Investigation of Morphological Changes in the Tamsui River Estuary Using an Integrated Coastal and Estuarine Processes Model

Hsieh, Tung-Chou; Ding, Yan; Yeh, Kuo‐Chen; Jhong, Ren Kai

doi:10.3390/w12041084

Cited by 11 publications

(8 citation statements)

References 38 publications

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“…The river mouth is bounded north eastly by the Fisherman's Wharf Causeway, south westly by the North Breakwater of the Taipei Port, and connected northwestward to the Taiwan Strait (Figure 3b). The littoral zone is subject to natural forcing such as tide, wave, river discharge, and typhoon (Hsieh et al., 2020). The semidiurnal M 2 tide is the principal constituent of astronomical tide.…”

Section: Methodsmentioning

confidence: 99%

Reservoir Mud Releasing May Suboptimize Fluvial Sand Supply to Coastal Sediment Budget: Modeling the Impact of Shihmen Reservoir Case on Tamsui River Estuary

Hsueh,

Wu,

et al. 2024

Water Resources Research

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Regular release of sediment from reservoir has been increasingly adopted as a strategy for sustainable management. Here, we use a process‐based morphodynamic model to simulate the estuarine sediment dynamics impacted by turbidity current venting implemented by the Shihmen Reservoir during three typhoon events in 2008. Upon validation with the post‐event bathymetries, the model hindcasts reveal that mud releasing can be effective in mitigating reservoir siltation, yet may be a suboptimal strategy for alleviating coastal sediment deficit. A vast majority of the released muds were delivered through the estuary and exported to offshore by flood advection, wave dispersion, and tidal flushing. The flood‐driven sands, sourced mainly from downstream tributaries, were instead the major contributor to coastal sediment budget. However, mud mantling (covering and immobilizing sand deposits by the reservoir‐released muds) reduced sand availability and thus sand delivery to the coast. For the present case, 25% of the released muds were deposited along the way, presence of these mud covers reduced sand delivery by 15%, compared to a hypothetical scenario of clear‐water flood releases. The relative sand transport deficit is found to increase linearly with the degree of bed mud saturation, 1–D/R, with D/R the ratio of single‐event mud deposit to release. Given broad relevance to global reservoirs encountering the problems of siltation and coastal sediment deficit, our findings highlight that sustainable management needs to look beyond just a bulk amount of sediment, but it is critical to consider how different sediment fractions are interacting and impacted by human activities.

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Section: Methodsmentioning

confidence: 99%

Reservoir Mud Releasing May Suboptimize Fluvial Sand Supply to Coastal Sediment Budget: Modeling the Impact of Shihmen Reservoir Case on Tamsui River Estuary

Hsueh,

Wu,

et al. 2024

Water Resources Research

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“…CCHE2D-Coast is another integrated wave-current-sediment model that has been verified and applied to practical projects. The model was developed at the National Center for Computational Hydroscience and Engineering (NCCHE) at the University of Mississippi [11,29] and has been applied to a range of coastal and estuary projects [30][31][32]. The flow model was based on CCHE2D [33].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Current–Wave–Sediment Model for Coastal and Estuary Simulation

Lai

2024

Water

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An integrated current–wave–sediment model is developed for coastal and estuary applications. The new model aims to improve the existing ones in both the physical process representation and the numerical techniques. Two areas of improvements are emphasized: the numerical procedure and a new general sediment sub-model. The numerical procedure adopts the one-model one-mesh approach to improve the model accuracy, efficiency and user friendliness. One model is developed which includes three major sub-models: current flow, wave dynamics and sediment transport. The three are tightly coupled during the solution process by exchanging data among sub-models within the same time step. Further, one unstructured geophysical mesh is adopted for all three sub-models and the mesh allows the most flexible polygonal shapes with an arbitrary number of sides. The current flow sub-model is an extension of the existing river hydraulic model (SRH-2D), the wave sub-model follows the third-generation theory implemented in SWAN which solves the multi-frequency multi-direction wave action balance equation and the sediment sub-model is a new development adopting a general multi-size non-equilibrium sediment transport formulation but tailored for coastal applications. In this paper, the theory, the governing equations and the numerical methods are presented; the new model is then verified and validated using selected experimental cases. It is shown that the new model may predict the current–wave–sediment dynamics well. In addition, model sensitivity results are also discussed to shed light on future needs.

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“…The sediment load in river basin system can influence dam filling, safety of fish and wildlife habitats, water pollution, understanding of flood volume and life of hydroelectric equipment etc. [3][4][5][6][7][8][9]. The harmful effects of suspended sediment yield (SSY) in a river system and the need to understand SSY behavior has drawn substantial attention over recent decades.…”

Section: Introductionmentioning

confidence: 99%

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

Yadav

Joshi

Kumar

et al. 2022

Water

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Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications.

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Investigation of Morphological Changes in the Tamsui River Estuary Using an Integrated Coastal and Estuarine Processes Model

Cited by 11 publications

References 38 publications

Reservoir Mud Releasing May Suboptimize Fluvial Sand Supply to Coastal Sediment Budget: Modeling the Impact of Shihmen Reservoir Case on Tamsui River Estuary

Reservoir Mud Releasing May Suboptimize Fluvial Sand Supply to Coastal Sediment Budget: Modeling the Impact of Shihmen Reservoir Case on Tamsui River Estuary

An Integrated Current–Wave–Sediment Model for Coastal and Estuary Simulation

Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India

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