• Title of article

    An analytical approach to the estimation of optimum river channel dimensions

  • Author/Authors

    Mahmoudi, M Faculty of Civil - Water and Environmental Engineering - Shahid Beheshti University, Tehran , Majdzadeh Tabatabai, M.R Faculty of Civil - Water and Environmental Engineering - Shahid Beheshti University, Tehran , Mousavi Nadoushani, S Faculty of Civil - Water and Environmental Engineering - Shahid Beheshti University, Tehran

  • Pages
    13
  • From page
    1169
  • To page
    1181
  • Abstract
    Extremal hypotheses without bank stability constraint typically over-predict and under-predict channel width in large rivers and natural streams, respectively. In general, results obtained from unconstrained extremal hypotheses show inappropriate agreement between predicted and observed dimensions of the rivers. One of the important factors in disparity of the data may be lack of appropriate relationships to assess bank vegetation of the rivers. For this reason, a modified analytical model was developed to reduce the effect of bias by considering bank stability and vegetation. The model took into account channel shape factor, a wide range of bed load equations in the form of excess shear stress, and vegetation quantication, which made it able to predict optimal channel geometry dimensions. Finally, the developed model was calibrated using the field data of the United Kingdom and Iran. In addition to indicating the effect of bank stability and vegetation on estimation of the geometric characteristics of the channel, the obtained results conrmed the effciency of the constrained model in comparison with the unconstrained model. This study also provides support for the use of the concepts of maximum sediment transporting capacity and minimum stream power for understanding the operation of alluvial rivers.
  • Keywords
    Extremal hypotheses , Bank vegetation , Analytical model
  • Journal title
    Scientia Iranica(Transactions A: Civil Engineering)
  • Serial Year
    2019
  • Record number

    2524817