Water Erosion Susceptibility Mapping Using Geomorphological Factors in Omarabad Watershed of Urmia

Document Type : Original Article

Authors

1 M.Sc. of Watershed, Urmia University

2 Department of Range and Watershed Management, Faculty of Agriculture and Natural Resources, Urmia University, Urmia, Iran

3 Assistant Professor, Department of Range and Watershed Management, Faculty of Agriculture and Natural Resources, Urmia University, Urmia, Iran

Abstract

Spatial distribution of erosion phenomena and susceptibility analysis of different parts of watersheds to their types, plays an important role in environmental planning, so that the preparation of susceptibility maps to types of erosion and identification of factors affecting it, can Reducing the risk of various erosive phenomena will lead to providing the necessary management measures for the relevant areas. In this research, a bivariate statistical method was investigated for analyzing the susceptibility of Surface, Rill, Channel and Riverbank erosion phenomena using eight factors of rock resistance, land use and land cover, slope percentage, slope aspect, plan curvature, slope length, stream power index and soil hydrologic group in Omarabad watershed of Urmia. The results showed that regarding the effect of various factors predisposing to the occurrence of Surface, Rill, Channel and Streambank erosion, acceptable results with a clear trend in surface and rill erosion were not obtained, However, in the case of channel and streambank erosion, each of the factors, according to the relevant classes, has a positive or negative effect on the occurrence of this type of erosion and the effect of one factor cannot be considered as complete, positive or negative. factors such as lithology, slope length, slope percentage, slope aspect and SPI have the most positive effect on the occurrence of Channel and Streambank erosion. On the other hand, according to the calibration of the susceptibility map to Channel and Streambank erosion, only 20% of the area with Channel and Streambank erosion related to calibration were in the range of areas with very high to severe sensitivity in the susceptibility map. This indicates the inefficiency of the susceptibility map produced using the above eight predisposing factors and emphasizes the research on other more important and effective factors in creating the desired erosion phenomena in the study area.

