Abstract
Water quality data in the sewer system are indispensable for modelling, but rarely available, as measurements in sewers are challenging to conduct. Optimal experimental design (OED) is a powerful tool to identify and maximize the information content of measurement data. This paper adopts a model-based OED methodology to efficiently plan a measurement campaign for final model calibration and validation of a new sewer water quality model. To do so, a preliminary calibrated model of the case study is used to evaluate the information content of different potential measurement locations and scenarios for suspended solids as measured variable. The case study first demonstrates how OED can identify the best measurement location within a complex sewer network. It secondly demonstrates that measuring the beginning of a big rain event results in the most information-rich data among all scenarios evaluated. Thirdly, it analyses in detail the information content of dry weather flow (DWF) data. In comparison to previous studies the methodology is improved by considering the actual measurement error characteristics when calculating the information content of measurement data.
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