Let's embrace space

Let’s embrace space Volume II

Enterprise and Industry

Acknowledgements Lec’b embaaóe bpaóe– eoldme II — ápaóe àebeaaóh aóhieeemencb dnôea che Àch Faamefoak Paogaamme ib pdblibheô bh che ápaóe Research and Development Unit in the European Commission’s Directorate-General for Industry and Enterprise. The editing of this book was undertaken by Reinhard áóhdlce—Baadókb– Pecea Baegea– Haacfig Bibóhof– áhlfia Borowiecka and áoia áaôiq. Articles published in this volume were reviewed by an editorial board consisting of Peter Baegea– Haacfig Bibóhof– Thieaah Baefoac– Mats Ljdngqeibc– àióhaaô Gilmoae– Tanja éegeab– Diak éimmea anô Hdgo Zunker. The eiefb expaebbeô in che aacióleb aae chobe of che adchoab– fhom che Edaopean Commibbion fibheb co chank foa cheia foak.

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CH AP T E R 1 6

AáIMãTH: applieô bimdlacionb and integrated modelling for the understanding of toxic and harmful algal blooms

Authors: Marcos Mateus, Julie Maguire, Hilda de Pablo, Kieran Lyons, Manuel Ruiz Villarreal, Caroline Cusack, Keith Davidson consortium Members: Daithi OãMurchu Marine Research Station, —reland; Marine —nstitute, —reland; —nstitut fran ais de recherche pour lãexploitation de la mer, France; —nstituto Español de Oceanografía, Spain; Scottish Association for Marine Science, United Kingdom; —nstituto Superior T cnico, Portugal; —nstituto Nacional de Recursos Biológicos/Instituto de Investigação das Pescas e do Mar, Portugal; –ocer, France; Nowcasting —nternational Ltd, —reland; Starlab, Spain; Numerics Warehouse Ltd, Ireland

AbSTRACT The nature of the problem of harmful algal bloomb (HABb) hab óhangeô bigniióanclh oeea aeóenc ôeóaôeb– fich a substantial increase in the number of oóódaaenóeb– che chpeb of aebodaóeb afeóceô anô che aebdlcing eóonomió lobbeb (Hallegaaef– 1993). Thebe blooms are triggered and controlled by a óomplex inceaplah of phhbióal– biologióal– geologióal anô óhemióal paocesses. AáIMãTH (hccp://fff.abimdch.

eu) aims to develop forecasting capabilities based on mathematical modelb co faan of impenôing HABb– chdb providing an operational tool for those responsible for the management of coastal resources. The main objectives of AáIMãTH inóldôe: (1) iôenciióacion of keh pabc HAB eeencb for reanalysis and for training of the moôelling bhbcem; (2) inóoapoaacion of the GMES marine core services with the above selected events to tune the system and move towards an operacional moôel foa foaeóabcing eeencb; (3) design of regional model systems and ôelieeah of nofóabcb foa bpeóiió HABb– using location information and remotely sensed data; (4) population of an HAB— distributed decision support system (HAB—bpeóiió chemació abbemblh óencae) faom aeleeanc ôaca; (5) paoeibion of expert interpretation of the available data by way of a web portal on a peaioôió babib anô– ôepenôing on aibk– issuing this assessment via a warning system to end users. The main outcomes expected for this paojeóc aae: (1) ôemonbcaacion chaodgh pilot tests based both on modelled data and on real events when possible; (2) a inanóiallh belf—bdbcainable foaeóabcing bhbcem foa HABb; (3) paoeibion

AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

of a business model for the operacional bdpplh of che paoôdóc– óoncaibuting directly to the sustainability and competitiveness of European valueadding services; (4) examination of the impact of this service in a socioeóonomió óoncexc– paaciódlaalh in inancially depressed peripheral regions fheae aqdaódlcdae hab faeqdenclh óaeated employment and where jobs have historically been lost due to mechanibacion of agaiódlcdae anô ibheaieb; (5) ôemonbcaacion of che mdcdal ôepenôenóh of ceóhnologh– oaganibational dynamics and societal issues as well as related legal/economic aspects in ôifeaenc beócoab; (6) ebcablibhmenc of close collaboration with representative user communities throughout Edaope (oóeanogaapheab– moôelleab– HAB expeac biologibcb– ibh/bhellibh faameab– paoóebboab– óonbdmeab anô regulators).

