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Modeling Earth Systems and Environment

, Volume 5, Issue 1, pp 291–306 | Cite as

Accessing the soil erosion rate based on RUSLE model for sustainable land use management: a case study of the Kotmale watershed, Sri Lanka

  • DMSLB DissanayakeEmail author
  • Takehiro Morimoto
  • Manjula Ranagalage
Review Article
First Online:
  • 51 Downloads

Abstract

Water based soil erosion is a serious socio-economic and environmental problem across the world especially in the tropical region. Assessing the soil erosion quantitatively and spatially provides information to prioritize the soil conservation area in sustainable land management view point. Among the other soil erosion approaches, erosion modeling has been playing a significant role and provides an accurate result in a cost-effective manner. In this study, revised universal soil loss equation (RUSLE) was integrated with remote sensing (RS) and geographic information system (GIS) to analyse the quantitative and spatial distribution of soil erosion across the entire Kotmale watershed which is located in the western part of the central mountain region in Sri Lanka. In the methodology, the parameters of the RUSLE model were estimated using pixel overlay method in ArcGIS software, both spatial data and remote sensing data facilitated with appropriate calibration. From the analysis, the annual soil erosion ranges from 0 to 472 t ha− 1 year− 1 with the mean and standard deviation 9.8 t ha− 1 year− 1 and 15.7 t ha− 1 year− 1 respectively. The mean erosion rate of the model was correlated with ground based data. After the final model was established, conservation priority area was identified by using hot and cold spot analysis. Here “hot spots” shows the area with high soil erosion clustering value, while “cold spot” refers to area with low soil erosion clustering. The soil conservation priority map has been produced and the result shows that approximately 25% represents hot sport. The result would be an aid and sources for soil and water conservation in the Kotmale watershed.

Keywords

RUSLE Kotmale watershed Soil erosion Erosion prone area Hot and cold spot Agriculture land sustainability 
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Notes

Acknowledgements

The authors would like to express their gratitude to anonymous reviewers for their valuable comments and suggestions.

Compliance with ethical standards

Conflict of interest

The author declares no conflicts of interest.

