Volume 21, Number 1 (Spring 2017)                   JWSS 2017, 21(1): 113-125 | Back to browse issues page



DOI: 10.18869/acadpub.jstnar.21.1.113

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Hayatzadeh M, Ekhtesasi M, Malekinezhad H, Fathzadeh A, Azimzadeh H. Determination of the Optimal Method to Estimate the Suspended Sediment Load in Arid Regions. Case Study: Fakhrabad basin of Mehriz (Yazd). JWSS. 2017; 21 (1) :113-125
URL: http://jstnar.iut.ac.ir/article-1-3212-en.html

Abstract:   (814 Views)

Soil erosion, is undoubtedly one of the most important problems in natural areas of Iran and has destructive effects on different ecosystems. Considering that the calculation of the sediment rate in sediment stations and direct measurements of erosion process is costly and difficult, it is critical to find ways to accurately estimate the amount of sediment yield in catchments especially in arid and hyper arid areas because of their high ecological sensitivity. One of the most commonly used methods in these areas is the sediment rating regression method. Therefore, in this study sediment observed data for 48 events (the corresponding discharge and sediment) in a 23-year period from Fkhrabad basin (Mehriz) were compared to the estimated data obtained from Multi-line rating method, extent middle class, middle class rating curve with correction factor QMLE, SMEARING correction coefficient FAO and Artificial Neural networks (ANNs). Finally, the accuracy of these methods were assessed using different evaluation criteria such as Root mean square error (RMSE), coefficient of determination (R2) and the standard Nash (ME). Results showed that ANN outperformed the other methods with the RMSE, R2 and ME of 203.3, 0.86 and 0.66, respectively. The results suggest that these methods should be used cautiously in estimating the suspended sediment load in arid and hyper arid regions due to the nature of the observed data and temporal and seasonal flow Systems in these regions. It was also indicated that the artificial neural network models have higher flexibility than other methods which makes them to be useful tools for modeling in poor data conditions.

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Type of Study: Research | Subject: Ggeneral
Received: 2016/02/4 | Accepted: 2016/07/27 | Published: 2017/06/6

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