AcHieVed resuLts Phytoplankton form the base of the aqdació fooô óhain. In maaine faceab– there are approximately 4 000 speóieb of phhcoplankcon– mobc of fhióh aae benign. Hofeeea– bome bpeóieb aae ‘haamfdl’– anô mah impaóc on hdman health through the production of a aange of pocenc nacdaal biocoxinb– oa damage the economy through their negaciee impaóc on ibh oa bhellibh farming or other human use of ecosyscem beaeióeb. Geneaallh– bdc noc exóldbieelh– hdman healch ib chaeaceneô bh low biomabb HABb (≈ few hundred to thousands of cells L—1)– che biocoxinb produced by these species being conóencaaceô bh ilcea feeôing bhellibh and other organisms that may be subbeqdenclh ingebceô bh hdmanb. High biomabb HABb óan aebdlc in oxhgen depletion when the bloom sinks and is

decomposed by bacteria in the bottom facea. Faameô ibh mah albo be killeô by smothering of the gills due to phytoplankton mucus production or gill abrasion by phytoplankton spines. HABb cheaefoae pobe beaiodb óonstraints to the sustainable development of coastal areas. Despite being ôiiódlc co qdancifh– che ebcimaceô total welfare cost in Europe (public healch– óommeaóial ibheah– aeóaeacion anô codaibm– monicoaing anô management) of HAB—aelaceô eeencb eaóh year is > EUR 800 million (Lorentzen anô Pecceabbon– 2005). The eóonomió cost of the Karenia bloomb of 2005 anô 2006 fhióh aebdlceô in mabb moacalicieb of óageô ibh in che bhelf beab of the north-east Atlantic remains to be qdanciieô bdc hab foaóeô che ólobdae of bome maaine inibh faamb (áilke ec al.– 2005; Daeiôbon ec al.– 2009). The paebenóe of ôiaaaheció bhellibh poiboning (DSP) toxins from Dinophysis spp. routinely cause prolongeô bhellibh ólobdaeb chac óan labc foa monchb in Poacdgal– ápain– Faanóe anô Iaelanô (Ebóaleaa ec al.– 2006; Taainea ec al.– 2010). Adcdmn bloomb of Gymnodinium catenatum (paralhció bhellibh poiboning coxinb) óan be ôeeabcacing co mdbbel aqdaódlcdae anô nacdaal bhellibh bankb paoôdócion in noach—febc Ibeaia. Foa example– in 2005– ólobdaeb labceô beeeaal monchb inóldôing chaodgh che fincea bhellibh harvest season. A substantial part of local famers’ annual income depends on these winter markets (Escalera et al.– 2010). ádóh inóiôencb aae bpoaaôió– largely unpredictable and may seriodblh ôibadpc paoôdócion planb of ibh anô bhellibh faamb. The impoacanóe of che Edaopean aqdaculture industry to peripheral regions in Europe cannot be overstated. Since the

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AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

European industry is more heavily regdlaceô chan mobc of cheia óompecicoab– it is essential that production schedules aae maincaineô co beódae paoicabilich. HAB—geneaaceô biocoxinb aebdlc in cempoaaah ólobdaeb of bhellibh haaeebcing aaeab– fich bdbbeqdenc lobb of inóome. More sustained closures can result in óomplece paoôdócion lobbeb– ab fdllh gaofn bhellibh (paaciódlaalh mdbbelb) can become too heavy for their growing structures and will eventually fall of paaciódlaalh ôdaing fincea bcoamb. If an early warning system were available production schedules could be adapted to suit each HAB bicdacion. A ôiiódlch in HAB foaeóabcing ib che enigmatic nature of bloom initiation. Neeeachelebb– paeôiócing anô monicoaing HAB ib óencaal co ôeeeloping paoactive strategies to ameliorate their

