Investigation of flocculation dynamics under changing hydrodynamic forcing on an intertidal mudflat

Guo, Chao; He, Qing; Prooijen, Bram C. van; Guo, Leicheng; Manning, Andrew J.; Bass, Sarah

doi:10.1016/j.margeo.2017.10.001

Cited by 34 publications

(20 citation statements)

References 51 publications

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“…Typical values were found to be 4 cm during calm conditions and about 13 cm during strong winds (6 Bft). The sediment on the flat is cohesive, with a median grain size less than 50 μm (Guo et al, ; Zhu, ). The suspended sediment concentration in the channels of the estuary fluctuates strongly in space and time, around an average of about 40 mg/L (van der Wal et al, ).…”

Section: Study Area and Field Datamentioning

confidence: 99%

Morphodynamic Feedback Loops Control Stable Fringing Flats

et al. 2018

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We apply a 2‐D horizontal process‐based model (Delft3D) to study the feedback mechanisms that control the long‐term evolution of a fringing intertidal flat in the Western Scheldt Estuary. The hydrodynamic model is validated using a comparison with measurements on the intertidal flat and the sediment transport module is calibrated against long‐term morphology data. First, the processes that lead to net sediment exchange between channel and flat are studied. Then, long‐term simulations are performed and the dependency of sediment fluxes on the tidal flat bathymetry, and the corresponding morphodynamic feedback mechanisms are explained. In the long run, relatively stable states can be approached, which are shown to be typical for wave‐dominated fringing mudflats. The system behavior can be explained by the typical feedback mechanisms between the intertidal bathymetry and the hydrodynamic forces on the flat. In the subtidal domain, the impact of small (5–10 cm) wind waves increases with a rising elevation due to decreasing water depths. In the intertidal domain, the wave impact increases with increasing cross‐sectional slope due to wave shoaling. These relationships result in negative (stabilizing) morphodynamic feedback loops. The tidal current velocities and tide‐induced bed shear stresses, on the other hand, are largely determined by the typical horizontal geometry. A stabilizing feedback loop fails, so that there is no trend toward an equilibrium state in the absence of wind waves.

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Section: Study Area and Field Datamentioning

confidence: 99%

Morphodynamic Feedback Loops Control Stable Fringing Flats

et al. 2018

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“…The reason for the added complexity is that cohesive particles can create aggregates, called flocs, which are orders of magnitude larger than the primary constituent grains of the aggregate (van Leussen, ; Figure ). Furthermore, the flocs can grow or shrink in size depending on environmental conditions such as sediment mineralogy, water column chemistry and organic content, suspended sediment concentration, turbulent shear stress, and floc shape (Dyer, ; Guo et al, ; Krone, ; Partheniades, ; Tang et al, ; Winterwerp & van Kesteren, ). In particular, sediment concentration, C (mass/vol), and the mean shear rate of turbulence, G (1/t or Hz; or turbulent shear rate), are often thought to be the key determiners of flocs size for a particular system with constant sediment type, biological content, and salinity.…”

Section: Introductionmentioning

confidence: 99%

A Shear‐Limited Flocculation Model for Dynamically Predicting Average Floc Size

2018

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The accuracy of sediment transport models depends on the selection of an appropriate sediment settling velocity. In general, settling velocity is primarily dependent on the size and density of the particles in the water column. Determining this value for mud suspensions can be difficult because the small cohesive particles can aggregate to form flocs whose sizes and density are a function of hydrodynamic and physiochemical conditions of the suspension. Here we present a new model for predicting floc size as a function of hydrodynamic conditions and inherited floc sizes. The new approach is a simple modification to the existing Winterwerp (1998, https://doi.org/10.1080/00221689809498621) floc size model. The modification is significant in that it yields predictions that are more inline with observations and theory regarding the upper limit on floc size. The modification we propose is to make the ratio of the applied stress on a floc, over the strength of the floc, a function of the floc size relative to the Kolmogorov microscale. The outcome of the modification is that flocs are not allowed to surpass the Kolmogorov microscale in size and that calibrated aggregation and breakup coefficients obtained at one suspended sediment concentration can be used to predict floc size under other concentrations without recalibration. In this paper, we present the motivation for the modification, the functionality of the modification, and we make a comparison of the updated model with laboratory and field data. Overall, the modification shows promise as a tool for improved prediction of cohesive mud transport.Plain Language Summary Rivers deliver muddy sediment to coastal regions. Upon arrival, mud can deposit in or move through the rivers, bays, marshes, and deltas of the region. Forecasting where the mud goes when it arrives is important for society. For example, accurately forecasting the movement and deposition of mud is important for predicting and evaluating the effectiveness of river diversion projects aimed at using sediment deposits to build deltaic landscape. Yet predicting the movement of mud is difficult because small mud particles tend to clump together and form aggregates, or flocs, which grow or shrink depending on turbulence levels and other water column properties. When these flocs grow or shrink in size, the rate at which they settle out of the water changes, creating dynamic settling speeds that are difficult to model. Here we present a new method for predicting floc size as a function of hydrodynamic conditions and inherited floc size. The new model is a simple modification to an existing method that yields predictions more inline with observations and theory. The model also performs better outside of the conditions for which it was calibrated than the previous method.

