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

Abstract

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 (R2 = 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.

Keywords

• green macroalgae
• annual maximum coverage (amc)
• the yellow sea
• porphyra mariculture
• extreme weather
• water quality