Skip to main content
SpringerLink
Log in

Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Meghalaya is well-known for its fragile ecosystem; because of its undulating landscape and high-intensity rainfall, Meghalaya faces severe soil erosion issues. This study aims to conduct a quantitative analysis to understand the soil loss in the region. Using various datasets covering rainfall, soil properties, topography, and land cover, this study employs a Geographic Information System (GIS) framework to apply the Revised Universal Soil Loss Equation (RUSLE) for soil erosion loss estimation. The study estimates the region’s mean soil erosion at 14 t ha−1 y−1, subsequently causing an annual loss of 5871.32 t ha−1 y−1. The sorting of the area into six risk zones reveals that 66% experience slight to moderate erosion, 18% experience high to very high erosion, and 16% encounter severe erosion. Study findings reveal that the LS factor significantly influences soil erosion. Different physiographic regions show varying erosion rates: Khasi Hills show the highest (20.94 t. ha–1. y–1), trailed by Jaintia Hills (13.35) and Garo Hills (5.47). The research highlights open and degraded forest areas with the highest erosion rates, followed by agricultural lands, range land, and barren land. Definite terrain characteristics, such as slope angles within 0 to 15 degrees and elevations greater than 1000 m, appear as erosion-prone areas. This research highlights the critical requirement for targeted preservation efforts and ecologically sound land use practices in Meghalaya. The findings provide essential guidance and regulation for stakeholders, policymakers, land managers, and conservationists to implement effective erosion control measures and protect the region’s valuable soil resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The datasets generated or analysed during this study are available from the corresponding author; they can be provided upon reasonable request.

Abbreviations

ANOVA:

Analysis of Variance

CRU:

Climate Research Unit

DEM:

Digital Elevation Model

FAO-UNESCO:

Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization

GIS:

Geographic Information System

IDW:

Inverse distance weighted

LULC:

Land Use Land Cover

NDVI:

Normalized Difference Vegetation Index

RS:

Remote Sensing

RUSLE:

Revised Universal Soil Loss Equation

USLE:

Universal Soil Loss Equation

References

  • Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027

    Article  Google Scholar 

  • Abdo, H., & Salloum, J. (2017). Mapping the soil loss in Marqya basin: Syria using RUSLE model in GIS and RS techniques. Environmental Earth Sciences, 76(3), 1–10. https://doi.org/10.1007/s12665-017-6424-0

    Article  Google Scholar 

  • Abu Hammad, A., Lundekvam, H., & Børresen, T. (2004). Adaptation of RUSLE in the eastern part of the Mediterranean region. Environmental Management, 34(6), 829–841. https://doi.org/10.1007/s00267-003-0296-7

    Article  Google Scholar 

  • Ahmad, W. S., Jamal, S., Taqi, M., El-Hamid, H. T. A., & Norboo, J. (2022). Estimation of soil erosion and sediment yield concentrations in Dudhganga watershed of Kashmir Valley using RUSLE & SDR model. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-022-02705-9

    Article  Google Scholar 

  • Aksoy, H., & Kavvas, M. L. (2005). A review of hillslope and watershed scale erosion and sediment transport models. CATENA, 64(2–3), 247–271. https://doi.org/10.1016/j.catena.2005.08.008

    Article  Google Scholar 

  • Almagro, A., Thomé, T. C., Colman, C. B., Pereira, R. B., Marcato Junior, J., Rodrigues, D. B. B., & Oliveira, P. T. S. (2019). Improving cover and management factor (C-factor) estimation using remote sensing approaches for tropical regions. International Soil and Water Conservation Research, 7(4), 325–334. https://doi.org/10.1016/j.iswcr.2019.08.005

    Article  Google Scholar 

  • Amsalu, T., & Mengaw, A. (2014). GIS-based soil loss estimation using RUSLE model: The case of Jabi Tehinan Woreda, ANRS, Ethiopia. Natural Resources, 5, 616–626. https://doi.org/10.4236/nr.2014.511054

