Factors Affecting Gully-Head Activity in a Hilly Area Under a Semiarid Climate in Iran

  • Narges Kariminejad
  • Mohsen Hosseinalizadeh
  • Hamid Reza PourghasemiEmail author
  • Majid Ownegh
  • Mauro Rossi
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Gully-head has been observed in a wide range of continuous and categorical conditioning factors in different countries. This study aimed to examine the association of gully-heads with the most effective hydrologic factors via univariate and bivariate analyses in the standard mode. A 2700 ha area in the loess-covered region of Iran was selected and the point map of 287 gully-heads prepared by unmanned aerial vehicle (UAV) images. The pattern of gully-heads was evaluated using univariate tests (O(r) &g(r)). The occurrence of gully-heads in relation to the linear features including road networks (RNS) and stream networks(SNS) was assessed using bivariate correlation tests(O12(r) g12(r)). The analysis mode in mark correlation function (kmm(r)) was applied for soil particles categorized into three groups by size including clay, sand, and silt content. The Mont Carlo simulation intervals were also conducted based on fifth highest and lowest values of the summary statistic of 199 simulated null model data sets. According to the results of the univariate spatial statistics, gully-heads had an aggregated distribution. The bivariate O-ring and pair correlation (g12(r)) test revealed that gully-heads had positive interactions with RNS and SNS. Based on mark correlation function kmm(r), clay content of nearby gully-heads was consistently smaller than the mean value of clay content (μ2 = 22.93%) in the study area. However, the silt contents of nearby gully-heads were significantly larger than the mean value of silt content (μ2 = 64.58%). The mean sand contents (μ2 = 14.75%) do not differ from the mean sand contents taken over all pair gully-heads. Consequently, compared to other interoperation, the suggested approach prepares a proper technique to erosion research community which would be of interest to policy makers and geomorphologists.


