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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Cited
15 citations as recorded by crossref.
- Distribution characteristics of green tides and its impact on environment in the Yellow Sea S. Wang et al. 10.1016/j.marenvres.2022.105756
- 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
- 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
- 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
- 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
- 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
- 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
- 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
13 citations as recorded by crossref.
- Distribution characteristics of green tides and its impact on environment in the Yellow Sea S. Wang et al. 10.1016/j.marenvres.2022.105756
- 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
- 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
- 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
- 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
- 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
- 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
- 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: 13 Dec 2024
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...