    Article  Google Scholar 

  • Angima, S. D., Stott, D. E., O’Neill, M. K., Ong, C. K., & Weesies, G. A. (2003). Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agriculture, Ecosystems and Environment, 97(1–3), 295–308. https://doi.org/10.1016/S0167-8809(03)00011-2

    Article  Google Scholar 

  • Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x

    Article  Google Scholar 

  • Ashiagbor, G., Forkuo, E. K., Laari, P., & Aabeyir, R. (2013). Modeling soil erosion using Rusle and Gis tools. International Journal of Remote Sensing & Geoscience, 2(4).

  • Avand, M., Nasiri Khiavi, A., Mohammadi, M., & Tiefenbacher, J. P. (2023). Prioritizing sub-watersheds based on soil-erosion potential by integrating RUSLE and game-theory algorithms. Advances in Space Research, 72(2), 471–487. https://doi.org/10.1016/j.asr.2023.03.031

    Article  Google Scholar 

  • Ayalew, D. A., Deumlich, D., Šarapatka, B., & Doktor, D. (2020). Quantifying the sensitivity of NDVI-based C factor estimation and potential soil erosion prediction using spaceborne earth observation data. Remote Sensing, 12(7). https://doi.org/10.3390/rs12071136

  • Beasley, D. B., & Huggins, L. F. (1981). ANSWERS Users Manual. U.S. Environmental Protection Agency, Region V.

  • Bircher, P., Liniger, H. P., & Prasuhn, V. (2022). Comparison of long-term field-measured and RUSLE-based modelled soil loss in Switzerland. Geoderma Regional, 31. https://doi.org/10.1016/j.geodrs.2022.e00595

  • Biswas, H., Raizada, A., Mandal, D., Kumar, S., Srinivas, S., & Mishra, P. K. (2015). Identification of areas vulnerable to soil erosion risk in India using GIS methods. Solid Earth, 6(4), 1247–1257. https://doi.org/10.5194/se-6-1247-2015

    Article  Google Scholar 

  • Chakrabortty, R., Pal, S. C., Sahana, M., Mondal, A., Dou, J., Pham, B. T., & Yunus, A. P. (2020). Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Natural Hazards, 104, 1259–1294. https://doi.org/10.1007/s11069-020-04213-3

    Article  Google Scholar 

  • Chakrabortty, R., Pal, S. C., Arabameri, A., Ngo, P. T. T., Chowdhuri, I., Roy, P., et al. (2022). Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India. Environment, Development and Sustainability, 24(3), 3518–3557. https://doi.org/10.1007/s10668-021-01576-w

    Article  Google Scholar 

  • Chuenchum, P., Xu, M., & Tang, W. (2020). Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang-Mekong River by using the modified RUSLE model. International Soil and Water Conservation Research, 8(3), 213–227. https://doi.org/10.1016/j.iswcr.2020.06.006

    Article  Google Scholar 

  • Da Cunha, E. R., Bacani, V. M., & Panachuki, E. (2017). Modeling soil erosion using RUSLE and GIS in a watershed occupied by rural settlement in the Brazilian Cerrado. Natural Hazards, 85(2), 851–868. https://doi.org/10.1007/s11069-016-2607-3

    Article  Google Scholar 

  • Das, B., Bordoloi, R., Thungon, L. T., Paul, A., Pandey, P. K., Mishra, M., & Tripathi, O. P. (2020). An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh. Journal of Earth System Science, 129. https://doi.org/10.1007/s12040-020-1356-6

  • Das, S., Bora, P. K., & Das, R. (2022). Estimation of slope length gradient (LS) factor for the sub-watershed areas of Juri River in Tripura. Modeling Earth Systems and Environment, 8, 1171–1177. https://doi.org/10.1007/s40808-021-01153-0

    Article  Google Scholar 

  • Das, J., Saha, P., Mitra, R., Alam, A., & Kamruzzaman, M. (2023). GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India. Heliyon, 9(5). https://doi.org/10.1016/j.heliyon.2023.e16186

