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Prediction of total sediment load: A case study of Wadi Arbaat in eastern Sudan

  • Aldrees, Ali (Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University) ;
  • Bakheit, Abubakr Taha (Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University) ;
  • Assilzadeh, Hamid (Institute of Research and Development, Duy Tan University)
  • Received : 2020.05.09
  • Accepted : 2020.11.21
  • Published : 2020.12.25
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Abstract

Prediction of total sediment load is essential in an extensive range of problems such as the design of the dead volume of dams, design of stable channels, sediment transport in the rivers, calculation of bridge piers degradation, prediction of sand and gravel mining effects on river-bed equilibrium, determination of the environmental impacts and dredging necessities. This paper is aimed to investigate and predict the total sediment load of the Wadi Arbaat in Eastern Sudan. The study was estimated the sediment load by separate total sediment load into bedload and Suspended Load (SL), independently. Although the sediment records are not sufficient to construct the discharge-sediment yield relationship and Sediment Rating Curve (SRC), the total sediment loads were predicted based on the discharge and Suspended Sediment Concentration (SSC). The turbidity data NTU in water quality has been used for prediction of the SSC in the estimation of suspended Sediment Yield (SY) transport of Wadi Arbaat. The sediment curves can be used for the estimation of the suspended SYs from the watershed area. The amount of information available for Khor Arbaat case study on sediment is poor data. However, the total sediment load is essential for the optimal control of the sediment transport on Khor Arbaat sediment and the protection of the dams on the upper gate area. The results show that the proposed model is found to be considered adequate to predict the total sediment load.

Keywords

Acknowledgement

This work was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project No 2019/01/1087.

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