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

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.

[1]  C. Gobler,et al.  Ecosystem disruptive algal blooms of the brown tide species, Aureococcus anophagefferens and Aureoumbra lagunensis , 2012 .

[2]  Kenneth H. Reckhow,et al.  Using generalized additive models for prediction of chlorophyll a in Lake Okeechobee, Florida , 1996 .

[3]  Gregory Leptoukh,et al.  Seasonal Variations of Chlorophyll $a$ Concentration in the Northern South China Sea , 2008, IEEE Geoscience and Remote Sensing Letters.

[4]  Chuanmin Hu,et al.  On the consistency of MODIS chlorophyll $a$ products in the northern South China Sea , 2014 .

[5]  T. Okuda,et al.  Spatial and temporal distributions of Secchi depths and chlorophyll a concentrations in the Suo Nada of the Seto Inland Sea, Japan, exposed to anthropogenic nutrient loading. , 2016, The Science of the total environment.

[6]  Lin Lin Xia,et al.  An overview of global ocean wind energy resource evaluations , 2016 .

[7]  T. Platt,et al.  Ocean surface partitioning strategies using ocean colour remote Sensing: A review , 2017 .

[8]  P. Shi,et al.  Seasonal phytoplankton blooms associated with monsoonal influences and coastal environments in the sea areas either side of the Indochina Peninsula , 2006 .

[9]  Shi-guo Xu,et al.  Temporal-spatial variations and developing trends of Chlorophyll-a in the Bohai Sea, China , 2016 .

[10]  D. Guiral,et al.  Wind Induced Resuspension in a Shallow Tropical Lagoon , 1993 .

[11]  Tae-Soo Chon,et al.  Self-Organizing Maps applied to ecological sciences , 2011, Ecol. Informatics.

[12]  K. An,et al.  Temporal and spatial variation of nutrients, suspended solids, and chlorophyll in Yeongsan watershed , 2018, Journal of Asia-Pacific Biodiversity.

[13]  Qiang Hao,et al.  Long-term environmental changes and the responses of the ecosystems in the Bohai Sea during 1960–1996 , 2010 .

[14]  Sufen Wang,et al.  Environmental Ecological Response to Increasing Water Temperature in the Daya Bay, Southern China in 1982-2012 , 2016 .

[15]  S. Smith,et al.  Optimality-based approach for computationally efficient modeling of phytoplankton growth, chlorophyll-to-carbon, and nitrogen-to-carbon ratios , 2018, Ecological Modelling.

[16]  A. Falcão,et al.  Phytoplankton community structures in shelf and oceanic waters off southeast Brazil (20°–25°S), as determined by pigment signatures , 2014 .

[17]  S. Gille,et al.  Southern Ocean wind‐driven entrainment enhances satellite chlorophyll‐a through the summer , 2015 .

[18]  P. Chigbu,et al.  Spatiotemporal patterns of phytoplankton composition and abundance in the Maryland Coastal Bays: The influence of freshwater discharge and anthropogenic activities , 2018, Estuarine, Coastal and Shelf Science.

[19]  Shuai Zhang,et al.  Remote sensing of spatial-temporal variations of Chlorophyll-a in Galveston Bay, Texas , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[20]  B. Pradhan,et al.  Spatial and seasonal variability of Chlorophyll-a and associated oceanographic events in Sabah water , 2012, 2012 Second International Workshop on Earth Observation and Remote Sensing Applications.

[21]  Xin Qian,et al.  Temporal and spatial variability of chlorophyll a concentration in Lake Taihu using MODIS time-series data , 2011, Hydrobiologia.

[22]  A. M. Kalteh,et al.  Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application , 2008, Environ. Model. Softw..

[23]  H. Stefan,et al.  Wind Control of Algae Growth in Eutrophic Lakes , 1976 .

[24]  Hisashi Yamaguchi,et al.  Seasonal and summer interannual variations of SeaWiFS chlorophyll a in the Yellow Sea and East China Sea , 2012 .

[25]  Shitao Peng,et al.  The nutrient, total petroleum hydrocarbon and heavy metal contents in the seawater of Bohai Bay, China: Temporal-spatial variations, sources, pollution statuses, and ecological risks. , 2015, Marine pollution bulletin.

[26]  L. Tosi,et al.  Interpreting the progressive eutrophication behind the world’s largest macroalgal blooms with water quality and ocean color data , 2015, Natural Hazards.

[27]  P. J. Werdell,et al.  Assessment of ocean color data records from MODIS-Aqua in the western Arctic Ocean , 2015 .

[28]  S. Saitoh,et al.  Satellite-derived measurements of spatial and temporal chlorophyll-a variability in Funka Bay, southwestern Hokkaido, Japan , 2008 .

[29]  Liis Sipelgas,et al.  Comparison of satellite sea surface temperature with in situ surface layer temperature , 2007, Estonian Journal of Ecology.

[30]  Deliang L. Chen,et al.  Analysis of microcystins in cyanobacteria blooms and surface water samples from Meiliang Bay, Taihu Lake, China. , 2003, Environment international.

[31]  Zhenhong Du,et al.  Multistep-ahead forecasting of chlorophyll a using a wavelet nonlinear autoregressive network , 2018, Knowl. Based Syst..

[32]  Danling Tang,et al.  World's Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations , 2015, Remote. Sens..

[33]  Qian Zhang,et al.  Watershed export of fine sediment, organic carbon, and chlorophyll-a to Chesapeake Bay: Spatial and temporal patterns in 1984-2016. , 2018, The Science of the total environment.

[34]  Daniel Berg.  Bankruptcy Prediction by Generalized Additive Models , 2006 .

[35]  Carlos M. Duarte,et al.  Nutrient and temperature control of the contribution of picoplankton to phytoplankton biomass and production , 2000 .

[36]  G J Joo,et al.  Modelling community changes of cyanobacteria in a flow regulated river (the lower Nakdong River, S. Korea) by means of a Self-Organizing Map (SOM) , 2005 .

[37]  Bertram Ostendorf,et al.  A review of methods for analysing spatial and temporal patterns in coastal water quality , 2011 .

[38]  T. Platt,et al.  Ecological indicators for the pelagic zone of the ocean from remote sensing , 2008 .

[39]  Xuan Chen,et al.  CMIP5-Based Wave Energy Projection: Case Studies of the South China Sea and the East China Sea , 2019, IEEE Access.

[40]  Qing Wang,et al.  An overview of medium- to long-term predictions of global wave energy resources , 2017 .

[41]  Zhenhong Du,et al.  Red tide time series forecasting by combining ARIMA and deep belief network , 2017, Knowl. Based Syst..

[42]  Bo Sun,et al.  MODIS ocean color product downscaling via spatio-temporal fusion and regression: The case of chlorophyll-a in coastal waters , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[43]  Ying Wang,et al.  Spatial-Temporal Variations of Chlorophyll-a in the Adjacent Sea Area of the Yangtze River Estuary Influenced by Yangtze River Discharge , 2015, International journal of environmental research and public health.