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

Abstract. Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomass are 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 and drift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronously between the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process is controlled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-Volume Community Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around the Yellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensing observation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial and temporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for 7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms in other regions.

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

10.5194/gmd-2021-20 ◽

2021 ◽

Author(s):

Fucang Zhou ◽

Jianzhong Ge ◽

Dongyan Liu ◽

Pingxing Ding ◽

Changsheng Chen

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Surface Wind ◽

Ocean Model ◽

Biological Data ◽

The Yellow Sea ◽

Green Tide ◽

Observation Data ◽

Green Tides ◽

Drift Model

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.

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Remote-Sensing Monitoring of Green Tide and Its Drifting Trajectories in Yellow Sea Based on Observation Data of Geostationary Ocean Color Imager

Acta Optica Sinica ◽

10.3788/aos202040.0301001 ◽

2020 ◽

Vol 40 (3) ◽

pp. 0301001

Author(s):

陈莹 Chen Ying ◽

孙德勇 Sun Deyong ◽

张海龙 Zhang Hailong ◽

王胜强 Wang Shengqiang ◽

丘仲锋 Qiu Zhongfeng ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Ocean Color ◽

Green Tide ◽

Observation Data ◽

Remote Sensing Monitoring

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Spatiotemporal Patterns and Morphological Characteristics of Ulva prolifera Distribution in the Yellow Sea, China in 2016–2018

Remote Sensing ◽

10.3390/rs11040445 ◽

2019 ◽

Vol 11 (4) ◽

pp. 445 ◽

Cited By ~ 10

Author(s):

Yingzhi Cao ◽

Yichen Wu ◽

Zhixiang Fang ◽

Xiaojian Cui ◽

Jianfeng Liang ◽

...

Keyword(s):

Yellow Sea ◽

Vegetation Index ◽

Morphological Changes ◽

Surface Wind ◽

Morphological Characteristics ◽

Ulva Prolifera ◽

Spatiotemporal Patterns ◽

The Yellow Sea ◽

Green Tide ◽

Sea Surface Wind

The world’s largest macroalgal blooms, Ulva prolifera, have appeared in the Yellow Sea every summer on different scales since 2007, causing great harm to the regional marine economy. In this study, the Normalized Difference of Vegetation Index (NDVI) index was used to extract the green tide of Ulva prolifera from MODIS images in the Yellow Sea in 2016–2018, to investigate its spatiotemporal patterns and to calculate its occurrence probability. Using the standard deviational ellipse (SDE), the morphological characteristics of the green tide, including directionality and regularity, were analyzed. The results showed that the largest distribution and coverage areas occurred in 2016, with 57,384 km2 and 2906 km2, respectively and that the total affected region during three years was 163,162 km2. The green tide drifted northward and died out near Qingdao, Shandong Province, which was found to be a high-risk region. The coast of Jiangsu Province was believed to be the source of Ulva prolifera, but it was probably not the only one. The regularity of the boundary shape of the distribution showed a change that was opposite to the variation of scale. Several sharp increases were found in the parameters of the SDE in all three years. In conclusion, the overall situation of Ulva prolifera was still severe in recent years, and the sea area near Qingdao became the worst hit area of the green tide event. It was also shown that the sea surface wind played an important part in its migration and morphological changes.

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Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign

Marine Pollution Bulletin ◽

10.1016/j.marpolbul.2018.05.035 ◽

2018 ◽

Vol 133 ◽

pp. 150-156 ◽

Cited By ~ 15

Author(s):

Qianguo Xing ◽

Lingling Wu ◽

Liqiao Tian ◽

Tingwei Cui ◽

Lin Li ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Early Stage ◽

The Yellow Sea ◽

Green Tide

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Multi-resource data-based research on remote sensing monitoring over the green tide in the Yellow Sea

Remote Sensing and Modeling of Ecosystems for Sustainability XIV ◽

10.1117/12.2271732 ◽

2017 ◽

Author(s):

Fuxiang Xu ◽

Xiangyu Zheng ◽

Zhiqiang Gao ◽

Debin Song ◽

Maosi Chen

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

The Yellow Sea ◽

Green Tide ◽

Remote Sensing Monitoring ◽

Resource Data

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Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007–2020) on the Basis of Satellite Remote Sensing

Remote Sensing ◽

10.3390/rs13193811 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3811

Author(s):

Deyu An ◽

Dingfeng Yu ◽

Xiangyang Zheng ◽

Yan Zhou ◽

Ling Meng ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Large Scale ◽

Marine Ecology ◽

The Yellow Sea ◽

Green Tide ◽

Southern Yellow Sea ◽

Simple Method ◽

Green Macroalgae ◽

The Southern Yellow Sea

Large scale green macroalgae blooms (MABs) caused by Ulva prolifera have occurred regularly in the Yellow Sea since 2007. In the MAB dissipation phase, the landing or sinking and decomposition of U. prolifera would alter the physical-chemical environment of seawater and cause ecological, environmental, and economic problems. To understand MAB dissipation features, we used multiple sensors to analyze the spatiotemporal variation of the MAB dissipation phase in the southern Yellow Sea. The results show the variation in the daily dissipation rate (DR) was inconsistent from year to year. Based on the DR variation, a simple method of estimating MAB dissipation days was proposed for the first time. Verification results of the method, from 2018 to 2020, showed the estimated dissipation days were relatively consistent with the results obtained by remote sensing imagery. From 2007 to 2020, the order in which macroalgae landed in the coastal cities of Shandong Peninsula can be roughly divided into two types. In one type, the macroalgae landed first in Rizhao, followed by Qingdao, Rushan, and Haiyang. In the other type, they landed in the reverse order. The MABs annual distribution density showed significant differences in the southern Yellow Sea. These results provided a basis for evaluating the MABs’ impact on marine ecology and formulating the green-tide prevention and control strategies.

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