.53) Upadhyay, R., Pandey, K., Upadhyay, S. K. and Bajpai, A., 2012, Annual sedimentation yield and sediment characteristics of upper lake, Bhopal, India. J. Chem. Sci., 2(2): 65-74. How to cite this article: Er.Shwetha, G., S. S. Shirahatti and Shirahatti, M. S. 2020. MUSLE Model Approach for Soil Loss Studies in Tonshyal Micro Watershed. Int.J.Curr.Microbiol.App.Sci. 9(10): 2048-2051. doi: https://doi.org/10.20546/ijcmas.2020.910.250 2051