Environmental Processes

, Volume 6, Issue 4, pp 883–913 | Cite as

Comparative Analysis between Morphometry and Geo-Environmental Factor Based Soil Erosion Risk Assessment Using Weight of Evidence Model: a Study on Jainti River Basin, Eastern India

  • Tusar Kanti HembramEmail author
  • Gopal Chandra Paul
  • Sunil Saha
Original Article


Assessment of spatial soil erosion risk is a viable effort signifying the needs of conservation measures due to the deterioration of land as well as soil quality degradation at various scales. Among several non-quantitative approaches regarding erosion risk prediction, watershed morphometry and other geo-environmental parameter based assessments were performed largely and separately which showed varied results. In the present work, using 15 morphometric and 13 geo-environmental parameters, spatial soil erosion risk was modelled in order to inspect the performances and consistency of both approaches in predicting Spatial Soil Erosion Risk (SSER). Field site erosion patch inventory (a total of 164 erosion patches), google earth imagery and a probabilistic model, i.e., Weight of Evidence (WoE) enabled the analysis. Training patches (115 patches) were used to model the SSER while validation patches (49 patches) were used to assess the consistency of model output. Both approaches quantify 25.41% and 20.18% of the area to high to very high susceptibility class, separately. The contribution of each factor of both parameter groups in risk predicting was analysed through Map Removal Sensitivity Analysis (MRSA). Further, the results of performance were evaluated through Repetitive Operator Choice (ROC) curve (success rate and prediction rate curves) measuring Area Under Curve (AUC). The success and prediction rate curves show that when considering morphometric parameters, the AUC is 0.775 and 0.729, respectively, whereas in the case of geo-environmental parameters, AUC = 0.892 and 0.878 accordingly. This reveals the better consistency of geo-environmental parameters in context with the spatial erosion risk zoning in the present scenario.


Basin morphometry Geo-environmental aspects Soil erosion risk Weight of evidence Sensitivity Receiver operating characteristic curve 


Supplementary material

40710_2019_388_MOESM1_ESM.docx (56 kb)
ESM 1 (DOCX 55 kb)


