Estimation of rainfall erosivity (R) using Geo-spatial technique for the state of Tripura, India: A comparative study

SUSANTA DAS; RANJIT DAS; PRADIP KUMAR BORA; MANISH OLANIYA

doi:10.56093/ijas.v92i7.104246

Authors

SUSANTA DAS Punjab Agricultural University, Ludhiana, Punjab
RANJIT DAS North Easter Space Application Centre, Umiam, Meghalaya
PRADIP KUMAR BORA North Eastern Regional Institute of Water and Land Management, Dolabari, Tezpur, Assam
MANISH OLANIYA Central Agricultural University, Imphal, Umiam, Meghalaya

https://doi.org/10.56093/ijas.v92i7.104246

Keywords:

rainfall erosivity, spatial variation, temporal variation

Abstract

The principal-agent of soil detachment is rainfall kinetic energy (KE), which must be assessed to understand
the nature of erosion, particularly in high rainfall regions, and is designated as a rainfall erosivity index (R). The
present study aimed to develop and choose an appropriate model for estimating the R factor in the Indian state of
Tripura. The study employed the following three models: KE>25 index model, average annual rainfall model, and
monthly and average annual rainfall model. The rainfall data were collected from MOSDAC and https://www.
worldweatheronline.com for the calculation of point R-value. The interpolation technique (Kriging) in the ArcGIS
environment was adopted to find the spatial variation of the rainfall and R factor over the region. The average annual R factor of the study area was 1089.89, 533.17, and 2452.27 MJ mm/ha/h/y as calculated by Model-1, Model-2,
and Model-3, respectively, for the study period (2008–17). The results show that Tripura has high rainfall erosivity
which may lead to soil erosion. The comparative analysis shows Model-2 has underestimated approximately 70%
whereas Model-3 has overestimated about 15% of the R factor values by considering Model-1 as base. The results
demonstrate that Model-2 can be used as an alternative for estimation of rainfall erosivity in an area where the daily
rainfall data is not available. These findings may help researchers to select a suitable method for the calculation of
rainfall erosivity factor in mountainous catchments.

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References

Angulo-Martinez M and Begueria S. 2009. Estimating rainfall erosivity from daily precipitation records: A comparison among methods using data from the Ebro Basin (NE Spain). Journal of Hydrol 379: 111–21. DOI: https://doi.org/10.1016/j.jhydrol.2009.09.051

Arnoldus H M L. 1980. An Approximation of Rainfall Factor in the Universal Soil Loss Equation Assessment of Erosion, pp. 127–32. M De Boodt, D Gabriels (Eds). Wiley Chichester, UK .

Bonilla C A and Vidal K L. 2011. Rainfall erosivity in Central Chile. Journal of Hydrology 410: 126–33. DOI: https://doi.org/10.1016/j.jhydrol.2011.09.022

Chauhan N, Kumar V, Paliwal R and Kakkar R. 2020. Quantifying the risks of soil erosion using Revised Universal Soil Loss Equation (RUSLE) and GIS technology for Ghaggar river Basin – A case of Lower Shivaliks. Mukt Shabd Journal 9(7): 221–32.

Das S, Deb P, Bora P K and Katre P. 2021. 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. DOI: https://doi.org/10.3390/su13010232

Dash C J, Das N K and Adhikary P P. 2019. Rainfall erosivity and erosivity density in Eastern Ghats highland of east India. Natural Hazards 97: 727–46. DOI: https://doi.org/10.1007/s11069-019-03670-9

Diodato N and Bellocchi G. 2009. Assessing and modelling changes in rainfall erosivity at different climate scales. Earth Surface Processes and Landforms 34: 969–80. DOI: https://doi.org/10.1002/esp.1784

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: 953–61. DOI: https://doi.org/10.1016/j.gsf.2015.10.007

Hudson N W. 1971. Soil conservation. B T Batsford Ltd, UK. Karthick P, Lakshumanan C and Ramki P. 2017. Estimation of soil erosion vulnerability in Perambalur Taluk Tamilnadu using revised universal soil loss equation model (RUSLE) and geoinformation technology. International Research Journal of Earth Sciences 5(8): 8–14.

