Insights and machine learning predictions of harmful algal bloom in the East China Sea and Yellow Sea

Abstract

The increase in harmful algal blooms (HABs) globally has been linked to climate change and anthropogenic activities such as agricultural runoff and urbanization. This study focused on analyzing the impact of these factors on HAB occurrences in the East China Sea and the Yellow Sea, identifying influential factors, and predicting future HAB events. For this study, random forest and numerical modeling were employed, with datasets encompassing physical and chemical properties of river water, seawater, and precipitation to assess the impact of discharge on HABs. Additionally, climate change scenarios derived from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were employed to predict future HAB occurrences, supported by a sensitivity analysis to identify influential factors affecting HAB occurrence. This study demonstrated that the growth rate and occurrence of HABs in the East China Sea (ECS) and Korean coastal waters (KCW) distinctively increased in July and November after the operation of the Three Gorges Dam (TGD). It is likely affected by the decreasing discharge from the Yangtze River (YR) owing to the operation of the TGD. Using the Random Forest model, future HAB events were predicted in good agreement with observations. The sensitivity results revealed that environmental properties, such as precipitation, water temperature, and salinity are major features affecting the HAB trends in both the KCW and YR basins. Moreover, based on the random forest model and climate change scenarios, HAB events were predicted to increase in frequency in July, September, and October. Therefore, the findings can contribute to preventing biological pollution of the ocean system in the ECS and KCW by supporting efficient environmental management.