Skip to main content

Advertisement

SpringerLink
Log in

Using Google Earth Engine and GIS for basin scale soil erosion risk assessment: A case study of Chambal river basin, central India

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

Abstract

In this study, soil loss by water in the Chambal river basin (CHB) has been estimated using Google Earth Engine and Geographic Information System platforms employing the Revised Universal Soil Loss Equation (RUSLE). Google Earth Engine, a cloud-based platform, has been used for generating land use/land cover map from the voluminous satellite remote sensing data. The observed mean rate of soil erosion in the CHB is 1.17 t/ha/yr. The study reveals that very severe type of soil erosion in the Chambal river basin is found in the gullies/ravines and barren lands with the highest mean rate of soil erosion in the gully areas at 13.44 t/ha/yr. Catchment-wise soil loss estimates suggest that the four catchments namely Kali Sindh, Lower Chambal, Upper Chambal and Parbati are experiencing much higher soil loss in comparison to others; hence these catchments are prioritised for soil conservation efforts. Sensitivity analysis of individual factors and their interaction effect indicates that LS factor is the most influential factor in the study area followed by C factor.

Research highlights

  • Estimates soil loss by water for the first time at basin scale in the Chambal river basin (mean rate 1.17 t/ha/yr) by integrating Google Earth Engine and GIS employing RUSLE.

  • Provides insight into the spatial pattern and status of soil erosion in the study area.

  • Reveals very severe type of soil erosion in the areas of gullies/ravines with the highest mean rate 13.44 t/ha/yr and 5.92 t/ha/yr, respectively.

  • LS factor is the most influential factor in the study area, followed by C factor.

  • Prioritises Kali Sindh, Lower Chambal, Upper Chambal and Parbati catchments for soil conservation efforts based on much higher soil loss.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  • AIS and LUP 1990 Watershed Atlas of India, All India Soil, and Land use Survey; Department of Agriculture and Cooperation, Government of India.

  • Alewell C, Borrelli P, Meusburger K and Panagos P 2019 Using the USLE: Chances, challenges and limitations of soil erosion modelling; Int. Soil Water Conserv. Res. 7(3) 203–225, https://doi.org/10.1016/j.iswcr.2019.05.004.

    Article  Google Scholar 

  • Amundson R, Berhe A A, Hopmans J W, Olson C, Sztein A E and Sparks D L 2015 Soil and human security in the 21st century; Science 348(6235) 1–6, https://doi.org/10.1126/science.1261071.

    Article  Google Scholar 

  • Belgiu M and Dragut L 2016 Random forest in remote sensing: A review of applications and future directions; ISPRS J. Photogramm. Remote Sens. 114 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011.

    Article  Google Scholar 

  • Biggelaar C D, Lal R, Wiebe K, Eswaran H, Breneman V and Reich P 2004 The global impact of soil erosion on productivity. II. Effects on crop yields and production over time; Adv. Agron. 81 49–95, https://doi.org/10.1016/S0065-2113(03)81002-7.

    Article  Google Scholar 

  • 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; Model. Earth Syst. Environ. 1(4) 1–13, https://doi.org/10.1007/s40808-015-0040-3.

    Article  Google Scholar 

  • Borrelli D A, Robinson L R, Fleischer E, Lugato C, Ballabio C, Alewell K, Meusburger S, Modugno S, Schütt B, Ferro V, Bagarello V, Oost V K, Montanarella L and Panagos P 2017 An assessment of the global impact of 21st century land use change on soil erosion; Nat. Commun. 8(1) 1–13, https://doi.org/10.1038/s41467-017-02142-7.

    Article  Google Scholar 

  • Borrelli P, Robinson D A, Panagos P, Lugato E, Yang J E, Alewell C and Ballabio C 2020 Land use and climate change impacts on global soil erosion by water (2015–2070); Proc. Natl. Acad. Sci. 117(36) 21,994–22,001, https://doi.org/10.1073/pnas.2001403117.