  • Dash, S. S., & Maity, R. (2023). Effect of climate change on soil erosion indicates a dominance of rainfall over LULC changes. Journal of Hydrology: Regional Studies, 47. https://doi.org/10.1016/j.ejrh.2023.101373

  • De Roo, A. P. J., Wesseling, C. G., Jetten, V. G., & Ritsema, C. J. (1996). LISEM: A physically-based hydrological and soil erosion model incorporated in a GIS. Application of geographic information systems in hydrology and water resources management. Proceedings of HydroGIS’96 conference, Vienna, 1996, (235), 395–403.

  • Erencin, Z. (2000). C-Factor Mapping Using Remote Sensing and GIS. A case study of Lom Sak/Lom Kao, Thailand. International Institute for Aerospace Survey and Earth Sciences (ITC), Enschede/Holland, Justus-Liebig-Universitat Giessen, 28 p.

  • Fayas, C. M., Abeysingha, N. S., Nirmanee, K. G. S., Samaratunga, D., & Mallawatantri, A. (2019). Soil loss estimation using rusle model to prioritize erosion control in KELANI river basin in Sri Lanka. International Soil and Water Conservation Research, 7(2), 130–137. https://doi.org/10.1016/j.iswcr.2019.01.003

    Article  Google Scholar 

  • Ferreira, V. A., & Smith, R. E. (1992). OPUS: An integrated simulation model for transport of nonpoint-source pollutants at the field scale, user manual. U.S. Agricultural Research Service.

  • Ferro, V., & Porto, P. (2000). Sediment Delivery Distributed (SEDD) Model. Journal of Hydrologic Engineering, 5(4), 411–422.

    Article  Google Scholar 

  • Flacke, W., Auerswald, K., & Neufang, L. (1990). Combining a modified Universal Soil Loss Equation with a digital terrain model for computing high resolution maps of soil loss resulting from rain wash. CATENA, 17(4–5), 383–397. https://doi.org/10.1016/0341-8162(90)90040-K

    Article  Google Scholar 

  • Fu, B. J., Zhao, W. W., Chen, L. D., Zhang, Q. J., Lü, Y. H., Gulinck, H., & Poesen, J. (2005). Assessment of soil erosion at large watershed scale using RUSLE and GIS: A case study in the Loess Plateau of China. Land Degradation and Development, 16(1), 73–85. https://doi.org/10.1002/ldr.646

    Article  Google Scholar 

  • Ganasri, B. P., & 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

    Article  Google Scholar 

  • Ghosal, K., & Bhattacharya, S. D. (2020). A Review of RUSLE Model. Journal of the Indian Society of Remote Sensing, 48(4), 689–707. https://doi.org/10.1007/s12524-019-01097-0

    Article  Google Scholar 

  • Gorrepotu, S. R., Debnath, K., & Mahapatra, R. N. (2023a). Multi-response optimization of the chemical treatment process parameters influencing the tensile, flexural, compression, and shear PROPERTIES of the injection moulded green composites. Journal of Polymers and the Environment, 31(1), 112–130. https://doi.org/10.1007/s10924-022-02613-z

    Article  CAS  Google Scholar 

  • Gorrepotu, S. R., Debnath, K., & Mahapatra, R. N. (2023b). Mechanical, thermal, and morphological behavior of pineapple leaf fibre and polylactic acid green composites fabricated by varying fiber loading, fiber length, and injection parameters. Polymer Engineering and Science, 63(8), 2498–2510. https://doi.org/10.1002/pen.26391

    Article  CAS  Google Scholar 

  • Gupta, S., & Kumar, S. (2017). Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape. Journal of Earth System Science, 126. https://doi.org/10.1007/s12040-017-0823-1

