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Operational monitoring and forecasting of bathing water quality
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through exploiting satellite Earth observation and models: the
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AlgaRisk demonstration service
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J. D. Shutler a*, M. A. Warren b, P.I. Miller b, R. Barciela c, R. Mahdon c, P. E.
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Land b, K. Edwards c,d, A. Wither d,f, P. Jonas d, N. Murdoch d, S.D. Roast d,g, O.
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Clements b, A. Kurekin b
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* corresponding author, Tel +44 (0)1752 633448
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a
University of Exeter, Penryn campus, TR10 9FE, UK
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b
Plymouth Marine Laboratory, Plymouth, PL13DH, UK.
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c
Met Office, Exeter, EX1 3PB, UK
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d
Environment Agency, Exeter, EX2 7LQ, UK
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e
now at Environment Agency, Exeter, EX2 7LQ, UK
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f
now at National Oceanography Centre, Liverpool, L3 5DA, UK.
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g
now at EDF Energy, Barnwood, GL4 3RS, UK.
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Abstract
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Coastal zones and shelf-seas are important for tourism, commercial fishing and
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aquaculture. As a result the importance of good water quality within these regions to
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support life is recognised worldwide and a number of international directives for
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monitoring them now exist. This paper describes the AlgaRisk water quality
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monitoring demonstration service that was developed and operated for the UK
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Environment Agency in response to the microbiological monitoring needs within the
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revised European Union Bathing Waters Directive. The AlgaRisk approach used
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satellite Earth observation to provide a near-real time monitoring of microbiological
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water quality and a series of nested operational models (atmospheric and
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hydrodynamic-ecosystem) provided a forecast capability. For the period of the
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demonstration service (2008-2013) all monitoring and forecast datasets were
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processed in near-real time on a daily basis and disseminated through a dedicated web
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portal, with extracted data automatically emailed to agency staff. Near-real time data
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processing was achieved using a series of supercomputers and an Open Grid
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approach. The novel web portal and java-based viewer enabled users to visualise and
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interrogate current and historical data. The system description, the algorithms
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employed and example results focussing on a case study of an incidence of the
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harmful algal bloom Karenia mikimotoi are presented. Recommendations and the
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potential exploitation of web services for future water quality monitoring services are
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discussed.
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Keywords
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Water quality, harmful algal blooms, remote sensing, ecosystem model, operational
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data processing, microbiological water quality.
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1. Introduction
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Coastal and shelf-seas (<200 m depth) waters are an important resource for food,
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industry and tourism. These regions are thought to support 10-15% of the global net
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primary production (the basis of the marine food chain) and more than 40% of the
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world's population live within 150 km of the sea (UN Atlas of the Oceans, 2012).
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The importance of monitoring microbiological water quality within these regions has
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been highlighted within the World Health Organisation report (WHO, 2003),
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prompting a number of International directives including the United States Beaches
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Environmental Assessment and Coastal Health (BEACH) Act and the revised
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European Bathing Waters Directive (EU DIRECTIVE 2006/7/EC). The latter of
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which requires all European agencies responsible for environmental issues to provide
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microbiological and bacterial water quality monitoring and forecasting of bathing
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waters by 2015. Where bathing waters are considered to be popular coastal beaches
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or inland sites where bathing is explicitly authorised or promoted (e.g. by the
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provision of associated facilities) and where bathing is practiced by large numbers of
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bathers.
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There are many different parameters that are used to monitor and assess water quality
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and these vary dependent upon the application of interest and the technology being
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employed. In situ based water quality monitoring of bathing waters can encompass a
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range of chemical, biological and physical characteristics and quantities. AlgaRisk
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focussed its efforts on techniques to monitor and study high biomass harmful algal
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(microbiological) species that can potentially impact bathing waters (tourism) and
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marine life during the summer months. The term 'harmful algal bloom' (HAB) refers
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to the increase in density of micro-algae leading to potentially or actual harmful
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effects. HAB forming species often occur naturally in the marine environment, but
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human activity is thought to play a role in their increasing occurrence (Hallegraeff
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2010). Dependent upon the species in question such events can affect human health,
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kill fish and/or result in the closure of commercial shellfish beds. In the US blooms
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of the dinoflagellate Karenia brevis occur annually which has prompted the
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development of a government funded operational monitoring system (Stumpf et al.
