A simple model for forecast of coastal algal blooms

In eutrophic sub-tropical coastal waters around Hong Kong and South China, algal blooms (more often called red tides) due to the rapid growth of microscopic phytoplankton are often observed. Under favourable environmental conditions, these blooms can occur and subside over rather short time scales—in the order of days to a few weeks. Very often, these blooms are observed in weakly flushed coastal waters under calm wind conditions—with or without stratification. Based on high-frequency field observations of harmful algal blooms at two coastal mariculture zones in Hong Kong, a mathematical model has been developed to forecast algal blooms. The model accounts for algal growth, decay, settling and vertical turbulent mixing, and adopts the same assumptions as the classical Riley, Stommel and Bumpus model (Riley, G.A., Stommel, H., Bumpus, D.F., 1949. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection Yale University 12, 1-169). It is shown that for algal blooms to occur, a vertical stability criterion, E < 4 μl²/ π², must be satisfied, where E, μ, l are the vertical turbulent diffusivity, algal growth rate, and euphotic layer depth respectively. In addition, a minimum nutrient threshold concentration must be reached. Moreover, with a nutrient competition consideration, the type of bloom (caused by motile or non-motile species) can be classified. The model requires as input simple and readily available field measurements of water column transparency and nutrient concentration, and representative maximum algal growth rate of the motile and non-motile species. In addition, with the use of three-dimensional hydrodynamic circulation models, simple relations are derived to estimate the vertical mixing coefficient as a function of tidal range, wind speed, and density stratification. The model gives a quick assessment of the likelihood of algal bloom occurrence, and has been validated against field observations over a 4-year period. The model helps to explain the observed spatial and temporal patterns of bloom occurrences in relation to the vertical turbulence and nutrient condition. The success of the model points the way to the development of real time management models for disaster mitigation.