Skip to main content

Advertisement

SpringerLink
Log in

Assessment of soil erosion and sediment yield in the Tamiraparani sub-basin, South India, using an automated RUSLE-SY model

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Soil erosion induces land degradation and minimizes the water-holding capacity, resulting in poor environmental quality at a basin scale. This becomes more serious due to long-term anthropogenic pressure including over exploitation of natural resources and lack of sustainable development. In this study, an automated RUSLE-SY model was developed to identify the potential soil erosion and sediment yield zones in Tamiraparani sub-basin, South India. The input parameters required to execute the RUSLE-SY model are digital elevation model, study area boundary, land-use/land-cover data, soil data, annual rainfall data, near-infrared and red bands from satellite imagery. The entire set of data was geoprocessed with standard RUSLE and sediment yield equations for generating soil erosion and sediment yield map. The distribution of soil erosion and sediment yield in the study area was analyzed, and its impacts were discussed. The result reveals that the high soil erosion is noticed in the western part of the sub-basin (31.25 ton ha−1 year−1). Moreover, the sediment yield in this area contributes around 14.54 ton ha−1 year−1. The low level of soil erosion and sediment yield (~95 % of the area) reflects the climatic variability and topography of Tamiraparani sub-basin. The resulting RUSLE-SY model provides a robust and easy to use soil erosion and conservation management tool for efficient planning in similar environments.

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

Similar content being viewed by others

References

  • Al-Quraishi AMF (2003) Soil erosion risk prediction with RS and GIS for the northwestern part of Hebei Province, China. Pak J Appl Sci 3(10–12):659–666

    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

    Article  Google Scholar 

  • Arnoldus HMJ (1980) An approximation of rainfall factor in the universal soil loss equation. In: De Boodt M, Gabriels D (eds) Assessment of erosion. Wiley, Chichester, pp 127–132

    Google Scholar 

  • Bartsch KP, Van Miegroet H, Boettinger J, Dobrwolski JP (2002) Using empirical erosion models and GIS to determine erosion risk at Camp Williams. J Soil Water Conserv 57:29–37

    Google Scholar 

  • Benkobi L, Trlica MJ, Smith JL (1994) Evaluation of a refined surface cover sub-factor for use in RUSLE. J Range Manag 47:74–78

    Article  Google Scholar 

  • Bhattarai R, Dutta D (2007) Estimation of soil erosion and sediment yield using GIS at catchment scale. Water Resour Manag 21:635–1647

    Article  Google Scholar 

  • Boggs G, Devonport C, Evans K, Puig P (2001) GIS-based rapid assessment of erosion risk in a small catchment in the wet/dry tropics of Australia. Land Degrad Dev 12:417–434

    Article  Google Scholar 

  • Boyce RC (1975) Sediment routing with sediment delivery ratios. In: Present and prospective technology for predicting sediment yields and sources, U.S. Department of Agriculture, ARS-S40, pp 61–65

  • CWRDM (1997) Attapady wasteland comprehensive environmental conservation project. Eco-restoration plan, vol 1. Center for Water Resources Development and Management, Kozhikode

  • Czubski K, Kozak J, Kolecka N (2013) Accuracy of SRTM-X and ASTER elevation data and its influence on topographical and hydrological modeling: case study of the Pieniny Mts, Poland. Int J Geoinf 9(2):7–17

    Google Scholar 

  • Dennis MF, Rorke MF (1999) The relationship of soil loss by interrill erosion to slope gradient. Catena 38:211–222

    Google Scholar 

  • Desmet PJ, Govers G (1996) A GIS-procedure for the automated calculation of the USLE LS-factor on topographically complex landscape units. J Soil Water Conserv 51:427–433

    Google Scholar 

  • ESRI (2000) ModelBuilder for ArcView Spatial Analyst 2. ESRI Press, Redlands

    Google Scholar 

  • Eswaran H, Lal R, Reich PF (2001) Land degradation: an overview. In: Bridges EM, Hannam ID, Oldeman LR, Penning de Vries FWT, Scherr SJ, Sombatpanit S (eds) Response to land degradation. Science Publishers Inc, Enfield, pp 20–35

    Google Scholar 

  • Fu BJ, Zhao WW, Chen LD, Zhang QJ, Lu YH, 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 Degrad Dev 16(1):73–85

    Article  Google Scholar 

  • Fu G, Chen S, McCool DK (2006) Modeling the impacts of no-tillage practices on soil erosion and sediment yield with RUSLE, SEDD and ArcView GIS. Soil Till Res 85(1–2):38–49

    Article  Google Scholar 

  • Goodchild MF (2005) GIS, spatial analysis and modeling overview. In: Maguire DJ, Batty M, Goodchild MF (eds) GIS, spatial analysis, and modeling. ESRI Press, Redlands, pp 1–18

    Google Scholar 

  • Gregory KJ, Walling DE (1973) Drainage basin form and processes: a geomorphological approach. Wiley, New York, p 458

