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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.

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one publication added to basket [349133]

A fast and effective parameterization of water quality models Khorashadi Zadeh, F.; Nossent, J.; Taddesse Woldegiorgis, B.; Bauwens, W.; Van Griensven, A. (2022). A fast and effective parameterization of water quality models. Environ. Model. Softw. 149: 105331. https://dx.doi.org/10.1016/j.envsoft.2022.105331 In: Environmental Modelling & Software. Elsevier: Oxford. ISSN 1364-8152; e-ISSN 1873-6726, meer

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Beschikbaar in	Auteurs
Waterbouwkundig Laboratorium: Open Repository 378390 [ download pdf ] Waterbouwkundig Laboratorium: Non-open access 371209 [ aanvragen ]

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Trefwoorden

Numerical modelling Water management > Hydrology > Conceptual models Water management > Statistics > Sensitivity analysis Water management > Statistics > Uncertainty analysis Water management > Water quality > Conceptual models

Author keywords

Parameterization; Sensitivity analysis; Uncertainty analysis; DREAM(ZS); Error model

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Ontwikkelen van conceptuele modellen, meer

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Khorashadi Zadeh, F. Nossent, J., meer Taddesse Woldegiorgis, B.	Bauwens, W., meer Van Griensven, A., meer

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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.

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