The development of a continuous model to simulate the behaviour of sewer systems requires detailed information on each component of the flows contributing to the global discharge. In this paper authors investigate a novel method based on signal processing and long time series data implemented with a 2 min time step (flow rate, conductivity, pH and turbidity) in order to identify the dry weather components in a separated stormwater sewer system draining an industrial catchment. The wavelet analysis is applied to the recorded data to identify main components in dry weather flow after the removing of the signal noise. This paper highlights also a method to detect inflow into sewer system and shows how hydrological modelling can be used to characterise the relevant components. These techniques could be used as a basis for several applications.
Keywords: dry weather flow, hydrological modelling, separated sewer, signal processing, time series data