Hydrography and distribution dynamics of larval and juvenile fishes in the coastal waters of the Tanshui River estuary, Taiwan, with reference to estuarine larval transport

1993 ◽  
Vol 116 (2) ◽  
pp. 205-217 ◽  
Author(s):  
W.-N. Tzeng ◽  
Y.-T. Wang
Keyword(s):  
Coastal Waters ◽  
River Estuary ◽  
Larval Transport ◽  
Distribution Dynamics ◽  
Tanshui River

scholarly journals Tidal Mixing Signatures in the Hong Kong Coastal Waters from Satellite-Derived Sea Surface Temperature

2018 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
R. Susanto ◽  
Jiayi Pan ◽  
Adam Devlin
Keyword(s):  
Hong Kong ◽  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Waters ◽  
River Estuary ◽  
Pearl River Estuary ◽  
Sea Surface ◽  
Tidal Mixing ◽  
Diurnal Tides

Tidal mixing in the coastal waters of Hong Kong was investigated using a combination of in situ observations and high-resolution satellite-derived sea surface temperature (SST) data. An indicator of tide-induced mixing is a fortnightly (spring-neap cycle) signature in SST due to nonlinear interactions between the two principal diurnal and the two principal semi-diurnal tides. Both semi-diurnal and diurnal tides have strong tidal amplitudes and currents near Hong Kong. As a result, both the near-fortnightly (Mf) and fortnightly (MSf) tides are enhanced due to nonlinear tidal signal interactions. In addition, these fortnightly tidal signals are modulated by seasonal variability, with the maximum seasonal modulation of fortnightly tides occurring during the monsoon transition periods in May and October. The largest fortnightly signals are found in the southwestern part of the Pearl River estuary. Tidal constituent properties vary by space and depth, and high-resolution SST plays a pivotal role in resolving the spatial characteristics of tidal mixing.

scholarly journals Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea

2019 ◽  
Vol 11 (7) ◽  
pp. 775 ◽  
Author(s):  
Deyong Sun ◽  
Xiaoping Su ◽  
Zhongfeng Qiu ◽  
Shengqiang Wang ◽  
Zhihua Mao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Yangtze River ◽  
River Estuary ◽  
Sea Surface Salinity ◽  
Yangtze River Estuary ◽  
The Yangtze River ◽  
Surface Salinity ◽  
The Southern Yellow Sea ◽  
The Yangtze River Estuary

Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak.

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