Field estimates of floc dynamics and settling velocities in a tidal creek with significant along-channel gradients in velocity and SPM

Schwarz, Christian; Cox, Tom J. S.; Engeland, Tom Van; Oevelen, Dick van; Belzen, Jim van; Koppel, Johan van de; Soetaert, Karline; Bouma, Tjeerd J.; Meire, Patrick; Temmerman, Stijn

doi:10.1016/j.ecss.2017.08.041

Cited by 22 publications

(20 citation statements)

References 60 publications

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“…A value of

k_{A}^{^{'}} = 0.5

and

k_{B}^{^{'}} = 5.0

E‐6 worked reasonably well for the Markussen and Andersen () data (Figure a). These same values for

k_{A}^{^{'}}

and

k_{B}^{^{'}}

also produced comparable, yet poorer, results between the observations and predictions for the Schwarz et al () case (Figure b); however, no pairing of data

k_{A}^{^{'}}

and

k_{B}^{^{'}}

values was found to do much better. This is perhaps due in part to fact that the measured floc sizes are inversely related to G over most of the time series but then decreases with G over the tail end of the time series.…”

Section: Model Validationsupporting

confidence: 51%

“…All times in the studies have been shifted to start at time equals 0 hr to better reflect the different time scales involved in each study. For (a) Markussen and Andersen () and (b) Schwarz et al (), the measured velocity ranged from −0.7 to 0.8 and ≈0 to 0.4 m/s, respectively, during the time window displayed.…”

Section: Model Validationmentioning

confidence: 97%

“…For testing of the model, we extracted time series data of average‐measured particles size, suspended sediment concentration, and fluid velocity from two tidally influenced field sites in the studies of Markussen & Andersen, (; measurements made in the Danish Wadden Sea) and Schwarz et al (; measurements made in a brackish intertidal creek of the Scheldt estuary in the Netherlands). These two studies were selected because they presented colocated, continuous time series of suspended particle (or aggregate) size, C , and velocity, U ; all particle size measurements were made with a LISST‐100, and flocculation was suspected as a key driver of measured variation in the suspended aggregate size.…”

Section: Model Validationmentioning

confidence: 99%

“…These two studies were selected because they presented colocated, continuous time series of suspended particle (or aggregate) size, C , and velocity, U ; all particle size measurements were made with a LISST‐100, and flocculation was suspected as a key driver of measured variation in the suspended aggregate size. The particular time window from which data was extracted in each study was t = 4:12 on 16 January 2011 to 18:42 on 17 January 2011 for Markussen and Andersen (); and t = 10:22 to 14:38 on 13 May 2015 for Schwarz et al (). The extracted data from each study were then interpolated to produce evenly spaced time series to use in the integration of equation .…”

Section: Model Validationmentioning

confidence: 99%

“…Based on the velocity time series from the field studies and reported ranges of G in estuaries, we have selected

G = 0.075 \sqrt{∣ U^{3} ∣ false/ ν}

as a reasonable estimate of G for the three sites (Figure ). Using this values yields G up to 40 to 50 s −1 for U up to 0.8 m/s in the Wadden Sea (Markussen & Andersen, ) and G up to 15 to 20 s −1 for U up to 0.4 m/s in the Scheldt estuary (Schwarz et al, ). While these values of G may not be entirely accurate, the time series in G should be reflective of the first‐order behavior and therefore suitable for using to examine the floc model behavior.…”

Section: Model Validationmentioning

confidence: 99%

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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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“…A value of

k_{A}^{^{'}} = 0.5

and

k_{B}^{^{'}} = 5.0

E‐6 worked reasonably well for the Markussen and Andersen () data (Figure a). These same values for

k_{A}^{^{'}}

and

k_{B}^{^{'}}

also produced comparable, yet poorer, results between the observations and predictions for the Schwarz et al () case (Figure b); however, no pairing of data

k_{A}^{^{'}}

and

k_{B}^{^{'}}

Section: Model Validationsupporting

confidence: 51%

Section: Model Validationmentioning

confidence: 97%

Section: Model Validationmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

“…Based on the velocity time series from the field studies and reported ranges of G in estuaries, we have selected

G = 0.075 \sqrt{∣ U^{3} ∣ false/ ν}

Section: Model Validationmentioning

confidence: 99%

See 3 more Smart Citations

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

2018

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Organic Matter Composition of Biomineral Flocs and its Influence on Suspended Particulate Matter Dynamics along a Nearshore to Offshore Transect

Fettweis

Schartau

Desmit

et al. 2021

Preprint

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The particulate organic matter (POM) is, together with particulate inorganic matter (PIM), the main component of the suspended particulate matter (SPM) in marine ecosystems. The flux and export of mineral and organic particles depends on flocculation processes, which influence the particles' composition and settling velocity. Flocculation combines organic and mineral particles into biomineral flocs (B.

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