Forecasting of environmental risk maps of coastal algal blooms |
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| Authors: | Ken T.M. Wong Joseph H.W. Lee Paul J. Harrison |
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| Affiliation: | 1. Strategic Research and Development, Deltares, The Netherlands;2. Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China;3. Atmospheric, Marine and Coastal Environment (AMCE) Program, The Hong Kong University of Science and Technology, Hong Kong, China;1. Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Laboratory of Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China;1. Environmental and Fisheries Sciences Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd E, Seattle, WA 98112, USA;2. Institute for Marine and Antarctic Studies, University of Tasmania, Private Bag 129, Hobart, Tasmania 7001, Australia;3. Ocean Sciences Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA;4. Plancton Andino spA, Laboratorio Puerto Varas, Terraplén 869, Puerto Varas, Chile;5. Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Padre Harter 574, Puerto Montt, Chile;6. Estuary & Ocean Science Center, Romberg Tiburon Campus, San Francisco State University, 3150 Paradise Dr., Tiburon, CA 94920, USA;1. Southeast Sea Fisheries Research Institute, National Fisheries Research & Development Institute (NFRDI), Tongyeong 650-943, Republic of Korea;2. Fishery & Ocean Information Division, National Fisheries Research & Development Institute (NFRDI), Busan 619-705, Republic of Korea;1. Environmental Protection Agency, Office of Evidence and Assessment, Richview, Clonskeagh Road, Dublin 14, Ireland;2. Environmental Protection Agency, Office of Evidence and Assessment, John Moore Road, Castlebar, Co. Mayo, Ireland;3. Marine Institute, Rinville, Oranmore, Co. Galway, Ireland;1. State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China;2. Key Laboratory of Coastal and Wetland Ecosystems, Ministry of Education/Fujian Provincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen, China;3. Department of Mathematics & Computer Science, Mount Allison University, Sackville, New Brunswick, Canada;4. Department of Environmental Sciences, School of the Coast & Environment, Louisiana State University, Baton Rouge, LA, 70803, USA;5. Ecosystem Dynamics Research Group, Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan;1. CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, Tasmania 7001, Australia;2. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania 7001, Australia;3. Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada |
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| Abstract: | We present the elements of an algal bloom risk forecast system which aims to provide a scientific prognosis of the likelihood of an algal bloom occurrence as a function of: (a) the nutrient concentration; and (b) the forecasted wind and tide-induced vertical mixing relative to the critical value defined by the environmental and algal growth conditions. The model is validated with high frequency field observations of algal blooms in recent years and only requires the input of readily available field measurements of water column transparency, nutrient concentration, representative maximum algal growth rate, and a simple estimate of vertical mixing as a function of tidal range, wind speed, and density stratification. The forecasted region-wide risk maps successfully predicted the observed algal bloom occurrences in Hong Kong waters over the past 20 years, with a correct prognosis rate of 87%. It is shown that algal blooms are to a large extent controlled by the interaction of physical and biological processes. This work provides a general framework to interpret the complex spatial and temporal dynamics of observed algal blooms, and paves the way for the development of real time algal bloom risk forecast systems. |
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