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

Abstract

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.

Estimation of Sediment Transport Parameters From Measured Suspended Concentration Time Series Under Waves and Currents With a New Conceptual Model

“…Zhang, Nielsen, Perrochet, and Jia (2021) used spectral analysis (SA) and bandpass filtering to distinguish the HA and LR components. One of the most promising ideas to estimate a group of sediment transport parameters is through the best fitting of measured SSC time series and those predicted by analytical (Lavelle et al., 1984; Zhang et al., 2024), numerical (Amoudry et al., 2014; Edge et al., 2023; Erikson et al., 2013; D. Wang et al., 2018; Yang & Hamrick, 2003), or conceptual models (Hill et al., 2003; Jago & Jones, 1998; Weeks et al., 1993).…”