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. Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomassare essential for effective marine management. In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM),was developed. Based on the tracking, replication, and extinction of Lagrangian particles, FMGDM is capable of determining the dynamic growth anddrift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronouslybetween the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process iscontrolled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-VolumeCommunity Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplanktonspecies causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around theYellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensingobservation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial andtemporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floatingmacroalgal blooms in other regions.

The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide