OPTICAL MONITORING AND FORECASTING SYSTEMS FOR HARMFUL ALGAL BLOOMS: POSSIBILITY OR PIPE DREAM?

Journal of Phycology - Tập 35 Số 6 - Trang 1477-1496 - 1999
Oscar Schofield1, Joseph J. Grzymski1, W. Paul Bissett2, Gary J. Kirkpatrick3, David F. Millie4, Mark A. Moline5, Collin S. Roesler6
1Coastal Ocean Observation Laboratory, Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 08901
2Florida Environmental Research Institute, 4807 Bayshore Boulevard, Suite 101, Tampa, Florida 33611
3Mote Marine Laboratory, 1600 Thompson Parkway, Sarasota, Florida 34236
4Agricultural Research Service, U.S. Department of Agriculture, New Orleans, Louisiana and Mote Marine Laboratory, 1600 Thompson Parkway, Sarasota, Florida 34236
5Department of Biological Sciences, California Polytechnic State University, San Luis Obispo, California 93407
6Bigelow Laboratory for Ocean Sciences, West Boothbay Harbor, Maine 04575

Tóm tắt

Monitoring programs for harmful algal blooms (HABs) are currently reactive and provide little or no means for advance warning. Given this, the development of algal forecasting systems would be of great use because they could guide traditional monitoring programs and provide a proactive means for responding to HABs. Forecasting systems will require near real‐time observational capabilities and hydrodynamic/biological models designed to run in the forecast mode. These observational networks must detect and forecast over ecologically relevant spatial/ temporal scales. One solution is to incorporate a multiplatform optical approach utilizing remote sensing and in situ moored technologies. Recent advances in instrumentation and data‐assimilative modeling may provide the components necessary for building an algal forecasting system. This review will outline the utility and hurdles of optical approaches in HAB detection and monitoring. In all the approaches, the desired HAB information must be isolated and extracted from the measured bulk optical signals. Examples of strengths and weaknesses of the current approaches to deconvolve the bulk optical properties are illustrated. After the phytoplankton signal has been isolated, species‐recognition algorithms will be required, and we demonstrate one approach developed for Gymnodinium breve Davis. Pattern‐recognition algorithms will be species‐specific, reflecting the acclimation state of the HAB species of interest.Field data will provide inputs to optically based ecosystem models, which are fused to the observational networks through data‐assimilation methods. Potential model structure and data‐assimilation methods are reviewed.

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Journal of Phycology

Tập 35 Số 6

1477-1496

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