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Soil loss estimation and prioritization of sub-watersheds of Kali River basin, Karnataka, India, using RUSLE and GIS

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Abstract

Most of the mountainous regions in tropical humid climatic zone experience severe soil loss due to natural factors. In the absence of measured data, modeling techniques play a crucial role for quantitative estimation of soil loss in such regions. The objective of this research work is to estimate soil loss and prioritize the sub-watersheds of Kali River basin using Revised Universal Soil Loss Equation (RUSLE) model. Various thematic layers of RUSLE factors such as rainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C), and support practice factor (P) have been prepared by using multiple spatial and non-spatial data sets. These layers are integrated in geographic information system (GIS) environment and estimated the soil loss. The results show that ∼42 % of the study area falls under low erosion risk and only 6.97 % area suffer from very high erosion risk. Based on the rate of soil loss, 165 sub-watersheds have been prioritized into four categories—very high, high, moderate, and low erosion risk. Anthropogenic activities such as deforestation, construction of dams, and rapid urbanization are the main reasons for high rate of soil loss in the study area. The soil erosion rate and prioritization maps help in implementation of a proper watershed management plan for the river basin.

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References

  • Adinarayana, J. (2003). Spatial decision support system for identifying priority sites for watershed management schemes. In First interagency conference on research in the watersheds (ICRW) (pp. 405–408). Arizona: Benson.

  • Aher, P. D., Adinarayana, J., & Gorantiwar, S. D. (2014). Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: a remote sensing and GIS approach. Journal of Hydrology, 511, 850–860.

    Article  Google Scholar 

  • Arekhi, S., Niazi, Y., & Kalteh, A. M. (2010). Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran. Arabian Journal of Geosciences, 5, 285–296.

    Article  Google Scholar 

  • Arnoldus, H. M. J. (1980). An approximation of the rainfall factor in the universal soil loss equation. In M. De Boodt & D. Gabriels (Eds.), Assessment of erosion (pp. 127–132). Chichester: Wiley.

    Google Scholar 

  • Badar, B., Romshoo, S. A., & Khan, M. A. (2013). Integrating biophysical and socioeconomic information for prioritizing watersheds in a Kashmir Himalayan lake: a remote sensing and GIS approach. Environmental Monitoring and Assessment, 8, 6419–6445.

    Article  Google Scholar 

  • Beasley, D. B., Huggins, L. F., & dan Monke, E. J. (1980). ANSWERS: a model for watershed planning. Transactions of the American Society of Agricultural Engineers, 23, 938–944.

    Article  Google Scholar 

  • Bewket, W., & Teferi, E. (2009). Assessment of soil erosion hazard and prioritization for treatment at the watershed level: case study in the Chemoga watershed, Blue Nile Basin, Ethiopia. Land Degradation and Development, 20(7), 609–622.

    Article  Google Scholar 

  • Birasal, N. R. (2014). Do we need some more dams? Past, present and future of freshwater bodies in Karnataka State—a perspective. International Journal of Scientific Research in Agricultural Sciences, 1(3), 32–42.

    Article  Google Scholar 

  • CGWB. (2012). Ground water information booklet, Uttara Kannda District Karnataka (p. 11). Bangalore: Central Ground Water Board.

    Google Scholar 

  • Chen, T., Niu, R. Q., Li, P. X., Zhang, L. P., & Du, B. (2011). Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China. Environmental Earth Sciences, 63, 533–541.

    Article  CAS  Google Scholar 

  • Chowdary, V. M., Yatindranath, S., & Adiga, S. (2004). Modelling of non point source pollution in a watershed using remote sensing and GIS. Journal of Indian Society of Remote Sensing, 32(1), 59–73.

    Article  Google Scholar 

  • Dabral, P. P., Baithuri, N., & Pandey, A. (2008). Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resources Management, 22, 1783–1798.

    Article  Google Scholar 

  • De Jong, S. M., Paracchini, M. L., Bertolo, F., Folving, S., Megier, J., & de Roo, A. P. J. (1999). Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. Catena, 37(3–4), 291–308.

