Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions

Luo, Wei; Shen, Fang; He, Qing; Cao, Fei; Zhao, Hang; Li, Mengyu

doi:10.1016/j.scitotenv.2021.150381

Cited by 33 publications

(23 citation statements)

References 46 publications

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“…In addition to the contributions of upstream sediment, downstream human activities add sediments from nearshore and cross-shore currents, contributing to the outward shifts of shoreline in Chongming Island [43,44]. A large number of wharf and bridge construction projects, reclamation activities, and channel dredging were carried out in the middle and lower reaches of the Yangtze River in recent years.…”

Section: Shoreline Dynamics and Factors Driving Chongming Island Areamentioning

confidence: 99%

“…For example, the deep-water channel project (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) dredged the Yangtze River estuary to a depth of 6.5-12.5 m [45,46]. These projects led to the resuspension and transport of sediments downstream, which eventually contributed to the continuous expansion of Chongming Island [43]. Moreover, the results of the research carried out by Chen et al showed that the submerged parts of the Yangtze River delta (the edges, tidal flats, or submerged small islands) were further eroded by tidal action, which was another source of the sediment inputs that caused the expansion of Chongming Island [45].…”

Section: Shoreline Dynamics and Factors Driving Chongming Island Areamentioning

confidence: 99%

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Shoreline Dynamics of Chongming Island and Driving Factor Analysis Based on Landsat Images

Wang

Zhang

et al. 2022

Remote Sensing

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Chongming Island, the third largest island in China, has experienced dramatic shoreline changes due to erosion, river deposits, and human activities. While previous studies have shown the capacity of Landsat series images to extract shoreline dynamics, the spatial variation of shoreline dynamics and their corresponding driving factors remain unclear. Therefore, we established a method to monitor the shoreline dynamics of Chongming Island from 1984 to 2020 and to evaluate the driving factors of shoreline changes using a novel approach to Landsat image analysis. The method, based on the LISA (local indicator of spatial autocorrelation) concept, automatically extracted the shoreline from Landsat imagery. The results show that the LISA method, based on the SWIR1 band, has a high capacity for shoreline extraction in Chongming Island. By distinguishing the responses of the eastern and northern shorelines to upstream sediment loads and comprehensively analyzing the driving factors of eastern and northern dynamics, we found that: (i) although upstream sediment loads decreased dramatically, the shoreline of Chongming Island is still expanding due to human activities (i.e., reclamation and an estuary project) and sediment re-suspension from near-shore or cross-shore currents; (ii) the expansion of Chongming Island was initially due to the dynamics at the eastern shoreline, but the expansion of the eastern shoreline slowed after 2008 as upstream sedimentation slowed, less construction of cofferdams took place, and the Qingcaosha Reservoir was constructed; (iii) the northern shoreline of Chongming Island expanded rapidly after 1999, due to the merger of Xinlongsha, Xincunsha, and Chongming Island, and the transport of coastal and offshore sediments by hydrodynamic processes; and (iv) the main driving factors of eastern shoreline movement on Chongming Island are cofferdam reclamation and coastal engineering, and the changes at the northern shoreline are mainly affected by reclamation projects, offshore sediment supplies, and upstream sediment inflow. The results of this study provide theoretical fundamentals for land reclamation and future urban planning for Chongming Island.

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Section: Shoreline Dynamics and Factors Driving Chongming Island Areamentioning

confidence: 99%

Section: Shoreline Dynamics and Factors Driving Chongming Island Areamentioning

confidence: 99%

Shoreline Dynamics of Chongming Island and Driving Factor Analysis Based on Landsat Images

Wang

Zhang

et al. 2022

Remote Sensing

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“…It has been observed in several estuarine networks that a reduced fluvial sediment input causes a downstream shift of ETMs. The construction of the Three Gorges Dam in the Yangtze River substantially reduced the fluvial sediment input into the Yangtze Estuary, with the river discharge almost unaffected, which causes the reduction in seaward net sediment transport in all distributary channels (Luo et al, 2022). By analysing in situ field measurements, combined with the Landsat images from 1979 to 2008 in the Yangtze Estuary, Jiang et al (2013) reported that lower fluvial sediment input caused ETMs to shift seaward.…”

