Arabian Journal of Geosciences

, Volume 8, Issue 6, pp 3697–3711 | Cite as

Identification of critical soil erosion prone areas and annual average soil loss in an upland agricultural watershed of Western Ghats, using analytical hierarchy process (AHP) and RUSLE techniques

Original Paper

Abstract

The present work integrates analytical hierarchy process (AHP) with Revised Universal Soil Loss Equation (RUSLE) model to determine the critical soil erosion prone areas along with the spatial pattern of annual average soil erosion rates of an upland agricultural sub-watershed in the Western Ghats of Kerala, India. The critical soil erosion prone areas were identified by integrating geo-environmental variables such as land use/land cover, geomorphology, drainage density, drainage frequency, lineament frequency, slope, and relative relief after determining its relative contribution in conditioning the terrain susceptible to soil erosion by AHP technique, in a raster-based Geographic Information Systems environment. The spatial pattern of average annual soil erosion rates was obtained by RUSLE model that consider five factors, viz., rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors. The soil erosion probability map prepared by the AHP method was reclassified into soil erosion severity map showing regions of different erosion probability. Among this, the critical erosion zone occupies 4.23 % of the total area followed by high erosion severity zone occupies 18.39 % of the study area. Nil and low zones together constitute 44.15 % of the total area. The assessed annual average soil loss from the watershed shows an increased value of 4,227 t−1 h−1 year−1 as the maximum loss. The cross-comparison of potential soil erosion severity map with annual average soil loss in the area validates the finding of the study by a high spatial correlation. More erosion proneness and annual loss were observed in areas where the side slope plateau, denudational slope, and valley fills comes with high slope and relative relief. The intense terrain modification in this area with improper soil conservation measures makes the watershed more vulnerable to soil erosion.

Keywords

AHP RUSLE Western Ghats Geomorphology Geographic information system 

Notes

Acknowledgments

The authors are grateful to the Head, Hazard Vulnerability and Risk Assessment (HVRA) Cell, Kerala State Disaster Management Authority, Department of Revenue and Disaster Management for providing the constant inspiration and support. The authors are thankful to the anonymous reviewers for constructive comments and suggestions.