Lee J H and Heo J H. 2011. Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. Journal of Hydrology 409: 30–48. DOI: https://doi.org/10.1016/j.jhydrol.2011.07.031

Lee J, Lee S, Hong J, Lee D, Bae J H, Yang J E, Kim J and Lim K J. 2021. Evaluation of rainfall erosivity factor estimation using machine and deep learning models. Water 13(3): 382. DOI: https://doi.org/10.3390/w13030382

Morgan R P C, Morgan D D V and Finney H J. 1982. Stability of agricultural eco-system: documentation of simple model for soil erosion assessment. International Institute for Applied Systems Analysis Collaborative paper, pp. 50–82.

Mukundan R, Pradhanang S M, Schneiderman E M, Pierson DC, Anandhi A, Zion M S, Matonse A H, Lounsbury D G and Steenhuis T S. 2013. Suspended sediment source areas and future climate impact on soil erosion and sediment yield in a New York City water supply watershed USA. Geomorphology 183: 110–19. DOI: https://doi.org/10.1016/j.geomorph.2012.06.021

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: 588–96. DOI: https://doi.org/10.4236/jgis.2012.46061

Rahaman A S, Aruchamy S, Jegankumar R and Abdul Ajeez S. 2015. Estimation of annual average soil loss based on RUSLE model in Kallar Watershed Bhavani Basin Tamil Nadu India. ISPRSANNALS II-2-W2 207–14. DOI: https://doi.org/10.5194/isprsannals-II-2-W2-207-2015

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). Agriculture Handbook, pp. 404. US Department of Agriculture, Washington DC, USA.

Routschek A, Schmidt J, Enke W and Deutschlaen T. 2014. Future soil erosion risk – results of GIS-based model simulations for a catchment in Saxony/Germany. Geomorphology 206: 299–06. DOI: https://doi.org/10.1016/j.geomorph.2013.09.033

Sepaskhah A R and Sarkhosh P. 2005. Estimating storm erosion index in southern region of IR Iran. Iranian Journal of Science & Technology Transaction A 31: 237–48.

Singh G, Chandra S and Babu R. 1981. Soil Loss and Pre-diction Research in India. Bulletin No T-12/D9 Central Soil and Water Conservation Research Training Institute Dehradun.

Singh J and Singh O. 2020. Assessing rainfall erosivity and erosivity density over a western Himalayan catchment India. Journal of Earth System Sciences 129: 97 DOI: https://doi.org/10.1007/s12040-020-1362-8

Sujay K, Syed D S and Topalakatti P M. 2021. Estimation of soil erosion in Doddahalla watershed, Chitradurga, Karnataka, Using Revised Universal Soil Loss Equation (RUSLE) and GIS. International Journal of Progressive Research in Science and Engineering 2(8): 731–40.

Thomas J, Joseph S and Thrivikramji K P. 2017. Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats India using RUSLE and GIS. Geoscience Frontiers 1–14. DOI: https://doi.org/10.1016/j.gsf.2017.05.011

Wischmeier W H and Smith D D. 1978. Predicting rainfall erosion losses: A guide to conservation planning Agriculture. Handbook 537. US Department of Agricultural Agricultural Research Service Washington DC, USA

Wischmeier W H and Smith D D. 1965. Predicting rainfall-erosion losses from cropland east of the Rocky Mountains. Agricultural Handbook 282, pp. 47. USDA ARS.

Zambon N, Johannsen LL, Strauss P, Dostal T, Zumr D, Neumann M, Cochrane T A and Klik A. 2020. Rainfall parameters affecting splash erosion under natural conditions. Applied Sciences 85(1): 79–85 DOI: https://doi.org/10.3390/app10124103