Preprints
https://doi.org/10.5194/gmd-2021-20
https://doi.org/10.5194/gmd-2021-20

Submitted as: model experiment description paper 29 Mar 2021

Submitted as: model experiment description paper | 29 Mar 2021

Review status: this preprint is currently under review for the journal GMD.

A Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application in the green tides of the Yellow Sea

Fucang Zhou1, Jianzhong Ge1,2, Dongyan Liu1,2, Pingxing Ding1,2, and Changsheng Chen3 Fucang Zhou et al.
  • 1State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
  • 2Institute of Eco-Chongming, No.20 Cuiniao Road, Chenjiazhen, Shanghai 202162, China
  • 3School for Marine Science and Technology, University of Massachusetts-Dartmouth, New Bedford, MA 02744, United States

Abstract. Massive floating macroalgal blooms in the ocean have had an array of ecological consequences; thus, tracking their drifting pattern and predicting their biomass are important for their effective management. However, a high-resolution ecological dynamics model is lacking. In this study, a physical–ecological model, Floating Macroalgal Growth and Drift Model (FMGDM v1.0), was developed to determine the dynamic growth and drift pattern of floating macroalgal, based on the tracking, replication and extinction of Lagrangian particles. The position, velocity, quantity and represented biomass of particles are updated synchronously between the tracking module and the ecological module. The former is driven by ocean flows and sea surface wind, while the latter is controlled by the temperature, salinity, and irradiation. Based on the hydrodynamic models of the Finite-Volume Community Ocean Model and parameterized using a culture experiment of Ulva prolifera, which caused the largest bloom worldwide of the green tide in the Yellow Sea, China, this model was applied to simulate the green tides around the Yellow Sea in 2014 and 2015. The simulation result, distribution and biomass of green tides, was validated using remote sensing observation data and reasonably modeled the entire process of green tide bloom and its extinction from early spring to late summer. Given the prescribed spatial initialization from remote sensing observation, the model could provide accurate short-term (7–8 d) predictions of the spatial and temporal developments of the green tide. With the support of the hydrodynamic model and biological data of macroalgae, this model can forecast floating macroalgae blooms in other regions.

Fucang Zhou et al.

Status: open (until 24 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-20', Anonymous Referee #1, 19 Apr 2021 reply

Fucang Zhou et al.

Data sets

Datasets of FVCOM model and satellite Jianzhong Ge and Fucang Zhou https://zenodo.org/record/4620534

Model code and software

Example for FMGDM_v1.0 2014/2015 cases Jianzhong Ge and Fucang Zhou https://doi.org/10.5281/zenodo.4607829

Source code of Floating Macroalgal Growth and Drift Model (FMGDM_v1.0) Jianzhong Ge and Fucang Zhou https://zenodo.org/record/4607771

Fucang Zhou et al.

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Short summary
Massive floating macroalgal blooms in the ocean have had an array of ecological consequences, tracking drifting pattern and predicting biomass are important for the effective management. Floating Macroalgal Growth and Drift Model (FMGDM v1.0) was developed to determine the dynamic growth and drift pattern of macroalgal blooms. This model had applied to simulate two massive green tides in Yellow Sea in 2014, 2015. This model can forecast similar floating macroalgae blooms in other regions.