Identification of Soil Erosion Prone Areas of Madhya Pradesh using USLE/RUSLE

Ashwini Suryawanshi; Anupam Kumar Nema; Rahul Kumar Jaiswal; Sukant Jain; Saswat Kumar Kar

doi:10.52151/jae2021581.1744

Authors

Ashwini Suryawanshi Author
Anupam Kumar Nema Author
Rahul Kumar Jaiswal Author
Sukant Jain Author
Saswat Kumar Kar Author

DOI:

https://doi.org/10.52151/jae2021581.1744

Keywords:

Soil erosion, universal soil loss equation, revised universal soil loss equation, remote sensing, conservation planning

Abstract

Soil erosion is caused due to the dynamic action of erosive agents, mainly water, and is a major threat to the environment. Primary aim of the present study was to study the soil loss dynamics, and identify the environmental hotspots in Madhya Pradesh to aid decision-makers to plan and prioritize appropriate conservation measures. Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE) models were applied for erosion rate estimation by generating thematic maps of R (Rainfall erosivity factor), K (Soil erodibility factor), LS (Topographic factor), C (Cover and management factor), and P (Support practice factor) factors by using several input parameters in QGIS software. Subsequently, the different classes of soil erosion and percentage area under these classes were identified. The average annual soil erosion for the entire state as obtained from the USLE and RUSLE model were 5.80 t.ha-1.yr-1 and 6.64 t.ha-1.yr-1, respectively. The areas under severe risk were 1.09 % and 1.80 %, and very severe risk areas were 1.57 % and 1.83 % as estimated by USLE and RUSLE model, respectively. As compared to RUSLE model, USLE model underestimated rate of soil erosion for most river basins of the state as well as for the entire state.

References

Adhikary P P; Tiwari S P; Mandal D; Lakaria B L; Madhu M. 2014. Geospatial comparison of four models to predict soil erodibility in a semi-arid region of Central India. Environ. Earth Sci., 72, 5049–5062.

Atoma H; Suryabhagavan K V; Balakrishnan M. 2020. Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia. Sustain. Water Resour. Manage., 6(1), 1-17.

Banerjee A; Chen R; Meadows M E; Singh R B; Mal S; Sengupta D. 2020. An analysis of long-term rainfall trends and variability in the Uttarakhand Himalaya using Google earth engine. Remote Sens., 12(4), 1-24. https://doi.org/10.3390/rs12040709

Belasri A; Lakhouili A. 2016. Estimation of soil erosion risk using the Universal Soil Loss Equation (USLE) and Geo-Information Technology in Oued El Makhazine watershed, Morocco. J. Geog. Info. Syst., 8, 98-107. https://dx.doi.org/10.4236/jgis.2016.81010

Biswas S. 2012. Estimation of soil erosion using remote sensing and GIS and prioritization of catchments. Int. J. Emerging Technol. Adv. Eng., 2 (7), 2250-2459.

Borrelli P; Robinson D A; Fleischer L R; Lugato E; Ballabio C; Alewell C; Meusburger K; Modugno S; Schütt B; Ferro V; Bagarello V. 2017. An assessment of the global impact of 21st century land use change on soil erosion. Nature Commun., 8(1), 1-13.

Divya P; Shetty A. 2021. Evaluation of CHIRPS satellite rainfall datasets over Kerala, India. In: Narasimhan M C; George V; Udaya Kumar G; Kumar A (Eds.), Trends in Civil Engineering and Challenges for Sustainability, Lecture Notes in Civil Engineering, Springer, Singapore, 99, 655-664. https://doi.org/10.1007/978-981-15-6828-2_49

Djoukbala O; Hasbaia M; Benselama O; Mazour M. 2019. Comparison of the erosion prediction models from USLE, MUSLE and RUSLE in a Mediterranean watershed, case of Wadi Gazouana (NW of Algeria). Modell. Earth Syst. Environ., 5(2), 725-743.

Fernandez C; Wu J Q. 2003. Estimating water erosion and sediment yield with GIS, RUSLE, and SEDD. J. Soil Water Conserv., 58 (3), 128-136.

Funk C; Peterson P; Landsfeld M; Pedreros D; Verdin J; Shukla S; Husak G; Rowland J; Harrison L; Hoell A; Michaelsen J. 2015. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data, 2 (1), 1-21. doi:10.1038/sdata.66

Ganasri B P; Ramesh H. 2016. Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin. Geosci. Front., 7(6), 953-961. https://doi.org/10.1016/j.gsf.2015.10.007

Gelagay A S; Minale H S. 2016. Soil loss estimation using GIS and Remote sensing techniques: A case of Koga watershed, Northwestern Ethiopia. Int. Soil Water Conserv. Res., 4, 126–136.

