Volume 26, Issue 1 (Spring 2022)                   JWSS 2022, 26(1): 71-83 | Back to browse issues page


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Moravejalahkami B, Rahimian M. A Two-Level Optimization of the Manning Roughness Coefficient Method Based on the Modification of USDA-NRCS Method for Basin Irrigation Evaluation. JWSS. 2022; 26 (1) :71-83
URL: http://jstnar.iut.ac.ir/article-1-4183-en.html
Agricultural Research, Education and Extension Organization (AREEO), Yazd , bita.moravej@gmail.com
Abstract:   (309 Views)
The current research was performed to present a quick and proper method for basin irrigation infiltration equation estimation by optimization of the Manning roughness coefficient. A two-level optimization of the Manning roughness coefficient method was presented by developing a zimod simulation model and initial intake families method, USDA-NRCS, (infiltration equation based on soil characteristics), and modified intake families (infiltration equation based on soil characteristics and inflow discharge). The investigation of the results of the model based on observed advance, recession, and surface storage showed the relative error of surface storage volume estimation was decreased by 38 to 50 % by adjusting the initial intake families method. The normalized root mean square error (NRMSE) of the advance estimation was between 0.22 to 0.85 for initial intake families and this parameter was between 0.09 to 0.5 for modified intake families. NRMSE of the recession estimation was between 0.13 to 0.75 for initial intake families and this parameter was between 0.09 to 0.19 for modified intake families. The presented method based on modified intake families increases the accuracy of infiltration estimation as compared to the initial intake families method and can evaluate basin irrigation acceptably. In addition, this method needs less time for basin irrigation evaluation as compared to the complete methods of optimization of infiltration parameters and roughness coefficient. 
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Type of Study: Research | Subject: Ggeneral
Received: 2021/07/3 | Accepted: 2021/08/24 | Published: 2022/05/22

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