References

  1. Abdo H, Salloum J (2017) Mapping the soil loss in Marqya basin: Syria using RUSLE model in GIS and RS techniques. Environ Earth Sci 76:1–10.  https://doi.org/10.1007/s12665-017-6424-0 Google Scholar
  2. Aiello A, Adamo M, Canora F (2015) Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. Catena 131:174–185.  https://doi.org/10.1016/j.catena.2015.04.003 Google Scholar
  3. Alexakis DD, Hadjimitsis DG, Agapiou A (2013) Integrated use of remote sensing, GIS and precipitation data for the assessment of soil erosion rate in the catchment area of “Yialias” in Cyprus. Atmos Res 131:108–124.  https://doi.org/10.1016/j.atmosres.2013.02.013 Google Scholar
  4. Balasubramani K, Veena M, Kumaraswamy K, Saravanabavan V (2015) Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (rusle) model through GIS. Model Earth Syst Environ 1:10.  https://doi.org/10.1007/s40808-015-0015-4 Google Scholar
  5. Batista PVG, Silva MLN, Silva BPC et al (2017) Modelling spatially distributed soil losses and sediment yield in the upper Grande River Basin—Brazil. Catena 157:139–150.  https://doi.org/10.1016/j.catena.2017.05.025 Google Scholar
  6. 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 1:42.  https://doi.org/10.1007/s40808-015-0040-3 Google Scholar
  7. De Silva RP, Chandrasekara M (2002) Impacts of land use changes on hydrological regime—a case study of Randenigala and Kotmale catchments. Sri Lanka 1:1–6Google Scholar
  8. Department of Census and Statistics (2016) Sri Lanka Labor Force Survey annual report—2016 Ministry of National Policies and Economic Affairs, Colombo, Sri Lanka, pp 1–120Google Scholar
  9. Devatha CP, Deshpande V, Renukaprasad MS (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 Google Scholar
  10. Diyabalanage S, Samarakoon KK, Adikari SB, Hewawasam T (2017) Impact of soil and water conservation measures on soil erosion rate and sediment yields in a tropical watershed in the Central Highlands of Sri Lanka. Appl Geogr 79:103–114.  https://doi.org/10.1016/j.apgeog.2016.12.004 Google Scholar
  11. Dutta S (2016) Soil erosion, sediment yield and sedimentation of reservoir: a review. Model Earth Syst Environ 2:123.  https://doi.org/10.1007/s40808-016-0182-y Google Scholar
  12. Dutta D, Das S, Kundu A, Taj A (2015) Soil erosion risk assessment in Sanjal watershed, Jharkhand (India) using geo-informatics, RUSLE model and TRMM data. Model Earth Syst Environ 1:37.  https://doi.org/10.1007/s40808-015-0034-1 Google Scholar
  13. ESRI (2016a) How hot spot analysis (Getis-Ord Gi*) works. Available online: http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm. Accessed on 30 Sept 2018
  14. ESRI (2016b) What is a z-score? What is a p-Value? Available online: http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/what-is-a-z-score-what-is-a-p-value.htm. Accessed on 30 Sept 2018
  15. FAO (2015) Global soil status, processes and trends. Status of the World’s Soil Resources (SWSR)—Main Report of the Food and Agriculture Organization, New York, United NationsGoogle Scholar
  16. Fenta AA, Yasuda H, Shimizu K et al (2016) Dynamics of soil erosion as influenced by watershed management practices: a case study of the Agula Watershed in the semi-arid highlands of Northern Ethiopia. Environ Manag 58:889–905.  https://doi.org/10.1007/s00267-016-0757-4 Google Scholar
  17. Fonseca F, de Figueiredo T, Nogueira C, Queirós A (2017) Effect of prescribed fire on soil properties and soil erosion in a Mediterranean mountain area. Geoderma 307:172–180.  https://doi.org/10.1016/j.geoderma.2017.06.018 Google Scholar
  18. Fu G, Chen S, McCool DK (2006) Modeling the impacts of no-till practice on soil erosion and sediment yield with RUSLE, SEDD, and ArcView GIS. Soil Tillage Res 85:38–49.  https://doi.org/10.1016/j.still.2004.11.009 Google Scholar
  19. Gajovic V, Todorovic B (2013) Spatial and temporal analysis of fires in Serbia for period 2000–2013. J Geogr Inst Jovan Cvijic SASA 63(3):297–312.  https://doi.org/10.2298/IJGI1303297G Google Scholar
  20. Ganasri H, Ramesh BP (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS—a case study of Nethravathi Basin. Geosci Front 7:953–961.  https://doi.org/10.1016/j.gsf.2015.10.007 Google Scholar
