A geoinformatics and RUSLE model-based soil erosion assessment in a tropical mountainous area of Chite watershed, Mizoram, India
DOI:
https://doi.org/10.15243/jdmlm.2024.113.5875Keywords:
Chite watershed, GIS, RUSLE, soil erosion, tropical mountainous regionAbstract
Soil erosion remains a persistent menace to the sustainability of agriculture and the environment in tropical mountainous regions. Soil erosion assessment is therefore necessary to identify degraded land areas for implementing effective conservation and management strategies. Hence, this study focuses on estimating potential soil erosion and analyzing their spatial patterns in the Chite watershed, situated in the Eastern Himalayas, India, using the Revised Universal Soil Loss Equation (RUSLE) model in Geographic Information System (GIS) platform. Various datasets encompassing remote sensing, ground observations, and laboratory analysis were employed to prepare the model’s input factors. The estimated mean erosion rate of the study area is 6.10 t ha-1 year-1, which produces a total soil loss of about 357580.90 t year-1. Spatial analysis reveals that about 5.79% of the watershed is under a relatively severe erosion category, contributing 70.13% of the total soil loss. Soil erosion appraisal with respect to the land use/ land cover (LULC) indicates a considerable consequence of various anthropogenic activities in the watershed. Higher rates of soil erosion are mainly observed on the bare land, cropland, and settlement areas which are characterized by steep and continuous slopes. The present findings were also validated with previous work undertaken in some comparable regions. This research can serve as a reliable tool towards the development of successful soil conservation measures and for promoting sustainable land use planning in this ecologically sensitive tropical mountainous region.
References
Alewell, C., Borrelli, P., Meusburger, K. and Panagos, P. 2019. Using the USLE: Chances, challenges and limitations of soil erosion modelling. International Soil and Water Conservation Research 7(3):203-225. https://doi.org/10.1016/j.iswcr.2019.05.004
Alexakis, D.D., Hadjimitsis, D.G. and Agapiou, A. 2013. Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of "Yialias" in Cyprus. Atmospheric Research 131:108-124. https://doi.org/10.1016/j.atmosres.2013.02.013
Angima, S.D., Stott, D.E., O'neill, M.K., Ong, C.K. and Weesies, G.A. 2003. Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agriculture, Ecosystems & Environment 97(1-3):295-308. https://doi.org/10.1016/S0167-8809(03)00011-2
Avwunudiogba, A. and Hudson, P.F. 2014. A review of soil erosion models with special reference to the needs of humid tropical mountainous environments. European Journal of Sustainable Development 3(4):299-299. https://doi.org/10.14207/ejsd.2014.v3n4p299
Babu, R., Dhyani, B.L. and Kumar, N. 2004. Assessment of erodibility status and refined Iso-Erodent Map of India. Indian Journal of Soil Conservation 32(2):171-177.
Bagarello, V., Stefano, C.D., Ferro, V., Giuseppe, G. and Iovino, M. 2009. A pedotransfer function for estimating the soil erodibility factor in Sicily. Journal of Agricultural Engineering 40(3):7-13. https://doi.org/10.4081/jae.2009.3.7
Barman, B.K., Rao, K.S., Sonowal, K., Prasad, N.S.R. and Sahoo, U.K. 2020. Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram, India. AIMS Geosciences 6(4):525-545. https://doi.org/10.3934/geosci.2020030
Biswas, S.S. and Pani, P. 2015. Estimation of soil erosion using RUSLE and GIS techniques: a case study of Barakar River basin, Jharkhand, India. Modeling Earth Systems and Environment 1:1-13. https://doi.org/10.1007/s40808-015-0040-3
Chatterjee, N. 2020. Soil erosion assessment in a humid, Eastern Himalayan watershed undergoing rapid land use changes, using RUSLE, GIS and high-resolution satellite imagery. Modeling Earth Systems and Environment 6(1):533-543. https://doi.org/10.1007/s40808-019-00700-0
Chatterjee, S., Krishna, A.P. and Sharma, A.P. 2014. Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India. Environmental Earth Sciences 71:357-374. https://doi.org/10.1007/s12665-013-2439-3
Chen, S., Liu, W., Bai, Y., Luo, X., Li, H. and Zha, X. 2021. Evaluation of watershed soil erosion hazard using combination weight and GIS: A case study from eroded soil in Southern China. Natural Hazards 109:1603-1628. https://doi.org/10.1007/s11069-021-04891-7
Choudhury, B.U., Nengzouzam, G., Ansari, M.A. and Islam, A. 2022. Causes and consequences of soil erosion in northeastern Himalaya, India. Current Science 122(7):772-789. https://doi.org/10.18520/cs/v122/i7/772-789
Conforti, M., Pascale, S., Robustelli, G. and Sdao, F. 2014. Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236-250. https://doi.org/10.1016/j.catena.2013.08.006
