Uncertainty Propagation in Integrated Urban Water Quality Modelling

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Abstract

Sensitivity and uncertainty assessment of integrated urban drainage water quality models are crucial steps in the evaluation of the reliability of model results. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. 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 paper presents the uncertainty assessment of an integrated urban drainage model developed in previous studies by means of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A straightforward approach based on the analysis of the coefficient of variation (Rxy). Rxy is defined as the ratio between the standard deviation (a) and the average (l) value of the model output of reference taken into account. The analysis has been applied to an experimental catchment in Bologna (Italy) which consists of a part of the Bologna sewer network and a reach of the Savena river. The results showed that the method can be a useful tool for uncertainty analysis and for guiding the operator in the choice of the modelling approach.
LinguaEnglish
Title of host publicationSpringer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN
Pagine799-806
Number of pages8
Publication statusPublished - 2019

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Mannina, G. (2019). Uncertainty Propagation in Integrated Urban Water Quality Modelling. In Springer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN (pagg. 799-806)

Uncertainty Propagation in Integrated Urban Water Quality Modelling. / Mannina, Giorgio.

Springer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN. 2019. pag. 799-806.

Risultato della ricerca: Chapter

Mannina, G 2019, Uncertainty Propagation in Integrated Urban Water Quality Modelling. in Springer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN. pagg. 799-806.
Mannina G. Uncertainty Propagation in Integrated Urban Water Quality Modelling. In Springer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN. 2019. pag. 799-806
Mannina, Giorgio. / Uncertainty Propagation in Integrated Urban Water Quality Modelling. Springer Nature Switzerland AG 2019 G. Mannina (Ed.): UDM 2018, GREEN. 2019. pagg. 799-806
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AB - Sensitivity and uncertainty assessment of integrated urban drainage water quality models are crucial steps in the evaluation of the reliability of model results. Indeed, the assessment of the reliability of the results of complex water quality models is crucial in understanding their significance. 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 paper presents the uncertainty assessment of an integrated urban drainage model developed in previous studies by means of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. A straightforward approach based on the analysis of the coefficient of variation (Rxy). Rxy is defined as the ratio between the standard deviation (a) and the average (l) value of the model output of reference taken into account. The analysis has been applied to an experimental catchment in Bologna (Italy) which consists of a part of the Bologna sewer network and a reach of the Savena river. The results showed that the method can be a useful tool for uncertainty analysis and for guiding the operator in the choice of the modelling approach.

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