    Article  Google Scholar 

  • Demirci, A., & Karaburun, A. (2012). Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed, northwest Turkey. Environmental Earth Sciences, 66(3), 903–913.

    Article  Google Scholar 

  • Dwarakish, G. S., & Jyothi, K. A. (2009). Preparation of land use/land cover map as an input to universal soil loss equation through remote sensing and GIS. In K. S. Jayappa & A. C. Narayana (Eds.), Coastal environments problems and perspectives (pp. 127–135). New Delhi: I.K. International publishing.

    Google Scholar 

  • Fernandez, C., Wu, J. Q., McCool, D. K., & Stockle, C. O. (2003). Estimating water erosion and sediment yield with GIS, RUSLE and SEDD. Journal of Soil and Water Conservation, 58, 128–136.

    Google Scholar 

  • Foster, G. R., & Lane, L. J. (1987). User requirements: USDA water erosion prediction project (WEPP). NSERL report no. 1. West Lafayette, Ind.: USDA‐ARS National Soil Erosion Research Laboratory.

  • Ismail, J., & Ravichandran, S. (2008). RUSLE2 model application for soil erosion assessment using remote sensing and GIS. Water Resources Management, 22, 83–102.

    Article  Google Scholar 

  • Jang, T., Vellidis, G., Hyman, J. B., Brooks, E., Kurkalova, L. A., Boll, J., & Cho, J. (2013). Model for prioritizing best management practice implementation: sediment load reduction. Environmental Management, 51, 209–224.

    Article  Google Scholar 

  • Karydas, C. G., Sekuloska, T., & Silleos, G. N. (2009). Quantification and site specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete. Environmental Monitoring and Assessment, 149, 19–28.

    Article  Google Scholar 

  • Khadse, G. K., Vijay, R., & Labhasetwar, P. K. (2015). Prioritization of catchments based on soil erosion using remote sensing and GIS. Environmental Monitoring and Assessment, 187, 333. doi:10.1007/s10661-015-4545-z.

    Article  Google Scholar 

  • Khosrokhani, M., & Pradhan, B. (2014). Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS. Geomatics, Natural Hazards and Risk, 5(3), 252–270.

    Article  Google Scholar 

  • King, C., Baghdadi, N., Lecomte, V., & Cerdan. (2005). The application of remote sensing data to monitoring and modeling of soil erosion. Catena, 62(79), 79–93.

    Article  Google Scholar 

  • Kouli, M., Soupios, P., & Vallianatos, F. (2009). Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environmental Geology, 57, 483–497.

    Article  Google Scholar 

  • Kumar, S., & Kushwaha, S. P. S. (2013). Modelling soil erosion risk based on RUSLE-3D using GIS in a Shivalik sub-watershed. Journal of Earth System Science, 122(2), 389–398.

    Article  Google Scholar 

  • Kumar, A., Devi, M., & Deshmukh, B. (2014). Integrated remote sensing and geographic information system based RUSLE modelling for estimation of soil loss in Western Himalaya, India. Water Resources Management, 28, 3307–3317.

    Article  Google Scholar 

  • Lin, C. Y., Lin, W. T., & Chou, W. C. (2002). Soil erosion prediction and sediment yield estimation: the Taiwan experience. Soil & Tillage Research, 68(2), 143–152.

    Article  Google Scholar 

  • Moore, I. D., & Burch, G. J. (1986a). Physical basis of the length slope factor in the universal soil loss equation. Soil Science Society of America Journal, 50(5), 1294–1298.

    Article  Google Scholar 

  • Moore, I. D., & Burch, G. J. (1986b). Modelling erosion and deposition. Topographic effects. Transactions of the ASABE, 29(6), 1624–1630.

    Article  Google Scholar 

  • Naqvi, H. R., Mallick, J., Devi, L. M., & Siddiqui, M. A. (2013). Multi-temporal annual soil loss risk mapping employing Revised Universal Soil Loss Equation (RUSLE) model in Nun Nadi Watershed, Uttrakhand (India). Arabian Journal of Geosciences, 6(10), 4045–4056.

