2017
DOI: 10.1177/1178622117731792
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Optimal Calibration and Uncertainty Analysis of SWAT for an Arid Climate

Abstract: One of the major issues for semidistributed models is calibration of sensitive parameters. This study compared 3 scenarios for Soil and Water Assessment Tool (SWAT) model for calibration and uncertainty. Roodan watershed has been selected for simulation of daily flow in southern part of Iran with an area of 10 570 km 2. After preparation of required data and implementation of the SWAT model, sensitivity analysis has been performed by Latin Hypercube One-factor-At-a-Time method on those parameters which are eff… Show more

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Cited by 9 publications
(5 citation statements)
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“…It should be emphasized that our results in this study are conditioned on the data and procedures used for this study. We are aware that input data [63], discretization of the region of study [64,65], regionalization of the parameters [66], method of calibration and the choice of objective function [67] all affect final parameter ranges and their sensitivities.…”
Section: Discharge Calibration and Validation Results In The Main Chamentioning
confidence: 99%
“…It should be emphasized that our results in this study are conditioned on the data and procedures used for this study. We are aware that input data [63], discretization of the region of study [64,65], regionalization of the parameters [66], method of calibration and the choice of objective function [67] all affect final parameter ranges and their sensitivities.…”
Section: Discharge Calibration and Validation Results In The Main Chamentioning
confidence: 99%
“…The initial step in the SWAT model calibration and validation procedure would be to choose the most sensitive parameters for a specific catchment [46]. This study set the global sensitivity method to select the most sensitive parameters during calibration [47][48][49][50].…”
Section: Model Calibration and Validationmentioning
confidence: 99%
“…NS ranges from -∞ to 1. The optimal value is 1, corresponding to the situation where the plot of observed data fits the simulation (Khoiab & Thomb 2015) perfectly, while values less than 0 indicate that the observed data mean is a more accurate predictor than the simulated output (Jajarmizadeh et al 2017). The target interval for PBIAS is zero to +25%, with zero corresponding to the optimum.…”
Section: Model Performance Measuresmentioning
confidence: 99%