OPTICAL MONITORING AND FORECASTING SYSTEMS FOR HARMFUL ALGAL BLOOMS: POSSIBILITY OR PIPE DREAM?
Version of Record online: 30 AUG 2002
Journal of Phycology
Volume 35, Issue 6, pages 1477–1496, December 1999
How to Cite
Schofield, O., Grzymski, J., Bissett, W. P., Kirkpatrick, G. J., Millie, D. F., Moline, M. and Roesler, C. S. (1999), OPTICAL MONITORING AND FORECASTING SYSTEMS FOR HARMFUL ALGAL BLOOMS: POSSIBILITY OR PIPE DREAM?. Journal of Phycology, 35: 1477–1496. doi: 10.1046/j.1529-8817.1999.3561477.x
- Issue online: 30 AUG 2002
- Version of Record online: 30 AUG 2002
- Cited By
- harmful algal blooms;
- remote sensing
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.