Gully-head Spatial modeling Road networks Stream networks Soil texture Iran 


  1. Baddeley, A., 2018. spatstat. local: Extension to’spatstat’ for local composite likelihood. URL https://CRAN.R, r package version 3.5–7
  2. Beretta, A.N., Silbermann, A.V., Paladino, L., Torres, D., Bassahun, D., Musselli, R., García-Lamohte, A., 2014. Soil texture analyses using a hydrometer: modification of the Bouyoucos method. Ciencia e Investigación Agraria, 41: 25–26CrossRefGoogle Scholar
  3. Choubin, B., Rahmati, O., Tahmasebipour, N., Feizizadeh, B., Pourghasemi, H.R., 2019. Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. In Natural hazards gis-based spatial modeling using data mining techniques (pp. 105–125). Springer, ChamCrossRefGoogle Scholar
  4. Evans, R. 1980. Mechanics of water erosion and their spatial and temporal controls: an empirical viewpoint. In: Kirkby MJ and Morgan RPC (eds) Soil erosion. Wiley: Chichester, 109–128Google Scholar
  5. Gan, M., Jia, Y., Shao, M. A., Guo, C., Li, T., 2018. Permanent gully increases the heterogeneity of soil water retention capacity across a slope-gully system. Agriculture, Ecosystems & Environment.Google Scholar
  6. Genet, A., Grabarnik, P., Sekretenko, O. Pothier, D., 2014. Incorporating the mechanisms underlying inter-tree competition into a random point process model to improve spatial tree pattern analysis in forestry. Ecological modelling, 288, pp.143–154Google Scholar
  7. Getzin, S., Wiegand, K., Yizhaq, H., Hardenberg, J. Meron, E., 2015. Adopting a spatially explicit perspective to study the mysterious fairy circles of Namibia. Ecography, 38,1–11CrossRefGoogle Scholar
  8. Hosseinalizadeh, M., Kariminejad, N., Campetella, G., Jalalifard, A., 2018. Spatial point pattern analysis of piping erosion in loess-derived soils in Golestan Province, Iran. Geoderma, 328: 20–29CrossRefGoogle Scholar
  9. Jafari Shalamzari, M., Zhang, W., 2018. Assessing water scarcity using the water poverty index (WPI) in Golestan province of Iran. Water, 10: 1–22CrossRefGoogle Scholar
  10. Jungerius, P. D., Matundura, J., Van De Ancker, J.A.M., 2002. Road construction and gully erosion in West Pokot, Kenya. Earth Surface Processes and Landforms, 27, 1237–1247CrossRefGoogle Scholar
  11. Keshavarzi B. 2014. A possible link between mineralogy of loess deposits and high incidence rate of esophageal cancer in Golestan province of Iran. Iranian Journal of Science and Technology 38: 281–287Google Scholar
  12. Liu, K., Ding, H., Tang, G., Song, C., Liu, Y., Jiang, L. Zhao, B., Gao, Y., Ma, R., 2018. Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information. Geomorphology, 314, 13–26CrossRefGoogle Scholar
  13. Makanzu Imwangana, F., Moeyersons, J., Ozer, P., Ntombi, M., & Dewitte, O., 2018. Factors controlling and triggering urban gullies in the high town of Kinshasa (DR Congo). In Geophysical Research Abstracts (Vol. 20, pp. EGU2018–7037). European Geophysical SocietyGoogle Scholar
  14. Maleki, S., Khormali, F., Bodaghabadi, M.B., Mohammadi, J., Kehl, M., Hoffmeister, D., 2017. Geological controlling soil organic carbon and nitrogen density in a hillslope landscape, semiarid area of Golestan province, Iran. 2: 221–228Google Scholar
  15. Morgan RPC. 2005. Soil erosion and conservation. Blackwell Publishing: The United Kingdom.Google Scholar
  16. Ollobarren Del Barrio, P., Campo-Bescós, M. A., Giménez, R., Casalí, J., 2018. Assessment of soil factors controlling ephemeral gully erosion on agricultural fields. Earth Surface Processes and LandformsGoogle Scholar
  17. Poesen, J., Nachtergaele, J., Verstraeten, G., Valentin, C., 2003. Gully erosion and environmental change: importance and research needs. Catena 50: 91–133CrossRefGoogle Scholar
  18. Shruthi, R.B., Kerle, N., Jetten, V., 2011. Object-based gully feature extraction using high spatial resolution imagery. Geomorphology, 134: 260–268CrossRefGoogle Scholar
  19. Stefanovic, J.R., Bryan, R.B. 2007. Experimental study of rill bank collapse. Earth Surface Processes and Landforms 32: 180–196CrossRefGoogle Scholar
  20. Tonini, M., Abellan, A., Pedrazzini, A., 2012. Cluster analysis of geological point processes with R free software. Open Source Geospatial Research and Education Symposium, SwitzerlandGoogle Scholar
  21. Valentin, C., Poesen, J., Li, Y., 2005. Gully erosion: Impacts, factors and control. Catena, 63: 132–153CrossRefGoogle Scholar
  22. Vandekerckhove, L., Poesen, J., Oostwoud Wijdenes, D., Gyssels, G., Beuselinck, L., De Luna, E., 2000. Characteristics and controlling factors of bank gullies in two semi-arid mediterranean environments. Geomorphology, 33: 37–58CrossRefGoogle Scholar
  23. Wiegand, T. and Moloney, K.A., 2004. Rings, circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229CrossRefGoogle Scholar
  24. Wiegand, T. and Moloney, K.A., 2014. Handbook of spatial point-pattern analysis in ecology. CRC Press, New York, 538 pGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Narges Kariminejad
    • 1
  • Mohsen Hosseinalizadeh
    • 1
  • Hamid Reza Pourghasemi
    • 2
    Email author
  • Majid Ownegh
    • 1
  • Mauro Rossi
    • 3
  1. 1.Department of Watershed and Arid Zone ManagementGorgan University of Agricultural Sciences and Natural ResourcesGorganIran
  2. 2.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran
  3. 3.Department of Research Institute for Geo-Hydrological Protection IRPIPerugiaItaly

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