Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability

RETRACTED ARTICLE: Uncertainty Analysis of a Continuous Hydrological Model Using DREAM-ZS Algorithm

“…The Editor-in-Chief has retracted this article [1] because significant parts of the text and the data were duplicated from a previously published article [2]. All authors agree to this retraction.…”

A hydrological toolkit to delineate patterns of blue and green water in a regional semi-arid climate in Iran via CMIP5 models

“…SWAT, as a continuous-term, semi-distributed, process-based model, is developed to assess alternative management strategies for short-and long-term decisions in large river basins (Arnold et al, 2012) by policymakers. SWAT model has been widely applied at regional (Rahimpour et al, 2020;Hassanzadeh et al, 2019;Samuels et al, 2018;Huang et al, 2017;Rodrigues et al, 2014), national (Liu et al, 2017;Zhu et al, 2015;Faramarzi et al, 2009) and continental scales (Giles et al, 2019;Faramarzi et al, 2013). The basin of this model is classified into multiple sub-basins, and then the soil and topographical features are classified into hydrological response units (HRUs) according to the combination of land uses (Afshar et al, 2018;Cuceloglu et al, 2017;Arnold et al, 2012).…”

“…SWAT was developed and employed in most studies in order to determine the effects of climate change in the quantity and spatiotemporal distribution of BW and GW, and also the impacts of human activity on agricultural yield, chemical, and stream flow in a large scale basin (Pandey et al, 2019;Dadfar et al, 2019;Fazeli Farsani et al, 2019;Masud et al, 2018;Afshar and Hassanzadeh, 2017;Veettil and Mishra, 2016;Besharat et al, 2015;Faramarzi et al, 2009Faramarzi et al, , 2013). The Calibration and Uncertainty Procedures (SWAT-CUP) computer program, which connects to SWAT model, is applied in order to investigate the sensitivity analysis, the model parameters, and the calibration and validation processes (Hassanzadeh et al, 2019;Shivhare et al, 2018;Afshar et al, 2018;Uniyal et al, 2015;Abbaspour et al, 2007). SWAT-CUP program also contains four algorithms to perform these processes, including a Monte Carlo Markov Chain (MCMC) algorithm, Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol), and Sequential Uncertainty Fitting program (SUFI-2).…”

“…SWAT-CUP program also contains four algorithms to perform these processes, including a Monte Carlo Markov Chain (MCMC) algorithm, Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol), and Sequential Uncertainty Fitting program (SUFI-2). Among them, SUFI-2 has extensively been employed in many parts of the world to optimize the parameters of the SWAT model, and it is found to be quite efficient in large scale models in comparison with another algorithms of SWAT-CUP program (Hassanzadeh et al, 2019;Shivhare et al, 2018;Li et al, 2017;Abbaspour et al, 2007).…”

Net Ecosystem Production of a River Relying on Hydrology, Hydrodynamics and Water Quality Monitoring Stations

“…Given that there was a set of unknown parameters governing the HSPF model, our study integrated a process for parameters calibration using the non-sorted genetic algorithm II (NSGA-II) [17,18]. This approach increased reliability under uncertainty of new hydrologic scenarios [19][20][21] and supported the use of an optimal solution [20,22,23] as the best set of parameters for the HSPF model. To guarantee satisfactory predictions, HSPEXP+ 2.0 was used to assess the calibrated HSPF model, as in the works of Xie et al [20] and Lampert and Wu [24].…”