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Soil Erosion Modeling Using Remote Sensing and GIS

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Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management

Part of the book series: Water Science and Technology Library ((WSTL,volume 105))

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Abstract

Besides a naturally occurring process, soil erosion results in a continuous loss of topsoil, ecological degradation, etc. Evaluating soil loss from watersheds is required while assessing the severity of soil erosion. The average annual soil loss from the Nathpa-Jhakri catchment has been estimated by employing the Revised Universal Soil Loss Equation (RUSLE) and Morgan-Morgan-Finney (MMF) models in the present study. The RUSLE factors and MMF parameters were calculated using meteorological data, FAO soil map, ASTER DEM map, European Space Agency (ESA) land use/cover map, and other reference studies. The model factors and parameters were integrated into the geographic information system (GIS) environment to estimate the soil loss. GIS was used in this study to generate, manipulate, and spatially organize disparate data for soil erosion modeling. The estimated average annual soil loss using the RUSLE and MMF models was 20.42 and 26.29 tons/ha/year, respectively. The coefficient of determination for sediment yield using the RUSLE and MMF models was 0.80 and 0.75, with a variation of 13.41% and 21.62%, respectively. Further, the total catchment area was categorized into the different erosion classes, viz., slight, moderate, high, very high, severe, and very severe. The RUSLE model showed that about 35.8% of the area of the Nathpa-Jhakri catchment lies in the slight to moderate, and 64.2% of the area lies in the high to very severe soil erosion classes. The soil loss estimated by MMF model showed that 13.88% of the Nathpa-Jhakri catchment area lies in the slight to moderate, and 86.12% of the area lies in the high to very severe soil erosion classes. The RUSLE model showed more precise results than the MMF model for the Nathpa-Jhakri catchment. Based on RUSLE model results, about 64.2% catchment area of the Nathpa-Jhakri needs immediate attention for proper land use management practices.

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References

  • Amsalu T, Mengaw A (2014) GIS-based soil loss estimation using RUSLE model: the case of jabitehinan woreda, ANRS, Ethiopia. Nat Res

    Google Scholar 

  • Angima SD, Stott DE, O’neill MK, Ong CK, Weesies GA (2003) Soil erosion prediction using RUSLE for central Kenyan highland conditions. Agric Ecosyst Environ 97(1–3):295–308

    Google Scholar 

  • Biswas SS, 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

    Article  Google Scholar 

  • Chen T, Niu RQ, Li PX, Zhang LP, Du B (2011) Regional soil erosion risk mapping using RUSLE, GIS, and remote sensing: a case study in Miyun Watershed, North China. Environ Earth Sci 63(3):533–541

    Article  Google Scholar 

  • Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using RUSLE, GIS and remote sensing. Water Resour Manage 22(12):1783–1798

    Google Scholar 

  • Das B, Paul A, Bordoloi R, Tripathi OP, Pandey PK (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 Envir 4(1):373–381

    Article  Google Scholar 

  • Demirci A, Karaburun A (2012) Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake Watershed, Northwest Turkey. Environ Earth Sci 66(3):903–913

    Article  Google Scholar 

  • De Roo APJ, Jetten VG (1999) Calibrating and validating the LISEM model for two data sets from the Netherlands and South Africa. Catena 37(3–4):477–493

    Google Scholar 

  • Devatha CP, Deshpande V, Renukaprasad MS (2015) Estimation of soil loss using RUSLE model for Kulhan Watershed, Chattisgarh—a case study. Aquat Procedia 4:1429–1436

    Google Scholar 

  • Dhruvanarayana VV, Babu R (1983) Estimation of soil erosion in India. J Irrig Drain Eng 109(4):419–434

    Google Scholar 

  • Diodato N, Bellocchi G (2010) MedREM, a rainfall erosivity model for the Mediterranean region. J Hydro 387(1–2):119–127

    Article  Google Scholar 

  • Eisazadeh L, Sokouti R, Homaee M, Pazira E (2012) Comparison of empirical models to estimate soil erosion and sediment yield in micro catchments. Eurasian J Soil Sci 1(1):28–33

    Google Scholar 

  • Fistikoglu O, Harmancioglu NB (2002) Integration of GIS with USLE in assessment of soil erosion. Water Res Manag 16(6):447–467

    Article  Google Scholar 

  • Gelagay HS, Minale AS (2016) Soil loss estimation using GIS and remote sensing techniques: a case of Koga Watershed, Northwestern Ethiopia. Int Soil Water Conser Res 4(2):126–136

