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

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