  1. Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2016) Landslide susceptibility mapping using analytic hierarchy process and information value methods along with a highway road section in Constantine, Algeria. Arab J Geosci 10(8).
  2. Agnesi V, Cappadonia C, Conoscenti C, Maggio C D, Rotigliano E (2007) Gully Erosion susceptibility assessment: a case study in the Magazzolo River Basin, Sicily, Italy. In 12th Belgium-France-Italy-Romania Geomorphological Meeting (pp. 31–32)Google Scholar
  3. Altaf S, Meraj G, Romshoo SA (2014) Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed. Environmental Monitoring and Assessment 186(12):8391–8412. Google Scholar
  4. Ameri AA, Pourghasemi HR, Cerda A (2017) Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: a comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Sci Total Environ 613-614:1385–1400. CrossRefGoogle Scholar
  5. Armas I (2012) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Natural Hazards, 60(3):937-950. Google Scholar
  6. Arnous MO, Aboulela HA, Green DR (2010) Geo-environmental hazards assessment of the north western Gulf of Suez, Egypt. J Coast Conserv 15(1):37–50. CrossRefGoogle Scholar
  7. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Journal, 24(1):43-69Google Scholar
  8. Bhunia GS, Samanta S, Pal B (2012) Quantitative analysis of relief characteristics using space technology. International Journal of Physical and Social Sciences 2(8):350–365Google Scholar
  9. Bonham-Carter GF (1994) Geographic information systems for geoscientists: modeling with GIS. In: Bonham-Carter F (ed.) Computer Methods in the Geosciences. Pergamon, Oxford, 13, 398Google Scholar
  10. Carranza EJM, Castro OT (2006) Predicting lahar-inundation zones: case study in West Mount Pinatubo, Philippines. Natural Hazards, 37(3):331-372Google Scholar
  11. Capra A, Ferro V, Porto P, Scicolone B (2012) Quantifying interrill and ephemeral gully erosion in a small Sicilian basin. Zeitschrift für Geomorphologie, Supplementary Issues 56(1):9–25Google Scholar
  12. Cevik E, Topal T (2003) GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environ Geol 44(8):949–962Google Scholar
  13. Chaplot V, Giboire G, Marchand P, Valentin C (2005) Dynamic modelling for linear erosion initiation and development under climate and land-use changes in northern Laos. Catena 63(2–3):318–328Google Scholar
  14. Chen W, Li W, Hou E, Zhao Z, Deng N, Bai H, Wang D (2014) Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arab J Geosci 7(11):4499–4511Google Scholar
  15. Choudhury MK, Nayak T (2003) Estimation of soil erosion in Sagar lake catchment of Central India. In: Proceedings of the International Conference on Water and Environment, Dec 15–18, 2003. Bhopal, India, pp 387–392Google Scholar
  16. Colaizzi PD, Evett SR, Agam N, Schwartz RC, Kustas WP (2016) Soil heat flux calculation for sunlit and shaded surfaces under row crops: 1. Model development and sensitivity analysis. Agriculture and Forest Meteorology 216:115–128Google Scholar
  17. Conforti M, Aucelli PP, Robustelli G, Scarciglia F (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (northern Calabria, Italy). Nat Hazards 56(3):881–898Google Scholar
  18. Conoscenti C, Agnesi V, Angileri S, Cappadonia C, Rotigliano E, Märker M (2013) A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ Earth Sci 70(3):1179–1195. CrossRefGoogle Scholar
  19. Dai FC, Lee CF, Li JXZW, Xu ZW (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391Google Scholar
  20. De Vente J, Poesen J, Govers G, Boix-Fayos C (2009) The implications of data selection for regional erosion and sediment yield modelling. Earth Surf Process Landf 34(15):1994–2007Google Scholar
  21. Deng Q, Qin F, Zhang B, Wang H, Luo M, Shu C, Liu G (2015) Characterizing the morphology of gully cross-sections based on PCA: a case of Yuanmou dry-Hot Valley. Geomorphology 228:703–713. CrossRefGoogle Scholar
  22. Dramis F, Gentili B (1977) Contribution to the study of slopes in the Umbrian Apennines, Marche. Stud Geol Camerti 3:153–164Google Scholar
  23. Dube F, Nhapi I, Murwira A, Gumindoga W, Goldin J, Mashauri DA (2014) Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District–Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C 67:145–152Google Scholar
  24. Esper Angillieri M (2008) Morphometric analysis of Colanguil River basin and flash flood hazard, San Juan, Argentina. Environ Geol 55:107–111Google Scholar
  25. Faniran A (1968) The index of drainage intensity—a provisional new drainage factor. Australian Journal Science 31:328–330Google Scholar
  26. Ferretti F, Saltelli A, Tarantola S (2016) Trends in sensitivity analysis practice in the last decade. Sci Total Environ 568:666–670. CrossRefGoogle Scholar
  27. Gayen A, Saha S (2017) Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India. Modeling Earth Systems and Environment 3(3):1123–1139Google Scholar
  28. Ghorbani Nejad S, Falah F, Daneshfar M, Haghizadeh A, Rahmati O (2016) Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International 32(2):167–187Google Scholar
  29. Ghosh KG, Saha S (2015) Identification of soil erosion susceptible areas in Hinglo River basin, eastern India based on geo-statistics. Universal Journal of Environmental Research & Technology 5(3)Google Scholar