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2003), as this particular species can impact on human health through respiratory
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distress (Hoagland et al. 2009). For UK waters, the dinoflagellate Karenia mikimotoi
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(hereafter K. mikimotoi) is the high-biomass HAB species of most concern (Davidson
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et al. 2009) and it has previously been identified in harmful concentrations in other
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waters around the world (Faust and Gulledge 2002, Haywood et al. 2004, Rhodes et
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al. 2004, Davidson et al. 2009). Karenia mikimotoi blooms can impact fish directly by
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clogging their gills, or indirectly by creating severe hypoxia; such impacts when
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reported in the press impact negatively on tourism. The frequency of the Karenia
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mikimotoi blooms appears to be increasing in European waters with events occurring
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in Scottish and Irish waters in 1980, 2003, 2005, 2006 and 2009 (Swan and Davidson
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2010) and in the Celtic sea and English Channel in 2000, 2002, 2006 and 2009
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(Groom et al. 2000, Kelly-Gerreyn et al. 2004, Garcia-Soto & Pingree 2009, Coates et
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al., 2009).
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In recent years, the operational delivery of near-real time satellite Earth observation
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(EO) data has become routine for agencies such as the European Space Agency (ESA)
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and the US National Aeronautics and Space Administration (NASA). For example,
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NASA provide the international community with near real-time ocean colour data
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from the Moderate Resolution Imaging Spectrometer (MODIS). The use of EO data
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allows the monitoring of large areas in comparison to spatially limited (and often
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expensive) ship- or buoy-based in situ data collection. Bathing water relevant quality
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parameters and indicators available from satellite Earth observation (and ecosystem
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models) include physical (e.g. sea surface temperature, turbidity), biological (e.g. total
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chlorophyll-a concentration, concentrations of some specific phytoplankton species)
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and optical parameters (e.g. in-water visibility). The use of EO data to aid water
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quality monitoring is an emerging field (e.g. Gohin et al., 2008; Ruddick et al 2008;
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Kurekin et al, 2014) and many different approaches are being developed. A range of
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studies have analysed visible spectrum EO data for detecting anomalously high levels
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of EO-derived chlorophyll-a as an indicator for a potential HAB (Stumpf et al., 2003,
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Miller et al., 2006, Gohin et al., 2008, Tomlinson et al., 2009, Shutler et al., 2012). A
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plethora of species-specific algorithms have also been developed (e.g. Subrananiam et
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al., 2002; Miller et al., 2006; Hu et al., 2010) and a full review of EO methods to
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study specific phytoplankton groups can be found in IOCCG (2014). Typically, the
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EO data used in all of these approaches have a spatial resolution of ~1 km, although,
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techniques are also being developed to allow the use of higher spatial resolution data
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towards coastal and estuarine monitoring (e.g. Shutler et al. 2007; Hu et al. 2010).
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Previous studies have illustrated the complementary nature of model and observation
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data for microbiological water quality monitoring (e.g. Davidson et al 2009). The
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operational running of forecast models for weather forecasting has been common
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place for many years and now wave and sea state models are also being run
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operationally (e.g. WaveWatchIII - Tolman et al., 2002). In contrast, the operational
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use of ecosystem models to forecast oceanic in-water conditions is in its infancy.
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However, since 2007 the UK National Centre for Ocean Forecasting has run an
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operational shelf-seas hydrodynamic-ecosystem model in nowcast and forecast mode.
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(Siddorn et al, 2007). This model is run on a supercomputer, forced using the North
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Atlantic and European (operational) atmospheric and ocean physics models and
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enables a 5 day forecast of a range of marine physical and biological variables to be
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generated.
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The AlgaRisk demonstration service was developed to provide a microbiological
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monitoring and forecast capability for dense algal blooms towards supporting the
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statutory obligations of bathing water regulatory agencies. AlgaRisk combined data
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from an operational hydrodynamic-ecosystem (physical-biological) model with near-
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real time satellite Earth observations (EO). All of these data were made available via
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a dedicated web portal, where the end-user could access and visualise daily and
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historical data. The combination of the model and EO data were used to guide
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targeted in situ sampling and monitoring to verify specific algal species. The
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AlgaRisk ‘demonstration service’ was implemented pre-operationally and exploited
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by agency staff between 2008 and 2010; the service continued (as a free to access
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service) until 2013. During this period it provided daily water quality monitoring data
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(from Earth observation) and five-day forecasts of water conditions (from the model)
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for the south-west UK and coastal waters in the Celtic and Irish Seas.
