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Summary: Mathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the “a priori” hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in “a priori” distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases. [Copyright &y& Elsevier]

The objective of this paper is the definition of a methodology to evaluate the impact of the temporal resolution of rainfall measurements in urban drainage modelling applications. More specifically the effect of the temporal resolution on urban water quality modelling is detected analysing the uncertainty of the response of rainfall-runoff modelling. Analyses have been carried out using historical rainfall-discharge data collected for the Fossolo catchment (Bologna, Italy). According to the methodology, the historical rainfall data are taken as a reference, and resampled data have been obtained through a rescaling procedure with variable temporal windows. The shape comparison between 'true' and rescaled rainfall data has been carried out using a non-dimensional accuracy index. Monte Carlo simulations have been carried out applying a parsimonious urban water quality model, using the recorded data and the resampled events. The results of the simulations were used to derive the cumulative probabilities of quantity and quality model outputs (peak discharges, flow volume, peak concentrations and pollutant mass) conditioned on the observation according to the GLUE (Generalized Likelihood Uncertainty Estimation) methodology. The results showed that when coarser rainfall information is available, the model calibration process is still efficient even if modelling uncertainty progressively increases especially with regards to water quality aspects. [ABSTRACT FROM AUTHOR]

The benefit of integrated analysis has been demonstrated in technical literature and it is also required by the EU Water Framework Directive 60/2000, which proposes a water-quality-orientated view of the whole urban drainage system and oversees new ways of assessing its performance. Integrated models, such as any complex modelling approach, often have prohibitive data availability requirements that reduce their applicability. Moreover, widely different approaches can be applied, ranging from simple conceptual models to complex physically based ones. In the present paper, two approaches have been compared using data from an experimental catchment in Bologna (Italy), which consists of a part of the Bologna sewer network and a reach of the Savena River. The paper shows how river modelling has the most relevant role in conditioning the overall response of the integrated approach. This behaviour is mainly attributable to the ephemeral characteristics of the river determining quality model parameter values much higher than those presented in literature for permanent rivers. [ABSTRACT FROM AUTHOR]