Author(s): Josie Ashe; Lie Grand-Clement; David Smith; Gan A. Savic; Richard E. Brazier
Linked Author(s):
Keywords: High-frequency monitoring; Information value; Multivariate; Visualization; Water quality
Abstract: Drinking water treatment works are increasingly placed under external stressors including climatic variability, land use and land management and pollution incidents. To address these problems, routine highfrequency water quality monitoring is an integral part of operational control. However, in order to further inform decision making using the complex, time-series of water quality data that are generated (and typically archived) there must be distinction between basic sensor errors, artefacts of system design and management, and process driven patterns. This paper explores the visualisation of these complex data in order to support synthesis of uncleaned (or raw) routinely collected, high-frequency data; extracting information value from complex data generated through catchment wide monitoring. The data are presented in a form that enhances the capability and capacity to utilise existing complex data; improves understanding of complex surface water systems; and helps facilitate data driven models to investigate and forecast the dynamics between water quality determinands during hard-to-treat spate (or flood-flow) events.
Year: 2018