Taking a high frequency pulse of rivers: the new wave of water-quality and pollution checks to support integrated real-time river basin management

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Pollution and water quality concerns within river networks are one of the biggest environmental and health problems facing the planet, impacting water, food, and ecosystem security. However, we are currently experiencing a technological revolution in environmental monitoring, changing paradigms in water quality and pollution sensing to new frontiers that open up unprecedented opportunities for taking the pulse of water quality extremes in complex landscapes. Instead of taking water samples in the field and transporting them back into the laboratory for subsequent analysis, the recent sensor revolution enables the monitoring of water quality in-situ, that is, in real-time and directly where it occurs. These technological advances enable to not only efficiently monitor the continuous long-term behaviour of water quality and pollution transport but also to more adequately capture the event characteristics of dynamic flow and pollution events, including water quality extremes. This project will work at the forefront of these developments to directly improve the way we detect, monitor, and prevent pollution of river networks. Recent interdisciplinary research has triggered the development of useful metrics for the identification of pollution source zone activation in river basins. However, so far such analyses have been limited to the monitoring of single pollutants and single locations.This PhD project will pioneer the combined and integrated development of novel types of water quality sensor networks and numerical models of in order improve the mechanistic understanding of the evolution of source area activation across the catchment continuum. It will therefore push current paradigms in in-situ water quality monitoring technologies (such as absorbance and fluorescence probes, Figure 1 left) as well as river basin scale water quality monitoring in order to identify event-based dynamics of pollution sources. The findings of this study will directly support the development of more evidence based prediction and management of river basin management.