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This paper describes the AlgaRisk system, its approach, presents some example
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output and demonstrates its use. The first part of the paper describes the methods
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employed within the operational modelling system, including its physical and
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biogeochemical components, the satellite Earth observation data and the products
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made available via the AlgaRisk web portal. The second part demonstrates the power
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of the approach via a case study of an occurrence of a bloom of K. Mikimotoi that
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formed during the 2008-2010 demonstration period. An initial analysis of the bloom
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incident is given (as carried out at the time of the incident) and this is then followed
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by a re-analysis of the event; both of these analyses used the AlgaRisk system.
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Finally, the discussion includes areas of future focus and highlights advancements in
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other scientific fields that could be used in future monitoring and forecasting efforts.
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2. Methods
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A schematic of the AlgaRisk system can be seen in Figure 1. This shows the
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monitoring (Earth observation) and model forecast (ecosystem and atmospheric)
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components of the approach. Throughout the demonstration study the data products
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were updated on a daily basis and automatically uploaded to the AlgaRisk web portal.
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In addition, extracted data for pre-defined regions and products were automatically
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emailed to a number of agency users. The individual components of the system are
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described below.
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[Figure 1 here]
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2.1 Model data
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A series of nested atmospheric and hydrodynamic-ecosystem (physical-biological)
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models provided the user with 5-day forecast data. Running the same model setup in
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nowcast mode provided an alternative data source for cases where the EO data may be
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unavailable (e.g. no EO data due to dense cloud cover). Additional atmospheric and
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weather related model parameters were also used within AlgaRisk to aid the
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interpretation of data. All of the models used within AlgaRisk are described below.
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These models were run on series of supercomputers. For the period 2008-2012, the
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hydrodynamic-ecosystem model used 1 node (6 cores) of a NEC SX-8 cluster for its
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operational configuration. System upgrades in 2012 meant that the model was then
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transferred to using 1 node (32 cores) of an IBM POWER-6 cluster and then later 1
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node (32 cores) of an IBM POWER-7 cluster. The Atlantic Margin Model, and
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Numerical Weather Prediction models were routinely operated on the same
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supercomputers.
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2.1.1 Operational hydrodynamic ecosystem model
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The Proudman Oceanographic Laboratory Coastal Ocean Modelling System
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(POLCOMS), coupled with the European Regional Seas Ecosystem Model (ERSEM),
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was run by the Met Office National Centre for Ocean Forecasting to provide nowcast
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and forecast data of the in-water conditions in the northeast European shelf waters
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(Siddorn et al, 2007). The physical oceanography component of the model
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(POLCOMS) was a three-dimensional baroclinic B-grid model (Holt and James,
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2001) solving primitive equations. The freshwater inputs included in POLCOMS
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were based on a long-term average (climatology) from over 300 rivers and the Baltic
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Sea. The ecosystem component of the model (ERSEM) is a complex ecosystem
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model with coupled pelagic and benthic sub-models. The POLCOMS-ERSEM
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coupling is described in Allen et al. (2001), Holt et al. (2005) and the application of
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ERSEM was essentially that of Siddorn et al. (2007). Several recent studies have
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extensively evaluated the POLCOMS-ERSEM model skill for the European shelf seas
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using a variety of univariate and multivariate methods (Holt et al., 2005, Holt et al.,
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2012, Lewis et al., 2006, Allen et al., 2007, Shutler et al., 2011). The model has a
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good ability to reproduce the sea surface temperature (absolute bias ranges from 0.2 to
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0.9 °C dependent on the region with a root mean squared error of 0.3 - 0.7 °C). Its
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precision (root mean squared error) of simulating chlorophyll-a is 1 mg m-3.
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The modeled region encompasses 12°W to 13°E and 48°N to 62°N with a grid of
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1/10° longitude by 1/15° latitude (equivalent to a spatial resolution of ~6 km) and 18
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vertical s-coordinate levels. The boundary of the model that connects with the open
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ocean follows the northwest European continental shelf break (approximate 200 m
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depth contour). At the open-ocean boundary, the model was forced by the larger
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spatial-scale (spatial resolution ~12 km) Atlantic Margin Model (AMM) version of
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POLCOMS (providing temperature, salinity and current data). Open boundary
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conditions for the AMM model were taken from the Met Office's operational 1/4° 
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1/4° spatial resolution global deep ocean Forecasting Ocean Assimilation Model
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(FOAM) (Bell et al., 2000) and a northeast Atlantic tidal model (Flather, 1981).
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Surface forcing (wind stress, sea surface pressure, heat and precipitation minus
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evaporation) were provided by the Met Office’s operational Numerical Weather
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Prediction (NWP) models. Example outputs from the model set up include sea
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surface temperature, phytoplankton biomass (dinoflagellates, flagellates, diatoms and
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picoplankton) and nutrient concentration (nitrates, phosphates and silicates).