    Google Scholar 

  • Hoyos N (2005) Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. Catena 63(1):85–108

    Article  Google Scholar 

  • Hu XL, Jiang XS, Yang SJ, Zhang CJ, Pan JJ (2006) Preliminary study on adaptability of rainfall erosivity model to different regions. Sci Soil Water Conserv 4(5):44–49

    Google Scholar 

  • Jasrotia AS, Singh R (2006) Modeling runoff and soil erosion in a catchment area, using the GIS, in the Himalayan region, India. Environ Geol 51:29–37

    Article  Google Scholar 

  • Karydas CG, Sekuloska T, Silleos GN (2009) Quantification and site specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean Island of Crete. Environ Monit Assess 149:19–28

    Article  Google Scholar 

  • Kinnell PIA (2000) The effect of slope length on sediment concentrations associated with side-slope erosion. Soil Sci Soc Am J 64:1004–1008

    Article  Google Scholar 

  • Kinnell PIA (2008) Sediment delivery from hillslopes and the universal soil loss equation: some perceptions and misconceptions. Hydrol Process 22:3168–3175

    Article  Google Scholar 

  • Kouli M, Soupios P, Vallianatos F (2009) Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece. Environ Geol 57:483–497

    Article  Google Scholar 

  • Krishna Bahadur KC (2009) Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa watershed, Nan Province, Thailand. Environ Geol 57:695–705

    Article  Google Scholar 

  • Lane LJ, Hernandez M, Nichols MH (1997) Processes controlling sediment yield from watersheds as functions of spatial scale. Environ Model Softw 12(4):355–369

    Article  Google Scholar 

  • Lane LJ, Nichols MH, Levick LR, Kidwell MR (2001) A simulation model for erosion and sediment yield at the hillslope scale. In: Harmon RS, Doe WW (eds) Landscape erosion and evolution modeling. Kluwer Academic/Plenum Publishers, New York, pp 201–237

    Chapter  Google Scholar 

  • Le Roux JJ, Morgenthal TL, Malherbe J, Pretorius DJ, Sumner PD (2008) Water erosion prediction at a national scale for South Africa. Water SA 34:305–314

    Google Scholar 

  • Lee GS, Lee KH (2006) Scaling effect for estimating soil loss in the RUSLE model using remotely sensed geospatial data in Korea. Hydrol Earth Syst Sci 3:135–157

    Article  Google Scholar 

  • Lim KJ, Myung Sagong M, Engel BA, Tang Z, Choi J, Kim KM (2005) GIS-based sediment assessment tool. Catena 64:61–80

    Article  Google Scholar 

  • Lu D, Li G, Valladares GS, Batistella M (2004) Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degrad Dev 15:499–512

    Article  Google Scholar 

  • Magesh NS, Chandrasekar N (2014) GIS model-based morphometric evaluation of Tamiraparani subbasin, Tirunelveli district, Tamil Nadu, India. Arab J Geosci 7(1):131–141

    Article  Google Scholar 

  • Manegold J (2003) Using the ModelBuilder of ArcGIS 9 for landscape modeling. In: Buchmann E, Ervin S (eds) Trends in landscape modeling. Proceedings at Anhalt University of Applied Sciences. Wichmann, Heidelberg, pp 240–245

    Google Scholar 

  • Mati BM (2000) Assessment of erosion hazard with USLE and GIS—a case study of the upper Ewaso Ng’iro basin of Kenya. Int J Appl Earth Obs 2:78–86

    Article  Google Scholar 

  • Matsuura H (2000) Design guidelines for mechanical soil and water conservation works and water resources development. Technical report for the Attapady wasteland comprehensive environmental conservation project (AWCECOP), ID-P111. Attapady Hills Area Development Society, Government of Kerala

  • Millward A, Mersey J (1999) Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38:109–129

    Article  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 50:1294–1298

    Article  Google Scholar 

  • Morgani RPC, Quintoni JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME (1998) The European soil erosion model (EUROSEM): a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surf Proc Land 23:527–544

    Article  Google Scholar 

  • Oh JH, Jung SG (2005) Potential soil prediction for land resource management in the Nakdong River basin. J Korean Soc Rural Plan 11(2):9–19

    Google Scholar 

  • Onori F, De Bonis P, Grauso S (2006) Soil erosion prediction at the basin scale using the revised universal soil loss equation (RUSLE) in a catchment of Sicily (southern Italy). Environ Geol 50:1129–1140

    Article  Google Scholar 

  • Pham H (2007) Evaluating potential of soil loss erosion in the Ba Be Lake basin in Vietnam, Project, Hanoi

  • Prasannakumar V, Shiny R, Geetha N, Vijith H (2011) Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India. Environ Earth Sci 64(4):965–972