Dabral, P.P., Baithuri, N. and 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. https://doi.org/10.1007/s11269-008-9253-9
Das, B., Paul, A., Bordoloi, R., Tripathi, O.P. and Pandey, P.K. 2018. Soil erosion risk assessment of hilly terrain through integrated approach of RUSLE and geospatial technology: a case study of Tirap District, Arunachal Pradesh. Modeling Earth Systems and Environment 4:373-381. https://doi.org/10.1007/s40808-018-0435-z
Das, S., Deb, P., Bora, P.K. and Katre, P. 2020. Comparison of RUSLE and MMF soil loss models and evaluation of catchment scale best management practices for a mountainous watershed in India. Sustainability 13(1):232. https://doi.org/10.3390/su13010232
El-Swaify, S.A. 1997. Factors affecting soil erosion hazards and conservation needs for tropical steeplands. Soil Technology 11(1):3-16. https://doi.org/10.1016/S0933-3630(96)00111-0
Feizizadeh, B. and Blaschke, T. 2014. An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. International Journal of Geographical Information Science 28(3):610-638. https://doi.org/10.1080/13658816.2013.869821
Ganasri, B.P. and Ramesh, H. 2016. Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geoscience Frontiers 7(6):953-961. https://doi.org/10.1016/j.gsf.2015.10.007
Jaiswal, M.K. and Amin, N. 2020. The impact of land use dynamics on the soil erosion in the Panchnoi river basin, northeast India. Journal of the Geographical Institute "Jovan Cvijic", SASA 70(1):1-14. https://doi.org/10.2298/IJGI2001001J
Karaburun, A. 2010. Estimation of C factor for soil erosion modeling using NDVI in Buyukcekmece watershed. Ozean Journal of Applied Sciences 3(1):77-85.
Kichu, R. and Dutta, M. 2022. Assessment of soil erosion using GIS and remote sensing techniques in Dzumah watershed of upper Dhansiri, Nagaland. The Pharma Innovation Journal 11(9):2266-2273.
Lu, D., Li, G., Valladares, G.S. and Batistella, M. 2004. Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degradation & Development 15(5):499-512. https://doi.org/10.1002/ldr.634
Millward, A.A. and Mersey, J.E. 1999. Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38(2):109-129. https://doi.org/10.1016/S0341-8162(99)00067-3
Mitasova, H., Hofierka, J., Zlocha, M. and Iverson, L.R. 1996. Modelling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information Systems 10(5):629-641. https://doi.org/10.1080/02693799608902101
Morgan, R.P.C. 2005. Soil Erosion and Conservation, 3rd Edition, John Wiley & Son, Inc., 111 River Street, Hoboken, New Jersey, USA.
Mosavi, A., Sajedi-Hosseini, F., Choubin, B., Taromideh, F., Rahi, G. and Dineva, A.A. 2020. Susceptibility mapping of soil water erosion using machine learning models. Water 12(7):1995. https://doi.org/10.3390/w12071995
Motsara M.R. and Roy R.N. 2008. Guide to laboratory establishment for plant nutrient analysis, FAO fertilizer and plant nutrition bulletin 19, FAO, Rome, Italy. 205p ISBN 978-92-5-105981-4.
Pandey, A., Mathur, A., Mishra, S.K. and Mal, B.C. 2009. Soil erosion modeling of a Himalayan watershed using RS and GIS. Environmental Earth Sciences 59:399-410. https://doi.org/10.1007/s12665-009-0038-0
Parveen, R. and Kumar, U. 2012. Integrated approach of Universal Soil Loss Equation (USLE) and Geographical Information System (GIS) for soil loss risk assessment in Upper South Koel Basin, Jharkhand. Journal of Geographic Information System 4:588-596. https://doi.org/10.4236/jgis.2012.46061
Prasannakumar, V., Vijith, H., Abinod, S. and Geetha, N.J.G.F. 2012. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers 3(2):209-215. https://doi.org/10.1016/j.gsf.2011.11.003
Pribyl, D. W. 2010. A critical review of the conventional SOC to SOM conversion factor. Geoderma 156(3-4):75-83. https://doi.org/10.1016/j.geoderma.2010.02.003
Renard, K.G., Foster. G.R., Weesies, G.A., McCool, D.K. and Yoder, D.C. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning With the Revised Universal Soil Loss Equation (RUSLE). U.S. Department of Agriculture, Agriculture Handbook No. 703. https://www.ars.usda.gov/arsuserfiles/64080530/rusle/ah_703.pdf.
Saha, R., Chaudhary, R.S. and Somasundaram, J. 2012. Soil health management under hill agroecosystem of North East India. Applied and Environmental Soil Science 2012. https://doi.org/10.1155/2012/696174
Sharma, P.D. 2004. Managing natural resources in the Indian Himalayas. Journal of the Indian Society of Soil Science 52(4):314-331.
United States Department of Agriculture (USDA). 2017. Soil Survey Manual: Soil Science Division Staff, U.S. Department of Agriculture Handbook No. ttps://www.nrcs.usda.gov/sites/default/files/2022-09/The-Soil-Survey-Manual.pdf.
Wischmeier, W.H. and Smith, D.D. 1978. Predicting soil erosion losses: A guide to conservation planning. U.S. Department of Agriculture Handbook, No. 537.
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