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2.1.2 Additional model data
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To complement the biological in-water model output, additional model data from the
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NWP models were delivered to the AlgaRisk portal; these included wind speed, wind
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direction, cloud cover, photosynthetically available radiation (PAR), mean air
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pressure at sea level, a stratification indicator, surface current velocity and daily tidal
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range data (Mahdon et al 2010).
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2.2 Near-real time Earth Observation data
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AlgaRisk focussed on the use of ~1km spatial resolution EO optical data (visible and
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thermal infra-red wavelengths) over the marine environment to provide near-coast
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estimates of sea surface temperature and ocean colour. All EO data were
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automatically processed using a 30-node Open Grid engine (typical node: dual core
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3.1 GHz, 8 GB of memory) and then made available on the portal in near-real time.
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This visible spectrum data processing consisted of ~13 GB day-1 of downloads,
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resulting ~22 hours of processing across multiple nodes. The thermal infra-red data
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processing consisted of ~1 GB day-1 of downloads resulting in ~5 hours of processing
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across multiple nodes. The Daily data and weekly mean composite data for all EO
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products were provided. The details of these data are described below.
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2.2.1 Earth Observation data sources
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Satellite data from two satellite sensors were exploited. The MODIS sensor on-board
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the Aqua platform provided visible spectrum data at a spatial resolution (nadir) of ~1
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km. The Advanced Very High Resolution Radiometer (AVHRR) series of sensors
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provided estimates of sea surface temperature at a spatial resolution (nadir) of ~1 km.
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The MODIS and AVHRR level 2 data (geo-located geophysical products) were
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provided in near-real time (within 2 - 3 hours of the satellite overpass) by the UK
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Natural Environment Research Council's Earth Observation Data Acquisition and
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Analysis Service (NEODAAS). These standard level 2 data were quality filtered and
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re-projected to a Mercator projection using existing techniques (Shutler et al., 2005;
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Miller et al., 1997).
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2.2.2 Chlorophyll-a, high biomass and Karenia data products
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The MODIS level 2 EO data were used to produce three additional products. Coastal
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and shelf waters (case 2) specific chlorophyll-a estimates (Figure 2a and b) were
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generated using an approach that was developed for waters within the English
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Channel and Bay of Biscay (Gohin et al., 2002). This approach is able to estimate the
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coastal chlorophyll-a concentrations with zero bias and an r2 of 0.7 (Gohin et al.,
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2002). Maps of the likelihood of the phytoplankton species Karenia mikimotoi
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existing within the water (Figure 2c and d) were generated (Miller et al., 2006;
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Kurekin et al., 2013). The accuracy of the likelihood approach has previously been
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characterised for the Western English Channel and Celtic Sea (Kurekin et al., 2013)
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resulting in a correct classification rate of 86% with a false alarm rate of 0.01
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(N=4662). Maps of the location of potential high biomass blooms (Shutler et al.,
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2012) were generated from the case 2 chlorophyll-a estimates (Figure 2e). This
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method exploits time series data to identify regions where the chlorophyll-a
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concentrations are higher than their background levels.
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approach has been previously characterised for monitoring K. mikimotoi in the Celtic
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sea and Western English Channel (Shutler et al., 2012) resulting in a correct
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classification rate of 68% with a false alarm rate of 0.24 (N=25).
The accuracy of the anomaly
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2.2.3 Dense bloom flag
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When dense coastal blooms are observed in EO chlorophyll-a data, the centre of the
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bloom is sometimes masked out. This is due to the existence of negative water
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leaving radiance (Lw) in some short wavelength bands, as this negative component
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prevents the estimation of chlorophyll-a concentrations. This can result in the densest
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blooms being masked as missing data (ie like cloud). From analysing historical data
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we found that negative Lw in the MODIS channel at 488 nm (blue part of the
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spectrum) was a reliable indicator (flag) of this incorrect masking. Hence in addition
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to the data products already described, we also highlighted such pixels in our
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chlorophyll-a data in a distinct colour to indicate blooms dense enough to prevent
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calculation of chlorophyll-a concentrations (Figure 2a and b). This is a simple
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alternative to detecting intense blooms compared to existing approaches that use the
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red part of the electromagnetic spectrum (Gower et al., 2005). In order to combine the
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cloud-free data into a 7-day composite, a second ‘mixed’ dense bloom flag was
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indicated if both valid chlorophyll-a values and dense blooms were observed for the
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same pixel during the period of compositing.