  • Gwapedza, D., Hughes, D. A., Slaughter, A. R., & Mantel, S. K. (2021). Temporal influences of vegetation cover (C) dynamism on musle sediment yield estimates: Ndvi evaluation. Water, 13(19). https://doi.org/10.3390/w13192707

  • Habtu, W., & Jayappa, K. S. (2022). Assessment of soil erosion extent using RUSLE model integrated with GIS and RS: the case of Megech-Dirma watershed, Northwest Ethiopia. Environmental Monitoring and Assessment, 194. https://doi.org/10.1007/s10661-022-09965-y

  • Hayicho, H., Alemu, M., & Kedir, H. (2019). Assessment of land-use and land cover change effect on Melka Wakena hydropower dam in Melka Wakena catchment of Sub-Upper Wabe-Shebelle Watershed, South Eastern Ethiopia. Agricultural Sciences, 10(06), 819–840. https://doi.org/10.4236/as.2019.106063

    Article  Google Scholar 

  • Iaaich, H., Moussadek, R., Baghdad, B., Mrabet, R., Douaik, A., Abdelkrim, D., & Bouabdli, A. (2016). Soil erodibility mapping using three approaches in the Tangiers province –Northern Morocco. International Soil and Water Conservation Research, 4(3), 159–167. https://doi.org/10.1016/j.iswcr.2016.07.001

    Article  Google Scholar 

  • Igwe P. U., Onuigbo, A. A., Chinedu, O. C., Ezezku, I. I., & Muoneke, M. M. (2017). Soil erosion: A review of models and applications. International Journal of Advanced Engineering Research and Science, 4(12), 138–150. https://doi.org/10.22161/ijaers.4.12.22

  • Imamoglu, A., & Dengiz, O. (2017). Determination of soil erosion risk using RUSLE model and soil organic carbon loss in Alaca catchment (Central Black Sea region, Turkey). Rendiconti Lincei, 28(1), 11–23. https://doi.org/10.1007/s12210-016-0556-0

    Article  Google Scholar 

  • Islam, M. R., Jaafar, W. Z. W., Hin, L. S., Osman, N., & Karim, M. R. (2020). Development of an erosion model for Langat River Basin, Malaysia, adapting GIS and RS in RUSLE. Applied Water Science, 10. https://doi.org/10.1007/s13201-020-01185-4

  • Islami, F. A., Tarigan, S. D., Wahjunie, E. D., & Dasanto, B. D. (2022). Accuracy assessment of land use change analysis using google earth in Sadar Watershed Mojokerto Regency. IOP Conference Series: Earth and Environmental Science, 950. https://doi.org/10.1088/1755-1315/950/1/012091

  • Jahun, B. G., Ibrahim, R., Dlamini, N. S., & Musa, S. M. (2015). Review of soil erosion assessment using RUSLE model and GIS. Journal of Biology, Agriculture and Healthcare, 5(9), 36–47.

    Google Scholar 

  • Jaiswal, M. K., Thakuria, G., Borah, A. C., & Saikia, R. (2014). Evaluation of parametic impact on soil loss of Panchnoi river basin, North-east India, using revised universal soil loss equation (rusle). The Clarion, 3(1), 51–60.

    Google Scholar 

  • Jena, R. K., Padua, S., Ray, P., Ramachandran, S., Bandyopadhyay, S., Deb Roy, P., et al. (2018). Assessment of soil erosion in sub tropical ecosystem of Meghalaya, India using remote sensing, GIS and RUSLE. Indian Journal of Soil Conservation, 46(3), 273–282.

    Google Scholar 

  • Joshi, V. U. (2018). Soil Loss Estimation based on RUSLE along the Central Hunter Valley Region, NSW, Australia. Journal of the Geological Society of India, 91(5), 554–562. https://doi.org/10.1007/s12594-018-0904-z

    Article  Google Scholar 

  • Joshi, P., Adhikari, R., Bhandari, R., Shrestha, B., Shrestha, N., Chhetri, S., et al. (2023). Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots. Heliyon, 9(5). https://doi.org/10.1016/j.heliyon.2023.e15800

  • 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.

