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Summary: The evaluation of urban stormwater quality is of relevant importance for urban drainage, and mathematical models may be of great interest in this respect. To date, several detailed mathematical models are available to predict stormwater quantity–quality characteristics in urban drainage systems. However, only a few models take sewer sediments into account, considering their cohesive-like properties that influence the build-up process of the pollutant load. Furthermore, the model data requirements, especially for the quality aspects, are extensive, which limit their applicability and affect model results with large uncertainty. Uncertainty analysis provides a measure or index regarding the significance and the accuracy of the results obtained by mathematical modelling and is therefore of high interest. Nevertheless, only few studies have been carried out in the urban drainage field, and very few deal with water quality issues. One of the main reasons for this lack of research is the computational burden required by detailed models that preserve this analysis and generally require several Monte Carlo simulation runs. A possible to this problem may be the adoption of simplified parsimonious models that generally require shorter computational times. In this context, this paper presents a parsimonious conceptual model for the evaluation of the pollutant load in-sewers. The model contains two modules: a hydrological and hydraulic module that calculates the hydrographs at the inlet and at the outlet of the sewer system, and a solid transfer module that calculates the pollutographs. The cohesive properties of sewer sediments were carefully considered. Further, the effectiveness of the innovative sewer sediment modelling approach has been verified by taking into account the uncertainty assessed according to the GLUE methodology. The model has been tested using experimental quantity–quality data gathered in two Italian catchments, Fossolo (Bologna) and Parco d’Orlèans (Palermo). [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]

In the past, emission standard indicators have been adopted by environmental regulation authorities in order to preserve the quality of a receiving water body. Such indicators are based on the frequency or magnitude of a polluted discharge that may be continuous or intermittent. In order to properly maintain the quality of receiving waters, the Water Framework Directive, following the basic ideas of British Urban Pollution Manual, has been established. The Directive has overtaken the emission-standard concept, substituting it with the stream-standard concept that fixes discharge limits for each polluting substance depending on the self-depurative characteristics of receiving waters. Stream-standard assessment requires the deployment of measurement campaigns that can be very expensive; furthermore, the measurement campaigns are usually not able to provide a link between the receiving water quality and the polluting sources. Therefore, it would be very useful to find a correlation between the quality status of the natural waters and the emission-based indicators. Thus, this study is aimed to finding a possible connection between the receiving water quality indicators drawn by environmental regulation authorities and emission-based indicators while considering both continuous (i.e. from the wastewater treatment plants) and intermittent pollution discharges (mainly from combined sewer overflows). Such research has been carried out by means of long-term analysis adopting a holistic modelling approach. The different parts of the integrated urban drainage system were modelled by a parsimonious integrated model. The analysis was applied to an ephemeral river bounding Bologna (Italy). The study concluded that the correlation between receiving water quality and polluting emissions cannot be generally stated. Nevertheless, specific analyses on polluting emissions were pointed out in the study highlighting cause-effect link between polluting sources and receiving water quality. [ABSTRACT FROM AUTHOR]

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]

Summary: Over the last few years, the use of mathematical models has gained importance in urban drainage system management; indeed, such models enable the combined analysis of different components that constitute a drainage system; the sewer system, wastewater treatment plant and the receiving water body. The effectiveness of an integrated approach has been widely demonstrated in the past and is presented in the EU Water Framework Directive, which also introduces a new point of view regarding the water quality management of the whole system, requiring a global analysis at the river basin scale for pollutant sources. However, integrated urban drainage models introduce several uncertain factors that are due to the high complexity level of the adopted approaches as well as to the lack of data for a robust model calibration. This paper presents the uncertainty assessment of an integrated urban drainage model developed in previous studies by means of the Generalized Likelihood Uncertainty Estimation methodology. The analysis has been applied to an experimental catchment in Bologna (Italy) which covers a part of the Bologna sewer network and a reach of the Savena River. [Copyright &y& Elsevier]

Abstract: In urban drainage, new computational possibilities have supported the development of new integrated approaches aimed at joint water quantity and quality analysis of the whole urban drainage system. Although the benefit of an integrated approach has been widely demonstrated, to date, several aspects prevent its applicability such as scarce availability of field data if compared with model complexity. These aspects sometimes prevent the correct estimation of parameters thus leading to large uncertainty in modelling response. This is a typical parameter identifiability problem that is discussed in the present paper evaluating the effect of identifiability procedures in increasing operator confidence in modelling results. The methodology presented has been applied to a home-made integrated urban drainage model that has been calibrated/validated considering field data collected in the Savena experimental catchment (Bologna, Italy). The results demonstrate the effectiveness of the identifiability analysis in obtaining a tool for urban integrated modelling applications and field data gathering campaigns. [Copyright &y& Elsevier]