    Article  Google Scholar 

  • Pan, J., & Wen, Y. (2014). Estimation of soil erosion using RUSLE in Caijiamiao watershed, China. Natural Hazards, 71(3), 2187–2205.

    Article  Google Scholar 

  • Pandey, A., Chowdary, V. M., & Mal, B. C. (2004). Morphological analysis and watershed management using GIS. Hydrology Journal India, 27(3–4), 71–84.

    Google Scholar 

  • Pandey, A., Chowdary, V. M., & Mal, B. C. (2007). Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resources Management, 21(21), 729–746.

    Article  Google Scholar 

  • Pandey, A., Mathur, A., Mishra, S. K., & Mal, B. C. (2009). Soil erosion modeling of a Himalayan watershed using RS and GIS. Environmental Earth Sciences, 59, 399–410.

    Article  Google Scholar 

  • Park, S., Oh, C., Jeon, S., Jung, H., & Choi, C. (2011). Soil erosion risk in Korean watersheds, assessed using the Revised Universal Soil Loss Equation. Journal of Hydrology, 399, 263–273.

    Article  Google Scholar 

  • Parysow, P., Wang, G. X., Gertner, G., & Anderson, A. B. (2003). Spatial uncertainty analysis for mapping soil erodibility based on joint sequential simulation. Catena, 53, 65–78.

    Article  Google Scholar 

  • Patel, D. P., Gajjar, C. A., & Srivastava, P. K. (2013). Prioritization of Malesari mini-watersheds through morphometric analysis: a remote sensing and GIS perspective. Environmental Earth Sciences, 69(8), 2643–2656.

    Article  Google Scholar 

  • Pradeep, G. S., Ninu Krishnan, M. V., & Vijith, H. (2014). Identification of critical soil erosion prone areas and annual average soil loss in an upland agricultural watershed of Western Ghats, using analytical hierarchy process (AHP) and RUSLE techniques. Arabian Journal of Geosciences, 8, 3697–3711.

    Article  Google Scholar 

  • Pradhan, B., Chaudhari, A., Adinarayana, J., & Buchroithner, M. F. (2012). Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environmental Monitoring and Assessment, 184, 715–727.

    Article  Google Scholar 

  • Prasannakumar, V., Shiny, R., Geetha, N., & Vijith, H. (2011a). Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environmental Earth Sciences, 64, 965–972.

    Article  Google Scholar 

  • Prasannakumar, V., Vijith, H., Geetha, N., & Shiny, R. (2011b). Regional scale erosion assessment of a sub-tropical highland segment in the Western Ghats of Kerala, South India. Water Resources Management, 25(14), 3715–3727.

    Article  Google Scholar 

  • Prasannakumar, V., Vijith, H., Abinod, S., & Geetha, N. (2012). Estimation of soil erosion risk in a small mountainous sub-watershed in Kerala, India, using RUSLE and geoinformation technology. Geoscience Frontiers, 3(2), 209–215.

    Article  Google Scholar 

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. C. (1997). Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Washington, DC: USDA Agricultural Research Services. Agriculture handbook no. 703.

    Google Scholar 

  • Rozos, D., Skilodimou, H. D., Loupasakis, C., & Bathrellos, G. D. (2013). Application of the revised universal soil loss equation model on landslide prevention. An example from N. Euboea (Evia) Island, Greece. Environmental Earth Sciences, 70(7), 3255–3266.

    Article  Google Scholar 

  • Shinde, V., Sharma, A., Tiwari, K. N., & Singh, M. (2011). Quantitative determination of soil erosion and prioritization of micro-watersheds using Remote Sensing and GIS. Journal of Indian Society of Remote Sensing, 39(2), 181–192.

    Article  Google Scholar 

  • Silva, R. M., Santos, C. A. G., & Montenegro, S. M. G. L. (2012). Integration of GIS and remote sensing for estimation of soil loss and prioritization of critical sub-catchments: a case study of Tapacura catchment. Natural Hazards, 62, 953–970.