    Google Scholar 

  • Kebede, B., Tsunekawa, A., Haregeweyn, N., Adgo, E., Ebabu, K., Meshesha, D. T., et al. (2021). Determining C- and P-factors of RUSLE for different land uses and management practices across agro-ecologies: Case studies from the Upper Blue Nile basin, Ethiopia. Physical Geography, 42(2), 160–182. https://doi.org/10.1080/02723646.2020.1762831

    Article  Google Scholar 

  • Kim, H. S., & Julien, P. Y. (2006). Soil erosion modeling using RUSLE and GIS on the Imha Watershed. Water Engineering Research, 7(1).

  • Knisel, W. G. (1982). CREAMS a field-scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Department of Agriculture.

    Google Scholar 

  • Koirala, P., Thakuri, S., Joshi, S., & Chauhan, R. (2019). Estimation of Soil Erosion in Nepal using a RUSLE modeling and geospatial tool. Geosciences, 9(4). https://doi.org/10.3390/geosciences9040147

  • Kulimushi, L. C., Choudhari, P., Mubalama, L. K., & Banswe, G. T. (2021). GIS and remote sensing-based assessment of soil erosion risk using RUSLE model in South-Kivu province, eastern, Democratic Republic of Congo. Geomatics, Natural Hazards and Risk, 12(1), 961–987. https://doi.org/10.1080/19475705.2021.1906759

    Article  Google Scholar 

  • Kumar, A., Satyannarayana, R., & Rajesh, B. G. (2022a). Correlation between SPT-N and shear wave velocity (VS) and seismic site classification for Amaravati city, India. Journal of Applied Geophysics, 205. https://doi.org/10.1016/j.jappgeo.2022.104757

  • Kumar, P., Garg, V., Mittal, S., & Murthy, Y. V. N. K. (2022b). GIS-based hazard and vulnerability assessment of a torrential watershed. Environment, Development and Sustainability, 24, 921–951. https://doi.org/10.1007/s10668-021-01476-z

    Article  Google Scholar 

  • Lane, L. J., & Nearing, M. A. (1989). USDA- Water Erosion Prediction Project: Hill Slope Profile Model Documentation. NSERL Report No. 2, USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, Indiana.

  • Lenka, N. K., Satapathy, K. K., Lal, R., Singh, R. K., Singh, N. A. K., Agrawal, P. K., et al. (2017). Weed strip management for minimizing soil erosion and enhancing productivity in the sloping lands of north-eastern India. Soil and Tillage Research, 170, 104–113. https://doi.org/10.1016/j.still.2017.03.012

    Article  Google Scholar 

  • Lu, D., Li, G., Valladares, G. S., & Batistella, M. (2004). Mapping soil erosion risk in Rondônia, Brazilian Amazonia: Using RUSLE, remote sensing and GIS. Land Degradation and Development, 15(5), 499–512. https://doi.org/10.1002/ldr.634

    Article  Google Scholar 

  • Maji, A.K., Reddy, G.P.O., & Sarkar, D. (2012). Degraded and Wastelands of India Status and Spatial Distribution. Indian Council of Agricultural Research and National Academy of Agricultural Sciences.

  • Mandal, S., & Gagoi, M. (2022). Eastern Himalayan Division : A Potential Zone to be Hub of Agriculture. Indian Farmer, 9(12), 559–566.

