We present a technique for using data assimilation to estimate uncertain model parameters and discuss its application within the context of coastal morphodynamic modelling. A key difficulty in the construction of a data assimilation algorithm is specification of the background error covariances. For parameter estimation, it is particularly important that the cross-covariances between the parameters and the state are given a good a priori specification. We have combined the methods of three dimensional variational data assimilation (3D Var) and the Kalman filter to produce a new hybrid data assimilation scheme that captures the flow dependent nature of the state-parameter cross covariances without explicitly propagating the full system covariance matrix. Here, an idealised two parameter 1D non-linear test model with pseudo-observations is used to demonstrate the method. The results are postive with the scheme able to recover the model parameters to a high level of accuracy. We believe that there is potential for successful application of the methodology to larger, more complex models.