  30. Hajam RA, Hamid A, Bhat S (2013) Application of morphometric analysis for geo-hydrological studies using geo-spatial technology-a case study of Vishav Drainage Basin. Hydrology Current Research 4(157):2Google Scholar
  31. Haregeweyn N, Tsunekawa A, Poesen J, Tsubo M, Meshesha DT, Fenta AA, Nyssen J, Adgo E (2017) Comprehensive assessment of soil erosion risk for better land use planning in river basins: case study of the upper Blue Nile River. Sci Total Environ 574:95–108Google Scholar
  32. Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Bulletin of the Geological Society of America, Vol 56:275–370Google Scholar
  33. Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926Google Scholar
  34. Jang T, Vellidis G, Hyman JB, Brooks E, Kurkalova LA, Boll J, Cho J (2013) Model for prioritizing best management practice implementation: sediment load reduction. Environ Manag 51(1):209–224. CrossRefGoogle Scholar
  35. Jasmin I, Mallikarjuna P (2013) Morphometric analysis of Araniar river basin using remote sensing and geographical information system in the assessment of groundwater potential. Arab J Geosci 6(10):3683–3692Google Scholar
  36. Kakembo V, Xanga WW, Rowntree K (2009) Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa. Geomorphology, 110(3-4):188-194Google Scholar
  37. Kottagoda SD, Abeysingha NS (2017) Morphometric analysis of watersheds in Kelani river basin for soil and water conservation. Journal of the National Science Foundation of Sri Lanka 45(3):6Google Scholar
  38. 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(3):483–497. CrossRefGoogle Scholar
  39. Kumar R, Kumar S, Lohani AK, Nema RK, Singh RD (2000) Evaluation of geomorphological characteristics of a catchment using GIS. GIS India 9(3):13–17Google Scholar
  40. Lodwick WA, Monson W, Svoboda L (1990) Attribute error and sensitivity analysis of map operations in geographical information systems: suitability analysis. International Journal of Geographical Information System 4:413–428Google Scholar
  41. Magliulo P (2012) Assessing the susceptibility to water-induced soil erosion using a geomorphological, bivariate statistics-based approach. Environ Earth Sci 67(6):1801–1820. CrossRefGoogle Scholar
  42. Malik M, Bhat M, Kuchay NA (2011) Watershed based drainage morphometric analysis of Lidder catchment in Kashmir valley using geographical information system. Recent Res Sci Technol 3(4):118–126Google Scholar
  43. Mararakanye N, Sumner PD (2017) Gully erosion: a comparison of contributing factors in two catchments in South Africa. Geomorphology 288:99–110Google Scholar
  44. Masselink RJ, Heckmann T, Temme AJ, Anders NS, Gooren H, Keesstra SD (2017) A network theory approach for a better understanding of overland flow connectivity. Hydrol Process 31(1):207–220Google Scholar
  45. Mesa LM (2006) Morphometric analysis of a subtropical Andean basin (Tucumán, Argentina). Environ Geol 50(8):1235–1242. CrossRefGoogle Scholar
  46. Moore ID, Burch GJ (1986) Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation 1. Soil Science Society of America Journal, 50(5):1294-1298Google Scholar
  47. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological and biological applications. Hydrol Process 5(1):3–30Google Scholar
  48. Morgan RPC (2009) Soil Erosion and Conservation. John Wiley & SonsGoogle Scholar
  49. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61:221-236Google Scholar
  50. Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58(4):275–287Google Scholar
  51. Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191Google Scholar
  52. Nekhay O, Arriaza M, Boerboom L (2009) Evaluation of soil erosion risk using analytic network process and GIS: a case study from Spanish mountain olive plantations. J Environ Manag 90(10):3091–3104. CrossRefGoogle Scholar
  53. Oh HJ, Kim YS, Choi JK, Park E, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172Google Scholar
  54. Okumura M, Araujo AG (2014) Long-term cultural stability in hunter–gatherers: a case study using traditional and geometric morphometric analysis of lithic stemmed bifacial points from southern Brazil. J Archaeol Sci 45:59–71. CrossRefGoogle Scholar
  55. Ozdemir A (2011) GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of Hydrology, 411(3-4):290-308Google Scholar
  56. Ozdemir A, Altural T (2013) A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. J Asian Earth Sci 64:180–197Google Scholar
  57. Ozdemir H, Bird D (2009) Evaluation of morphometric parameters of drainage networks derived from topographic maps and DEM in point of floods. Environ Geol 56(7):1405–1415. CrossRefGoogle Scholar
  58. Park NW (2015) Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Science 73(3):937–949Google Scholar
  59. Phillips JD (1990) Relative Importance of Factors Influencing Fluvial Soil Loss At The Global Scale. Science, 290:547-568Google Scholar
  60. Pike R (2000) Geomorphometry - diversity in quantitative surface analysis. Prog Phys Geogr 24(1):1–20. CrossRefGoogle Scholar
  61. Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84Google Scholar
  62. Pourghasemi HR, Moradi HR, Aghda SF (2013a) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779Google Scholar
  63. Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2013b) A comparative assessment of prediction capabilities of Dempster–Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics, Natural Hazards and Risk 4(2):93–118Google Scholar
