The management of water quality in rivers, urban drainage and water supply networks is essential for ecological and human well-being. Predicting the effects of management strategies requires knowledge of the hydrodynamic processes covering spatial scales of a few millimetres (turbulence) to several hundred kilometres (catchments), with a similarly large range of timescales from milliseconds to weeks. Predicting underlying water quality processes and their human and ecological impact is complicated as they are dependent on contaminant concentration. Current water quality modelling methods range from complex three dimensional computational fluid dynamics (3D CFD) models, for short time and small spatial scales, to one-dimensional (1D) time dependent models, critical for economic, fast, easy-to-use applications within highly complex situations in river catchments, water supply and urban drainage systems. Mixing effects in channels and pipes of uniform geometry can be represented with some confidence in highly turbulent, steady flows. However, in the majority of water networks, the standard 1D model predictions fall short because of knowledge gaps due to low turbulence, 3D shapes and unsteady flows. This Fellowship will work to address the knowledge gaps, delivering a step change in the predictive capability of 1D water quality network models. It will achieve this via the strategic leadership of a programme of laboratory and full-scale field measurements, the implementation of system identification techniques and active engagement with primary users. The proposal covers aspects from fundamental research, through applications, to end-user delivery, by providing a new modelling methodology to inform design, appraisal and management decisions made by environmental regulators, engineering consultants and water utilities.