Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6049-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6049-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
11 Oct 2021
Model experiment description paper |
| 11 Oct 2021
| 11 Oct 2021
The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide
Fucang Zhou
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Institute of Eco-Chongming, No.20 Cuiniao Road, Chenjiazhen, Shanghai 202162, China
Dongyan Liu
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Institute of Eco-Chongming, No.20 Cuiniao Road, Chenjiazhen, Shanghai 202162, China
Pingxing Ding
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 200241, China
Institute of Eco-Chongming, No.20 Cuiniao Road, Chenjiazhen, Shanghai 202162, China
Changsheng Chen
School for Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, MA 02744, USA
Xiaodao Wei
Institute of Eco-Chongming, No.20 Cuiniao Road, Chenjiazhen, Shanghai 202162, China
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- Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea Y. Men et al. 10.3390/jmse11122212
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- Exploring the Green Tide Transport Mechanisms and Evaluating Leeway Coefficient Estimation via Moderate-Resolution Geostationary Images M. Ji et al. 10.3390/rs16162934
- Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea M. Ji et al. 10.1016/j.marpolbul.2023.115420
- Predicting massive floating macroalgal blooms in a regional sea F. Zhou et al. 10.1016/j.envsoft.2024.106310
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- Strong interannual variation of green tides in the southern Yellow Sea: Crucial factors and implications on management strategies Y. Li et al. 10.1016/j.marpolbul.2025.117989
- Modeling of algal blooms: Advances, applications and prospects Y. Wang et al. 10.1016/j.ocecoaman.2024.107250
- Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery L. Gao et al. 10.5194/essd-16-4189-2024
- A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides X. Geng et al. 10.3389/fmars.2025.1546289
- Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery S. Xu et al. 10.3390/rs15082196
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- Shifting Dynamics for Coastal Upwelling and Surface Cold Water Patch: A Synergy of HF-Radar, In-Site Observation, Satellite, and Numerical Model Y. Wang et al. 10.1109/TGRS.2025.3559935
- Forecasting green tide events in a semi-closed tidal flat using artificial intelligence and environmental big data E. Yauchi et al. 10.1080/24705357.2023.2204107
- Simulation of red tide drift-diffusion process in the Pearl River Estuary and its response to the environment Y. Xu et al. 10.3389/fmars.2023.1096896
- A NEW APPROACH FOR PREDICTING GREEN TIDES IN THE YATSU TIDAL FLATS USING ARTIFICIAL INTELLIGENCE E. YAUCHI et al. 10.2208/kaigan.77.2_I_931
23 citations as recorded by crossref.
- Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model M. Ji & C. Zhao 10.3390/rs17091636
- Distribution characteristics of green tides and its impact on environment in the Yellow Sea S. Wang et al. 10.1016/j.marenvres.2022.105756
- Environmental factors affecting Ulva prolifera blooms in the South Yellow Sea: Insights from deep learning models with attention mechanisms Y. Wang et al. 10.1016/j.rsma.2025.104304
- Biophysical interactions control the progression of harmful algal blooms in Chesapeake Bay: A novel Lagrangian particle tracking model with mixotrophic growth and vertical migration J. Xiong et al. 10.1002/lol2.10308
- Approaches, challenges and prospects for modeling macroalgal dynamics in the green tide: The case of Ulva prolifera H. Chang et al. 10.1016/j.marpolbul.2025.117897
- Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea Y. Men et al. 10.3390/jmse11122212
- Characterizing distribution patterns of small algae patches across the northern and southern sides of the Yellow Sea front using synchronous CZI-MODIS images Z. Yin et al. 10.1080/01431161.2025.2492410
- Estimating windage coefficient of floating marine green tide of Ulva prolifera using UAV optical images X. Zheng et al. 10.1016/j.marpolbul.2025.117834
- Monitoring, modeling and projection of harmful algal blooms in China W. Guan et al. 10.1016/j.hal.2021.102164
- Storm-induced coastward expansion of Margalefidinium polykrikoides bloom in Chesapeake Bay J. Xiong et al. 10.1016/j.marpolbul.2022.114187
- Exploring the Green Tide Transport Mechanisms and Evaluating Leeway Coefficient Estimation via Moderate-Resolution Geostationary Images M. Ji et al. 10.3390/rs16162934
- Identification and assessment of the drift velocity of green tides using the maximum cross-correlation method in the Yellow Sea M. Ji et al. 10.1016/j.marpolbul.2023.115420
- Predicting massive floating macroalgal blooms in a regional sea F. Zhou et al. 10.1016/j.envsoft.2024.106310
- Numerical Modeling of a Green Tide Migration Process with Multiple Artificial Structures in the Western Bohai Sea, China X. Han et al. 10.3390/app12063017
- Research on the ensemble forecasting method for green tide paths in the Yellow Sea based on parameter perturbation Y. Zhu et al. 10.1016/j.marpolbul.2025.117717
- Strong interannual variation of green tides in the southern Yellow Sea: Crucial factors and implications on management strategies Y. Li et al. 10.1016/j.marpolbul.2025.117989
- Modeling of algal blooms: Advances, applications and prospects Y. Wang et al. 10.1016/j.ocecoaman.2024.107250
- Weekly green tide mapping in the Yellow Sea with deep learning: integrating optical and synthetic aperture radar ocean imagery L. Gao et al. 10.5194/essd-16-4189-2024
- A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides X. Geng et al. 10.3389/fmars.2025.1546289
- Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery S. Xu et al. 10.3390/rs15082196
- Temporal and spatial distribution of floating marine macro litter in the offshore waters of the Bohai Sea and Yellow Sea (BYS) G. Teng et al. 10.1016/j.marpolbul.2024.117232
- Shifting Dynamics for Coastal Upwelling and Surface Cold Water Patch: A Synergy of HF-Radar, In-Site Observation, Satellite, and Numerical Model Y. Wang et al. 10.1109/TGRS.2025.3559935
- Forecasting green tide events in a semi-closed tidal flat using artificial intelligence and environmental big data E. Yauchi et al. 10.1080/24705357.2023.2204107
2 citations as recorded by crossref.
Latest update: 03 Jul 2025
Short summary
In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed to determine the dynamic growth and drifting pattern of floating macroalgae. Based on Lagrangian tracking, the macroalgae bloom is jointly controlled by ocean flows, sea surface wind, temperature, irradiation, and nutrients. The FMGDM was robust in successfully reproducing the spatial and temporal dynamics of the massive green tide around the Yellow Sea.
In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model...
