A fast and effective parameterization of water quality models. (March 2022)

• Record Type: Journal Article
• Authors: Khorashadi Zadeh, Farkhondeh; Nossent, Jiri; Woldegiorgis, Befekadu Taddesse; Bauwens, Willy; Van Griensven, Ann
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Parameterization -- Sensitivity analysis -- Uncertainty analysis -- DREAM(ZS) -- Error model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105331
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Water quality (WQ) models parameterization remains a challenging task, as these models are typically characterized by a high number of parameters. The objective of this study was to present a solution to the WQ parameterization problem by the use of a fast sensitivity analysis (SA) method and a manual calibration. For this purpose, we applied the simple screening LH-OAT method to the conceptual WQ model of the River Dender, Belgium. To evaluate the effectiveness of LH-OAT to identify the influential parameters, the advanced PAWN method was applied. A manual calibration was done using the influential parameters. LH-OAT provided a parameter ranking that was very similar to the one of PAWN but in a much more efficient way. The Bayesian uncertainty assessment showed the effectiveness of the LH-OAT results. To conclude, a fast screening method is preferred over an advanced SA method to identify the influential parameters for the calibration. Highlights: Water quality models parameterization is still a challenging task. The use of a fast sensitivity analysis (SA) method and a manual calibration is proposed as a solution to the parameterization problem. LH-OAT provides a parameter ranking that is very similar to the one of PAWN but in a much more efficient way. For parameter screening, a simple method is preferred over an advanced method.