    Google Scholar 

  • Mandal, D., & Sharda, V. N. (2013). Appraisal of soil erosion risk in the eastern himalayan region of india for soil conservation planning. Land Degradation and Development, 24(5), 430–437. https://doi.org/10.1002/ldr.1139

    Article  Google Scholar 

  • Masroor, M., Sajjad, H., Rehman, S., Singh, R., Hibjur Rahaman, M., Sahana, M., et al. (2022). Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin, India. Geoscience Frontiers, 13(2), 101312. https://doi.org/10.1016/j.gsf.2021.101312

    Article  Google Scholar 

  • Mohammed, S., Alsafadi, K., Talukdar, S., Kiwan, S., Hennawi, S., Alshihabi, O., et al. (2020). Estimation of soil erosion risk in southern part of Syria by using RUSLE integrating geo informatics approach. Remote Sensing Applications: Society and Environment, 20. https://doi.org/10.1016/j.rsase.2020.100375

  • Morgan, R. P. C., Quinton, J. N., Smith, R. E., Govers, G., Poesen, J. W. A., Auerswald, K., et al. (1998). The European soil erosion model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms, 23(6), 527–544. https://doi.org/10.1002/(SICI)1096-9837(199806)23:6%3c527::AID-ESP868%3e3.0.CO;2-5

    Article  Google Scholar 

  • Mutti, P. R., Dubreuil, V., Bezerra, B. G., Arvor, D., de Oliveira, C. P., & Santos e Silva, C. M. (2020). Assessment of Gridded CRU TS Data for Long-Term Climatic Water Balance Monitoring over the São Francisco Watershed, Brazil. Atmosphere, 11. https://doi.org/10.3390/atmos11111207

  • Narayana, D. V. V., & Babu, R. (1983). Estimation of soil erosion in India. Journal of Irrigation and Drainage Engineering, 109(4), 419–434. https://doi.org/10.1061/(asce)0733-9437(1983)109:4(419)

    Article  Google Scholar 

  • Nasir Ahmad, N. S. B., Mustafa, F. B., & Muhammad Yusoff, S. Y. (2023). Spatial prediction of soil erosion risk using knowledge-driven method in Malaysia’s Steepland Agriculture Forested Valley. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03251-8

    Article  Google Scholar 

  • Oldeman, L. R., Hakkeling, R. T., & Sombroek, W. G. (1990). ISRIC Report 1990/07: World map of the status of human-induced soil degradation: An Explanatory note. Wageningen.

    Google Scholar 

  • Olika, G., Fikadu, G., & Gedefa, B. (2023). GIS based soil loss assessment using RUSLE model: A case of Horo district, western Ethiopia. Heliyon, 9(2). https://doi.org/10.1016/j.heliyon.2023.e13313

  • Opeyemi, O. A., Abidemi, F. H., & Victor, O. K. (2019). Assessing the impact of soil erosion on residential areas of Efon-Alaaye Ekiti, Ekiti-State, Nigeria. International Journal of Environmental Planning and Management, 5(1), 23–31.

    Google Scholar 

  • Pal, S. C., & Chakrabortty, R. (2019). Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Advances in Space Research, 64(2), 352–377. https://doi.org/10.1016/j.asr.2019.04.033

    Article  Google Scholar 

  • Pal, S. C., Chakrabortty, R., Roy, P., Chowdhuri, I., Das, B., Saha, A., & Shit, M. (2021). Changing climate and land use of 21st century influences soil erosion in India. Gondwana Research, 94, 164–185. https://doi.org/10.1016/j.gr.2021.02.021

    Article  Google Scholar 

  • Pan, J., & Wen, Y. (2014). Estimation of soil erosion using RUSLE in Caijiamiao watershed, China. Natural Hazards, 71(3), 2187–2205. https://doi.org/10.1007/s11069-013-1006-2

    Article  Google Scholar 

  • Pandey, A., Himanshu, S. K., Mishra, S. K., & Singh, V. P. (2016). Physically based soil erosion and sediment yield models revisited. CATENA, 147, 595–620. https://doi.org/10.1016/j.catena.2016.08.002

    Article  Google Scholar 

  • Poreba, G. J., & Prokop, P. (2011). Estimation of soil erosion on cultivated fields on the hilly Meghalaya Plateau, North-East India. Geochronometria, 38(1), 77–84. https://doi.org/10.2478/s13386-011-0008-7