    Article  Google Scholar 

  • Ghosal K, Bhattacharya SD (2020) A review of RUSLE model. J Indian Soc Remote Sens 48(4):689–707

    Google Scholar 

  • Griffin ML, Beasley DB, Fletcher JJ, Foster GR (1988) Estimating soil loss on topographically non-uniform field and farm units. J Soil Water Conser 43(4):326–331

    Google Scholar 

  • Igwe PU, Onuigbo AA, Chinedu OC, Ezeaku II, Muoneke MM (2017) Soil erosion: a review of models and applications. Int J Adv Eng Res Sci 4(12):237341

    Google Scholar 

  • Jain MK, Kothyari UC (2000). Estimation of soil erosion and sediment yield using GIS. Hydrol Sci J 45(5):771–786

    Google Scholar 

  • Jain MK, Kumar S, Varghese J (2001) Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour Manage 15(1):41–54

    Article  Google Scholar 

  • Jain MK, Mishra SK, Shah RB (2010) Estimation of sediment yield and areas vulnerable to soil erosion and deposition in a Himalayan watershed using GIS. Curr Sci 213–221

    Google Scholar 

  • Langdale GW, West LT, Bruce RR, Miller WP, Thomas AW (1992) Restoration of eroded soil with conservation tillage. Soil Technol 5(1):81–90

    Article  Google Scholar 

  • McCool DK, Brown LC, Foster GR, Mutchler CK, Meyer LD (1987) Revised slope steepness factor for the Universal Soil Loss Equation. Transactions of the ASAE 30(5):1387–1396

    Google Scholar 

  • Meyer LD, Wischmeier WH (1969) Mathematical simulation of the process of soil erosion by water. Trans ASAE 12(6):754–0758

    Google Scholar 

  • 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

    Google Scholar 

  • Mondal A, Khare D, Kundu S (2017) Uncertainty analysis of soil erosion modelling using different resolution of open-source DEMs. Geocarto Int 32(3):334–349

    Google Scholar 

  • Moore ID, Burch GJ (1986) Physical basis of the length‐slope factor in the universal soil loss equation. Soil Sci Soc Am J 50(5):1294–1298

    Google Scholar 

  • Morgan RPC, Morgan DDV, Finney HJ (1984) A predictive model for the assessment of soil erosion risk. J Agric Eng Res 30:245–253

    Google Scholar 

  • Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece. Int J Remote Sens 27(21):4819–4838

    Article  Google Scholar 

  • Onstad CA, Piest RF, Saxton KE. In: 3rd Federal interagency sedimentation conference proceeding water resouces council, Washington DC, USA

    Google Scholar 

  • Onyando JO, Kisoyan P, Chemelil MC (2005) Estimation of potential soil erosion for river perkerra catchment in Kenya. Water Res Manag 19(2):133–143

    Article  Google Scholar 

  • Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion prone areas in the small agricultural watershed using RUSLE, GIS and remote sensing. Water Resour Manage 21(4):729–746

    Article  Google Scholar 

  • Pandey A, Chowdary VM, Mal BC, Billib M (2008) Runoff and sediment yield modeling from a small agricultural watershed in India using the WEPP model. J Hydrol 348(3–4):305–319

    Google Scholar 

  • Pandey A, Gautam AK, Chowdary VM, Jha CS, Cerdà A (2021) Uncertainty assessment in soil erosion modelling using RUSLE, multisource and multiresolution DEMs. J Indian Soc Remote Sens 1–19

    Google Scholar 

  • Pandey A, Mathur A, Mishra SK, Mal BC (2009) Soil erosion modelling of a Himalayan watershed using RS and GIS. Envir Earth Sci 59(2):399–410

    Article  Google Scholar 

  • Pandey A, Mishra SK, Gautam AK (2015) Soil erosion modelling using satellite rainfall estimates. J Water Res Hydra Eng 4(4):318–325

    Google Scholar 

  • Prasannakumar V, Vijith H, Abinod S, Geetha NJGF (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

    Article  Google Scholar 

  • Ranzi R, Le TH, Rulli MC (2012) A RUSLE approach to model suspended sediment load in the Lo river (Vietnam): effects of reservoirs and land use changes. J Hydrol 422:17–29

    Google Scholar 

  • Rapp JF (1994) Error assessment of the Revised Universal Soil Loss Equation using natural runoff plot data. M.S. thesis, University of Arizona, Tucson, Arizona. http://hdl.handle.net/10150/291699

  • Raymo ME, Ruddiman WF (1992) Tectonic forcing of late Cenozoic climate. Nature 359(6391):117–122

    Article  Google Scholar 

  • Renard KG (1997) Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). United States Government Printing

