Probabilistic Backtracing of Drinking Water Contamination Events in a Stochastic World

Abstract In this paper, we investigate the relevance of the stochastic nature of water demand for backtracing of contaminations in drinking water distribution networks. We present an approach to deal with the uncertainty introduced by stochastic demand, which is applied to a full detail part (all pipes) of a hydraulic model of a distribution network in the Netherlands. It is demonstrated that stochastic water demand can introduce significant amounts of uncertainty for backtracing in some parts of tertiary (reticulation) networks in specific, looped configurations. In other parts, the additional uncertainty introduced by stochastic water demand can be limited.