Gulati A; Rajkhowa A; Sharma P; Pravesh. 2017. Making Rapid Strides-Agriculture in Madhya Pradesh: Sources, Drivers, and Policy Lessons. Indian Council for Research on International Economic Relations, Working Paper No. 339, pp: 51. http://hdl.handle.net/11540/7030

Gupta V; Jain M K; Singh P K; Singh V. 2020. An assessment of global satellite‐based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India. Int. J. Climatol., 40(8), 3667– 3688. https://doi.org/10.1002/joc.6419

Khan S; Singh A; Bhadauria S S; Yadav S S; Sharma A. 2018. Soil loss estimation of a watershed of central India with integration of geospatial techniques and universal soil loss equation. Int. J. Agric. Environ. Biotechnol., 11(4), 703-711. DOI:10.30954/0974- 1712.08.2018.13

López-García E M; Torres-Trejo E; López-Reyes L; Flores-Domínguez Á D; Peña-Moreno R D; López-Olguín J F. 2019. Estimation of soil erosion using USLE and GIS in the locality of Tzicatlacoyan, Puebla, México. Soil Water Res., 15(1), 9-17. https://doi.org/10.17221/165/2018-SWR

Mitasova H; Barton C M; Ullah I; Hofierka J; Harmon R S. 2013. GIS based soil erosion modeling. Treatise Geomorphol., 3, 228–258. https://doi.org/10.1016/B978-0-12-374739-6.00052-X

Mondal A; Khare D; Kundu S. 2016. A comparative study of soil erosion modelling by MMF, USLE and RUSLE. Geocarto Int., 33(1), 89-103. https://doi.org/10.1080/10106049.2016.1232313

Parmar S. 2018. Sediment yield assessment using SAGA GIS and USLE model: A case study of watershed – 63 of Narmada River, Gujarat, India. Int. J. Eng. Trends Technol., (IJETT), 67(8), 1-13.

Patil R J; Sharma S K; Tignath S; Sharma A P M. 2017. Use of remote sensing, GIS and C++ for soil erosion assessment in the Shakkar River basin, India. Hydrol. Sci. J., 62(2), 217-231. DOI: 10.1080/02626667.2016.1217413

Prakash S. 2019. Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India. J. Hydrol., 571, 50-59. https://doi.org/10.1016/j.jhydrol.2019.01.036

Prasannakumar V; Vijith H; Abinod S; Geetha N. 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. Geosci. Front., 3(2), 209- 215. https://doi.org/10.1016/j.gsf.2011.11.003

Rambabu; Tejwani K K; Agrawal M C; Bhusan L S. 1979. Rainfall Intensity Duration-Return Equation and Nomographs of India. ICAR-Central Soil and Water Conservation Research and Training Institute (CSWCRTI), Dehradun, India, Bulletin No. 3, pp: 70.

Rapp J F; Lopes V L; Renard K G. 2001. Comparing Soil Erosion Estimates from RUSLE and USLE on Natural Runoff Plots. In: Ascough J CII; Flanagan D C (Eds.), Soil Erosion Research for the 21st Century, Proc. Int. Symp., Honolulu, HI, St. Joseph, USA, MI: ASAE.701P0007, 24-27. doi:10.13031/2013.3193

Saroha D J. 2017. Soil erosion: Causes, extent and management in India. Int. J. Creative Res. Thoughts, 5(4), 2320-2882.

Tamgadge D B; Raja P; Gaikawad S T; Sehgal J L; Gajbhiye K S; Singh S R. 2000. Assessment of soil degradation status in Madhya Pradesh. J. Indian Soc. Soil Sci., 48(3), 581-586.

Trivedi T P. 2010. Degraded and Wastelands of India: Status and Spatial Distribution. Directorate of Information and Publications of Agriculture, Indian Council of Agricultural Research, Krishi Anusandhan Bhavan, Pusa, New Delhi, pp: 158.

Van der Knijff J M; Jones R J A; Montanarella L. 2000. Soil Erosion Risk Assessment in Europe. European Soil Bureau- Joint Research Centre, EUR 19044 EN, Office for Official Publications of the European Communities, Luxembourg, pp: 34.

Verma S; Dar J A; Malasiya D; Khare P K; Dayanandan S; Khan M L. 2018. A MODIS-based spatiotemporal assessment of agricultural residue burning in Madhya Pradesh, India. Ecol. Indic., 105, 496-504.

Warwade P; Hardaha M K; Kumar D; Chadniha S K. 2014. Estimation of soil erosion and crop suitability for a watershed through remote sensing and GIS approach. Indian J. Agric. Sci., 84(1), 18-23.

Wischmeier W H; Smith D D. 1965. Predicting Rainfall Erosion Losses from Cropland East of the Rocky Mountains. U.S. Department of Agriculture Science and Education Administration, Washington, DC, USA, Agriculture Handbook No. 282, pp: 47.

Wolka K; Tadesse H; Garedew E; Yinner F. 2015. Soil erosion risk assessment in the Chaleleka wetland watershed, Central Rift Valley of Ethiopia. Environ. Syst. Res., 4(1), 1-12. https://doi.org/10.1186/s40068-015-0030-5