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Red Tide Field Monitoring and Forecasting at Kat O and Lamma Island, HK. Ken T.M. Wong Department of Civil Engineering, The University of Hong Kong. Algal Bloom / Red Tide - rapid increase in number of microalgae - discoloration of water . Harmful Algal Blooms (HABs) - beach closure
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Red Tide Field Monitoring and Forecasting at Kat O and Lamma Island, HK Ken T.M. Wong Department of Civil Engineering, The University of Hong Kong
Algal Bloom / Red Tide - rapid increase in number of microalgae - discoloration of water Harmful Algal Blooms (HABs) - beach closure - mariculture loss due to fish intoxication, oxygen depletion or fish gill irritation. - human intoxication through shellfish-vectored poisoning or direct contact with the toxic species Massive Red Tide in 1998 - estimated mariculture loss over HK$315 Million
Kat O Luk Chau Wan Continuous Field Monitoring Oceanic (dinoflagellates become more active) Estuarine (diatom dominate)
Monitoring Parameters Meteorological Information Wind, Air Temperature, Solar Radiation, Photosynthetical Active Radiation (PAR) Hydrographic Information Tidal Level, Tidal Current Water Quality Water Temperature, Dissolved Oxygen (DO), pH, Salinity, Chlorophyll, Secchi Depth, Suspend Solid (SS), Cell Count, Nutrient (DIN, ON, PO4+, Si)
Telemetry System Thermistor box Magnetic valves DO meter & probe Micrologger Fluorometer Relay box Peristaltic pump Anemometer Pyranometer Water Profiler Acoustic Doppler Current Meter
Telemetry Data of a Typical Day (11Dec00) CTD Profile 12:00 24:00 06:00 18:00
Biweekly Water Sampling Cell sample - live and fixed Nutrient - DIN, PO4, Si, ON Chlorophyll-a, SS, Secchi Depth
Red Tide Early Detection Alarming Level 24hrs Field Survey
Kat O Station Lamma Station Red Tides observed from 2000 to 2004
Transportation of red tide patch (from Lee and Qu, 2004) Tidal Flushing (NEST Lagrangian particle method)
Red Tide Organisms Algae – plants require light (energy source) and nutrient (material source) for photosynthesis Non-motile algae (diatoms) Rapid growing (1-2 doubles per day) Move passively by water turbulence Motile algae (dinoflagellates) Slow growing (less than 1 double per day) Swimming actively and aggregation
06:00 06:00 12:00 12:00 18:00 18:00 24:00 24:00 Observed Vertical Structure Diatom Bloom (August 2000) Dinoflagellate Bloom (March 2001) Depth (m) Chlorophyll Fluorescence (g/L) Depth (m) Dissolved Oxygen (mg/L)
irradiance irradiance surface surface net net photic zone growth growth (thickness = l) (thickness = l) m m rate = rate = turbulent turbulent diffusivity diffusivity E E sinking sinking loss loss velocity velocity v v rate rate = = d d non non - - productive productive lower segment lower segment depth z depth z simplified simplified growth function growth function Condition of red tide formation by non-motile species Consider the effect of turbulent diffusion, sinking and growth/mortality on the algal concentration . . . For a typical diatom growth rate =2day-1, depth of photic zone l=5m, the criteria for bloom formation is E<2.3×10-4ms-2
Condition of red tide formation by motile species Consider aggregation of motile species with vertical migration . . . For a typical dinoflagellate swimming speed v = 1mhr-1, migration distance =5m, the criteria for bloom formation is E < 1.4×10-4ms-2
non-motile species motile species swim down nutrient source Competition Threshold (for bottom nutrient source) • For motile species, they swim down to acquire bottom nutrient • For non-motile specie, nutrient has to be supplied through turbulent diffusion . . . For a typical dinoflagellate growth rate ’=0.5day-1, algal nitrogen concentration for bloom CN=100gL-1, depth of photic zone l=5m, bottom nitrogen concentration at bloom N0=200 gL-1, the competition threshold E < 3.6×10-5ms-2
Triggering Nutrient Level nutrient utilisation stability criteria giving a typical triggering nitrogen concentration around 100gL-1 to 200 gL-1 (a typical value of 120gL-1) Similar triggering level has been observed based historical records of red tides in Hong Kong
Red Tide Prediction Model triggering level stability criteria competition threshold
Comparison of predicted bloom occurrences with field observation
Comparison of predicted blooms of motile and non-motile species with field observations
Conclusions • An online early warning system has been set up • The system is capable to detect and give extensive data on red tide • A simple vertical stability theory has been devised to determine the condition necessary for red tide occurrence • A practical mathematical model has been constructed to predict the likelihood of red tide occurrence with readily available field measurements • The mathematical can help understand the characteristics and reasons for red tide occurrences at the red tide blackspots