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Integrating autonomous data acquisition and forecasting into regional monitoring efforts; a synopsis of the merhab 2002: eastern gulf of mexico sentinel program
Author(s) -
Bendis B.,
Fries D.,
Haywood A.,
Kirkpatrick G.,
Millie D.,
Orsi T.,
Scholin C.,
Steidinger K.,
Stumpf R.,
Walsh J.,
Weisberg B.
Publication year - 2003
Publication title -
journal of phycology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.85
H-Index - 127
eISSN - 1529-8817
pISSN - 0022-3646
DOI - 10.1111/j.0022-3646.2003.03906001_7.x
Subject(s) - dissemination , citizen science , sampling (signal processing) , algal bloom , dinoflagellate , adaptive sampling , environmental resource management , phytoplankton , environmental monitoring , remote sensing , oceanography , computer science , environmental science , telecommunications , ecology , biology , geography , statistics , botany , mathematics , detector , geology , nutrient , monte carlo method
Blooms of the red‐tide dinoflagellate, Karenia brevis , occur annually in coastal waters of the eastern Gulf of Mexico (GOMx) and impact marine resources, public health, and community economics. Early forecasting of and subsequent mitigation for blooms are objectives of agency, academic, and private partnerships. The 5‐year, NOAA‐funded MERHAB 2002: Eastern GOMx Sentinel Program will develop and assess the utility of a networked system of autonomous sampling platforms. The goal of the program is to become a strategic, proactive hardware‐ and information‐technology for operational GOMx coastal observatories and as such, facilitate model initializations and state‐wide, adaptive field sampling. Monitoring platforms will utilize both existing and newly established buoys, and autonomous, water‐column profiling vehicles incorporating physical, chemical, and biological sensors. The biological sensors, including a bio‐optical phytoplankton discriminator and a processor containing a molecular‐probe array, previously have provided consistent results in laboratory and field trials and show great promise for remote, autonomous discrimination of K. brevis . Generated data will initialize a coupled bio‐physical forecast model and validate remote sensing algorithms for K. brevis blooms. Data acquisition and forecasting efforts will be integrated with a geographically‐comprehensive, rapid response component that incorporates adaptive, field sampling and a volunteer sampling network. NOAA's National Coastal Data Development Center will manage instrument data streams and disseminate and integrate all data into existing (and future) coastal partnerships with their associated management and communication network.