Urban water quality management often requires use of numerical models allowing the evaluation of the cause–effect relationship between the input(s) (i.e. rainfall, pollutant concentrations on catchment surface and in sewer system) and the resulting water quality response. The conventional approach to the system (i.e. sewer system, wastewater treatment plant and receiving water body), considering each component separately, does not enable optimisation of the whole system. However, recent gains in understanding and modelling make it possible to represent the system as a whole and optimise its overall performance. Indeed, integrated urban drainage modelling is of growing interest for tools to cope with Water Framework Directive requirements. Two different approaches can be employed for modelling the whole urban drainage system: detailed and simplified. Each has its advantages and disadvantages. Specifically, detailed approaches can offer a higher level of reliability in the model results, but can be very time consuming from the computational point of view. Simplified approaches are faster but may lead to greater model uncertainty due to an over-simplification. To gain insight into the above problem, two different modelling approaches have been compared with respect to their uncertainty. The first urban drainage integrated model approach uses the Saint-Venant equations and the 1D advection-dispersion equations, for the quantity and for the quality aspects, respectively. The second model approach consists of the simplified reservoir model. The analysis used a parsimonious bespoke model developed in previous studies. For the uncertainty analysis, the Generalised Likelihood Uncertainty Estimation (GLUE) procedure was used. Model reliability was evaluated on the basis of capacity of globally limiting the uncertainty. Both models have a good capability to fit the experimental data, suggesting that all adopted approaches are equivalent both for quantity and quality. The detailed model approach is more robust and presents less uncertainty in terms of uncertainty bands. On the other hand, the simplified river water quality model approach shows higher uncertainty and may be unsuitable for receiving water body quality assessment.
Keywords: mathematical modelling, river water quality, sensitivity analysis, uncertainty analysis, urban drainage integrated modelling