Keywords

References

Abedini M., and Yaghubnejad Asl N. 2017. Assessment and zoning of the soil erosion risk in Balekhli river watershed of Yamci dam, using fuzzy model. Quantitative geomorphological Research, 6(1):137-155. (In Persian)
Abolfathi D., and Kiani M. 2007. Soil erosion and sedimentation rate equivalent to flood rate in Farsban Watershed using GIS. Applied Research in Geographical Sciences, 7(20): 158-172. (In Persian)
Amiri M., Pourghasemi HR., Ghanbarian GA., and Afzali SF. 2019. Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma, 340: 55-69.‏
Angileri SE., Conoscenti C., Hochschild V., Märker M., Rotigliano E., and Agnesi V. 2016. Water erosion susceptibility mapping by applying stochastic gradient treeboost to the imera Meridionale River basin (Sicily, Italy). Geomorphology, 262: 61-76.
Arabkhedri M. 2014. A Review on major water erosion factors in Iran. Journal of land Management, 2(1): 17-26. (In Persian)
Arabameri AR., Pradhan B., Pourghasemi HR., Rezaei K., and Kerle N. 2018. Spatial modelling of gully erosion using GIS and R programing: a comparison among three data mining algorithms. Applied Sciences, 8(8): 1-22.‏
Arabameri AR., Pradhan B., and Rezaei K. 2019. Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, 23(4):669-686.
Azareh A., Rahmati O., Rafiei-Sardooi E., Sankey JB., Lee S., Shahabi H. and Ahmad BB. 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655:684-696.
Bayati Khatibi M., Karami F., and Mokhtari D. 2006. Investigation and analysis of Stream erosion based on geomorphological evidences using quantitative and classical methods. Human Sciences Modarres, 10(2): 83-1010. (In Persian)
Belayneh M., Yirgu T., and Tsegaye D. 2019. Potential soil erosion estimation and area prioritization for better conservation planning in Gumara watershed using RUSLE and GIS techniques. Environmental Systems Research, 8(1): 20.‏
Bonham-Carter G.F. 1994. Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists, 13: 398p.
Conforti M., Aucelli PP., Robustelli G., and Scarciglia F. 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural hazards, 56(3): 881-898.‏
Conoscenti C., Agnesi V., Angileri S., Cappadonia C., Rotigliano E., and Märker M. 2013. A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environmental Earth Sciences, 70(3): 1179-1195.
Dube F., Nhapi I., Murwira A., Gumindoga W., Goldin J. and 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: 145–152.
Ebrahimi M, Amir Ahmadi A, and Zangeneh Asadi M A. 2018. Comparison of gully erosion susceptibility mapping using weight of evidence and frequency ratio models at sanganeh Kalat Basin. Geographical Researches, 32 (4) :105-126. (In Persian)
Esmaili R., and Shokati R. 2015. Evaluation of Gully erosion susceptibility using logistic regression, in SalavatAbad, Kordestan Province.  Arid Regions Geographic Studies, 5(20): 91-104. (In Persian)
Gong JG., Jia YW., Zhou ZH., Wang Y., Wang WL. and Peng H. 2011. An experimental study on dynamic processes of ephemeral gully erosion in loess landscapes. Geomorphology, 125 (1): 201-213.
Gutiérrez Á.G., Schnabel S., and Contador J.F.L. 2009. Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecological Modelling, 220(24): 3630-3637.
Halefom, A., and Teshome A. 2019. Modelling and mapping of erosion potentiality watersheds using AHP and GIS technique: a case study of Alamata Watershed, South Tigray, Ethiopia. Modeling Earth Systems and Environment, 5(3):819-831.‏
Jenks GF. 1989. Geographic logic in line generalization. Cartographica, 26(1):27–42.
Kanti Hembram T., Paul GC., and Saha S. 2019. Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: a comparison of information value and logistic regression models. Modeling Earth Systems and Environment, 5(2): 689-708. ‏
Lucà F., Conforti M. and Robustelli G. 2011. Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology, 134(3-4): 297-308. ‏
Mehreban M., Golkarian A., and Khosravi Kh. 2018. Assessment of sensitivity to Gully erosion using maximum entropy model (Case study: Shorluq region of Khorasan Razavi province) 3rd National Conference on Soil Conservation and Watershed Management, 18-19 June, Tehran (In Persian)
Melelli L., Vergari F., Liucci L., and Del Monte M. 2017. Geomorphodiversity index: Quantifying the diversity of landforms and physical landscape. Science of the Total Environment, 584: 701-714. ‏
Meliho M., Khattabi A., and Mhammdi N. 2018. A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco. Environmental Earth Sciences, 77(18):655.
Najafi S., and Sadeghi S.H.R. 2013. Comparative study of applying soil erosion mapping, fingerprinting and field measurement techniques in estimation of sediment sources. Journal of Watershed Engineering and Management, 5 (3): 165-178 (In Persian)
Novara A., Pisciotta A., Minacapilli M., Maltese A., Capodici F., Cerdà A. and Gristina L. 2018. The impact of soil erosion on soil fertility and vine vigor. A multidisciplinary approach based on field, laboratory and remote sensing approaches. Science of the Total Environment, 622:474-480.
Prosdocimi M., Burguet M., Di Prima S., Sofia G., Terol E., Comino JR., and Tarolli P. 2017. Rainfall simulation and Structure-from-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards. Science of the Total Environment, 574: 204-215. ‏
Rahmati O., Pourghasemi HR. and Zeinivand H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golestan Province, Iran. Geocarto International, 31(1):42-70.
Zakerinejad R., and Maerker M. 2015. An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Natural Hazards, 79(1): 25-50.
Zhu B., Yu J., Rioual P., Gao Y., Zhang Y., and Min L. 2015. Geomorphoclimatic characteristics and landform information in the Ejina Basin, Northwest China. Environmental Earth Sciences, 73(11): 7547-7560.
Applied Soil Research
Volume 9, Issue 3
December 2021
Pages 43-56

Files

History

  • Receive Date: 22 October 2020
  • Revise Date: 25 April 2021
  • Accept Date: 26 April 2021

Share

How to cite

Statistics