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1 Operational monitoring and forecasting of bathing water quality 2 through exploiting satellite Earth observation and models: the 3 AlgaRisk demonstration service 4 5 J. D. Shutler a*, M. A. Warren b, P.I. Miller b, R. Barciela c, R. Mahdon c, P. E. 6 Land b, K. Edwards c,d, A. Wither d,f, P. Jonas d, N. Murdoch d, S.D. Roast d,g, O. 7 Clements b, A. Kurekin b 8 9 * corresponding author, Tel +44 (0)1752 633448 10 a University of Exeter, Penryn campus, TR10 9FE, UK 11 b Plymouth Marine Laboratory, Plymouth, PL13DH, UK. 12 c Met Office, Exeter, EX1 3PB, UK 13 d Environment Agency, Exeter, EX2 7LQ, UK 14 e now at Environment Agency, Exeter, EX2 7LQ, UK 15 f now at National Oceanography Centre, Liverpool, L3 5DA, UK. 16 g now at EDF Energy, Barnwood, GL4 3RS, UK. 17 18 Abstract 19 Coastal zones and shelf-seas are important for tourism, commercial fishing and 20 aquaculture. As a result the importance of good water quality within these regions to 21 support life is recognised worldwide and a number of international directives for 22 monitoring them now exist. This paper describes the AlgaRisk water quality 23 monitoring demonstration service that was developed and operated for the UK 24 Environment Agency in response to the microbiological monitoring needs within the 25 revised European Union Bathing Waters Directive. The AlgaRisk approach used 26 satellite Earth observation to provide a near-real time monitoring of microbiological 27 water quality and a series of nested operational models (atmospheric and 28 hydrodynamic-ecosystem) provided a forecast capability. For the period of the 29 demonstration service (2008-2013) all monitoring and forecast datasets were 30 processed in near-real time on a daily basis and disseminated through a dedicated web 31 portal, with extracted data automatically emailed to agency staff. Near-real time data 32 processing was achieved using a series of supercomputers and an Open Grid 33 approach. The novel web portal and java-based viewer enabled users to visualise and 34 interrogate current and historical data. The system description, the algorithms 35 employed and example results focussing on a case study of an incidence of the 36 harmful algal bloom Karenia mikimotoi are presented. Recommendations and the 37 potential exploitation of web services for future water quality monitoring services are 38 discussed. 39 40 Keywords 41 Water quality, harmful algal blooms, remote sensing, ecosystem model, operational 42 data processing, microbiological water quality. 43 44 1. Introduction 45 Coastal and shelf-seas (<200 m depth) waters are an important resource for food, 46 industry and tourism. These regions are thought to support 10-15% of the global net 47 primary production (the basis of the marine food chain) and more than 40% of the 48 world's population live within 150 km of the sea (UN Atlas of the Oceans, 2012). 49 The importance of monitoring microbiological water quality within these regions has 50 been highlighted within the World Health Organisation report (WHO, 2003), 51 prompting a number of International directives including the United States Beaches 52 Environmental Assessment and Coastal Health (BEACH) Act and the revised 53 European Bathing Waters Directive (EU DIRECTIVE 2006/7/EC). The latter of 54 which requires all European agencies responsible for environmental issues to provide 55 microbiological and bacterial water quality monitoring and forecasting of bathing 56 waters by 2015. Where bathing waters are considered to be popular coastal beaches 57 or inland sites where bathing is explicitly authorised or promoted (e.g. by the 58 provision of associated facilities) and where bathing is practiced by large numbers of 59 bathers. 