impact on human health and the economics of coastal communities. In manh aegionb– paaciódlaalh in Edaope– ac the moment there is no warning when a bloom will occur. The main outcome of AáIMãTH ib che development of forecasting capabilicieb co faan of impenôing HABb (Figdae 1). HABb óan be geneaaceô bh a very diverse range of causative organibmb– fich ôifeaenc bloom ôhnamiób anô chpe of impaócb– bo cheia bcdôh óallb foa a óooaôinaceô bóienciió anô management approach. The steps to achieve this include a series of scienciió anô ceóhnióal objeócieeb fhióh fill enable the modelling of physical–biological interactions leading to the foreóabcing of coxin eeencb– ibh moacalicieb or ecological disruption associated with HAB.

Figure 1. Diagram of ASIMUTH main methodologies and outcomes. See the text for more details on the complementary characteristic of the methodologies and their contribution to the project’s main outcome, namely the warning system for coastal resource managers, ishermen, aquaculture operators and public health oicials.

AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

Combining methodologies

Remote sensing

Oda dnôeabcanôing of che biologióal– chemical and physical processes that aebdlc in HABb in che oóean ôepenôb on the scope and scale of our obsereacionb. åhen aôeqdace obbeaeacional coverage in time and space is lacking– fe óan bdpplemenc oda knofleôge through mathematical models that simulate the oceanographic conditions and their impact on the ecosystem.

Since the early days of ocean modelling– aemocelh benbeô ôaca hab alfahb played a fundamental role in the valiôacion of moôel aebdlcb. Toôah– moae chan eeea– chib link becfeen bacellice imagery and model predictions is still ealiô (Figdae 2) anô ib ac che óoae of che AáIMãTH moôelling appaoaóh.

Monicoaing paogaammeb foa HABb aae chpióallh aeaóciee– bafegdaaôing hdman healch chaodgh che ólobdae of aqdaculture areas once harmful species oa cheia coxinb aae ôeceóceô– bdc dbdally without means for advance warning of chebe eeencb. Gieen chib– che development of algal forecasting systems would be of great use in guiding traditional monitoring programmes and providing a proactive means for aebponôing co HABb. Foaeóabcing bhbcemb aeqdiae neaa real-time observational capabilities and hydrodynamic/biological models designed to run in forecast mode. AáIMãTH ib cheaefoae ôebigneô co make dbe of che odcpdcb (bacellice– in-situ and model products) from the GMES marine core services (MCS) for launching a downstream service in HAB foaeóabc. The AáIMãTH expeac bhbcem fill paoduce HAB bdllecinb ac eaaiodb biceb along Europe’s Atlantic coast based on model foaeóabcb– bacellice imageah anô in situ networks that the MCS and intermediate users (the AáIMãTH óonboacidm) fill paoeiôe– óombineô fich an aaaah of biological samples collected and the knowledge provided by HAB biologh expeacb. The combination of these methodologies has previously been shown to proeiôe an efeóciee meanb co paeôióc HABb (ácdmpf ec al.– 2009).

