Model calibration and uncertainty analysis of runoff in the Zayanderood River basin using generalized likelihood uncertainty estimation (GLUE) method

Abstract

This study is designed to consider the uncertainty in the kinematic runoff and erosion model named KINEROS2. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used for assessing the uncertainty associated with model predictions, which assumes that due to the limitations in model structure, data and calibration scheme, many different parameter sets can make acceptable simulations. GLUE is a Bayesian approach based on the Monte Carlo method for model calibration and uncertainty analysis. The assessment was performed in the Zayanderood River basin located in Central Iran. To make an accurate calibration, five runoff events were selected from three different gauging stations for the purpose. Statistical evaluations for streamflow prediction indicate that there is good agreement between the measured and simulated flows with Nash–Sutcliffe values of efficiency of 0.85 and 0.79 for calibration and validation periods respectively. Uncertainty analysis was carried out on the new distribution of input parameters for model validation.