60 61 There are many different parameters that are used to monitor and assess water quality 62 and these vary dependent upon the application of interest and the technology being 63 employed. In situ based water quality monitoring of bathing waters can encompass a 64 range of chemical, biological and physical characteristics and quantities. AlgaRisk 65 focussed its efforts on techniques to monitor and study high biomass harmful algal 66 (microbiological) species that can potentially impact bathing waters (tourism) and 67 marine life during the summer months. The term 'harmful algal bloom' (HAB) refers 68 to the increase in density of micro-algae leading to potentially or actual harmful 69 effects. HAB forming species often occur naturally in the marine environment, but 70 human activity is thought to play a role in their increasing occurrence (Hallegraeff 71 2010). Dependent upon the species in question such events can affect human health, 72 kill fish and/or result in the closure of commercial shellfish beds. In the US blooms 73 of the dinoflagellate Karenia brevis occur annually which has prompted the 74 development of a government funded operational monitoring system (Stumpf et al. 75 2003), as this particular species can impact on human health through respiratory 76 distress (Hoagland et al. 2009). For UK waters, the dinoflagellate Karenia mikimotoi 77 (hereafter K. mikimotoi) is the high-biomass HAB species of most concern (Davidson 78 et al. 2009) and it has previously been identified in harmful concentrations in other 79 waters around the world (Faust and Gulledge 2002, Haywood et al. 2004, Rhodes et 80 al. 2004, Davidson et al. 2009). Karenia mikimotoi blooms can impact fish directly by 81 clogging their gills, or indirectly by creating severe hypoxia; such impacts when 82 reported in the press impact negatively on tourism. The frequency of the Karenia 83 mikimotoi blooms appears to be increasing in European waters with events occurring 84 in Scottish and Irish waters in 1980, 2003, 2005, 2006 and 2009 (Swan and Davidson 85 2010) and in the Celtic sea and English Channel in 2000, 2002, 2006 and 2009 86 (Groom et al. 2000, Kelly-Gerreyn et al. 2004, Garcia-Soto & Pingree 2009, Coates et 87 al., 2009). 88 89 In recent years, the operational delivery of near-real time satellite Earth observation 90 (EO) data has become routine for agencies such as the European Space Agency (ESA) 91 and the US National Aeronautics and Space Administration (NASA). For example, 92 NASA provide the international community with near real-time ocean colour data 93 from the Moderate Resolution Imaging Spectrometer (MODIS). The use of EO data 94 allows the monitoring of large areas in comparison to spatially limited (and often 95 expensive) ship- or buoy-based in situ data collection. Bathing water relevant quality 96 parameters and indicators available from satellite Earth observation (and ecosystem 97 models) include physical (e.g. sea surface temperature, turbidity), biological (e.g. total 98 chlorophyll-a concentration, concentrations of some specific phytoplankton species) 99 and optical parameters (e.g. in-water visibility). The use of EO data to aid water 100 quality monitoring is an emerging field (e.g. Gohin et al., 2008; Ruddick et al 2008; 101 Kurekin et al, 2014) and many different approaches are being developed. A range of 102 studies have analysed visible spectrum EO data for detecting anomalously high levels 103 of EO-derived chlorophyll-a as an indicator for a potential HAB (Stumpf et al., 2003, 104 Miller et al., 2006, Gohin et al., 2008, Tomlinson et al., 2009, Shutler et al., 2012). A 105 plethora of species-specific algorithms have also been developed (e.g. Subrananiam et 106 al., 2002; Miller et al., 2006; Hu et al., 2010) and a full review of EO methods to 107 study specific phytoplankton groups can be found in IOCCG (2014). Typically, the 108 EO data used in all of these approaches have a spatial resolution of ~1 km, although, 109 techniques are also being developed to allow the use of higher spatial resolution data 110 towards coastal and estuarine monitoring (e.g. Shutler et al. 2007; Hu et al. 2010). 111 112 Previous studies have illustrated the complementary nature of model and observation 113 data for microbiological water quality monitoring (e.g. Davidson et al 2009). The 114 operational running of forecast models for weather forecasting has been common 115 place for many years and now wave and sea state models are also being run 116 operationally (e.g. WaveWatchIII - Tolman et al., 2002). In contrast, the operational 117 use of ecosystem models to forecast oceanic in-water conditions is in its infancy. 