Predicting massive floating macroalgal blooms in a regional sea

  • Fucang Zhou
  • , Zhi Chen
  • , Zaiyang Zhou
  • , Bing Cao
  • , Lili Xu
  • , Dongyan Liu
  • , Ruishan Chen
  • , Karline Soetaert
  • , Jianzhong Ge*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Increasingly frequent and severe floating macroalgal blooms present significant challenges to coastal and ocean environments. Here a short-term forecast system of floating macroalgal blooms was developed to predict the physical-biogeochemical environment and macroalgal ecodynamic processes in a regional ocean. Predictions of macroalgal ecodynamic processes are influenced by oceanic conditions (hydrodynamics, temperature, and nutrients), as well as atmospheric conditions (wind). The system's effectiveness is demonstrated by successfully hindcasting the June 2021 green tide bloom event in the Yellow Sea and using real-time satellite data to make reliable and robust continuous short-term predictions for 2022 and 2023. The prediction accuracy of coverage reaches 87.5%, and the minimum transport error of the green tide center of mass is 6.09 nautical miles over an 7-day prediction duration. Supported by regional marine physics and biogeochemistry and macroalgal physiological characteristic datasets, this system may serve as a crucial cornerstone for similar floating macroalgal disaster prevention.

Original languageEnglish
Article number106310
JournalEnvironmental Modelling and Software
Volume185
DOIs
StatePublished - Feb 2025
Externally publishedYes

Keywords

  • Biomass
  • Coverage
  • Green tide
  • Prediction
  • The yellow sea
  • Ulva prolifera

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