Sediment sorting and bedding dynamics of tidal flat wetlands: Modeling the signature of storms

Zhou, Zeng; Wu, Yingliang; Fan, Daidu; Wu, Guoxiang; Luo, Feng; Yao, Peng; Gong, Zheng; Coco, Giovanni

doi:10.1016/j.jhydrol.2022.127913

Cited by 11 publications

(7 citation statements)

References 55 publications

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Section: Methodsmentioning

confidence: 99%

“…In salt marshes, the bed form typically consists of silt and clay (Zhou et al., 2022). Compared to sand‐dominated beaches, sediment in marshes is mainly transported through the suspended load (Zhou et al., 2022). Moreover, owing to cohesive forces within particles, the critical bed shear stress for the incipient motion is significantly higher for cohesive mud compared to non‐cohesive sand of similar median grain size (M. Zhang & Yu, 2017).…”

Section: Methodsmentioning

confidence: 99%

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Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet‐Flow

Chen,

Luo,

et al. 2024

JGR Earth Surface

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The two‐way interactions between biological and physical processes, bio‐geomorphic feedback, play a vital role in landscape formation and evolution in salt marshes. Patchy vegetation represents a typical form of scale‐dependent feedback in salt marshes and is primarily responsible for the formation of efficient drainage networks. The intuitive manifestation of scale‐dependent feedback is the heterogeneity of flow and landscape. Process‐based modeling is an essential tool for exploring flow heterogeneity, but calculations for small spatial scales and over long time frames can be prohibitively costly. In this study, we proposed a deep learning model architecture, UNet‐Flow, based on convolutional neural networks (CNNs), which is used to build a surrogate model to simulate a flow field induced by salt marsh patchy vegetation. After optimizing and evaluating the model, we discovered that UNet‐Flow exhibits a speed improvement of over four orders of magnitude compared to single‐process simulations using the free surface flow model TELEMAC‐2D, with acceptable levels of error. Furthermore, we proposed an approach that combines the process‐based model SISYPHE with the deep learning method to model geomorphic heterogeneity. After numerous simulations of flow heterogeneity modeling using UNet‐Flow, we obtained a significant logarithmic relationship between scale‐dependent feedback strength and vegetation stem density, and an ascending‐descending trend in feedback strength was observed as the number or surface area of vegetation patches increased. Finally, we investigated the relationship between geomorphic heterogeneity and vegetation‐related variables. This study represents a noteworthy effort to study bio‐geomorphology using deep learning methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet‐Flow

Chen,

Luo,

et al. 2024

JGR Earth Surface

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“…In order to reduce complexity and restrict the degrees of freedom (and thus number of simulations) we strongly schematized tidal basin geometry and boundary conditions. We have quantified the effects of sand-mud interaction by defining a number of morphologic metrics, but we also observe that some of these metrics vary even more in reality due to for instance the large-scale variability in tidal prism, storminess or suspended mud concentrations (Zhou et al, 2021(Zhou et al, , 2022 and the basin geometry on a smaller scale (e.g., comparing adjacent Wadden Sea basins; Cleveringa & Oost, 1999;Ridderinkhof et al, 2014). And even though our model simulations fit within the range of morphological metrics that describe the basins in the Wadden Sea, they are most useful to analyze in comparison with each other.…”

Section: Using Schematized Modelsmentioning

confidence: 99%

Morphodynamic Modeling of Tidal Basins: The Role of Sand‐Mud Interaction

Colina Alonso,

van Maren,

van Weerdenburg

et al. 2023

JGR Earth Surface

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The morphology of tide‐dominated systems is progressively influenced by human activities and climate change. Quantitative approaches aiming at understanding or forecasting the effects of interventions and climate change are often aggregated, thereby simplifying or schematizing the investigated area. In this work, we advance on the knowledge of sediment transport processes shaping tidal systems and on methodologies translating schematized model output into physically realistic variables. In terms of improved physics, we systematically evaluate the influence of sand‐mud interaction processes. Most tidal systems are shaped by a mixture of sand and mud. Morphological models typically compute transport of sand and mud independently, despite studies clearly demonstrating that their physical behavior is mutually dependent. We investigate the effects of two interaction mechanisms (erosion interaction and roughness interaction, applied with varying mud erodibility) with a schematized process‐based morphodynamic model. We convert model output into metrics that describe the meso‐scale configuration of the modeled systems, allowing a quantitative comparison of scenarios. Modeled patterns and intertidal flat shape, size and composition widely vary with mud erodibility settings, but equally depend on the evaluated sand‐mud interaction mechanisms (with erosion interaction having a larger effect than roughness interaction). Sand‐mud interaction thus needs to be accounted for from a physical point of view, but also to improve predictions of tidal basin evolution models, particularly the (bimodally distributed) sediment composition of intertidal flats.

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“…Tidal flats are a crucial geomorphic unit of the coastal zone and are mainly composed of muddy deposits. Tidal flats can provide a habitat for flora and fauna to protect biodiversity, protect the coasts from storms, and increase the coastal protection capacity (Murray et al, 2019;Zhou et al, 2022). With the progress of society and the development of the economy, the reclamation of upper tidal flats has become the primary engineering method of solving the problem of land scarcity in many coastal cities worldwide, promoting economic development (Sengupta et al, 2018;Chu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Impacts of tidal flat reclamation on suspended sediment dynamics in the tidal-dominated Wenzhou Coast, China

Zhang

Chen

et al. 2023

Front. Mar. Sci.

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Reclamation of tidal flats is one of the engineering measures of expanding land area and developing the economy in coastal areas; however, this process disturbs the natural processes of the tidal flat system. Taking the flood-tidal dominant Oufei tidal flat at the Wenzhou coast as a study area, the influences of tidal flat reclamation on tidal and suspended sediment dynamics were comprehensively investigated via numerical modeling. Before the reclamation, the Eulerian residual current flows alongshore and the residual sediment transport on-shore in the tidal flat area. The reclamation reduces the tidal flat area, shifting the M4 co-phase line toward the coast and squeezing the flood-dominated area. Consequently, the tidal current is weakened and the suspended sediment concentration (SSC) is reduced, but the residual sediment transport change insignificantly. The residual sediment transport in the Feiyunjiang Estuary is controlled by the tidal pumping effect. The upper estuary shows a net sediment transport landward, while a seaward transport is observed in the lower estuary, which further splits into two circulation outside the estuary. The south Oufei dike construction interferes the northern sediment circulation, resulting in the alteration of local SSC and enhancing landward sediment transport inside the estuary. The methodology and understandings arising from this study could be a good reference for the analysis of suspended sediment transport under tidal flat reclamation effects at other sites.

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Sediment sorting and bedding dynamics of tidal flat wetlands: Modeling the signature of storms

Cited by 11 publications

References 55 publications

Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet‐Flow

Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet‐Flow

Morphodynamic Modeling of Tidal Basins: The Role of Sand‐Mud Interaction

Impacts of tidal flat reclamation on suspended sediment dynamics in the tidal-dominated Wenzhou Coast, China

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