118 However, since 2007 the UK National Centre for Ocean Forecasting has run an 119 operational shelf-seas hydrodynamic-ecosystem model in nowcast and forecast mode. 120 (Siddorn et al, 2007). This model is run on a supercomputer, forced using the North 121 Atlantic and European (operational) atmospheric and ocean physics models and 122 enables a 5 day forecast of a range of marine physical and biological variables to be 123 generated. 124 125 The AlgaRisk demonstration service was developed to provide a microbiological 126 monitoring and forecast capability for dense algal blooms towards supporting the 127 statutory obligations of bathing water regulatory agencies. AlgaRisk combined data 128 from an operational hydrodynamic-ecosystem (physical-biological) model with near- 129 real time satellite Earth observations (EO). All of these data were made available via 130 a dedicated web portal, where the end-user could access and visualise daily and 131 historical data. The combination of the model and EO data were used to guide 132 targeted in situ sampling and monitoring to verify specific algal species. The 133 AlgaRisk ‘demonstration service’ was implemented pre-operationally and exploited 134 by agency staff between 2008 and 2010; the service continued (as a free to access 135 service) until 2013. During this period it provided daily water quality monitoring data 136 (from Earth observation) and five-day forecasts of water conditions (from the model) 137 for the south-west UK and coastal waters in the Celtic and Irish Seas. 138 139 This paper describes the AlgaRisk system, its approach, presents some example 140 output and demonstrates its use. The first part of the paper describes the methods 141 employed within the operational modelling system, including its physical and 142 biogeochemical components, the satellite Earth observation data and the products 143 made available via the AlgaRisk web portal. The second part demonstrates the power 144 of the approach via a case study of an occurrence of a bloom of K. Mikimotoi that 145 formed during the 2008-2010 demonstration period. An initial analysis of the bloom 146 incident is given (as carried out at the time of the incident) and this is then followed 147 by a re-analysis of the event; both of these analyses used the AlgaRisk system. 148 Finally, the discussion includes areas of future focus and highlights advancements in 149 other scientific fields that could be used in future monitoring and forecasting efforts. 150 151 2. Methods 152 A schematic of the AlgaRisk system can be seen in Figure 1. This shows the 153 monitoring (Earth observation) and model forecast (ecosystem and atmospheric) 154 components of the approach. Throughout the demonstration study the data products 155 were updated on a daily basis and automatically uploaded to the AlgaRisk web portal. 156 In addition, extracted data for pre-defined regions and products were automatically 157 emailed to a number of agency users. The individual components of the system are 158 described below. 159 160 [Figure 1 here] 161 162 2.1 Model data 163 A series of nested atmospheric and hydrodynamic-ecosystem (physical-biological) 164 models provided the user with 5-day forecast data. Running the same model setup in 165 nowcast mode provided an alternative data source for cases where the EO data may be 166 unavailable (e.g. no EO data due to dense cloud cover). Additional atmospheric and 167 weather related model parameters were also used within AlgaRisk to aid the 168 interpretation of data. All of the models used within AlgaRisk are described below. 169 These models were run on series of supercomputers. For the period 2008-2012, the 170 hydrodynamic-ecosystem model used 1 node (6 cores) of a NEC SX-8 cluster for its 171 operational configuration. System upgrades in 2012 meant that the model was then 172 transferred to using 1 node (32 cores) of an IBM POWER-6 cluster and then later 1 173 node (32 cores) of an IBM POWER-7 cluster. The Atlantic Margin Model, and 174 Numerical Weather Prediction models were routinely operated on the same 175 supercomputers. 