References

  1. Ahmed P (2009) Impact of change in forest cover on soil status in Kahmil Watershed, J&K, using Geo-spatial tools. e-J Earth Sci India 2(3):187–195Google Scholar
  2. 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 Perspect Precipitation Sci I 131:108–124Google Scholar
  3. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena 114:21–36CrossRefGoogle Scholar
  4. Anon (2008) Annual report (2007–08): Ministry of Agriculture, Govt. of IndiaGoogle Scholar
  5. Anon (2009) State of environment report. Ministry of Environment and Forest, Govt. of IndiaGoogle Scholar
  6. Arar A, Chenchouni H (2013) A simple geomatics-based approach for assessing water erosion hazard at montane areas. Arab J Geosci 7(1):1–12CrossRefGoogle Scholar
  7. Arekhi S, Niazi Y, Kalteh AM (2012) Soil erosion and sediment yield modeling using RS and GIS techniques: a case study, Iran. Arab J Geosci 5(1):285–296CrossRefGoogle Scholar
  8. 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, UK, 127--132Google Scholar
  9. Bonilla CA, Reyes JL, Magri A (2010) Water erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, central Chile. Chil J Agric Res 70(1):159–169CrossRefGoogle Scholar
  10. Chandio IA, Matori ANB, WanYusof KB, Talpur MAH, Balogun AL, Lawal DU (2013) GIS-based analytic hierarchy process as a multicriteria decision analysis instrument: a review. Arab J Geosci 6(8):3059–3066CrossRefGoogle Scholar
  11. Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of north eastern India using USLE, GIS and remote sensing. Water Resour Manag 22:1783–1798CrossRefGoogle Scholar
  12. Fattahi H, Farsangi MAE, Shojaee S, Mansouri H (2014) Selection of a suitable method for the assessment of excavation damage zone using fuzzy AHP in Aba Saleh Almahdi tunnel, Iran. Arab J Geosci. doi: 10.1007/s12517-014-1280-7 Google Scholar
  13. Hlaing KT, Haruyama S, Aye MM (2008) Using GIS-based distributed soil loss modeling and morphometric analysis to prioritize water shed for soil conservation in Bago river basin of Lower Myanmar. Front Earth Sci China 2(4):465–478Google Scholar
  14. Hoyos N (2005) Spatial modeling of soil erosion potential in a tropical watershed of the Colombian Andes. Catena 63(1):85–108CrossRefGoogle Scholar
  15. Intarawichian N, Dasananda S (2010) Analytical hierarchy process for landslide susceptibility mapping in lower Mae Chaem watershed, northern Thailand. Suranaree J Sci Technol 17(3):277–292Google Scholar
  16. Kaliraj S, Chandrasekar N, Magesh NS (2013) Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arab J Geosci. doi: 10.1007/s12517-013-0849-x Google Scholar
  17. Khosrokhani M, Pradhan B (2013) Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS. Geomatics Nat Haz Risk. doi: 10.1080/19475705.2013.794164 Google Scholar
  18. Kim S-M, Choi Y, Suh J, Oh S, Park H-D, Yoon S-H (2012) Estimation of soil erosion and sediment yield from mine tailing dumps using GIS: a case study at the Samgwang mine, Korea. Geosyst Eng 15(1):2–9CrossRefGoogle Scholar
  19. 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 57:83–497CrossRefGoogle Scholar
  20. Lal R (1994) Soil erosion research method, 2nd edn. Soil and Water Conservation Society, Ankeny, 352. ppGoogle Scholar
  21. Lee S (2004) Soil erosion assessment and its verification using the Universal Soil Loss Equation and geographic information system: a case study at Boun, Korea. Environ Geol 45:457–465CrossRefGoogle Scholar
  22. Malczewski J (1999) GIS and multi-criteria decision analysis, 1st edn. John Wiley and Sons, New York, 392 ppGoogle Scholar
  23. Meusburger K, Konz N, Schaub M, Alewell C (2010) Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment. Int J Appl Earth Obs Geoinfo 12(3):208–215CrossRefGoogle Scholar
  24. Millward AA, Mersey JE (1999) Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena 38(2):109–129CrossRefGoogle Scholar
  25. Misra N, Satyanarayana T, Mukherjee RK (1984) Effect of top elements on the sediment production rate from sub-watershed in Upper Damodar Valley. J Agric Eng 21(3):65–70Google Scholar
  26. Moore ID, Burch GJ (1986a) Physical basis of the length slope factor in the Universal Soil Loss Equation. Soil Sci Soc Am 50(5):1294–1298CrossRefGoogle Scholar
  27. Moore ID, Burch GJ (1986b) Modeling erosion and deposition. Topographic effects. Trans Am Soc Agric Eng 29(6):1624–1630CrossRefGoogle Scholar
  28. Naqvi HR, Mallick J, Devi LM, Siddiqui MA (2013) Multi-temporal annual soil loss risk mapping employing Revised Universal Soil Loss Equation (RUSLE) model in Nun Nadi Watershed, Uttrakhand (India). Arab J Geosci 6(10):4045–4056CrossRefGoogle Scholar
  29. Narayan VVD, Babu R (1983) Estimation of soil erosion in India. J Irrig Drain Eng 109(4):419–434CrossRefGoogle Scholar
  30. Navarro EM, Martínez-Pérez S, Sastre-Merlín A, Bienes-Allas R (2014) Catchment erosion and sediment delivery in a limno-reservoir basin using a simple methodology. Water Resour Manag. doi: 10.1007/s11269-014-0601-7 Google Scholar
  31. Ni JR, Li YK (2003) Approach to soil erosion assessment in terms of land-use structure changes. J Soil Water Conserv 58(3):158–169Google Scholar
  32. Pandey A, Chowdary VM, Mal BC (2007) Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Res Manag 21:729–746CrossRefGoogle Scholar