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“…Flocs are totally different from primary sediment particles in terms of their larger sizes, lower excess density, and higher settling velocity in water [2,6]. Studying cohesive sediment flocculation in a turbulent flow environment is essential because it plays an important role in affecting the morphological changes to coastal areas, dredging operations in navigational canals, and sediment siltation in reservoirs and 2 of 20 lakes [7,8]. Since some pollutants (such as heavy metals) and nutrients are absorbed on the surfaces of cohesive sediment particles due to the electrochemical attraction of clay particles and/or organic matter contained in the sediment, the flocculation of cohesive sediment is also a vital element in investigating the variation of water quality and ecosystem function in some waters such as lakes and estuarine and coastal waters, which contain an abundance of cohesive sediment [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Some experimental works have been performed to investigate the median value of the size distribution of the flocs at the steady state of flocculation with respect to various flow shear conditions (e.g., References [8,14,[24][25][26][27]. These studies reported that the median floc size decreases as the flow shear stress increases.…”

Section: Introductionmentioning

confidence: 99%

A Simple Explicit Expression for the Flocculation Dynamics Modeling of Cohesive Sediment Based on Entropy Considerations

Zhu

2018

Entropy

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The flocculation of cohesive sediment plays an important role in affecting morphological changes to coastal areas, to dredging operations in navigational canals, to sediment siltation in reservoirs and lakes, and to the variation of water quality in estuarine waters. Many studies have been conducted recently to formulate a turbulence-induced flocculation model (described by a characteristic floc size with respect to flocculation time) of cohesive sediment by virtue of theoretical analysis, numerical modeling, and/or experimental observation. However, a probability study to formulate the flocculation model is still lacking in the literature. The present study, therefore, aims to derive an explicit expression for the flocculation of cohesive sediment in a turbulent fluid environment based on two common entropy theories: Shannon entropy and Tsallis entropy. This study derives an explicit expression for the characteristic floc size, assumed to be a random variable, as a function of flocculation time by maximizing the entropy function subject to the constraint equation using a hypothesis regarding the cumulative distribution function of floc size. It was found that both the Shannon entropy and the Tsallis entropy theories lead to the same expression. Furthermore, the derived expression was tested with experimental data from the literature and the results were compared with those of existing deterministic models, showing that it has good agreement with the experimental data and that it has a better prediction accuracy for the logarithmic growth pattern of data in comparison to the other models, whereas, for the sigmoid growth pattern of experimental data, the model of Keyvani and Strom or Son and Hsu model could be the better choice for floc size prediction. Finally, the maximum capacity of floc size growth, a key parameter incorporated into this expression, was found to exhibit an empirical power relationship with the flow shear rate.

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Investigation of flocculation dynamics under changing hydrodynamic forcing on an intertidal mudflat

Cited by 34 publications

References 51 publications

Morphodynamic Feedback Loops Control Stable Fringing Flats

Morphodynamic Feedback Loops Control Stable Fringing Flats

A Shear‐Limited Flocculation Model for Dynamically Predicting Average Floc Size

A Simple Explicit Expression for the Flocculation Dynamics Modeling of Cohesive Sediment Based on Entropy Considerations

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