176 177 2.1.1 Operational hydrodynamic ecosystem model 178 The Proudman Oceanographic Laboratory Coastal Ocean Modelling System 179 (POLCOMS), coupled with the European Regional Seas Ecosystem Model (ERSEM), 180 was run by the Met Office National Centre for Ocean Forecasting to provide nowcast 181 and forecast data of the in-water conditions in the northeast European shelf waters 182 (Siddorn et al, 2007). The physical oceanography component of the model 183 (POLCOMS) was a three-dimensional baroclinic B-grid model (Holt and James, 184 2001) solving primitive equations. The freshwater inputs included in POLCOMS 185 were based on a long-term average (climatology) from over 300 rivers and the Baltic 186 Sea. The ecosystem component of the model (ERSEM) is a complex ecosystem 187 model with coupled pelagic and benthic sub-models. The POLCOMS-ERSEM 188 coupling is described in Allen et al. (2001), Holt et al. (2005) and the application of 189 ERSEM was essentially that of Siddorn et al. (2007). Several recent studies have 190 extensively evaluated the POLCOMS-ERSEM model skill for the European shelf seas 191 using a variety of univariate and multivariate methods (Holt et al., 2005, Holt et al., 192 2012, Lewis et al., 2006, Allen et al., 2007, Shutler et al., 2011). The model has a 193 good ability to reproduce the sea surface temperature (absolute bias ranges from 0.2 to 194 0.9 °C dependent on the region with a root mean squared error of 0.3 - 0.7 °C). Its 195 precision (root mean squared error) of simulating chlorophyll-a is 1 mg m-3. 196 197 The modeled region encompasses 12°W to 13°E and 48°N to 62°N with a grid of 198 1/10° longitude by 1/15° latitude (equivalent to a spatial resolution of ~6 km) and 18 199 vertical s-coordinate levels. The boundary of the model that connects with the open 200 ocean follows the northwest European continental shelf break (approximate 200 m 201 depth contour). At the open-ocean boundary, the model was forced by the larger 202 spatial-scale (spatial resolution ~12 km) Atlantic Margin Model (AMM) version of 203 POLCOMS (providing temperature, salinity and current data). Open boundary 204 conditions for the AMM model were taken from the Met Office's operational 1/4° 205 1/4° spatial resolution global deep ocean Forecasting Ocean Assimilation Model 206 (FOAM) (Bell et al., 2000) and a northeast Atlantic tidal model (Flather, 1981). 207 Surface forcing (wind stress, sea surface pressure, heat and precipitation minus 208 evaporation) were provided by the Met Office’s operational Numerical Weather 209 Prediction (NWP) models. Example outputs from the model set up include sea 210 surface temperature, phytoplankton biomass (dinoflagellates, flagellates, diatoms and 211 picoplankton) and nutrient concentration (nitrates, phosphates and silicates). 212 213 2.1.2 Additional model data 214 To complement the biological in-water model output, additional model data from the 215 NWP models were delivered to the AlgaRisk portal; these included wind speed, wind 216 direction, cloud cover, photosynthetically available radiation (PAR), mean air 217 pressure at sea level, a stratification indicator, surface current velocity and daily tidal 218 range data (Mahdon et al 2010). 219 220 2.2 Near-real time Earth Observation data 221 AlgaRisk focussed on the use of ~1km spatial resolution EO optical data (visible and 222 thermal infra-red wavelengths) over the marine environment to provide near-coast 223 estimates of sea surface temperature and ocean colour. All EO data were 224 automatically processed using a 30-node Open Grid engine (typical node: dual core 225 3.1 GHz, 8 GB of memory) and then made available on the portal in near-real time. 226 This visible spectrum data processing consisted of ~13 GB day-1 of downloads, 227 resulting ~22 hours of processing across multiple nodes. The thermal infra-red data 228 processing consisted of ~1 GB day-1 of downloads resulting in ~5 hours of processing 229 across multiple nodes. The Daily data and weekly mean composite data for all EO 230 products were provided. The details of these data are described below. 231 232 2.2.1 Earth Observation data sources 233 Satellite data from two satellite sensors were exploited. The MODIS sensor on-board 234 the Aqua platform provided visible spectrum data at a spatial resolution (nadir) of ~1 235 km. The Advanced Very High Resolution Radiometer (AVHRR) series of sensors 236 provided estimates of sea surface temperature at a spatial resolution (nadir) of ~1 km. 237 The MODIS and AVHRR level 2 data (geo-located geophysical products) were 238 provided in near-real time (within 2 - 3 hours of the satellite overpass) by the UK 239 Natural Environment Research Council's Earth Observation Data Acquisition and 240 Analysis Service (NEODAAS). These standard level 2 data were quality filtered and 241 re-projected to a Mercator projection using existing techniques (Shutler et al., 2005; 242 Miller et al., 1997). 