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[Figure 2 here]
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2.3 Provision of data to the users
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All of the data products described in sections 2.1 and 2.2 were generated in near-real
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time on a daily basis using previously developed automatic systems (e.g. Shutler et
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al., 2005; Miller et al., 1997; Siddorn et al., 2007). The hydrodynamic-ecosystem
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model was run in nowcast and forecast mode providing a nowcast and 5-day forecast
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of the water conditions. All of the EO and model data were automatically uploaded
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onto the AlgaRisk web portal allowing users to easily access and compare the data.
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The web portal was the main interface between users and data, allowing users to
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quickly visualise and assess the data to support decision-making. The portal allowed
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all of the data (EO and model) to be viewed alongside each other and included an
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interactive java-based image viewer that allowed users to overlay a number or data
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products. For a number of user-defined regions statistical parameters (such as mean,
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median and standard deviation) were routinely calculated for each of the datasets and
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these were automatically emailed each day to agency users in a comma separated
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variable (csv) format. This feature was provided to allow the users to load the data
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into a spreadsheet for studying patterns and analysis.
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3. Results
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3.1 Example data products and web view
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Example data products produced from the EO data are shown in Figure 2. These
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include daily and composite images of chlorophyll with dense bloom flag identifiers,
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sea surface temperature images and HAB likelihood maps. Figure 3 shows an
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example screenshot of the AlgaRisk web portal. Figure 3 illustrates that the portal
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allowed users to access historical data, view time series data and view daily and
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weekly composite data. The java-based image viewer enabled users to select and
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view multiple datasets, overlay annotation, alter the colour palette, zoom the view,
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and to retrieve geophysical parameter values at a given latitude and longitude (e.g.
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chlorophyll concentrations in mg m-3). An example screenshot of the image viewer
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showing EO chlorophyll estimates with overlaid surface wind speeds can be seen in
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Figure 4.
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[Figure 3 here]
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[Figure 4 here]
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3.2 Case study: Karenia mikimotoi bloom in South West UK 2009
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In August 2009 an incidence of the flagellate Karenia mikimotoi in very high
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concentrations was detected along the coasts of Cornwall and Devon in the south west
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of the UK. Routine in situ sampling by the Environment Agency (England & Wales)
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on the 11-12th August identified K. mikimotoi as being present in the coastal waters of
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St Austell Bay in Cornwall (Crinnis, Porthpean, Par and Charlestown) and South
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Devon (mouth of the River Yealm) in concentrations higher than the background
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concentrations for these regions. From the shoreline in St Austell Bay, Environment
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Agency staff also observed dead marine animals (dogfish, turbot, eel, dover sole and
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sea potatoes), fish swimming at the surface of the water and the water appeared to be
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red in colour. Independent in situ sampling by the Plymouth Marine Laboratory in St
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Austell Bay on the 13th August 2009 confirmed the species as K. mikimotoi in a
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concentration of 5,400,000 cells l-1. Background concentrations in UK waters are
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normally of the order of a few thousand cells per litre (Davidson et al., 2009). Figure
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5 shows a map of the region and the locations where in situ samples were collected.
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Extrapolating between the in situ sampling locations and assuming that each
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confirmed instance was part of the same bloom instance suggested that >55 km of
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coastline was covered by the bloom (Figure 5); potentially impacting ~20 bathing
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beaches. The locations and numbers of bathing beaches potentially impacted were
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determined using the UK Good Beach guide (UK Beach guide 2014). Observations
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of water discolouration by a scuba diving company based in Porthkerris (Cornwall)
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extended the potential coastal coverage further west to a total of >85 km, potentially
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impacting a total of ~30 bathing beaches. The high concentrations, the dead marine
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animals observed by the Environment Agency staff and the apparent discolouration of
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the water suggested a dense algal bloom. It was assumed that the animals died from
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hypoxia (due to K. mikimotoi depleting dissolved oxygen concentrations) following
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the rapid formation of the algal bloom (e.g. Brand et al., 2012). Karenia mikimotoi is
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also thought to produce toxins that are harmful to fish (Brand et al., 2012) so this may
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have also played a role in the mortalities. As a precautionary measure shellfish
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harvesting within four regions along the southern coastline of Cornwall and Devon
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was halted, the general public were advised against collecting any dead marine
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animals and the local council authority (Cornwall Council) advised people not to
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bathe in discoloured waters and recommend that anybody coming into prolonged
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contact with scum or foam should wash the exposed skin with clean water. The high
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algal concentrations remained in the coastal waters for a number of weeks after the
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initial report.
