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Real-Time Data Driven Forecast System for Coastal Algal Blooms

Author(s): Jiuhao Guo; Joseph H. W. Lee

Linked Author(s): Joseph Hun-Wei Lee

Keywords: Coastal algal bloom; Water column stability; Real-time prediction; Daily forecast system; Fisheries management

Abstract: relative to a critical value (�(cid:3030)) dependent on the algal growth rate and photic depth. Based on 3D hydrodynamic modeling and data driven models, the turbulent diffusivity � for a given semi-enclosed water on any given day We present a new modeling system for prognostic daily forecasting of algal bloom (Chlorophyll-a > 10 density stratification. The critical diffusivity �(cid:3030) can be determined from water temperature and light extinction can be estimated from the predicted tidal range and hydro-meteorological data – and accounting for effects of data. This bloom forecast framework has been validated against extensive biweekly and monthly water quality data (1986 to 2018). Using high-frequency data (10 min) on salinity and temperature at various depths, together with readily available stability risk (�/�(cid:3030)). Combining the stability risk with a nutrient availability factor estimated from water quality hydro-meteorological data (solar radiation, air temperature, wind speed and rainfall) the vertical density gradient on the next day can be predicted using an artificial neural network (ANN) model - and hence the water column monitoring, the algal bloom risk on the next day can be predicted. The forecast system has been validated against 4 years of high frequency data for the Yim Tin Tsai marine fish culture zone in Tolo Harbour, Hong Kong.


Year: 2020

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