Quantification and relative comparison of different types of uncertainties in sewer water quality modeling

Abstract

Quantifiable sources of uncertainty have been identified for a case study of integrated modeling of a sewer system with a more downstream wastewater treatment plant and storage sedimentation tank. The different sources were classified in model input and model-structure-related uncertainties. They were quantified and propagated towards the uncertainty in the event-based prediction of sewer emissions (flow, and physico-chemical water quality concentrations and loads). Based on the concept of variance decomposition, the total prediction uncertainty was split into the contributions of the various uncertainty sources and the different submodels. Although the results strongly depend on the water quality variable considered, it is in most general terms concluded that the uncertainty contribution by the water quality submodels is an order of magnitude higher than that for the flow submodels. Future model improvement should therefore mainly focus on water quality data collection, which would reduce current problems of spurious model calibration and verification, but also of knowledge gaps in in-sewer processes.