| 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
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| Beschikbaar in | Auteurs |
- Waterbouwkundig Laboratorium: Open Repository 378390 [ OWA ]
- 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
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| Author keywords |
Parameterization; Sensitivity analysis; Uncertainty analysis; DREAM(ZS); Error model |
| Project | Top | Auteurs |
- Ontwikkelen van conceptuele modellen
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| Auteurs | | Top |
- Khorashadi Zadeh, F.
- Nossent, J.
- Taddesse Woldegiorgis, B.
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- Bauwens, W.
- Van Griensven, A.
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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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