  • Singh, R., & Phadke, V. S. (2006). Assessing soil loss by water erosion in Jamni River Basin, Bundelkhand region, India, adopting universal soil loss equation using GIS. Current Science, 90(10), 1431–1435.

    Google Scholar 

  • Srinivas, C. V., Maji, A. K., Reddy, G. P. O., & Chary, G. R. (2002). Assessment of soil erosion using remote sensing and GIS in Nagpur district, Maharashtra for prioritisation and delineation of conservation units. Journal of Indian Society of Remote Sensing, 30(4), 197–212.

    Article  Google Scholar 

  • Stone, R. P., & Hilborn, D. (2000). Fact sheet, universal soil loss equation (p. 12). Ontario: Ministry of Agriculture Food and Rural Affairs.

    Google Scholar 

  • Terranova, O., Antronico, L., Coscarelli, R., & Iaquinta, P. (2009). Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: an application model for Calabria (southern Italy). Geomorphology, 112, 228–245.

    Article  Google Scholar 

  • Tian, Y. C., Zhou, Y. M., Wu, B. F., & Zhou, W. F. (2009). Risk assessment of water soil erosion in upper basin of Miyun Reservoir, Beijing, China. Environmental Geology, 57, 937–942.

    Article  Google Scholar 

  • Tripathi, M. P., Panda, R. K., & Raghuwanshi, N. S. (2003). Identification and prioritization of critical sub-watersheds for soil conservation management using the SWAT model. Biosystems Engineering, 85(3), 365–379.

    Article  Google Scholar 

  • United States Department of Agriculture, USDA (1981). Handbook no. 282.

  • Vemu, S., & Udayabhaskar, P. (2010). An integrated approach for prioritization of reservoir catchment using remote sensing and geographic information system techniques. Geocarto International, 25, 149–168.

    Article  Google Scholar 

  • Vijith, H., Suma, M., Rekha, V. B., Shiju, C., & Rejith, P. G. (2012). An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS. Arabian Journal of Geosciences, 5, 797–805.

    Article  Google Scholar 

  • Wang, G., Wente, S., Gertner, G. Z., & Anderson, A. (2002). Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat thematic mapper images. International Journal of Remote Sensing, 23(18), 3649–3667.

    Article  Google Scholar 

  • Williams, J. R. (1975). Sediment yield prediction with universal equation using runoff energy factor. In Present and prospective technology for predicting sediment yields and sources (pp. 244–252). Agricultural Research Service, US Department of Agriculture.

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses. USDA Agricultural Research Services Handbook 537. Washington, DC: USDA. 57p.

    Google Scholar 

  • Wu, L., Long, T., Liu, X., & Mmereki, D. (2012). Simulation of soil loss processes based on rainfall runoff and the time factor of governance in the Jialing River Watershed, China. Environmental Monitoring and Assessment, 184, 3731–3748.

    Article  Google Scholar 

  • Young, R. A., Onstad, C. A., Bosch, D. D., & Anderson, V. P. (1989). AGNPS—a non-point source pollution model for evaluating agricultural watersheds. Journal of Soil and Water Conservation, 44(2), 168–173.

    Google Scholar 

  • Zhou, P., Luukkanen, O., Tokola, T., & Nieminen, J. (2008). Effect of vegetation cover on soil erosion in a mountainous watershed. Catena, 75(3), 319–325.

    Article  Google Scholar 

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Acknowledgments

The authors are thankful to all the anonymous reviewers for their valuable suggestions which helped us to improve the quality of the manuscript. We are also thankful to Mr. Pattabhirama Somayaji, Professor of English, University College, Mangaluru, for kindly editing the manuscript and rectifying the serious problems in grammar, sentences, and paragraphs. The authors are also thankful to the Ministry of Earth Sciences, New Delhi for the financial assistance in the form of research project (MoES/11-MRDF/1/35/P/08-PC-III).

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Markose, V.J., Jayappa, K.S. Soil loss estimation and prioritization of sub-watersheds of Kali River basin, Karnataka, India, using RUSLE and GIS. Environ Monit Assess 188, 225 (2016). https://doi.org/10.1007/s10661-016-5218-2

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