Comparison of two data assimilation algorithms for shallow water flows

This article presents the comparison of two algorithms for data assimilation of two  dimensional shallow water flows. The first algorithm is based on a  linearization of the model equations and a quadratic programming (QP)  formulation of the problem. The second algorithm uses Ensemble  Kalman Filtering (EnKF) applied to the non-linear two dimensional  shallow water equations. The two methods are implemented  on a scenario in which boundary conditions and Lagrangian  measurements are available. The performance of the methods is  evaluated using twin experiments with experimentally measured  bathymetry data and boundary conditions from a river located in the  Sacramento Delta. The sensitivity of the algorithms to the number of  drifters, low or high discharge and time sampling frequency is  studied.