Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed Zhanjiang Bay, China

Abstract

Typhoons can induce variations in hydrodynamic conditions and biogeochemical processes, potentially escalating the risk of algal bloom occurrences impacting coastal ecosystems. However, the impacts of typhoons on instantaneous changes and the mechanisms behind typhoon-induced algal blooms remain poorly understood. This study utilized high-frequency in situ observation and machine learning model to track the dynamic variations in meteorological, hydrological, physicochemical, and Chlorophyll-a (Chl-a) levels through the complete Typhoon Talim landing in Zhanjiang Bay (ZJB) in July 2023. The results showed that a delayed onset of algal bloom occurring 10 days after typhoon's arrival. Subsequently, as temperatures reached a suitable range, with an ample supply of nutrients and water stability, Chl-a peaked at 121.49 μg L^-1 in algal bloom period. Additionally, water temperature and air temperature decreased by 1.61 °C and 2.8 °C during the typhoon, respectively. In addition, wind speed and flow speed increased by 1.34 and 0.015 m s^-1 h^-1 to peak values, respectively. Moreover, the slow decline of 8.2 % in salinity suggested a substantial freshwater input, leading to an increase in nutrients. For instance, the mean DIN and DIP were 2.2 and 8.5 times higher than those of the pre-typhoon period, resulting in a decrease in DIN/DIP (closer to16) and the alleviation of P limitation. Furthermore, pH and dissolved oxygen (DO) were both low during the typhoon period and then peaked at 8.93 and 19.05 mg L^-1 during the algal bloom period, respectively, but subsequently decreased, remaining lower than those of the pre-typhoon period. A preliminary learning machine model was established to predict Chl-a and exhibited good accuracy, with R² of 0.73. This study revealed the mechanisms of eutrophication status formation and algal blooms occurrence in the coastal waters, providing insights into the effects of typhoon events on tropical coastal biogeochemistry and ecology.

Keywords: Algal bloom; Chl-a; Coastal water; Machine learning model; Nutrients; Typhoon.