While it has been recognized for some time that large-amplitude nonlinear internal waves (NLIW) can mobilise and transport sediment, quantitative observations of this process are rare. Rarer still are accompanying measurements or even estimates of suspended sediment mass concentration (SSC) during the passage of NLIW. Here we present high resolution observations of NLIW and the SSC response within the bottom boundary layer. The observations were made in 2017 in the Browse Basin on Australia’s Northwest Shelf in 250 m of water. We compare two direct calibration methods designed to overcome the inherent difficulty of directly observing SSC in deeper ocean environments, and employ Bayesian methods to estimate the uncertainty in SSC. Both calibration methods were used as bench-marking to infer SSC from a range of instrumentation deployed on a bottom-lander frame (acoustics, optical, and laser scattering). Estimates of near-bed SSC, with uncertainty, during NLIW passages are presented for each instrument. During a large NLIW event, the peak mean SSC estimate was 102 mg L$^{-1}$ with 95\% credible intervals of 93 and 112 mg L$^{-1}$, 0.87 m above the sea bed. We also examine the propagation of uncertainty to several derived quantities, such as SSC gradients. This work is the first step towards the quantitative analysis of sediment dynamics needed to develop parameterized models associated with the passage of NLIW.

We describe a framework for the simultaneous estimation of model parameters in a partial differential equation using sparse observations. Monte Carlo Markov Chain (MCMC) sampling is used in a Bayesian framework to estimate posterior probability distributions for each parameter. We describe the necessary components of this approach and its broad potential for application in models of unsteady processes. The framework is applied to three case studies, of increasing complexity, from the field of cohesive sediment transport. We demonstrate that the framework can be used to recover posterior distributions for all parameters of interest and the results agree well with independent estimates (where available). We also demonstrate how the framework can be used to compare different model parameterizations and provide information on the covariance between model parameters.