243 244 2.2.2 Chlorophyll-a, high biomass and Karenia data products 245 The MODIS level 2 EO data were used to produce three additional products. Coastal 246 and shelf waters (case 2) specific chlorophyll-a estimates (Figure 2a and b) were 247 generated using an approach that was developed for waters within the English 248 Channel and Bay of Biscay (Gohin et al., 2002). This approach is able to estimate the 249 coastal chlorophyll-a concentrations with zero bias and an r2 of 0.7 (Gohin et al., 250 2002). Maps of the likelihood of the phytoplankton species Karenia mikimotoi 251 existing within the water (Figure 2c and d) were generated (Miller et al., 2006; 252 Kurekin et al., 2013). The accuracy of the likelihood approach has previously been 253 characterised for the Western English Channel and Celtic Sea (Kurekin et al., 2013) 254 resulting in a correct classification rate of 86% with a false alarm rate of 0.01 255 (N=4662). Maps of the location of potential high biomass blooms (Shutler et al., 256 2012) were generated from the case 2 chlorophyll-a estimates (Figure 2e). This 257 method exploits time series data to identify regions where the chlorophyll-a 258 concentrations are higher than their background levels. 259 approach has been previously characterised for monitoring K. mikimotoi in the Celtic 260 sea and Western English Channel (Shutler et al., 2012) resulting in a correct 261 classification rate of 68% with a false alarm rate of 0.24 (N=25). The accuracy of the anomaly 262 263 2.2.3 Dense bloom flag 264 When dense coastal blooms are observed in EO chlorophyll-a data, the centre of the 265 bloom is sometimes masked out. This is due to the existence of negative water 266 leaving radiance (Lw) in some short wavelength bands, as this negative component 267 prevents the estimation of chlorophyll-a concentrations. This can result in the densest 268 blooms being masked as missing data (ie like cloud). From analysing historical data 269 we found that negative Lw in the MODIS channel at 488 nm (blue part of the 270 spectrum) was a reliable indicator (flag) of this incorrect masking. Hence in addition 271 to the data products already described, we also highlighted such pixels in our 272 chlorophyll-a data in a distinct colour to indicate blooms dense enough to prevent 273 calculation of chlorophyll-a concentrations (Figure 2a and b). This is a simple 274 alternative to detecting intense blooms compared to existing approaches that use the 275 red part of the electromagnetic spectrum (Gower et al., 2005). In order to combine the 276 cloud-free data into a 7-day composite, a second ‘mixed’ dense bloom flag was 277 indicated if both valid chlorophyll-a values and dense blooms were observed for the 278 same pixel during the period of compositing. 279 280 [Figure 2 here] 281 282 2.3 Provision of data to the users 283 All of the data products described in sections 2.1 and 2.2 were generated in near-real 284 time on a daily basis using previously developed automatic systems (e.g. Shutler et 285 al., 2005; Miller et al., 1997; Siddorn et al., 2007). The hydrodynamic-ecosystem 286 model was run in nowcast and forecast mode providing a nowcast and 5-day forecast 287 of the water conditions. All of the EO and model data were automatically uploaded 288 onto the AlgaRisk web portal allowing users to easily access and compare the data. 289 The web portal was the main interface between users and data, allowing users to 290 quickly visualise and assess the data to support decision-making. The portal allowed 291 all of the data (EO and model) to be viewed alongside each other and included an 292 interactive java-based image viewer that allowed users to overlay a number or data 293 products. For a number of user-defined regions statistical parameters (such as mean, 294 median and standard deviation) were routinely calculated for each of the datasets and 295 these were automatically emailed each day to agency users in a comma separated 296 variable (csv) format. This feature was provided to allow the users to load the data 297 into a spreadsheet for studying patterns and analysis. 