Remote sensing for the detection of surface pigments or temperature signatures has been utilised for HAB ôeceócion oeea che pabc 30 heaab. Ddaing chib period there has been much discussion of satellites being able to track surface algal bloomb– aebdlcing in che paoôdótion of some services that purport to be HAB nofóabcb anô foaeóabcb. Neaa bdafaóe high óhloaophhll a (Chla) ioneb– faeqdenclh abboóiaceô fich HABb like the Karenia bpp. bloomb– óan be eabilh ôeceóceô anô caaókeô bh aiaóaat anô satellite aemoce benbing ceóhniqdeb. Hofeeea– bdbbdafaóe HAB bloomb like the Dinophysis spp. blooms cannot be detected directly with satellite remote sensing. Such blooms can persist in the water for weeks unnoticed and appear as sudden events without early warning. Understanding the drivers behind these biological phenomena in che oóean cheaefoae aeqdiaeb a moae complex approach than simple remote benbing– chodgh cheae ib bome meaic in using satellite-derived Chla images to delineate high-biomabb– neaa—bdafaóe algal blooms. Ocean colour satellites provide large synoptic bampling– impobbible co óollect with standard in situ ceóhniqdeb. Hofeeea– peabibcenc ólodô óoeea óan pocenciallh óadbe ôaca gapb in pabbiee– aemocelh benbeô ôaca. Neeeachelebb– advances in optical instrumentation may provide rapid spatial coverage for

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AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

HABb anô– óodpleô fich ôaca—abbimilaciee moôelling– fill paoeiôe che

necessary components for building an HAB ôeceócion anô foaeóabcing bhbcem.

Figure 2. Comparison of remote sensing data with model results. Example of a validation report of a MOHID forecast model for the Iberia domain.

AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

Model applications Deóaôeb of laboaacoah anô ielô aebeaaóh hab bigniióanclh inóaeabeô our knowledge on the ecological interrelationships of HAB óommdnicieb. Thib enableb che iôenciióacion of bome basic bloom triggers and contributes to the construction of bloom models that eventually will lead to the forecast of bloom initiation. HAB foaeóabcb neeô co faócoa in changes in both water column structure (including likely areas where HAB species will be retained) and transport pathways in order for such a forecast to be realistic. The forecast should albo inóldôe all aeailable biocoxin– phhtoplankton count and bioassay data to support the model forecast and satellite imagery of a bloom. Current bloom loóacionb– fdcdae bloom loóations and areas of impacts are critical components of these forecasts. From a managemenc peabpeóciee– a majoa concern is also the ability to predict where a bloom is likely to be transported over a few days from its last known location. A stepwise approach will be followed to achieve the desired HAB foaeóabc óapability in AáIMãTH moôel bimdlacionb. The ability to forecast where a feature is likely to be transported is a critical step towards developing an ecological forecasting system. Even a rudimentary forecast system can be useful and could be used as a baseline for future impaoeemencb (äelo—ád aei ec al.– 2010; åhnne ec al.– 2011). Onóe a feacdae oa algal bloom hab been iôenciieô in imageah– ic ib nof pobbible co caaók bloom pobicion– chdb óompenbacing foa mibbing imageah– anô óaeacing a nofcast and forecast for potential impacted aaeab (bee che example in Figdae 3).

The assessment of skill of the operational forecasts depends on the ability of the model to forecast conditions at an appaopaiace aeboldcion– fichin che constraints of the available validation data. Coupling of a hydrodynamic model and routine satellite imagery will allow visualisation of forecast bloom moeemenc anô– ac che bame cime– ealiôace che moôel oa paoeiôe insightful clues to tune the modelling system. The outcome There is now scope to provide a worthwhile HAB foaeóabcing ôofnbcaeam beaeióe co che aqdaódlcdae inôdbcah dbing che óombineô moôel foaeóabcb– satellite imagery and in situ networks that the MCS and intermediate dbeab fill paoeiôe– óombineô fich an the array of biological samples collected in monitoring programmes and the expertise provided by HAB biology experts. Through AáIMãTH– bóiencibcb anô inôdbcah in iee Edaopean óodncaieb fill aoll odc che iabc aealibció HAB foaeóabcing óapabilich ab a GMES ôofnbcaeam beaeióe co dbeab– in chib óabe che Edaopean aqdaódlcdae inôdbtry along Europe’s Atlantic margin. The early warning of severe blooms fill allof ibh faameab anô bhellibh farmers to adapt their culture and haaeebcing paaócióeb in cime– in oaôea to reduce potential losses and in turn increase their productivity. For the model results to be useful in a managemenc óoncexc– cheh neeô co be run in near real time and have a mechanism in place for rapid dissemination of forecasts and nowcasts. A forecast could become available to the research and management community in the form of a weekly or biweekly report.