Section: Fluvial Sediment Inputmentioning

confidence: 99%

“…This causes the locations of the resulting ETMs to move up-and downstream during a spring-neap cycle and may occasionally even shift from one channel to another. Furthermore, satellite images and also in situ data of the Yangtze Estuary in China suggest that there is a global decrease in suspended sediment concentration after the upstream construction of the Three Gorges Dam that reduces the fluvial sediment input (Luo et al, 2022). They also show that the construction of two training walls (narrowing) in one of the channels, called the North Passage, reduced the sediment concentration in nearby channels.…”

Section: Introductionmentioning

confidence: 97%

Turbidity maxima in estuarine networks: Dependence on fluvial sediment input and local deepening/narrowing with an exploratory model

2022

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An estuarine turbidity maximum (ETM) results from various subtidal sediment transport mechanisms related to, e.g., river, tides, and density gradients, which have been extensively analysed in single-channel estuaries. However, ETMs have also been found in estuaries composed of multiple interconnected tidal channels, where the water and suspended fine sediments are exchanged at the junctions with possible occurrence of sediment overspill. The overall aim of this study is to understand the processes that determine the ETM dynamics in such channel networks. Specifically, focusing on the ETMs formation due to sediment transport by river flow and density-driven flow, the dependence of ETM locations in an idealised three-channel network on fluvial sediment input and the local deepening and narrowing of a seaward channel is investigated. It is found that the ETM dynamics in channels of a network is coupled, and hence, changes in one channel affect the ETM pattern in all channels. Sensitivity results show that, keeping river discharge fixed, a larger fluvial sediment input leads to the upstream shift of ETMs and an increase in the overall sediment concentration. Both deepening or narrowing of a seaward channel may influence the ETMs in the entire network. Furthermore, the effect of either deepening or narrowing of a seaward channel on the ETM locations in the network depends on the system geometry and the dominant hydrodynamic conditions. Therefore, the response of the ETM location to local geometric changes is explained by analysing the dominant sediment transport mechanisms. In addition to the convergence of sediment transport mechanisms in single-estuarine channels, ETM dynamics in networks is found to be strongly affected by net exchange of sediment between the branches of a network. We find that considering the sensitivity of net sediment transport to geometric changes is needed to understand the changing ETM dynamics observed in a real estuarine network.

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“…Due to the different river discharges, the SPM of the Yangtze estuary exhibits seasonal variations (Shen et al, 2013), with SPM in the upper estuary (lower estuary) during flood season significantly higher (lower) than that during the dry season. Over the past 37 years, SPM in the Yangtze estuary demonstrated an overall declining pattern (Luo et al, 2022), with SPM in the inner estuary responding most promptly (40.3 % reduction) after the operation of Three Gorges Dam. Chl-a also shows seasonal variations in the Yangtze estuary, ranging from 0.03 to 3.10 mg m −3 and from 0.88 to 31.5 mg m −3 during spring and summer seasons of 2008, respectively (Shen et al, 2010b).…”

Section: Yangtze Estuarymentioning

confidence: 99%

The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters

Lavigne

Dogliotti

Doxaran

et al. 2022

Earth Syst. Sci. Data

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Abstract. Because of the large diversity of case 2 waters ranging from extremely absorbing to extremely scattering waters and the complexity of light transfer due to external terrestrial inputs, retrieving main biogeochemical parameters such as chlorophyll-a or suspended particulate matter concentration in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from 1 to 700 FNU and from 0.9 to 180 mg m−3 respectively, the HYPERMAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms. The HYPERMAQ dataset provides biogeochemical parameters (i.e. turbidity, pigment and chlorophyll-a concentration, suspended particulate matter), apparent optical properties (i.e. water reflectance from above water measurements) and inherent optical properties (i.e. absorption and attenuation coefficients) from six different study areas. These study areas include large estuaries (i.e. the Rio de la Plata in Argentina, the Yangtze estuary in China, and the Gironde estuary in France), inland (i.e. the Spuikom in Belgium and Chascomùs lake in Argentina), and coastal waters (Belgium). The dataset is available from Lavigne et al. (2022) at https://doi.org/10.1594/PANGAEA.944313.

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Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions

Cited by 33 publications

References 46 publications

Shoreline Dynamics of Chongming Island and Driving Factor Analysis Based on Landsat Images

Shoreline Dynamics of Chongming Island and Driving Factor Analysis Based on Landsat Images

Turbidity maxima in estuarine networks: Dependence on fluvial sediment input and local deepening/narrowing with an exploratory model

The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters

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