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  • 标题:Description of the G-A Infiltration Model Using Chu and Chow Viewpoints
  • 本地全文:下载
  • 作者:M Vachefi ; H Rahideh
  • 期刊名称:Journal of Applied Sciences and Environmental Management
  • 印刷版ISSN:1119-8362
  • 出版年度:2011
  • 卷号:15
  • 期号:1
  • DOI:10.4314/jasem.v15i1.65671
  • 语种:English
  • 出版社:Department of Pure & Industrial Chemistry, University of Port Harcourt
  • 摘要:The soil infiltration process is one of the hydrological cycle processes, attracting the attention of the hydrologists more than any other process. This process provides conversion of the raw rainfall into the excess rainfall; and ultimately the excess rainfall is used for describing the rainfall-runoff models. In most of the infiltration models, the rainfall is introduced to the model as steady and is converted into the excess rainfall. In this paper, through using Green-Ampt model and the double-ring tests results performed in seven pits in one of the indicator catchment south of the country, the model parameters have been estimated, and the excess rainfall resulting from the unsteady raw rainfall has been calculated using deterministic model and using two entirely different viewpoints. After making sure of the conditions and limitations of both viewpoints, the necessary grounds have been provided for distinguishing between time scale implemented in the model and time scale used for observation of the unsteady rainfall as well as their influence on such indexes as the runoff depth, the runoff peak and the cumulative infiltration. The bright point of this paper is consideration of the difference between the concepts of “Model Time Scale” and “Observation Time Scale”. Taking into notice of the change trend of infiltration, run-off and their change ratios against the changes of these two time scales, the researcher must suitably select between the “Model Time Scale” and “Observation Time Scale” of the rainfall and considers the optimum value of these time intervals in calculation of the excess rainfall and their influence on the run-off rainfall models.
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