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Figure 3. Model forecast (MOHID–PCOMS) for potential impacted areas by the tracking of bloom position. Simulation made in a preliminary stage of ASIMUTH for the irst record of Ostreopsis proliferations in the continental Portuguese coast. he forecast was used for the decision-making process that led to the closure of several beaches in a major tourist spot in Portugal (panel in the lower-right side; image retrieved from a national newspaper)

CONCLUSIONS Reasons for the emergent interebc in HABb aae abdnôanc– inóldôing concerns associated with human healch– aôeeabe efeócb on biologióal aebodaóeb– eóonomió lobbeb accaibdceô co aeóaeacion– codaibm anô beafooô— aelaceô inôdbcaieb– anô che óobc of maintaining public advisory services and monitoring programmes for shellibh coxinb anô water qdalich. In che ódaaenc eóonomió ólimace– ic ib imperative that the activities in this domain are not wasteful or a repeticion of paeeiodb efoacb. Nef inbighcb need to be gained in the study of HAB

formation and impact on human activicieb– anô che nef appaoaóheb neeô co bring added value in terms of methodologies and results. AáIMãTH ib che iabc bcep co ôeeelop short-term HAB aleac bhbcemb foa Atlantic Europe. This will be achieved using information on the most curaenc maaine óonôicion (feachea– facea óhaaaóceaibciób– coxióich– haamfdl algal paebenóe– ecó.) óombineô fich high aebolution local numerical predictions. Thib incegaaceô– mdlciôibóiplinaah– trans-boundary approach to the study of HAB ôeeelopeô ôdaing AáIMãTH fill lead to a better understanding of the

AáIMãTH: APPLIED áIMãLATIONá AND INTEGàATED MODELLING FOà THE ãNDEàáTANDING OF TOçIC AND HAàMFãL ALGAL BLOOMá

phhbióal– óhemióal anô eóologióal faócoab óoncaolling chebe bloomb– ab fell as their impact on human activities. The outcome will be an appropriate aleac bhbcem foa an efeóciee management of areas that are usually associated with HAB eeencb anô fheae chebe epiboôeb mah haee a moae bigniióanc negative impact on human activities. ápeóiióallh foa che aqdaódlcdae inôdbcah– che infoamacion paoeiôeô fill enable farmers to adapt their working practices in time to prevent morcalicieb in inibh faamb anô/oa manage cheia bhellibh haaeebc moae efeótively. This will lead to the ‘improvement of European competitiveness and sustainable development’ in this area.

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Hallegaaef– G. M.– ‘A review of harmful algae blooms and cheia appaaenc global inóaeabe’– Phycologia– 32– 1993– pp. À9–99.

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áilke– J.– O’Beian– F. anô Caonin– M.– ‘Karenia mikimotoi: an exceptional ôinolagellace bloom in febcean Iaibh faceab– bdmmea 2005’– Marine Environment and Health Series– 21– 2005– Maaine Inbcicdce– Galfah– Iaelanô– 2005– 44 pp.– online ac http://www.marine.ie.

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ácdmpf– à. P.– Tomlinbon– M. C.– Calkinb– J. A.– Kiakpacaiók– B.– Fibhea– K.– Nieaenbeag– K.– Cdaaiea– à. anô åhnne T. T.– ‘ákill abbebbmenc foa an operational algal bloom forecast bhbcem’– Journal of Marine Systems– À6(1–2)– 2009– pp. 151–161.

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Taainea– ä. L.– Picóhea– G. C.– àegdeaa– B. anô ámahôa– T. J.– ‘The distribution and impacts of harmful algal bloom species in eastern bodnôaah dpfelling bhbcemb’– Progress in Oceanography– Á5(1–2)– 2010– pp. 33–52.

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