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The algal anomaly data (Figure 2e) showed elevated algal concentrations along most
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of the Cornish and Devon coastlines. The EO Karenia probability maps showed
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elevated probability of K. mikimotoi in regions adjacent to the south coast (Figure 2c
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and d). At the time of the bloom the dense bloom approach was under development.
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Offline analysis of the EO dense bloom flag data showed that the regions of coastline
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covered by the dense bloom flag data were adjacent to the offshore areas identified as
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potential K. mikimotoi in the HAB likelihood maps. The dense bloom flag data
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suggested that a much larger coastal region was affected by this bloom incident than
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that suggested by the in situ data alone (Figure 2b). Extrapolating between regions of
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dense blooms along the coast in the EO data for the 01-07 August suggested that a
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further >90 km of coastline was potentially impacted. This meant that >175 km of the
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south coast of the UK from Lizard Point to Exeter was impacted, potentially
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impacting ~100 bathing beaches. The bloom areal coverage offshore seemed to be
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greatest between St Austell Bay and Plymouth (Figure 6).
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[Figure 5 here]
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[Figure 6 here]
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The AlgaRisk system and supporting meteorological data were later used to re-
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analyse the development and extent of the algal bloom to identify potential drivers for
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the high algal concentrations. The EO dense bloom flag algorithm showed high levels
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of chlorophyll-a and/or dense algal concentrations along the south coast of England
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between the 5 August and the 17 September, as shown in Figure 6d-f. From the EO
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data the greatest coastal coverage or extent of the bloom was during 4 to 8 September.
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The EO dense bloom data suggested that the extent of the bloom had begun to reduce
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from the 13 September and no evidence of the bloom was visible after the 18
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September. The model nowcast data showed an increase in total chlorophyll-a
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concentration during August in the St. Austell Bay area (increasing from 4 to 15 mg
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m-3 between 4th-9th August and then varying between to 4 - 8 mg m-3 for the
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remainder of August), with flagellates being the dominant species (K. mikimotoi is a
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dinoflagellate). The model estimated that nutrient levels within the St Austell Bay
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area were very low (< 0.5 mg m-3 (P ,Si) and 2.0 mg m-3 (N)) in the upper 15 m of the
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water column. Higher nutrient concentrations were apparent at lower depths, below
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the mixed layer and the thermocline. Meteorological records (NOAA, 2014) for the
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Plymouth region (Bigbury Island) showed 32 days of rain between 04 July – 04
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August, followed by a period of reduced cloud cover, low or no wind and calm sea
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conditions. The rainfall exceeded 1 mm h-1 (average over a 3 hour period) six times
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during this period, with the highest rate of 3 mm h-1 (average over a 3 hour period) on
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the 29 July (see Figure 7). This rainfall over a relatively long period of time suggests
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that elevated nutrient levels from land run-off may have provided the nutrients
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required for the rapid increase in the K. mikimotoi concentrations and the formation of
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a dense bloom. The river flow data from the four major rivers that flow into the sea in
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this region (the Fal, Tamar, Fowey and Exe) all showed high flows (7-160 m3 s-1) that
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peaked on the 31st July, supporting this hypothesis. The hydrodynamic-ecosystem
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model used in the AlgaRisk system uses climatological nutrients and so it would not
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have captured the impact of the rain events. The linkage between increased rainfall
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over land resulting in increased nutrient availability in coastal areas and thus
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increasing the potential for these naturally occurring algae to bloom in harmful
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concentrations has previously been documented (Anderson et al., 2002), further
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supporting our hypothesis.
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The meteorological data (NOAA, 2014) for the Plymouth region (Bigbury) showed an
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increase in ground swell waves on the morning of the 9 September, increasing from
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~1 to 1.3 m on the 08 September to ~1.4-1.7 m on the 9 September (see Figure 7, 9
407
September marked by a vertical line). The wind direction also changed direction on
408
the 9 September. Prior to the 9 September the wind was predominantly from the west
409
and/or south (cross or onshore) whereas on the morning of the 9 September the wind
410
changed to a northerly (offshore). As already discussed the EO data showed that the
411
greatest coastal coverage or extent of the bloom was during 4 to 8 September; the last
412
day of which was the day before the change in environmental conditions. From this
413
we conclude that the change in the wind conditions and the increase in the ground
414
swell waves caused the breakup of the bloom.
