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

Na Zhao; Guanlan Zhang; Sha Zhang; Yun Bai; Shahazad Ali; Jiahua Zhang

doi:10.1109/ACCESS.2019.2950833

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

Na Zhao, Guanlan Zhang, Sha Zhang, Yun Bai, Shahazad Ali, Jiahua Zhang^*

^*Corresponding author for this work

Research output: Contribution to journal › Journal Article › peer-review

27 Citations (Scopus)

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/m³ in July, and the highest value 3.43mg/m³ 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.

Original language	English
Article number	8889733
Pages (from-to)	160947-160960
Number of pages	14
Journal	IEEE Access
Volume	7
DOIs	https://doi.org/10.1109/ACCESS.2019.2950833
Publication status	Published - 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Chl-a
environmental factors
generalized additive model
self-organizing mapping neural network
the Bohai sea and Yellow sea

Access to Document

10.1109/ACCESS.2019.2950833

Cite this

@article{6df809a67400493fb689cf017d6cc6ea,

title = "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/m3 in July, and the highest value 3.43mg/m3 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.",

keywords = "Chl-a, environmental factors, generalized additive model, self-organizing mapping neural network, the Bohai sea and Yellow sea",

author = "Na Zhao and Guanlan Zhang and Sha Zhang and Yun Bai and Shahazad Ali and Jiahua Zhang",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2019",

doi = "10.1109/ACCESS.2019.2950833",

language = "English",

volume = "7",

pages = "160947--160960",

journal = "IEEE Access",

issn = "2169-3536",

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}

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T1 - Temporal-Spatial Distribution of Chlorophyll-a and Impacts of Environmental Factors in the Bohai Sea and Yellow Sea

AU - Zhao, Na

AU - Zhang, Guanlan

AU - Zhang, Sha

AU - Bai, Yun

AU - Ali, Shahazad

AU - Zhang, Jiahua

PY - 2019

Y1 - 2019

N2 - 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/m3 in July, and the highest value 3.43mg/m3 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.

AB - 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/m3 in July, and the highest value 3.43mg/m3 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.

KW - Chl-a

KW - environmental factors

KW - generalized additive model

KW - self-organizing mapping neural network

KW - the Bohai sea and Yellow sea

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UR - https://openalex.org/W2985241013

UR - https://www.scopus.com/pages/publications/85077987477

U2 - 10.1109/ACCESS.2019.2950833

DO - 10.1109/ACCESS.2019.2950833

M3 - Journal Article

SN - 2169-3536

VL - 7

SP - 160947

EP - 160960

JO - IEEE Access

JF - IEEE Access

M1 - 8889733

ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

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