Calibrated Suspended Sediment Observations Beneath Large Amplitude Non‐Linear Internal Waves

Abstract

Internal waves are waves that travel along layers of different density within the ocean. They can travel through the ocean and have the potential to generate strong currents as they move into shallow water on the continental shelf. This process can lift and transport local sea bed sediment if the near-bed currents are strong enough. This process is short-lived, unpredictable, and generally occurs in deeper waters, making high quality observations rare. We present detailed observations of ocean currents and the sediment response during the passage of several large internal waves. The observations were captured on the Northwest Shelf of Australia in 250 m depth for two months in 2017. We used a range of instruments to measure the properties of sediment in the water. These data sets were converted to estimates of suspended sediment mass concentration by calibration. The observed near-bed currents created by internal waves resulted in significant sediment transport, which was trapped in a region close to the sea bed due to density layers.

In-situ estimation of erosion model parameters using an advection-diffusion model and Bayesian inversion

“…In addition, the erosion model itself could be re-parameterised to reduce parameter covariance. In this case study we attempt to estimate unobserved parameters using 2017 field observations from the Northwest Shelf of Australia (Zulberti et al, 2022;Edge et al, 2021).…”

“…Study 2 is a synthetic data set generated from the numerical model itself, with the inputs designed to mimic typical unsteady tidal forcing. Case Study 3 is an application of the method to a set of complex (unsteady and noisy) real-world observations from Edge et al (2021). Our goal is to use these case studies of sediment transport to gradually introduce complexity and demonstrate the efficacy of the method for estimating un-observable, yet important, parameters used in the prediction of the response of a geophysical system.…”

“…Points close to the sea bed where the ADCP returned poor vertical velocity data were linearly extrapolated to zero at the bed. Both the BBL height and vertical velocities were lowpass filtered.The observations of C, taken fromEdge et al (2021), were chosen at two discrete heights, 1.4 m and 9.5 m ASB [Figure10, bottom]. In addition, we performed the inference with a single point measurement (at 1.4 m ASB) in order to test the method sensitivity and applicability to cases where only a single observation height was available.…”

“…The settling velocity, w s , was estimated to be around 0.16 × 10 −4 m s −1 slower in E2 compared to E1. Mean particle size and effective density measurements fromEdge et al (2021) gave an estimated mean w s of 0.6 × 10 −4 m s −1 using Stokes'…”

In Situ Estimation of Erosion Model Parameters Using an Advection‐Diffusion Model and Bayesian Inversion

“…Case Study 2 is a synthetic data set designed to mimic typical unsteady tidal forcing. Case Study 3 is an application of the method to a set of complex (unsteady and noisy) real-world observations from Edge et al (2021). Our goal is to use these case studies of sediment transport to demonstrate the efficacy of the method for estimating unobservable, yet important, parameters used in the prediction of the response of a geophysical system.…”