    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. Geosci Front 3(2):209–215

    Article  Google Scholar 

  • Renard KG, Foster GR (1983) Soil conservation: principles of erosion by water. In: Dregne HE, Willis WO (eds) Dryland agriculture. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, pp 155–176

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, Porter JP (1991) RUSLE: revised Universal Soil Loss Equation. J Soil Water Conserv 46(1):30–33

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture Handbook No. 703. US Department of Agriculture, Washington

    Google Scholar 

  • Roehl JE (1962) Sediment source areas, delivery ratios and influencing morphological factors. Int Assoc Sci Hydrol 59:202–213

    Google Scholar 

  • Rosso R, Rulli MC (2002) An integrated simulation method for flash-flood risk assessment. 2. Effects of changes in land use under a historical perspective. Hydrol Earth Syst Sci 6(2):285–294

    Article  Google Scholar 

  • Rulli MC, Rosso R (2005) Modeling catchment erosion after wildfires in the San Gabriel Mountains of southern California. Geophys Res Lett 32:L19401. doi:10.1029/2005GL023635

    Article  Google Scholar 

  • Saha SK, Pande LM (1993) Integrated approach towards soil erosion inventory for environmental conservation using satellite and agrometeorological data. Asia-Pac Remote Sens J 5(2):21–28

    Google Scholar 

  • Schwab GO, Frevert RK, Edminster TW, Barnes KK (1981) Soil water conservation engineering, 3rd edn. Wiley, New York

    Google Scholar 

  • Shiono T, Kamimura K, Okushima S, Fukumoto M (2002) Soil loss estimation on a local scale for soil conservation planning. Jarq-Jpn Agric Res Q 36(3):157–161

    Article  Google Scholar 

  • Terranova O, Antronico R, 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 112:228–245

    Article  Google Scholar 

  • Tian YC, Zhou YM, Wu BF, Zhou WF (2009) Risk assessment of water soil erosion in upper basin of Miyun Reservoir, Beijing, China. Environ Geol 57:937–942

    Article  Google Scholar 

  • Troeh F, Hobbs J, Donahue R (1999) Soil and water conservation: productivity and environment protection. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • UNEP (1991) Status of desertification and Implementation of the UN Plan of Action to Combat Desertification. UNEP, Nairobi

    Google Scholar 

  • USDA-SCS (1972) Sediment sources, yields, and delivery ratios. National engineering handbook. Section 3 Sedimentation. Report No. 54

  • Van der Knijff JM, Jones RJA, Montanarella L (2000) Soil erosion risk assessment in Europe EUR 19044 EN. Office for Official Publications of the European Communities, Luxembourg, p 34

    Google Scholar 

  • Vanoni VA (1975) Sedimentation engineering. Manuals and reports on engineering practice—No. 54. American Society of Civil Engineers, New York

    Google Scholar 

  • Walling DE (1988) Erosion and sediment yield—some recent perspectives. J Hydrol 100:113–141

    Article  Google Scholar 

  • Wang GGG, Fang S, Anderson AB (2003) Mapping multiple variables for predicting soil loss by geostatistical methods with TM images and a slope map. Photogramm Eng Remote Sens 69:889–898

    Article  Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide to conservation. Agricultural Handbook 537. US Department of Agriculture, Washington

    Google Scholar 

  • Wischmeier WH, Johnson CB, Cross BV (1971) A soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv 26:189–193

    Google Scholar 

  • Xu Y, Zhou Q, Li S (2005) An analysis on spatial–temporal distribution of rainfall erosivity in Guizhou province. Bull Soil Water Conserv 4:11–14

    Google Scholar 

  • Yue-qing X, Jian P, Xiao-mei S (2009) Assessment of soil erosion using RUSLE and GIS: a case study of the Maotiao River watershed, Guizhou Province, China. Environ Geol 56:1643–1652

    Article  Google Scholar 

  • Yuksel A, Gundogan R, Akay AE (2008) Using the remote sensing and GIS technology for erosion risk Mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey. Sensors 8:4851–4865

    Article  Google Scholar 

  • Zhao WW, Zhu J, Guo WW (2007) Estimation of monthly rainfall erosivity based on rainfall amount and rainfall time: a case study in hilly and gully area of Loess Plateau in Northern Shaanxi. Sci Soil Water Conserv 5(6):8–14

    Google Scholar 

  • Zhou P, Luukkanen O, Tokola T, Nieminen J (2008) Effect of vegetation cover on soil erosion in a mountainous watershed. Catena 75:319–325

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Geotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, 627012, India

    N. S. Magesh & N. Chandrasekar

Authors
  1. N. S. Magesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Chandrasekar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. S. Magesh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magesh, N.S., Chandrasekar, N. Assessment of soil erosion and sediment yield in the Tamiraparani sub-basin, South India, using an automated RUSLE-SY model. Environ Earth Sci 75, 1208 (2016). https://doi.org/10.1007/s12665-016-6010-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-016-6010-x

Keywords

Search

Navigation