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Seasonal particle responses to near-bed shear stress in a shallow, wave- and current-driven environment

Citation

Chang, Grace et al. (2021), Seasonal particle responses to near-bed shear stress in a shallow, wave- and current-driven environment, Dryad, Dataset, https://doi.org/10.5061/dryad.8sf7m0cpn

Abstract

Novel analysis of in-situ acoustic and optical data collected in a shallow, wave- and current-driven environment enabled determination of: (1) particle characteristics that were most affected by near-bed physical forcing over seasonal scales, and (2) characteristic shear stress, tchar, at which the rate of change to particle characteristics was most pronounced. Near-bed forcing and particle responses varied by season. Results indicated that moderate tchar values of 0.125 Pa drove changes in particle composition during summer. In winter, particle concentration effects were most affected at tchar of 0.05 Pa, suggesting dominance of fluff layer resuspension. Changes to particle size were most relevant during a biologically productive springtime period, with initiation of particle disaggregation occurring most commonly at tchar of 0.25 Pa. These results suggest that it may be more important to parameterize tchar, as opposed to critical shear stress for erosion, for sediment transport models.

Methods

This repository contains field data collected as part of the NSF OCE-1736668 project examining boundary layer and sediment dynamics in South San Francisco Bay that are associated with the paper, “Seasonal Particle Responses to Near-Bed Shear Stress in a Shallow, Wave- and Current-Driven Environment”. The project consisted of three field deployments: Summer (July - August 2018), Winter (January - February 2019), and Spring (April - May 2019). The data set currently includes data products computed from measurements taken by acoustic Doppler velocimeters (turbulent momentum and sediment fluxes), CTD (salinity, temperature, pressure), LISST (suspended sediment particle size distributions), backscattering meters, and an absorption-attenuation meter. We encourage the free use of this dataset, provided that it is properly cited.

The data set currently includes time series data products derived from measurements taken by Nortek Vector acoustic Doppler velocimeters (turbulent momentum and sediment fluxes; collected at 8 Hz for 14 minute burst periods centered around the top of every hour, i.e. starting 7 minutes before the hour, and ending 7 minutes after, at heights of 5 and 15 cmab), Sea-Bird Scientific, Inc. CTD (salinity, temperature, pressure; collected at a one-minute interval), Sequoia Scientific Inc. LISST (suspended sediment particle size distributions; measurements were taken over 60 seconds each hour, at a measurement height of 15 cmab), Sea-Bird Scientific, Inc. backscattering meters at 15 and 45 cmab (burst sampled every 15 min), and Sea-Bird Scientific, Inc. absorption-attenuation meter at 15 cmab (burst sampled every hour).

Analysis relevant to this paper are described here. Acoustic Doppler velocimeter (ADV) data at 5 cm above the bed (cmab) were rotated into major and minor directions of the tidal ellipse that were estimated from the first and second principal components of the depth-averaged acoustic Doppler current profiler (ADP) mean velocity time series for each deployment. Here, we denote the major velocity component by u and the minor component by v, and w denotes the vertical component. Each velocity component was decomposed as: u = u + u + u’, where the overbar indicates burst-averaged, u is the wave velocity, and u’ represents the turbulent fluctuations in velocity.

We derived combined current and wave shear stress over the periods of the three field experiments following: τc+w = r u*2, where r is the density of seawater, determined from CTD measurements, and u* is friction (or shear) velocity, determined as: u* = -u'w'- uw, where u'w' and uw represent the turbulent Reynolds stress and wave momentum flux, respectively (overbars denote averages). The decomposition was performed following the phase method of Bricker and Monismith (2007).

Suspended particle characteristics, co-located with ADV measurements, were derived from in-situ optical properties. We define characteristic particle size as the median particle diameter, D50, derived from the LISST-measured particle size distributions (PSDs) at 15 cmab. Estimates of optically-derived particle concentration were obtained through a two-step process: (1) log-linear regression between ADV-measured acoustic backscatter (ABS) and suspended sediment concentration (SSC), where water samples collected from study site were used to calibrate the ADV ABS in the laboratory across varying concentrations (details can be found in Egan et al. 2020a), and (2) linear regression between ABS-derived SSC and bbp(660).

Particle composition was inferred from the bulk refractive index of particles, np, which was derived from optical properties. The parameter, np, is described in terms of the particulate backscattering ratio, b bp(660), and the hyperbolic (Junge-like) slope of the PSD, g (Twardowski et al. 2001): np = 1 + b bp(660)0.5377 + 0.4867 2 (1.4676 + 2.2950 g2 + 2.3113 g4), where b bp(660) = b bp (660) b p (660)

. The variable, bbp(660), is obtained from ECObb measurements and bp(660) is total particulate scattering, determined from ac-9 measurements. The parameter, g, represents the slope of the particulate attenuation spectrum, derived from ac-9 measurements of attenuation and modeled partitioned spectral absorption (Roesler et al. 1989), and has been shown to be directly related to the hyperbolic distribution of the PSD (Boss et al. 2001a,b). Oceanic particle values of np range between 1.0 and 1.26 (relative to seawater) and give an indication of the composition of particles. Lower values of np typically represent less dense particles (e.g., organic) and higher values generally indicate denser particles (e.g., inorganic) (Aas 1996; Lide 1997).

Usage Notes

QA/QC procedures followed IOOS standards for relevant variables, as well as community-accepted optical data analysis correction and calibration protocols. Vectrino data are QC/QC’ed in the following manner: any measurement with signal-to-noise ratio (SNR) < 10 or correlation < 20 counts is NaN'd out. Measurements below the bed are NaN'd out, where the bed position is determined through the Vectrino boundary finding algorithm. This is relatively minimal QC, so the beam correlations and SNR are included for further processing. The ADP data are not filtered in time or space, though measurements above the free surface are replaced by NaN. ADV outliers were removed and interpolated over. Outliers were defined as data points with any beam correlation < 30 counts, a velocity measurement outside of 5 standard deviations of the median, or above a certain magnitude (which varied for each data set). LISST data were processed using manufacturer provided scripts. Additional QA/QC processing involved removal of scintillation effects, identified by comparing volume PSD data across size bins. Erroneous data were identified as data spikes of 40% or greater across consecutive size bins.

Funding

National Science Foundation, Award: OCE-1736668