    Article  Google Scholar 

  • Bruinsma J 2003 World agriculture: Towards 2015/2030: An FAO perspective; Earthscan, London, UK.

  • Das D C 1985 Problem of soil erosion and land degradation in India; Paper presented at National Seminar on Soil Conservation and Watershed Management, September 17–18.

  • 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; Model. Earth Syst. Environ. 4(1) 373–381, https://doi.org/10.1007/s40808-018-0435-z.

    Article  Google Scholar 

  • Devatha C P, Deshpande V and Renukaprasad M S 2015 Estimation of soil loss using USLE model for Kulhan Watershed, Chattisgarh: A case study; Aquat. Procedia 4 1429–1436, https://doi.org/10.1016/j.aqpro.2015.02.185.

    Article  Google Scholar 

  • Dwivedi R S and Ramana K V 2003 The delineation of reclamative groups of ravines in the Indo-Gangetic alluvial plains using IRS-1D LISS-III data; Int. J. Remote Sens. 24(22) 4347–4355, https://doi.org/10.1080/0143116031000116994.

    Article  Google Scholar 

  • Elkhrachy I 2018 Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia; Ain Shams Eng. J. 9(4) 1807–1817, https://doi.org/10.1016/j.asej.2017.01.007.

    Article  Google Scholar 

  • Eswaran H, Lal R and Reich P F 2001 Land degradation: An overview; In: Responses to land degradation (eds) Bridges E M, Hannam I D, Oldeman L R, Pening de Vries F W T, Scherr S J and Sompatpanit S, Proceedings 2nd International Conference on Land Degradation and Desertification, Khon Kaen, Thailand. New Delhi, India: Oxford Press, pp. 20–35.

  • FAO (Food and Agriculture Organization) 2005 The state of food insecurity in the world 2005: Eradicating world hunger key to achieving the millennium development goals.

  • FAO (Food and Agriculture Organization) 2006 The state of food and agriculture 2006; Food aid for food security (No. 37).

  • FAO (Food and Agriculture Organization) 2015 Healthy soils are the basis for healthy food production; Food and Agriculture Organization of the United Nations 1–4, http://www.fao.org/documents/card/en/c/645883cd-ba28-4b16-a7b8-34babbb3c505/.

  • 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; Geosci. Front. 7(6) 953–961, https://doi.org/10.1016/j.gsf.2015.10.007.

    Article  Google Scholar 

  • Gelfand I, Sahajpal R, Zhang X, Izaurralde R C, Gross K L and Robertson G P 2013 Sustainable bioenergy production from marginal lands in the US Midwest; Nature 493(7433) 514–517, https://doi.org/10.1038/nature11811.

    Article  Google Scholar 

  • Hickey R 2001 Slope angle and slope length solutions for GIS; Cartogr. J. 29(1) 1–8, https://doi.org/10.1080/00690805.2000.9714334.

    Article  Google Scholar 

  • Jain S K, Kumar S and Varghese J 2001 Estimation of soil erosion for a Himalayan watershed using GIS technique; Water Resour. Manag. 15(1) 41–54, https://doi.org/10.1023/A:1012246029263.

    Article  Google Scholar 

  • Jain M K, Mishra S K and Shah R B 2010 Estimation of sediment yield and areas vulnerable to soil erosion and deposition in a Himalayan watershed using GIS; Curr. Sci. 98(2) 213–221.

    Google Scholar 

  • Jones D S, Kowalski D G and Shaw R B 1996 Calculating revised universal soil loss equation (RUSLE) estimates on department of defense lands: A review of RUSLE factors and US army land condition-trend analysis (LCTA) data gaps; Center for Ecological Management of Military Lands, Colorado State University.

  • Joshi V U 2014 The Chambal Badlands; In: Landscapes and Landforms of India (ed.) Kale V S, Springer, Netherlands, pp. 143–149, https://doi.org/10.1007/978-94-017-8029-2_13.