  21. Gelagay HS, Minale AS (2016) Soil loss estimation using GIS and remote sensing techniques: a case of Koga watershed, Northwestern Ethiopia. Int Soil Water Conserv Res 4:126–136.  https://doi.org/10.1016/j.iswcr.2016.01.002 Google Scholar
  22. Gong G, Mattevada S, O’Bryant SE (2014) Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ Res 130:59–69.  https://doi.org/10.1016/j.envres.2013.12.005 Google Scholar
  23. Gunaalan K, Ranagalage M, Gunarathna M et al (2018) Application of geospatial techniques for groundwater quality and availability assessment: a case study in Jaffna Peninsula, Sri Lanka. ISPRS Int J Geo-Inf 7:20.  https://doi.org/10.3390/ijgi7010020 Google Scholar
  24. Handayani HH, Estoque RC, Murayama Y (2018) Estimation of built-up and green volume using geospatial techniques: a case study of Surabaya, Indonesia. Sustain Cities Soc 37:581–593.  https://doi.org/10.1016/j.scs.2017.10.017 Google Scholar
  25. Hewawasam T (2010) Effect of land use in the upper Mahaweli catchment area on erosion, landslides and siltation in hydropower reservoirs of Sri Lanka. J Nat Sci Found Sri Lanka 38:3–14Google Scholar
  26. Jayasooriya HJC, Aheeyar MMM (2016) Adoption and factors affecting on adoption of integrated pest management among vegetable farmers in Sri Lanka. Procedia Food Sci 6:208–212.  https://doi.org/10.1016/j.profoo.2016.02.052 Google Scholar
  27. Johansson D (1989) The Kotmale environment: a study of the impact of the Kotmale hydropower project in Sri Lanka. SIDA, Stockholm, pp 1–80Google Scholar
  28. Karamage F, Zhang C, Kayiranga A et al (2016) USLE-based assessment of soil erosion by water in the nyabarongo river catchment, Rwanda. Int J Environ Res Public Health 13:1–16.  https://doi.org/10.3390/ijerph13080835 Google Scholar
  29. Karamage F, Zhang C, Liu T et al (2017) Soil erosion risk assessment in Uganda. Forests 8:1–20.  https://doi.org/10.3390/f8020052 Google Scholar
  30. Li Z, Liu S, Zhang X, West TO, Ogle SM, Zhou N (2016) Evaluating land cover influences on model uncertainties—a case study of cropland carbon dynamics in the mid-continent intensive campaign region. Ecol Model 337:176–187.  https://doi.org/10.1016/j.ecolmodel.2016.07.002 Google Scholar
  31. Lu D, Li G, Valladares GS, Batistella M (2004) Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degrad Develop 15:499–512.Google Scholar
  32. Mahaweli Authority of Sri Lanka (2017) Uma Oya Gauging Station at welimada—water and sediment discharge, 1st edn. Environment and forest conservation division, Mahaweli Authority of Sri Lanka, Riverside, Lanka, pp 1–20Google Scholar
  33. Mapa PB, Somasiri S, Nagarajah S(1999) Soil of the wet zone of Sri Lanka. Soil Science Society of Sri Lanka, Colombo, pp 12–136Google Scholar
  34. Marco A (2004) Rainfall erosivity map for Brazil. 57:251–259.  https://doi.org/10.1016/j.catena.2003.11.006
  35. Ministry of Environment and Renewable Energy in Sri Lanka (2017) National action program (NAP) for combating land degradation in Sri Lanka 2015–2024. Natural Resources Management Division, Ministry of Environment and Renewable Energy, Sri Lanka, pp 1–146Google Scholar
  36. Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geog Anal 27:286–306.  https://doi.org/10.1111/j.1538-4632.1995.tb00912.x Google Scholar
  37. Ostovari Y, Ghorbani-dashtaki S, Bahrami H (2017) Soil loss prediction by an integrated system using RUSLE, GIS and remote sensing in semi-arid region. Geoderma Reg.  https://doi.org/10.1016/j.geodrs.2017.06.003 Google Scholar
  38. Ozsoy G, Aksoy E, Dirim MS, Tumsavas Z (2012) Determination of soil erosion risk in the mustafakemalpasa river basin, Turkey, using the revised universal soil loss equation, geographic information system, and remote sensing. Environ Manag 50:679–694.  https://doi.org/10.1007/s00267-012-9904-8 Google Scholar
  39. 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 64:965–972.  https://doi.org/10.1007/s12665-011-0913-3 Google Scholar
  40. 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 3:209–215.  https://doi.org/10.1016/j.gsf.2011.11.003 Google Scholar
  41. Rahman MR, Shi ZH, Chongfa C (2009) Soil erosion hazard evaluation—an integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies. Ecol Model 220:1724–1734.  https://doi.org/10.1016/j.ecolmodel.2009.04.004 Google Scholar