415
416
[Figure 7 here]
417
418
We hypothesise that the natural occurrence of the algal species in these coastal waters,
419
the abundance of nutrients from land run-off and increased river outflow due to heavy
420
rain combined followed by increased sunlight and calm sea conditions led to the
421
formation of the high algal concentrations. This instance of a bloom of K. mikimotoi
422
was present along the southern English coast in concentrations greater than the
423
background levels for 44 days (5 August 2009 to 17 September 2009) and it
424
affected >175 km of the coastline. A change in the environmental conditions (wind
425
and sea state), nutrient exhaustion and low oxygen levels within the coastal waters are
426
the likely reason for the bloom subsiding.
427
428
4. Discussion
429
The case study in section 3.2 has highlighted the importance of river gauging inputs
430
when monitoring coastal microbiological water quality, both to help parameterise the
431
hydrodynamic-ecosystem model and to aid the user interpretation of the
432
complementary datasets. In the absence of such data, rainfall and other
433
meteorological parameters would be useful to aid interpretation of the environmental
434
conditions. The re-analysis of the case study illustrated that using climatological
435
nutrient data meant that the model was (understandably) unable to capture the sudden
436
increase in nutrients and its impact on the phytoplankton concentrations. Clearly
437
alternative methods for providing nutrient data for driving hydrodynamic-ecosystem
438
models for forecasting conditions in these coastal waters should be investigated.
439
Alternatively some mechanism to interpret the impact of the rain over the land should
440
be investigated as a proxy for river flow data.
441
442
With respect to the additional meteorological parameters used for the re-analysis
443
(hindcast, nowcast and forecast), many of these datasets are already freely available
444
through the internet e.g. windguru http://www.windguru.cz and the U.S. NOAA
445
National Centre for Environmental Protection (NCEP) forecasts can be accessed via
446
their Thematic Real-time Environmental Distributed Data Services (THREDDS)
447
servers.
448
449
Since the demonstration period of the AlgaRisk study, the Met Office no longer
450
operate the POLCOMS-ERSEM model and instead use the Nucleus for European
451
Modelling of the Ocean (NEMO)-ERSEM model. These updates to the operational
452
model are described and validated in Edwards et al (2012).
453
454
The next generation of observing satellite sensors are likely to improve our capability
455
to observe the water quality of our oceans. The European Copernicus programme
456
(formally known as the Global Monitoring for Environment and Security, GMES,
457
programme) is expected to provide a long term monitoring solution for sea state and
458
biology (Aschbacher and Milagro-Pérez, 2012). Water quality monitoring will also
459
benefit from initiatives designed to support the Copernicus programme and services.
460
One example of this is the ESA Felyx project (http://felyx.org ) which is developing
461
an open source solution to allow routine monitoring of Earth observation data
462
streams. One of its design objectives is to provide capability for monitoring EO data
463
within single geographical positions and regions, and the project has defined water
464
quality monitoring as one of its initial user-driven test cases.
465
466
The web portal developed for AlgaRisk was relatively simple but functional. The
467
development of web map servers (WMS) offers a more flexible and scalable approach
468
enabling data from different (and remote) servers to be easily combined, viewed and
469
mined. For example, such a system could exploit the NOAA THREDDS service
470
mentioned above. Water quality monitoring efforts are likely to benefit from online
471
plotting, comparison and analysis tools and recent WMS developments also include
472
the ability to create user-defined workflows. These tools allow users to analyse data
473
and develop their own fuzzy logic combinations of data whilst online, removing the
474
need for a user to download (or be emailed) datasets. These WMS can be consumed
475
and integrated into a web-based portal with ease (e.g. using OpenLayers or
476
GoogleMaps). A rich web-based data exploration and visualisation application could
477
be developed by combining a suite of web-based tools with a diverse EO, model and
478
in situ approach like AlgaRisk.
479
480
Despite the apparent complexity of using EO data it can provide a cost-effective
481
approach for monitoring large areas. For example, a water quality monitoring service,
482
based on EO and in situ data, is annually purchased (2011-2014) by a group of
483
Scottish Aquaculture companies through the Scottish Salmon Producers’
484
Organisation.
485
486
5. Conclusions
487
The AlgaRisk system and web portal have been described. The satellite Earth
488
observation data used within the AlgaRisk system provided a near-real time
489
microbiological monitoring capability, and the model data (from a series of nested
490
operational models) provided a forecast capability. The nowcast model data provided
491
a backup solution for monitoring if persistent cloud meant that no Earth observation
492
data were available. The satellite Earth observation and modelling components of the
493
AlgaRisk system exploited published research, and data were processed or generated
494
in near-real time using an Open Grid approach (for Earth observation data) and a
495
series of super-computers (for all of the model data). This distributed approach meant
496
that during the demonstration period all Earth observation and model data were made
497
available through the dedicated web portal within 2 to 3 hours of their availability.
