Sewer modelling based on highly distributed calibration data sets and multi-objective auto-calibration schemes

Abstract

Pollutant load modelling for sewer systems is state-of-the-art, especially for the estimation of discharged pollutant loads and development of sewer management strategies. However, conventionally obtained calibration data sets are often not exhaustive and have significant drawbacks. In the Graz West catchment area (Graz, Austria), continuous high-resolution long-term online measurements for discharge and pollutant concentration have been carried out since 2002. In this paper, the application of single- and multi-objective auto-calibration schemes based on evolution strategies for a deterministic hydrological pollutant load model will be discussed. Three approaches for pollutant load modelling are examined and compared: using a constant storm weather concentration and two build-up wash-off approaches with basic respectively extended wash-off equations. It is shown that the applied auto-calibration method leads to very satisfying results for both the calibration and the validation data set, and also for the dry and the storm weather runoff. However, until now, convective storms have not been convincingly represented. The build-up wash-off approach using the basic wash-off equation shows the best correlations between measured data and simulation results. As one of the chosen objectives for the multi-objective optimisation reacted highly sensitively to measurement errors, additional improvements can be expected after refining the criteria used in this algorithm.