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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The Amazon River has the highest discharge in the world. Nevertheless, there is still a lack of the research on the interaction between river-diluted water and the ocean. This study used the remote sensing data (2008–2017) of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite, and data of the currents, wind fields, sea surface temperature, and water depth. The river–sea interaction off the Amazon estuary was studied by analyzing the diffusion of river-diluted water and the distribution of surface suspended particulate matter (SPM). The results revealed that the Amazon estuary has a “filter effect,” whereby the distribution of the surface SPM exhibited significant spatial characteristics of being high in the nearshore area and low in the offshore area. Most of the SPM accumulated within the estuary in a fan shape, although some was distributed in the shallow water region of the continental shelf along the coasts on both sides of the estuary. The currents were found to limit the diffusion range of SPM. The flow direction and velocity of the North Brazil Current and the North Equatorial Countercurrent, which are largely driven by the magnitude of the trade wind stress, are the main forces controlling the long-distance diffusion of diluted water, thus forming unique river–sea interaction patterns in the Amazon estuary. This research provides a supplement and reference for the study of the diffusion process of SPM and river-diluted water, and on the estuarine river–sea interactions of other large rivers worldwide.

Details

Title
The River–Sea Interaction off the Amazon Estuary
Author
Yu, Di 1   VIAFID ORCID Logo  ; Liu, Shidong 2 ; Li, Guangxue 1   VIAFID ORCID Logo  ; Zhong, Yi 1 ; Liang, Jun 3 ; Shi, Jinghao 4 ; Liu, Xue 1 ; Wang, Xiangdong 4 

 Key Laboratory of Submarine Sciences and Prospecting Techniques, MOE, Ocean University of China, Qingdao 266100, China; [email protected] (D.Y.); [email protected] (G.L.); [email protected] (Y.Z.); [email protected] (X.L.) 
 Key Laboratory of Submarine Sciences and Prospecting Techniques, MOE, Ocean University of China, Qingdao 266100, China; [email protected] (D.Y.); [email protected] (G.L.); [email protected] (Y.Z.); [email protected] (X.L.); College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China 
 College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China; [email protected] 
 Qingdao Blue Earth Big Data Technology Co., Ltd., Qingdao 266400, China; [email protected] (J.S.); [email protected] (X.W.) 
First page
1022
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2633143736
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.