498
The overall system was developed as a decision-support tool to allow national
499
agencies to make informed decisions and to help guide in-situ sampling. The benefits
500
of the AlgaRisk system approach, that of using a combination of satellite Earth
501
observation, model and in situ data to monitor microbiological water quality in coastal
502
areas, has been demonstrated through the case study of a harmful algal bloom that
503
occurred in European waters in 2009. A subsequent re-analysis of this event suggests
504
that river flow data is important for near-coast microbiological water quality
505
monitoring and forecasting. We recommend that future approaches for coastal water
506
quality monitoring and forecasting should exploit satellite Earth observation, a range
507
of forecast models (hydrodynamic-ecosystem, atmospheric/weather and waves) and
508
will require some method of accounting for real-time river flow and run-off from the
509
land.
510
511
Acknowledgements
512
This work was funded by the Environment Agency (England & Wales) through
513
Science Project SC070082/SR1 and the ESA Felyx project (contract
514
4000107654/13/I-AM). The Earth observation systems used were maintained by the
515
UK NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)
516
and the operational models were maintained through the UK National Centre for
517
Ocean Forecasting (NCOF).
518
519
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Figure 1: Schematic of the AlgaRisk system and its components.
736
737
a)
b)
Dense bloom:
mixed
pure
c)
d)
HAB risk
no bloom
coastline
HAB risk, and chl-a > 10 mg m-3
HAB risk, and 5< chl-a < 10 mg m-3
HAB risk, and chl-a < 5 mg m-3
not classsified
coastline
land or no data
harmless algae
not classsified
land or no data
f)
e)
algal anomaly
coastline
land or no anomaly
738
739
coastline
mg m-3
land or no data
coastline
°C
land or no data
Figure 2: Example satellite Earth observation data products of the Celtic Sea on the
10 September 2009 from MODIS at 1335 UTC and AVHRR at 0616 UTC . a)
MODIS OC5 chlorophyll-a estimates, b) MODIS OC5 chlorophyll-a estimates with
regions of dense blooms labelled, c) MODIS Karenia mikimotoi likelihood using
Kurekin et al., (2013), d) MODIS Karenia mikimotoi likelihood classified into
chlorophyll-a concentrations using Kurekin et al., (2013), e) MODIS algal
anomalies using Shutler et al., (2012) and f) AVHRR sea surface temperature using
Miller et al., 1997.
Figure 3: Example of AlgaRisk web portal showing SST over the period 8th – 14th
April 2010 illustrating one way in which time series data could be viewed. The
selection of available Earth observation and model data products can be seen in the
left hand menu. The buttons at the top of the screen allowed the user to navigate
through time. The image on the top right of the screen is the weekly composite. The
other images are the daily data.
740
741
742
743
744
Figure 4: Example Chlorophyll-a composite (in mg m-3) image with modelled surface
wind speed vector field (in m s-1) overlaid (white arrows). Land and/or missing data is
black and the UK and French coastlines are in white. The pop up window is
displaying the cursor’s geographical position and the chlorophyll-a value. The javabased viewer allowed users to zoom in or out, interrogate the data layers, alter the
colour palette, select the overlaid data and to save the data in common bitmap
formats.
745
746
Figure 5: Map of the case study region in the South West UK showing line
representations of the extent of the bloom described in the case study and how it
progressed based on availability of in situ data, diver information and EO data.
747
748
749
a) 7 Aug 2009
b) 25 Aug 2009
c) 20 Sep 2009
d) 1-7 Aug 2009
e) 19-25 Aug 2009
f) 14-20 Sep 2009
Dense bloom
750
Figure 6: EO monitoring of Karenia mikimotoi HAB event at St Austell, Cornwall, Aug. 2009, using
dense bloom flag (magenta colour) to indicate pixels where chlorophyll-a could not be estimated due to
atmospheric correction failure (negative water-leaving radiance at 488nm): (a)-(c) single chlorophyll-a
scenes from Aqua-MODIS; (d)-(f) 7-day composite chlorophyll-a maps, adding a second ‘mixed’
dense bloom flag (light magenta) to indicate pixels for which both dense bloom flags and valid data
were acquired.
Figure 7. Time series of meteorological data, for July-October 2009, over the
Plymouth (Bigbury) region. Crosses represent 3 hourly data and the lines are the daily
average, except for total rain where the line represents daily total. Note increased
rainfall during month of July, followed by a period of calm conditions (low wind,
wave and rainfall), and wind direction change on 9th September, the date highlighted
by a solid vertical line.
751
752