    Article  Google Scholar 

  • 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. Geoscience Frontiers, 3(2), 209–215. https://doi.org/10.1016/j.gsf.2011.11.003

    Article  Google Scholar 

  • Räsänen, T. A., Tähtikarhu, M., Uusi-Kämppä, J., Piirainen, S., & Turtola, E. (2023). Evaluation of RUSLE and spatial assessment of agricultural soil erosion in Finland. Geoderma Regional, 32. https://doi.org/10.1016/j.geodrs.2023.e00610

  • Rawat, K. S., & Singh, S. K. (2018). Appraisal of soil conservation capacity using NDVI model-based C factor of RUSLE model for a semi arid ungauged watershed: A case study. Water Conservation Science and Engineering, 3, 47–58. https://doi.org/10.1007/s41101-018-0042-x

    Article  Google Scholar 

  • Renard, K. G., Foster, G. R., Weesies, G. A., & Porter, J. P. (1991). RUSLE: Revised universal soil loss equation. Journal of Soil & Water Conservation, 46(1), 30–33.

    Google Scholar 

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., & Yoder, D. (1997). Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook No. 703. Washington, D.C.

  • Rose, C. W., Coughlan, K. J., & Fentie, B. (1998). Griffith University Erosion System Template (GUEST). In D. Boardman, J., Favis-Mortlock (Ed.), Modelling Soil Erosion by Water (pp. 399–412). NATO ASI Series. https://doi.org/10.1007/978-3-642-58913-3_30

  • Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use / land cover classification using remote sensing and GIS. International Journal of Geosciences, 8, 611–622. https://doi.org/10.4236/ijg.2017.84033

    Article  Google Scholar 

  • Schmidt, J. (1991). A mathematical model to simulate rainfall erosion. Catena Supplement (Giessen), 19, 101–109.

    Google Scholar 

  • Schmidt, S., Alewell, C., & Meusburger, K. (2018). Mapping spatio-temporal dynamics of the cover and management factor (C-factor) for grasslands in Switzerland. Remote Sensing of Environment, 211, 89–104. https://doi.org/10.1016/j.rse.2018.04.008

    Article  Google Scholar 

  • Schönbrodt, S., Saumer, P., Behrens, T., Seeber, C., & Scholten, T. (2010). Assessing the USLE crop and management factor C for soil erosion modeling in a large mountainous watershed in Central China. Journal of Earth Science, 21(6), 835–845. https://doi.org/10.1007/s12583-010-0135-8

    Article  Google Scholar 

  • Schramm, M. (1994). Ein Erosionsmodell mit zeitlich und raumlich veranderlicher Rillengeometrie. Mitt Inst Wasserbau Und Kulturtechnik, 190.

  • Shafizadeh-moghadam, H., Asghari, A., Taleai, M., & Tayyebi, A. (2017). Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata. GIScience & Remote Sensing, 54(5), 639–656. https://doi.org/10.1080/15481603.2017.1309125

    Article  Google Scholar 

  • Sharda, V. N., & Ojasvi, P. R. (2016). A revised soil erosion budget for India: Role of reservoir sedimentation and land-use protection measures. Earth Surface Processes and Landforms, 41(14), 2007–2023. https://doi.org/10.1002/esp.3965

    Article  Google Scholar 

  • Sharpley, A. N., & Williams, J. R. (1990). EPIC: The erosion-productivity impact calculator. Model documentation. U.S. Department of Agriculture Technical Bulletin No. 1768, 235.

  • Shit, P. K., Nandi, A. S., & Bhunia, G. S. (2015). Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India. Modeling Earth Systems and Environment, 1(3), 1–12. https://doi.org/10.1007/s40808-015-0032-3

    Article  Google Scholar 

  • Singh, S., & Dubey, A. (2002). Gully erosion and management methods and application (A field manual) (pp. 1–2). New Academic Publishers.

  • Sivapalan, M., Viney, N. R., Zammit, C., Singh, V. P., & Frevert, D. K. (2002). LASCAM: Large scale catchment model. Mathematical Models of Large Watershed Hydrology, 579–648.

