Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention

Green macroalgae bloom (GMB), with the dominant species of Ulva prolifera, has regularly occurred since 2007 along the China coast. Although disaster prevention and control achieved favorable results in 2020, the satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level. The reasons for this rebound and the significant interannual variability over past 15 years are still open questions. Here, by using long-term time-series (2007-2022) optical and Synthetic Aperture Radar satellite observations (1000+ scenes), meteorological data and water quality statistics, the mechanism analysis was performed by exploring effects from natural factors and human activities. Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area (the Subei Shoal) during a critical period (from April to May 20) and the nutrient availability. Furthermore, by using these two parameters, a novel model for AMC prediction (R (2 )= 0.87, p < 0.01) is proposed and independently validated, which can reasonably explain the significant interannual variability (2014-2021) and agree well with the latest observation in 2022 (percentage difference 12%). Finally, suggestions are proposed for future disaster prevention and alleviation. This work may aid future bloom prediction and management measure optimization.

Automated summary

Green macroalgae bloom (GMB), dominated by Ulva prolifera, has been occurring regularly along the China coast since 2007. The satellite-observed GMB annual maximum coverage (AMC) rebounded sharply in 2021 to an unprecedented level, which raises questions about the reasons for this rebound and the significant interannual variability. Through the analysis of long-term satellite observations, meteorological data, and water quality statistics, two key determinants for AMC were identified as the macroalgae distribution in a key area and nutrient availability. A novel model for AMC prediction was proposed and validated, which can explain the significant interannual variability and align well with the latest observation in 2022.