  • Kaushik P and Ghosh P 2015 Geomorphic evolution of Chambal river origin in Madhya Pradesh using remote sensing and GIS; Int. J. Adv. Remote Sens. GIS 4(1) 1130–1141, http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-394.

  • Koirala P, Thakuri S, Joshi S and Chauhan R 2019 Estimation of soil erosion in Nepal using a RUSLE modeling and geospatial tool; Geosci. J. 9(4) 1–19, https://doi.org/10.3390/geosciences9040147.

    Article  Google Scholar 

  • Kothyari U C 1996 Erosion and sedimentation problems in India; IAHS Publ. – Series of Proceedings and Reports – Proc. Int. Assoc. Hydrol. Sci. 236 531–540.

    Google Scholar 

  • Kumar R and Deshmukh B 2018 Soil loss assessment using geospatial technology: Challenges and opportunities with reference to the Chambal basin, central India; In: Environmental and Ecological Sustainability (eds) Rupini B, Baskar S, Sahoo, S and Kapur N S, Proceedings International Conference on Environmental and Ecological Sustainability – Engaging the Stakeholders, IGNOU, New Delhi, India, pp. 304–316, ISBN: 978-93-87960-94-7.

  • Kumar A, Devi M and Deshmukh B 2014 Integrated remote sensing and Geographic Information System based RUSLE modelling for estimation of soil loss in western Himalaya, India; Water Resour. Manag. 28 3307–3317, https://doi.org/10.1007/s11269-014-0680-5.

    Article  Google Scholar 

  • Lambin E F, Gibbs H K, Ferreira L, Grau R, Mayaux P, Meyfroidt P and Munger J 2013 Estimating the world’s potentially available cropland using a bottom-up approach; Glob. Environ. Change 23(5) 892–901, https://doi.org/10.1016/j.gloenvcha.2013.05.005.

    Article  Google Scholar 

  • Mannering J V 1981 Use of soil loss tolerances as a strategy for soil conservation; In: Soil conservation problems and prospects, Proceedings of Conservation 80, Int. Soil Water Conserv. Silsoe, Beford, UK, 21st–25th July 1980.

  • Merritt W S, Letcher R A and Jakeman A J 2003 A review of erosion and sediment transport models; Environ. Model. Softw. 18(8–9) 761–799, https://doi.org/10.1016/S1364-8152(03)00078-1.

    Article  Google Scholar 

  • Montanarella L 2015 Agricultural policy Govern our soils; Nat. News. 528(7580) 1–32.

    Article  Google Scholar 

  • Narayana D V and Babu R 1983 Estimation of soil erosion in India; J. Irrig. Drain. Eng. 109(4) 419–434, https://doi.org/10.1061/(ASCE)0733-9437(1983).

    Article  Google Scholar 

  • National Commission on Agriculture 1976 Report part V; Ministry of Agriculture, New Delhi, 443p.

  • NBSS LUP 2005 Study of Gangtok, Sikkim (representing eastern Himalayan mountain region) and Meerut, U.P. (representing the Indo-Gangetic alluvial plains); conducted jointly by NBSS&LUP and NRSA at Nagpur.

  • Nguyen P, Shearer E J, Tran H, Ombadi M, Hayatbini N, Palacios T and Kuligowski B 2019 The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data; Sci. Data 6(1) 1–10, https://doi.org/10.1038/sdata.2018.296.

    Article  Google Scholar 

  • Panagos P, Borrelli J, Poesen C, Ballabio E, Lugato M K, Montanarella L and Alewell C 2015 The new assessment of soil loss by water erosion in Europe; Environ. Sci. Policy 54 438–447, https://doi.org/10.1016/j.envsci.2015.08.012.

