Bayesian Estimation of Return Periods of CSO Volumes for Decision-Making in Sewer System Management

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Decisions on the rehabilitation of a sewer system are usually based on a single computation of CSO volumes using a time series of rainfall as system loads. A shortcoming of this method is that uncertainties in knowledge of sewer system dimensions are not taken into account. Besides, statistical uncertainties are left aside. This paper presents the effect of variations in sewer system dimensions on return periods of calculated CSO volumes. As an example the sewer system of ‘De Hoven’ (the Netherlands) is used. CSO volumes per storm event are computed using Monte Carlo simulations with a reservoir model of the sewer system. In each Monte Carlo run random values for the sewer system dimensions are drawn and substituted in the model. With regard to the computed CSO volumes probability distributions are estimated taking into account the statistical uncertainties involved. For this purpose so-called Bayes factors are used to determine weights that describe how well a probability distribution fits the computed data, i.e. the better the tit, the higher the weighing. With the fitted probability distributions the 95% uncertainty intervals of calculated CSO volumes and their corresponding return periods are computed. The results show that uncertainties in knowledge of sewer system dimensions cause a considerable variability in return periods of calculated CSO volumes.