Abstract
Harmful algal blooms (HABs) are types of phytoplankton overgrowth that adversely affect marine ecosystems and aquaculture resources. One such HAB species, Cochlodinium polykrikoides, occurs irregularly and causes significant damage to the aquaculture industry along the coastal regions of Korea. In this study, we developed and implemented an integrated system to detect and predict HAB occurrences in real time. This system comprises four main components: (1) a real-time detection system utilizing acoustic sensing, ocean weather, water temperature, salinity, and chlorophyll, satellite images, genetic analysis, and optics; (2) a prediction model system based on current and tidal, HAB occurrence, and HAB movement and diffusion models; (3) an additional data based on HAB information of sampling data and HAB information of GPS data, and (4) an integrated information system utilizing data storage servers and a visualization platform. We applied and assessed the efficiency of this integrated system in the South Sea of Korea from 2017 to 2019. Particularly, HABs occurred significantly in 2019, and the system demonstrated the feasibility of detection and prediction under field conditions. Implementing a more advanced integrated detection and prediction system in the field is anticipated to minimize the damage caused by irregular HAB occurrences every year.
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