    Google Scholar 

  • Risse LM, Nearing MA, Laflen JM, AD Nicks (1993) Soil Sci Soc Am J 57:3

    Google Scholar 

  • Rodrigo-Comino J (2018) Five decades of soil erosion research in “terroir”. The State-of-the-Art. Earth-Sci Rev 179:436–447

    Google Scholar 

  • Sharma A (2010) Integrating terrain and vegetation indices for identifying potential soil erosion risk area. Geo-Spatial Inf Sci 13(3):201–209

    Google Scholar 

  • Shrestha DP (1997) Soil erosion modelling. ILWIS application guide

    Google Scholar 

  • Singh G, Babu R, Narain P, Bhushan LS, Abrol IP (1992) Soil erosion rates in India. J Soil Water Conserv 47(1):97–99

    Google Scholar 

  • Singh G, Panda RK (2017) Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using RUSLE, GIS and remote sensing: a case study in the Kapgari watershed, India. Int Soil Water Conserv Res 5(3):202–211

    Google Scholar 

  • Thampapillai DJ, Anderson JR (1994) A review of the socio-economic analysis of soil degradation problems for developed and developing countries. Rev Mark Agric Econ 62(430-2016-31509):291–315

    Google Scholar 

  • Tiwari AK, Risse LM, Nearing MA (2000) Evaluation of WEPP and its comparison with RUSLE and RUSLE. Trans ASAE 43(5):1129

    Article  Google Scholar 

  • Udayakumara EPN, Shrestha RP, Samarakoon L, Schmidt-Vogt D (2010) People’s perception and socioeconomic determinants of soil erosion: a case study of Samanalawewa watershed, Sri Lanka. Int J Sed Res 25(4):323–339

    Google Scholar 

  • USDA ARS (Agricultural Research Service) (2008) Draft science documentation, Revised Universal Soil Loss Equation Version 2. USDA Agricultural Research Service, Washington, DC. http://www.ars.usda.gov/sp2UserFiles/Place/64080510/RUSLE/RUSLE2_Science_Doc.pdf

  • Wijesundara NC, Abeysingha NS, Dissanayake DMSLB (2018) GIS-based soil loss estimation using RUSLE model: a case of Kirindi Oya river basin, Sri Lanka. Model Earth Syst Environ 4(1):251–262

    Article  Google Scholar 

  • Williams JR, Berndt HD (1972) Sediment yield computed with universal equation. J Hydraul Div 98(12):2087–2098

    Google Scholar 

  • Williams JR, Berndt HD (1977) Sediment yield prediction based on watershed hydrology. Trans ASAE 20(6):1100–1104

    Google Scholar 

  • Wischmeier WH, Smith DD (1965) Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: guide for selection of practices for soil and water conservation (No. 282). Agricultural Research Service, US Department of Agriculture

    Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science, and Education Administration

    Google Scholar 

  • Wuepper D, Borrelli P, Finger R (2020) Countries and the global rate of soil erosion. Nat Sustain 3(1):51–55

    Article  Google Scholar 

  • Yoder DC, Ketchem AJ, Whittemore DA, Porter JP, Weesies GA, Renard KG (1992) RUSLE user guide. Predicion soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE)

    Google Scholar 

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Authors and Affiliations

  1. Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India

    Osama Mirran Hussien Al-Qaim, Vikas G. Jadhao & Ashish Pandey

Authors
  1. Osama Mirran Hussien Al-Qaim
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  2. Vikas G. Jadhao
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  3. Ashish Pandey
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Correspondence to Ashish Pandey .

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Editors and Affiliations

  1. Department of Biological and Agricultural Engineering, Zachry Department of Civil and Environment Engineering, Texas A&M University, College Station, TX, USA

    Vijay P. Singh

  2. Department of Civil Engineering, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India

    Shalini Yadav

  3. Faculty of Science, Madhyanchal Professional University, Bhopal, India

    Krishna Kumar Yadav

  4. IHE Delft Institute for Water Education, Delft, The Netherlands

    Gerald Augusto Corzo Perez

  5. The School of Natural Resources and Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

    Francisco Muñoz-Arriola

  6. Madhyanchal Professional University, Bhopal, India

    Ram Narayan Yadava

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Al-Qaim, O.M.H., Jadhao, V.G., Pandey, A. (2022). Soil Erosion Modeling Using Remote Sensing and GIS. In: Singh, V.P., Yadav, S., Yadav, K.K., Corzo Perez, G.A., Muñoz-Arriola, F., Yadava, R.N. (eds) Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management. Water Science and Technology Library, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-031-14096-9_8

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