Real-time water quality forecasting in rivers using satellite data and dynamic models: an online system for operational management, control and citizen science - Whitehead, Paul G.; Edmunds, Paul; Bussi, Gianbattista; O’Donnell, Séamus; Futter, Martyn; Groom, Steve; Rampley, Cordelia; Szweda, Chris; Johnson, David; Triggs Hodge, Andy; Porter, Tim; Castro, Geraldine (Frontiers in Environmental Science, 2024)
Increasingly scarce water resources and growing global populations have exacerbated the problems of water quality in river systems and freshwaters in general. New monitoring methodologies and tools to democratize access to water quality information are needed if we are to reach ambitious societal objectives such as the UN Sustainable Development Goals and the European Green Deal. Here we present a cloud-based system for producing publicly accessible real time water quality forecasts coupled to novel biosensor technology. Short term forecasts of water quality impairments, e.g., as cyanobacteria blooms, sediment plumes and toxic pollution incidents are increasingly relevant both to citizens and stakeholders. Here, we present a new cloud based system that utilizes satellite data to produce real time forecasts of flow and water quality using a chain of dynamic catchment-scale models at multiple locations in a river network. We demonstrate this new system using two case studies: the River Thames and the Essex Colne River (UK). These rivers are key water supply sources for London and South-East England, respectively and are of high interest to recreational water users. We show how the new system can predict and forecast water quality, estimate toxicity and connect to citizen science observations using an App (www.aquascope.com) for information synthesis and delivery.