298 299 3. Results 300 3.1 Example data products and web view 301 Example data products produced from the EO data are shown in Figure 2. These 302 include daily and composite images of chlorophyll with dense bloom flag identifiers, 303 sea surface temperature images and HAB likelihood maps. Figure 3 shows an 304 example screenshot of the AlgaRisk web portal. Figure 3 illustrates that the portal 305 allowed users to access historical data, view time series data and view daily and 306 weekly composite data. The java-based image viewer enabled users to select and 307 view multiple datasets, overlay annotation, alter the colour palette, zoom the view, 308 and to retrieve geophysical parameter values at a given latitude and longitude (e.g. 309 chlorophyll concentrations in mg m-3). An example screenshot of the image viewer 310 showing EO chlorophyll estimates with overlaid surface wind speeds can be seen in 311 Figure 4. 312 313 [Figure 3 here] 314 [Figure 4 here] 315 316 3.2 Case study: Karenia mikimotoi bloom in South West UK 2009 317 In August 2009 an incidence of the flagellate Karenia mikimotoi in very high 318 concentrations was detected along the coasts of Cornwall and Devon in the south west 319 of the UK. Routine in situ sampling by the Environment Agency (England & Wales) 320 on the 11-12th August identified K. mikimotoi as being present in the coastal waters of 321 St Austell Bay in Cornwall (Crinnis, Porthpean, Par and Charlestown) and South 322 Devon (mouth of the River Yealm) in concentrations higher than the background 323 concentrations for these regions. From the shoreline in St Austell Bay, Environment 324 Agency staff also observed dead marine animals (dogfish, turbot, eel, dover sole and 325 sea potatoes), fish swimming at the surface of the water and the water appeared to be 326 red in colour. Independent in situ sampling by the Plymouth Marine Laboratory in St 327 Austell Bay on the 13th August 2009 confirmed the species as K. mikimotoi in a 328 concentration of 5,400,000 cells l-1. Background concentrations in UK waters are 329 normally of the order of a few thousand cells per litre (Davidson et al., 2009). Figure 330 5 shows a map of the region and the locations where in situ samples were collected. 331 Extrapolating between the in situ sampling locations and assuming that each 332 confirmed instance was part of the same bloom instance suggested that >55 km of 333 coastline was covered by the bloom (Figure 5); potentially impacting ~20 bathing 334 beaches. The locations and numbers of bathing beaches potentially impacted were 335 determined using the UK Good Beach guide (UK Beach guide 2014). Observations 336 of water discolouration by a scuba diving company based in Porthkerris (Cornwall) 337 extended the potential coastal coverage further west to a total of >85 km, potentially 338 impacting a total of ~30 bathing beaches. The high concentrations, the dead marine 339 animals observed by the Environment Agency staff and the apparent discolouration of 340 the water suggested a dense algal bloom. It was assumed that the animals died from 341 hypoxia (due to K. mikimotoi depleting dissolved oxygen concentrations) following 342 the rapid formation of the algal bloom (e.g. Brand et al., 2012). Karenia mikimotoi is 343 also thought to produce toxins that are harmful to fish (Brand et al., 2012) so this may 344 have also played a role in the mortalities. As a precautionary measure shellfish 345 harvesting within four regions along the southern coastline of Cornwall and Devon 346 was halted, the general public were advised against collecting any dead marine 347 animals and the local council authority (Cornwall Council) advised people not to 348 bathe in discoloured waters and recommend that anybody coming into prolonged 349 contact with scum or foam should wash the exposed skin with clean water. The high 350 algal concentrations remained in the coastal waters for a number of weeks after the 351 initial report. 352 353 The algal anomaly data (Figure 2e) showed elevated algal concentrations along most 354 of the Cornish and Devon coastlines. The EO Karenia probability maps showed 355 elevated probability of K. mikimotoi in regions adjacent to the south coast (Figure 2c 356 and d). At the time of the bloom the dense bloom approach was under development. 