Environmental Science and Pollution Research

, Volume 25, Issue 31, pp 30979–30997 | Cite as

Tracing sediment sources in a mountainous forest catchment under road construction in northern Iran: comparison of Bayesian and frequentist approaches

  • Kazem NosratiEmail author
  • Arman HaddadchiEmail author
  • Adrian L. Collins
  • Saeedeh Jalali
  • Mohammad Reza Zare
Research Article


Development and land use change lead to accelerated soil erosion as a serious environmental problem in river catchments in Iran. Reliable information about the sources of sediment in catchments is therefore necessary to design effective control strategies. This study used a composite sediment source tracing procedure to determine the importance of forest road cuttings as a sediment source in a mountainous catchment located in northern Iran. A fallout radionuclide (137Cs) and 12 geochemical tracers (Ca, Cu, Fe, K, Mg, Mn, Na, Ni, OC, Pb, Sr and TN) were used to determine the relative contributions of three sediment source types (hillslopes, road cuttings and channel banks) to both suspended and bed sediment samples. Two mixing models based on different mathematical concepts were used to apportion the sediment sources: the mixture sampling importance resampling Bayesian model which incorporates the mass-balance matrix and a distribution model using normal and summed probability of normal distributions. The results of both mixing models indicated that sub-soil erosion from road cuttings and channel banks dominated the sources of river bed and suspended sediment samples, respectively. These results therefore highlight that conservation that works in the study area to remedy the sediment problem should initially focus on stabilisation and rehabilitation of road cuttings and channel banks. This successful application of a composite (radionuclide and geochemical) tracing technique for discriminating source end members characterised by different erosion processes underscores the importance of sub-soil erosion in this case study.


Sediment tracing Sub-surface erosion Geochemical tracers 137Cs MixSIR Bayesian model 



The authors would like to express their gratitude to the Iran National Science Foundation (INSF) for financial support (grant numbers of 89002624). This project was also funded by a grant from the research council of Shahid Beheshti University, Tehran, Iran (grant number 600.3847). Rothamsted Research receives strategic funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC), and the input to this work by ALC was funded by grant BBS/E/C/000I0330.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Physical Geography, Faculty of Earth SciencesShahid Beheshti UniversityTehranIran
  2. 2.National Institute of Water and Atmospheric ResearchChristchurchNew Zealand
  3. 3.Sustainable Agriculture Sciences DepartmentRothamsted ResearchOkehamptonUK
  4. 4.Department of PhysicsMalek-Ashtar UniversityShahinshahrIran

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