  64. Pradhan B, Lee S (2009) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759Google Scholar
  65. Pradhan B, Pirasteh P (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Advances 3(2):26–34Google Scholar
  66. Prasad K, Gopi S, Rao R (1992) Demarcation of priority macro-watersheds in Mahbubnagar district, AP using remote sensing techniques (pp. 263-269). Tata McGraw-HillGoogle Scholar
  67. Pulice I, Cappadonia C, Scarciglia F, Robustelli G, Conoscenti C, De Rose R, Rotigliano E, Agnesi V (2012) Geomorphological, chemical and physical study of “calanchi” landforms in NW Sicily (southern Italy). Geomorphology 153:219–231Google Scholar
  68. Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258Google Scholar
  69. Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017) Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: an integrated framework. Sci Total Environ 579:913–927Google Scholar
  70. Raja NB, Cicek I, Turkoglu N, Aydin O, Kawasaki A (2016) Landslide susceptibility mapping of the Sera River basin using logistic regression model. Nat Hazards 85(3):1323–1346Google Scholar
  71. Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2012) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742. CrossRefGoogle Scholar
  72. 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). Washington, DC: United States Department of Agriculture, Vol. 703Google Scholar
  73. Rodrigo-Comino J, Iserloh T, Lassu T, Cerdà A, Keestra SD, Prosdocimi M, Brings C, Marzen M, Ramos MC, Senciales JM, Ruiz Sinoga JD, Seeger M, Ries JB (2016) Quantitative comparison of initial soil erosion processes and runoff generation in Spanish and German vineyards. Sci Total Environ 565:1165–1174. CrossRefGoogle Scholar
  74. Samani AN, Ahmadi H, Jafari M, Boggs G, Ghoddousi J, Malekian A (2009) Geomorphic threshold conditions for gully erosion in southwestern Iran (Boushehr-Samal watershed). J Asian Earth Sci 35(2):180–189Google Scholar
  75. Sar N, Khan A, Das A, Mipun BS, Chatterjee S (2016) Coupling of analytical hierarchy process and frequency ratio based spatial prediction of soil erosion susceptibility in Keleghai river basin. India International Soil and Water Conservation Research.
  76. Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Geological society of America bulletin, 67(5):597-646Google Scholar
  77. Sharma NK, Singh RJ, Mandal D, Kumar A, Alam NM, Keesstra S (2017) Increasing farmer's income and reducing soil erosion using intercropping in rainfed maize wheat rotation of Himalaya, India. Agriculture Ecosystems and Environment 247:43–53Google Scholar
  78. Shit PK, Nandi AR, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on Jhargram sub-division at West Bengal in India. Modeling Earth System and Environment 1(28):1–12Google Scholar
  79. Shrimali SS, Aggarwal SP, Samra JS (2001) Prioritizing erosion-prone areas in hills using remote sensing and GIS—a case study of the Sukhna Lake catchment, northern India. Int J Appl Earth Obs Geoinf 3(1):54–60Google Scholar
  80. Singh S, Dubey A (1994) Geoenvironmental planning of watershed in India, Chugh publications, Allahabad. India pp 28-69Google Scholar
  81. Smith GH (1935) The relative relief of Ohio. Geogr Rev 25:272–284Google Scholar
  82. Sreedevi PD, Owais S, Khan HH, Ahamed S (2009) Morphometric analysis of a watershed of South India using SRTM data and GIS. J Geol Soc India 73(4):543–552. CrossRefGoogle Scholar
  83. Srivastava VK (2003) Role of GIS in natural resources management. In: Thakur B (ed) Perspectives in resource Management in Developing Countries. Concept Publishing Company, New Delhi, pp 479–484Google Scholar
  84. Stewart BA, Lal R, El-Swaify SA, Eswaran H (1990) Sustaining the soil resource base of an expanding world agriculture. In transactions, 14th International Congress of Soil Science, Kyoto, Japan, august 1990, volume VII. pp. 296-301Google Scholar
  85. Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Geol Soc Am Bull 63:1117Google Scholar
  86. Svoray T, Markovitch H (2009) Catchment scale analysis of the effect of topography, tillage direction and unpaved roads on ephemeral gully incision. Earth Surf Process Landf 34(14):1970–1984Google Scholar
  87. Takken I, Croke J, Lane P (2008) Thresholds for channel initiation at road drain outlets. Catena 75(3):257–267Google Scholar
  88. Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101Google Scholar
  89. Thiemann S, Schütt B, Förch G (2005) Assessment of erosion and soil erosion processes – a case study from the northern Ethiopian Highland. FWU Water Resource Publications 3:173–185Google Scholar
  90. Valentin C, Poesen J, Li Y (2005) Gully erosion: impacts, factors and control. Catena 63(2–3):132–153Google Scholar
  91. Yesilnacar EK, (2005) The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. (Ph.D Thesis). Department of Geomatics the University of Melbourne, p. 423Google Scholar
  92. Zabihi M, Mirchooli F, Motevalli A, Darvishan AK, Pourghasemi HR, Zakeri MA, Sadighi F (2018) Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena 161:1–13Google Scholar
  93. Zakerinejad R, Maerker M (2015) An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Nat Hazards 79(S1):25–50. CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of GeographyUniversity of Gour BangaMaldaIndia

Personalised recommendations