Search

Abstract: In urban water quality management, several models are connected and integrated for analysing the fate of pollutants from the sources in the urban catchment to the final recipient; classical problems connected with the selection and calibration of parameters are amplified by the complexity of the modelling approach increasing their uncertainty. The present paper aims at studying the influence of reductions in available data on the modelling response uncertainty with respect to the different integrated modelling outputs (both considering quantity and quality variables). At this scope, a parsimonious integrated home-made model has been used allowing for analysing the combinative effect of data availability regarding the different parts of the integrated urban drainage system; the uncertainty analysis approach has been applied to an experimental catchment in Bologna (Italy). The number of available data points has been fictitiously reduced obtaining data sets ranging between 25% and 100% of the actually measured data. For each of the data sets, uncertainty has been evaluated and its propagation from the upstream sub-model to the downstream ones has been assessed. The present study demonstrates that model calibration and modelling efficiency assessment may induce the operator to be excessively confident in the model results when available data are scarce. Quite the opposite is indeed true, that limited data availability increases modelling uncertainty. A conclusion of this article is that uncertainty analysis should always be conducted in order to effectively evaluate model reliability. [Copyright &y& Elsevier]

Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by means of a formal distribution of residuals between the model and measured data (GLUE_f). The uncertainty assessment of the models enabled evaluation of the advantages and limitations of the three methodologies adopted. The models were then tested using the quantity–quality data gathered for the Fossolo catchment in Bologna, Italy. The results revealed that all of the models evaluated here provided good calibration results, even if the model reliability (in terms of related uncertainty) varied, which suggests the adoption of a specific modelling approach with respect to the others. Additionally, a comparison of uncertainty analysis approaches showed that, regarding the models evaluated here, the classical Bayesian method is more effective at discriminating models according to their uncertainty, but the GLUE approach performs similarly when it is based on the same founding assumptions as the Bayesian method. [Copyright &y& Elsevier]