A depth-averaged 2-D model of flow and sediment transport in coastal waters

Sánchez, Alejandro; Wu, Weiming; Beck, Tanya M.

doi:10.1007/s10236-016-0994-3

Cited by 20 publications

(5 citation statements)

References 45 publications

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“…DELFT3D has been applied to simulate coastal systems (e.g., Elias et al, 2012a). The CMS is an integrated suite of numerical models for simulating flow, waves, sediment transport, and morphology change (Buttolph et al, 2006;Larson et al, 2011;Lin et al, 2011;Sanchez et al, 2014Sanchez et al, , 2016. The CMS has been broadly used by the USACE and other researchers/practitioners in quantifying tidal inlet processes (e.g., Demirbilek et al, 2015a;Demirbilek et al, 2015b).…”

Section: Morphodynamics Methods and Tools Supporting Regional Sediment Managementmentioning

confidence: 99%

Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA

Beck¹,

Wang²

2021

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The temporal and spatial scales controlling the morphodynamics of barrier-inlet systems are critical components of regional sediment management practice. This paper discusses regional sediment management methods employed at multiple barrier-inlet systems, with case studies from West-Central Florida. A decision-support tool is proposed for regional sediment management with discussion of its application to barrier-inlet systems. Connecting multiple barrier islands and inlets at appropriate spatio-temporal scales is critical in developing an appropriately scoped sediment management plan for a barrier-inlet system. Evaluating sediment bypassing capacity and overall inlet morphodynamics can better inform regional sand sharing along barrier-inlet coastlines; particularly where sediment resources are scarce and a close coupling between inlet dredging and beach placement is vital to long-term sustainable management. Continued sea-level rise and anthropogenic activities may intensify the need for investigating longer-term processes and expanding regional planning at a centennial timescale and are acknowledged as challenging tasks for RSM studies. Specifically, we suggested that a regionally focused, multi-inlet study was necessary for management plan of individual inlet for the west-central Florida case studies. Key recommendations based on the case studies are included.

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Section: Morphodynamics Methods and Tools Supporting Regional Sediment Managementmentioning

confidence: 99%

Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA

Beck¹,

Wang²

2021

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“…Temporally, the behavior-based models are in decades to centuries, while the process-based ones are in hours to years [1,2]. Behavior-based models resort to too much empiricism and may have limited applicability and high uncertainty [3].…”

Section: Introductionmentioning

confidence: 99%

“…CMS-Wave is an integrated wave-current-sediment model developed at the U.S. Army Corps of Engineers [10]. The current flow model was based on CMS2D (Coastal Modeling System two-dimensional) [23], the wave model by Lin et al [10], and the sediment model by Sanchez et al [3]. CMS-Wave also solved the spectral wave action balance equation but was modified from the full equation of SWAN.…”

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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“…However, most empirical models describe single features [9]. Numerical models, on the other hand, typically include more complex relations and time-varying physical interactions between different features such as wave-current interactions, current-induced sediment transport, and tides [10]. As such, physics-based numerical models are valuable tools for complex processes because they can generally capture high dimensionality with significant accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

Abouhalima,

das Neves,

Taveira-Pinto

et al. 2024

JMSE

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The integration of machine learning (ML) techniques in coastal engineering marks a paradigm shift in how coastal processes are modeled and understood. While traditional empirical and numerical models have been stalwarts in simulating coastal phenomena, the burgeoning complexity and computational demands have paved the way for data-driven approaches to take center stage. This review underscores the increasing preference for ML methods in coastal engineering, particularly in predictive tasks like wave pattern prediction, water level fluctuation, and morphology change. Although the scope of this review is not exhaustive, it aims to spotlight recent advancements and the capacity of ML techniques to harness vast datasets for more efficient and cost-effective simulations of coastal dynamics. However, challenges persist, including issues related to data availability and quality, algorithm selection, and model generalization. This entails addressing fundamental questions about data quantity and quality, determining optimal methodologies for specific problems, and refining techniques for model training and validation. The reviewed literature paints a promising picture of a future where ML not only complements but significantly enhances our ability to predict and manage the intricate dynamics of coastal environments.

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A depth-averaged 2-D model of flow and sediment transport in coastal waters

Cited by 20 publications

References 45 publications

Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA

Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA

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

Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives

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