    Article  Google Scholar 

  • Pandey A, Chowdary V M and Mal B C 2007 Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing; Water Resour. Manag. 21(4) 729–746, https://doi.org/10.1007/s11269-006-9061-z.

    Article  Google Scholar 

  • Pani P and Carling P 2013 Land degradation and spatial vulnerabilities: A study of inter-village differences in Chambal Valley, India; Asian Geogr. 30(1) 65–79, https://doi.org/10.1080/10225706.2012.754775.

    Article  Google Scholar 

  • Pani P and Mohapatra S N 2001 Delineation and monitoring of gullied and ravinous lands in a part of lower Chambal Valley, India, using remote sensing and GIS; Paper presented at 22nd Asian Conference on Remote Sensing, 5–9 November 2001, Singapore, pp. 1–5.

  • Pelletier C, Valero S, Inglada J, Champion N and Dedieu G 2016 Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas; Remote Sens. Environ. 187 156–168, https://doi.org/10.1016/j.rse.2016.10.010.

    Article  Google Scholar 

  • Pimentel D, Harvey C, Resosudarmo P, Sinclair K, Kurz D, McNair M and Blair R 1995 Environmental and economic costs of soil erosion and conservation benefits; Science 267(5201) 1117–1123, https://doi.org/10.1126/science.267.5201.1117.

    Article  Google Scholar 

  • Poesen J 2018 Soil erosion in the Anthropocene: Research needs; Earth Surf. Process Landf. 43(1) 64–84, https://doi.org/10.1002/esp.4250.

    Article  Google Scholar 

  • Rajeevan M and Bhate J 2009 A high resolution daily gridded rainfall dataset (1971–2005) for mesoscale meteorological studies; Curr. Sci. 96(4) 558–562, https://www.jstor.org/stable/24105470.

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

    Google Scholar 

  • Renard K G, Foster G R, Weesies G A, McCool and Yoder 1997 Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE); 703. Washington, DC: United States Department of Agriculture.

  • Risse L M, Nearing M A, Laflen J M and Nicks A D 1993 Error assessment in the universal soil loss equation; Soil Sci. Soc. Am. J. 57(3) 825–833, https://doi.org/10.2136/sssaj1993.03615995005700030032x.

    Article  Google Scholar 

  • SAC Report 2016 Desertification and Land Degradation Atlas of India (based on IRS AWiFS data of 2011–13 and 2003–05); Space Applications Centre, ISRO, Ahmedabad, India, 219p.

  • Saha S K 2003 Water and wind induced soil erosion assessment and monitoring using remote sensing and GIS; Satellite remote sensing and GIS applications in agricultural meteorology, pp. 315–330.

  • Sartori M, Philippidis G, Ferrari E, Borrelli P, Lugato E, Montanarella L and Panagos P 2019 A linkage between the biophysical and the economic: Assessing the global market impacts of soil erosion; Land Use Policy 86 299–312, https://doi.org/10.1016/j.landusepol.2019.05.014.

    Article  Google Scholar 

  • Sehgal J L and Abrol I P 1994 Soil degradation in India: Status and impact, Oxford & IBH Publ Co., ISBN: 9788120409316.

  • Shyampura R L and Sehgal J 1995 Soils of Rajasthan for optimising land use. Soils of India Series; NBSS Publ. 51(b), ISBN: 81-85460-13-2.

  • Singh I B 1996 Geological evolution of Ganga Plain an overview; J. Palaeontol. Soc. India 41 99–137.

    Google Scholar 

  • Singh G, Chandra S and Babu R 1981 Soil loss and prediction research in India; Central Soil and Water Conservation Research Training Institute, Bulletin No T-12 D: 9.

  • Singh G, Babu R, Narain P, Bhushan L S and Abrol I P 1992 Soil erosion rates in India; J. Soil Water Conserv. 47(1) 97–99.