  • Tagung, T., Singh, S. K., Singh, P., Kashiwar, S. R., Singh, K. K., & Singh, A. (2022). A review on assessment of soil loss through erosion using revised universal soil loss equation (RUSLE) model. The Pharma Innovation Journal, 11(8), 486–493.

    Google Scholar 

  • Taye, G., Vanmaercke, M., Poesen, J., Wesemael, B. V., Tesfaye, S., Teka, D., et al. (2018). Determining RUSLE P- and C-factors for stone bunds and trenches in rangeland and cropland, North Ethiopia. Land Degradation and Development, 29(3), 812–824. https://doi.org/10.1002/ldr.2814

    Article  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(3–4), 228–245. https://doi.org/10.1016/j.geomorph.2009.06.009

    Article  Google Scholar 

  • Thapa, P. (2020). Spatial estimation of soil erosion using RUSLE modeling: A case study of Dolakha district, Nepal. Environmental Systems Research, 9. https://doi.org/10.1186/s40068-020-00177-2

  • Thomas, J., Joseph, S., & Thrivikramji, K. P. (2018). Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats, India using RUSLE and GIS. Geoscience Frontiers, 9(3), 893–906. https://doi.org/10.1016/j.gsf.2017.05.011

    Article  CAS  Google Scholar 

  • Tian, P., Zhu, Z., Yue, Q., He, Y., Zhang, Z., Hao, F., et al. (2021). Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province, China. International Soil and Water Conservation Research, 9(3), 433–444. https://doi.org/10.1016/j.iswcr.2021.04.007

    Article  Google Scholar 

  • Vatandaşlar, C., & Yavuz, M. (2017). Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed. Environmental Earth Sciences, 76(2). https://doi.org/10.1007/s12665-017-6388-0

  • Vijith, H., Seling, L. W., & Dodge-Wan, D. (2018). Estimation of soil loss and identification of erosion risk zones in a forested region in Sarawak, Malaysia, Northern Borneo. Environment, Development and Sustainability, 20(3), 1365–1384. https://doi.org/10.1007/s10668-017-9946-4

    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 yield and sources. USDA, Agricultural Research Service.

  • Williams, J. R. (1995). The EPIC model. In V. P. Singh (Ed.), Computer models of watershed hydrology, Chapter 25. Water Resources Publications.

  • Wischmeier, W. H., & Smith, D. D. (1972). Rainfall-erosion losses from cropland east of the rocky mountains: Guide for selection of practices for soil and water conservation. USDA agricultural handbook No.282, Washington, DC.

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting Rainfall Erosion losses. A guide to conservation planning. The USDA agricultural handbook No. 537, Washington, DC.

  • Woolhiser, D. A., Smith, R. E., & Goodrich, D. C. (1990). KINEROS, A Kinematic Runoff and Erosion Model: Documentation and user manual. USDA, Agricultural research service, ARS-77.

  • Young, R. A., Onstad, C. A., Bosch, D. D., & Anderson, W. P. (1987). AGNPS, Agricultural Non-PointSource Pollution Model - A watershed analysis tool. USDA, Conservation Research Report-35, Albancy, CA.

Download references

Acknowledgements

The authors wish to thank the anonymous reviewers for their constructive comments and insightful suggestions which helped us to improve the overall quality of the manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Meghalaya, Shillong, Meghalaya, 793003, India

    Naveen Badavath, Smrutirekha Sahoo & Rasmiranjan Samal

Authors
  1. Naveen Badavath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Smrutirekha Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rasmiranjan Samal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naveen Badavath.

Ethics declarations

Conflict of interest

The author declares no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Badavath, N., Sahoo, S. & Samal, R. Assessing soil erosion risk in Meghalaya, India: integrating geospatial data with RUSLE model. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04855-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10668-024-04855-4

Keywords

Search

Navigation