  42. Ranagalage M, Estoque RC, Murayama Y (2017) An urban heat island study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017). ISPRS Int J Geo-Inf 6:189.  https://doi.org/10.3390/ijgi6070189 Google Scholar
  43. Ranagalage M, Estoque RC, Zhang X, Murayama Y (2018a) Spatial changes of urban heat island formation in the Colombo District, Sri Lanka: Implications for sustainability planning. Sustainability (Switzerland).  https://doi.org/10.3390/su10051367 Google Scholar
  44. Ranagalage M, Estoque RC, Handayani HH, Zhang X, Morimoto T, Tadono T, Murayama Y (2018b) Relation between urban volume and land surface temperature: a comparative study of planned and traditional cities in Japan. Sustainability 10(7):1–17.  https://doi.org/10.3390/su10072366 Google Scholar
  45. Ranagalage M, Dissanayake D, Murayama Y, Zhang X, Estoque RC, Perera E, Morimoto T (2018c) Quantifying Surface urban heat island formation in the world heritage tropical mountain City of Sri Lanka. ISPRS Int J Geo-Inf 7(9):341.  https://doi.org/10.3390/ijgi7090341 Google Scholar
  46. Renard K, Foster G, Weesies G et al (1997) Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE). Agric Handb No 703:404Google Scholar
  47. Ricker MC, Odhiambo BK, Church JM (2008) Spatial analysis of soil erosion and sediment fluxes: a paired watershed study of two Rappahannock river tributaries, Stafford County, Virginia. Environ Manag 41:766–778.  https://doi.org/10.1007/s00267-008-9094-6 Google Scholar
  48. Rodriguez Lopez JM, Heider K, Scheffran J (2017) Frontiers of urbanization: identifying and explaining urbanization hot spots in the south of Mexico City using human and remote sensing. Appl Geogr 79:1–10.  https://doi.org/10.1016/j.apgeog.2016.12.001 Google Scholar
  49. Senanayake SS, Munasinge AMK, Wickramasinge WMADB (2013) Use of Erosion hazard assessments for reginal scale crop suitability mapping in Uwa province. Ann Sri Lanka Dep Agric 15:127–141Google Scholar
  50. Shit PK, Nandi AS, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India. Model Earth Syst Environ 1:28.  https://doi.org/10.1007/s40808-015-0032-3 Google Scholar
  51. Singh G, Panda RK (2017) Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: A case study in the Kapgari watershed, India. Int Soil Water Conserv Res 5:202–211.  https://doi.org/10.1016/j.iswcr.2017.05.006 Google Scholar
  52. The Mahaweli Authority of Sri Lanka (2012) Annual report. Mahaweli Authority of Sri Lanka, Colombo, pp 1–164Google Scholar
  53. Thomas J, Joseph S, Thrivikramji KP (2017) Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats, India using RUSLE and GIS. Geosci Front.  https://doi.org/10.1016/j.gsf.2017.05.011 Google Scholar
  54. Wasowicz P (2014) Non-native species in the vascular flora of highlands and mountains of Iceland. PeerJ Preprints.  https://doi.org/10.7287/peerj.preprints.488v1 Google Scholar
  55. Weerasinghe HMSPM (2007) Variation in soil fertility and capacity for supplying soil nutrients in a hedgerow intercropping system with different tree species in the upcountry wet zone of Sri Lanka. Ceylon J Sci 36:65–79Google Scholar
  56. Wijesekara S, Samarakoon L (2001) Extraction of parameters and modelling soil erosion using GIS in a grid environment. J Chem Inf Model 53:1689–1699.  https://doi.org/10.1017/CBO9781107415324.004 Google Scholar
  57. 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.  https://doi.org/10.1007/s40808-018-0419-z Google Scholar
  58. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. Agric Handb 537:285–291.  https://doi.org/10.1029/TR039i002p00285 Google Scholar
  59. Zubair L (2001) Challenges for environmental impact assessment in Sri Lanka. Environ Impact Assess Rev 21:469–478Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Graduate School of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan
  2. 2.Department of Environmental Management, Faculty of Social Sciences and HumanitiesRajarata University of Sri LankaMihintaleSri Lanka
  3. 3.Faculty of Life and Environmental SciencesUniversity of TsukubaTsukubaJapan

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