Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The Al- gaRisk demonstration service J.D. Shutler a,n , M.A. Warren b , P.I. Miller b , R. Barciela c , R. Mahdon c , P.E. Land b , K. Edwards c,d , A. Wither d,1 , P. Jonas d , N. Murdoch d , S.D. Roast d,2 , O. Clements b , A. Kurekin b a University of Exeter, Penryn campus, TR10 9FE, UK b Plymouth Marine Laboratory, Plymouth PL13DH, UK c Met Office, Exeter EX1 3PB, UK d Environment Agency, Exeter EX2 7LQ, UK article info Article history: Received 3 June 2014 Received in revised form 26 November 2014 Accepted 16 January 2015 Available online 19 January 2015 Keywords: Water quality Harmful algal blooms Remote sensing Ecosystem model Operational data processing Microbiological water quality 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 Environ- ment 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 ser- vice (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. Recommenda- tions and the potential exploitation of web services for future water quality monitoring services are discussed. & 2015 Elsevier Ltd. All rights reserved. 1. Introduction Coastal and shelf-seas ( o200 m depth) waters are an im- portant resource for food, industry and tourism. These regions are thought to support 10–15% of the global net primary production (the basis of the marine food chain) and more than 40% of the world's population live within 150 km of the sea (UN Atlas of the Oceans, 2012). The importance of monitoring microbiological water quality within these regions has been highlighted within the World Health Organisation report (WHO, 2003), prompting a number of International directives including the United States Beaches Environmental Assessment and Coastal Health (BEACH) Act and the revised European Bathing Waters Directive (EU DI- RECTIVE 2006/7/EC). The latter of which requires all European agencies responsible for environmental issues to provide micro- biological and bacterial water quality monitoring and forecasting of bathing waters by 2015. Where bathing waters are considered to be popular coastal beaches or inland sites where bathing is explicitly authorised or promoted (e.g. by the provision of asso- ciated facilities) and where bathing is practiced by large numbers of bathers. There are many different parameters that are used to monitor and assess water quality and these vary dependent upon the ap- plication of interest and the technology being employed. In situ based water quality monitoring of bathing waters can encompass a Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cageo Computers & Geosciences http://dx.doi.org/10.1016/j.cageo.2015.01.010 0098-3004/& 2015 Elsevier Ltd. All rights reserved. n Corresponding author. E-mail address: j.d.shutler@exeter.ac.uk (J.D. Shutler). 1 National Oceanography Centre, Liverpool L3 5DA, UK. 2 EDF Energy, Barnwood GL4 3RS, UK. Computers & Geosciences 77 (2015) 87–96