Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

Abstract

Better understanding of the temporal-spatial distribution of chlorophyll-a concentration (Chl-a) is crucial in controlling harmful water blooms. In this study, the dynamical change of Chl-a over the Bohai Sea and Yellow Sea from 2003-2017 were analyzed by using the MODIS/Aqua satellite data, and the effects of sea surface temperature (SST), wind and wave were investigated. The typical distribution modes of long-term surface Chl-a were extracted by using the Self-organizing Mapping (SOM), neural network model. The results showed distinct seasonal variations of the Chl-a along with a gradual increase in the study period. The total Chl-a of the whole area reached the lowest value of 2.41mg/m 3 in July, and the highest value 3.43mg/m 3 in April; though in Laizhou Bay, the Chl-a concentration was significantly higher than other regions and the value reached at the peak in September. The spatial distribution showed that Chl-a decreased from inshore to offshore. Meanwhile, from clear mode to low, medium, and high concentration modes, the Chl-a gradually increased in coverage and concentration, and modes extracted by the SOM neural network have effectively elucidated the trend of Chl-a in spatial, seasonal, and interannual variability. The Generalized Additive Model (GAM) was used to evaluate the effect of SST, wind, and wave on the changing patterns of Chl-a. It was found that there is a significant nonlinear correlation between Chl-a and SST, wind speed, mean wave direction and significant height of the wave. These influencing factors accounted for 47.9% of the change of Chl-a, which had significant effects on Chl-a change. Compared with wind speed, mean wave direction and significant height of wave, SST can better explain the change of Chl-a. Besides, wind direction and increased human activity (e.g., river discharge) played a significant role in changing the Chl-a distribution in the Bohai Sea and Yellow Sea. INDEX TERMS Chl-a, self-organizing mapping neural network, generalized additive model, environmental factors, the Bohai sea and Yellow sea.

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“…Na Zhao et al (2019) suggested the Generalized Additive Model (GAM) for chlorophyll-a of environmental factors in the yellow sea and Bohai sea. In the meantime, the Chlorophyll-a has gradually increased scope and focus, and modes have taken out by the neural network have efficiently elucidated the Chlorophyll-a pattern in seasonal,inter annual and spatial variation, from simple modes to high, low,and medium-content modes.The improvements in Chlorophyll-a, which had significant impacts on Chl-a transition, accounted for 47.9 %.…”

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

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Distribution, Occurrence, and Fate of Biogenic Dimethylated Sulfur Compounds in the Yellow Sea and Bohai Sea During Spring

“…In the study area, the concentration of Chl a ranged from 0.20 to 5.83 μg/L with a mean value of 1.57 μg/L. Different from those in summer and autumn (Zhou et al, 2017), the area with high concentrations of Chl a extended to the center of the SYS. This was attributed to the fact that the cold water mass had not fully formed and its impact was weak (W. Li et al, 2012;Y.…”