    Google Scholar 

  • Singh S P, Ram J, Walia C S, Sachdev C B, Dhankar R P, Rana K P C and Gajbhiye K S 2004 Soils of Uttar Pradesh for optimising land use; NBSS Publ. 68(b) 1–91.

    Google Scholar 

  • Stone R P and Hilborn D 2000 Universal Soil Loss Equations Fact Sheet (USLE); Ministry of Agriculture, Food and Rural Affairs Version 2 (RUSLE2).

  • Swarnkar S, Malini A, Tripathi S and Sinha R 2018 Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: An application to the Garra River basin, India; Hydrol. Earth Syst. Sci. 22(4) 2471–2485, https://doi.org/10.5194/hess-22-2471-2018.

    Article  Google Scholar 

  • Tamgadge D B 1996 Soils of Madhya Pradesh for optimising land use; National Bureau of Soil Survey & Land Use Planning Publ. 59(b), ISBN: 81-85460-32-9.

  • Teluguntla P, Thenkabail P S, Oliphant A, Xiong J, Gumma M K, Congalton R G and Huete A 2018 A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform; ISPRS J. Photogramm. Remote Sens. 144 325–340, https://doi.org/10.1016/j.isprsjprs.2018.07.017.

    Article  Google Scholar 

  • Teng H, Rossel R A V, Shi Z, Behrens T, Chappell A and Bui E 2016 Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia; Environ. Model. Softw. 77 156–167, https://doi.org/10.1016/j.envsoft.2015.11.024.

    Article  Google Scholar 

  • Van R, Maichle R and Hickey R 2004 Computing the RUSLE LS factor based on array-based slope length processing of digital elevation data using a C++ executable; Comput. Geosci. 30(9–10) 1–43.

    Google Scholar 

  • Wanielista M P and Yousef Y A 1992 Stormwater management; John Wiley & Sons.

  • Wischmeier W H and Smith D D 1965 Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: A guide for selecting practices for soil and water conservation; USDA Agricultural Handbook No. 282, http://www.ars.usda.gov/SP2UserFiles/Place/64080530/RUSLE/AH_282.pdf.

  • Wischmeier W H and Smith D D 1978 Predicting rainfall erosion losses: A guide to conservation planning; USDA, Science and Education Administration, Department of Agriculture, Science and Education Administration 537 1–58.

Download references

Acknowledgements

Authors acknowledge USGS (https://earthexplorer.usgs.gov) for making SRTM DEM 30 m data available for download, GEE for hosting Sentinel data and also to provide platform to run specific codes for LULC, and IMD for making monthly rainfall data available. RK is thankful to IGNOU for providing financial support in the form of IGNOU-Research fellowship during the period 2018–2019. RK acknowledges Akshita for her assistance in data processing and proofreading. Authors acknowledge Rajesh Kaliraman and Ashish Sharma for their assistance in sensitivity analysis.

Author information

Authors and Affiliations

  1. Discipline of Geology, School of Sciences, Indira Gandhi National Open University, New Delhi, 110 068, India

    Rohit Kumar & Benidhar Deshmukh

  2. Environmental Technology Division, CSIR-Institute of Himalayan Bioresource Technology, Council of Scientific and Industrial Research, Palampur, Himachal Pradesh, 176 061, India

    Amit Kumar

Authors
  1. Rohit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benidhar Deshmukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RK and BD conceptualised the study, designed the methodology and interpreted the results. RK acquired and processed the data and drafted the manuscript. AK assisted in data processing. BD and AK edited and improved the manuscript. BD supervised the study.

Corresponding author

Correspondence to Benidhar Deshmukh.

Additional information

Communicated by Arkoprovo Biswas

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, R., Deshmukh, B. & Kumar, A. Using Google Earth Engine and GIS for basin scale soil erosion risk assessment: A case study of Chambal river basin, central India. J Earth Syst Sci 131, 228 (2022). https://doi.org/10.1007/s12040-022-01977-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12040-022-01977-z

Keywords

Search

Navigation