Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service. (April 2015)

• Record Type: Journal Article
• Authors: Shutler, J.D.; Warren, M.A.; Miller, P.I.; Barciela, R.; Mahdon, R.; Land, P.E.; Edwards, K.; Wither, A.; Jonas, P.; Murdoch, N.; Roast, S.D.; Clements, O.; Kurekin, A.
• Is Part Of: Computers & geosciences. Volume 77(2015)
• Page Start: 87
• Page End: 96
• Publication Date: 2015-04
• Subjects: Water quality -- Harmful algal blooms -- Remote sensing -- Ecosystem model -- Operational data processing -- Microbiological water quality; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2015.01.010
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008–2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed. Highlights: Successful development of a coastal microbiological water quality monitoring and forecasting system (called AlgaRisk). System exploits satellite Earth observation and model forecasts to guide decisions and in situ sampling. Operational system run for 6 years (2008–2013) to support decision making within UK Environment Agency. System exploits open grid and supercomputing approaches and disseminated all data via a web interface and email.