357 Offline analysis of the EO dense bloom flag data showed that the regions of coastline 358 covered by the dense bloom flag data were adjacent to the offshore areas identified as 359 potential K. mikimotoi in the HAB likelihood maps. The dense bloom flag data 360 suggested that a much larger coastal region was affected by this bloom incident than 361 that suggested by the in situ data alone (Figure 2b). Extrapolating between regions of 362 dense blooms along the coast in the EO data for the 01-07 August suggested that a 363 further >90 km of coastline was potentially impacted. This meant that >175 km of the 364 south coast of the UK from Lizard Point to Exeter was impacted, potentially 365 impacting ~100 bathing beaches. The bloom areal coverage offshore seemed to be 366 greatest between St Austell Bay and Plymouth (Figure 6). 367 368 [Figure 5 here] 369 [Figure 6 here] 370 371 The AlgaRisk system and supporting meteorological data were later used to re- 372 analyse the development and extent of the algal bloom to identify potential drivers for 373 the high algal concentrations. The EO dense bloom flag algorithm showed high levels 374 of chlorophyll-a and/or dense algal concentrations along the south coast of England 375 between the 5 August and the 17 September, as shown in Figure 6d-f. From the EO 376 data the greatest coastal coverage or extent of the bloom was during 4 to 8 September. 377 The EO dense bloom data suggested that the extent of the bloom had begun to reduce 378 from the 13 September and no evidence of the bloom was visible after the 18 379 September. The model nowcast data showed an increase in total chlorophyll-a 380 concentration during August in the St. Austell Bay area (increasing from 4 to 15 mg 381 m-3 between 4th-9th August and then varying between to 4 - 8 mg m-3 for the 382 remainder of August), with flagellates being the dominant species (K. mikimotoi is a 383 dinoflagellate). The model estimated that nutrient levels within the St Austell Bay 384 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 385 water column. Higher nutrient concentrations were apparent at lower depths, below 386 the mixed layer and the thermocline. Meteorological records (NOAA, 2014) for the 387 Plymouth region (Bigbury Island) showed 32 days of rain between 04 July – 04 388 August, followed by a period of reduced cloud cover, low or no wind and calm sea 389 conditions. The rainfall exceeded 1 mm h-1 (average over a 3 hour period) six times 390 during this period, with the highest rate of 3 mm h-1 (average over a 3 hour period) on 391 the 29 July (see Figure 7). This rainfall over a relatively long period of time suggests 392 that elevated nutrient levels from land run-off may have provided the nutrients 393 required for the rapid increase in the K. mikimotoi concentrations and the formation of 394 a dense bloom. The river flow data from the four major rivers that flow into the sea in 395 this region (the Fal, Tamar, Fowey and Exe) all showed high flows (7-160 m3 s-1) that 396 peaked on the 31st July, supporting this hypothesis. The hydrodynamic-ecosystem 397 model used in the AlgaRisk system uses climatological nutrients and so it would not 398 have captured the impact of the rain events. The linkage between increased rainfall 399 over land resulting in increased nutrient availability in coastal areas and thus 400 increasing the potential for these naturally occurring algae to bloom in harmful 401 concentrations has previously been documented (Anderson et al., 2002), further 402 supporting our hypothesis. 403 404 The meteorological data (NOAA, 2014) for the Plymouth region (Bigbury) showed an 405 increase in ground swell waves on the morning of the 9 September, increasing from 406 ~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 References 520 Allen J.I., Blackford J.C., Holt J., Proctor R., Ashworth M., Siddorn J., 2001, A 521 highly spatially resolved ecosystem model for the North West European Continental 522 Shelf. 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WHO Geneva, ISBN 92 4 154580 1 729 730 731 732 733 734 735 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