  33. Park S, Oh S, Jeon S, Jung H, Choi C (2011) Soil erosion risk in Korean watersheds, assessed using the Revised Universal Soil Loss Equation. J Hydrol 399(3–4):263–273CrossRefGoogle Scholar
  34. Pazand K, Hezarkhani A, Ghanbari Y (2014) Fuzzy analytical hierarchy process and GIS for predictive Cu porphyry potential mapping: a case study in Ahar–Arasbaran Zone (NW, Iran). Arab J Geosci 7(1):241–251CrossRefGoogle Scholar
  35. Pourghasemi HR, Pradhan B, Gokceoglu C (2012) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63(2):965–996CrossRefGoogle Scholar
  36. Prasannakumar V, Shiny R, Geetha N, Vijith H (2011a) 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 64:965–972CrossRefGoogle Scholar
  37. Prasannakumar V, Vijith H, Geetha N, Shiny R (2011b) Regional scale erosion assessment of a sub-tropical highland segment in the Western Ghats of Kerala, South India. Water Res Manag 25(14):3715–3727CrossRefGoogle Scholar
  38. Prasannakumar V, Vijith H, Abinod S, Geetha N (2012) Estimation of soil erosion risk in a small mountainous sub-watershed in Kerala, India, using RUSLE and geoinformation technology. Geosci Front 3(2):209–215CrossRefGoogle Scholar
  39. Rahman MJ, 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(13–14):1724–1734CrossRefGoogle Scholar
  40. 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). Agricultural Handbook, Vol 703 US Department of Agriculture, Washington, pp 1--251Google Scholar
  41. Rozos D, Skilodimou HD, Loupasakis C, Bathrellos GD (2013) Application of the Revised Universal Soil Loss Equation model on landslide prevention. An example from N. Euboea (Evia) Island, Greece. Environ Earth Sci 70(7):3255–3266CrossRefGoogle Scholar
  42. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:57--68Google Scholar
  43. Saaty TL (1980) The analytical hierarchy process. McGraw Hill, New York, p 350Google Scholar
  44. Saaty TL (1990) The analytic hierarchy process: planning, priority setting, resource allocation, 1st edn. RWS Publications, Pittsburgh, 502 ppGoogle Scholar
  45. Saaty TL (1994) Fundamentals of decision making and priority theory with analytic hierarchy process, 1st edn. RWS Publications, Pittsburgh, 527 ppGoogle Scholar
  46. Saaty TL, Vargas LG (2001) Models, methods, concepts, and applications of the analytic hierarchy process, 1st edn. Kluwer Academic, Boston, 333 ppCrossRefGoogle Scholar
  47. Sharda VN, Mandal D, Ojasvi PR (2013) Identification of soil erosion risk areas for conservation planning in different states of India. J Environ Biol 34:219–226Google Scholar
  48. Singh G, Babu R, Narain P, Bhusan LS, Abrol IP (1992) Soil erosion rates in India. J Soil Water Conserv 47(1):97–99Google Scholar
  49. USDA (1978) Predicting rainfall erosion losses. Aguide to conservation planning, Washington DCGoogle Scholar
  50. 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, 34 ppGoogle Scholar
  51. Vijith H, Madhu G (2007) Application of GIS and frequency ratio model in mapping the potential surface failure sites in the Poonjar sub-watershed of Meenachil river in Western Ghats of Kerala. J Indian Soc Remote Sens 35(3):261–271CrossRefGoogle Scholar
  52. Vijith H, Madhu G (2008) Estimating potential landslide sites of an upland sub- watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol 55:1397–1405CrossRefGoogle Scholar
  53. Vijith H, Rejith PG, Madhu G (2009) Using Infoval method and GIS techniques for the spatial modelling of landslide susceptibility in the upper catchment of River Meenachil in Kerala. J Indian Soc Remote Sens 37:1–11CrossRefGoogle Scholar
  54. Vijith H, Suma M, Rekha VB, Shiju C, Rejith PG (2012) An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS. Arab J Geosci 5(4):797–805CrossRefGoogle Scholar
  55. Wischmeier WH (1971) A soil erodibility nomograph for farmland and construction sites. J Soil Water Conserv 26:189–193Google Scholar
  56. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses—a guide to conservation planning. Agriculture Handbook No. 537. US Department of Agriculture Science and Education Administration, Washington, DC, USA,. 163 ppGoogle Scholar
  57. Wu Q, Wang M (2007) A framework for risk assessment on soil erosion by water using an integrated and systematic approach. J Hydrol 337(1–2):11–21CrossRefGoogle Scholar
  58. Yang Q, Xie Y, Li W, Jiang Z, Li H, Qin X (2014) Assessing soil erosion risk in karst area using fuzzy modeling and method of the analytical hierarchy process. Environ Earth Sci 71(1):287–292CrossRefGoogle Scholar
  59. Yasser M, Jahangir K, Mohmmad A (2013) Earth dam site selection using the analytic hierarchy process (AHP): a case study in the west of Iran. Arab J Geosci 6(9):3417–3426CrossRefGoogle Scholar
  60. Youssef MA, Pradhan B, Tarabees E (2011) Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arab J Geosci 4:463–473CrossRefGoogle Scholar
  61. Zhang Y, Degroote J, Wolter C, Sugumaran R (2009) Integration of Modified Universal Soil Loss Equation (MUSLE) into a GIS framework to assess soil erosion risk. Land Degrad Dev 20:84–91CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2014

Authors and Affiliations

  • G. S. Pradeep
    • 1
  • M. V. Ninu Krishnan
    • 1
  • H. Vijith
    • 1
  1. 1.Hazard Risk and Vulnerability Analysis (HVRA) Cell, Kerala State Disaster Management AuthorityInstitute of Land and Disaster ManagementThiruvanathapuramIndia

Personalised recommendations