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RESEARCH PRODUCT

Uncertainty in water quality modelling: The applicability of Variance Decomposition Approach

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Quantification of uncertainty is of paramount interest in integrated urban drainage water quality modelling. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. However, the state of knowledge regarding uncertainties in urban drainage models is poor. In the case of integrated urban drainage water quality models, due to the fact that integrated approaches are basically a cascade of sub-models (simulating the sewer system, wastewater treatment plant and receiving water body), uncertainty produced in one sub-model propagates to the following ones in a manner dependent on the model structure, the estimation of parameters and the availability and uncertainty of measurements in the different parts of the system. Uncertainty basically propagates throughout a chain of models in which the simulation output from upstream models is transferred to the downstream ones as input. The Variance Decomposition Approach tracks uncertainty propagation commonly assuming that the correlation among error sources is negligible. In complex environmental models, the overall uncertainty can differ significantly from the simple sum of uncertainties generated in each sub-model, showing the well-known uncertainty accumulation problems due to non-linearity in the model and correlation among the sources of uncertainty. This work discusses the importance of such issues in the application of a complex integrated urban drainage model with the aim of evaluating the applicability of Variance Decomposition Approach. The integrated model and the methodology for the uncertainty decomposition were then applied to a complex integrated catchment: the Nocella basin (Italy). The results showed that the Variance Decomposition Approach can be a powerful tool for uncertainty analysis, but a possible correlation among uncertainty sources should be considered because it can greatly affect the analysis.