10.4122/1.1000000605
Madsen, Henrik
Henrik
Madsen
hem@dhi.dk
Hartnack, Johan
Johan
Hartnack
jnh@dhi.dk
Tornfeldt Sørensen, Jacob
Jacob
Tornfeldt Sørensen
jts@dhi.dk
Madsen, Henrik
Henrik
Madsen
hem@dhi.dk
Data assimilation in a flood modelling system using the ensemble Kalman filter
XVI International Conference on Computational Methods in Water Resources
2006
2006
Data assimilation in a combined 1D-2D numerical flood modelling system is
considered. The model is based on a dynamic linking between existing and well-
established 1D and a 2D numerical modelling systems enhanced with new features which
are targeted specifically towards modelling of floods. This combination ensures a
maximum of flexibility by allowing modelling some areas in 2D detail (floodplain),
while other areas can be modelled in 1D (river network).
For this combined modelling system data assimilation facilities have been
implemen¬ted for assimilation of water level measurements. The data assimilation
system is based on the ensemble Kalman filter (EnKF) methodology. In the EnKF the
probability density of the model state is represented by an ensemble of model
states. In a model forecast each ensemble member is propagated according to the
dynamical system subjected to model errors, and the resulting ensemble then provides
estimates of the forecast state vector and the corresponding covariance matrix. When
measurements are available, the ensemble is updated using the standard Kalman filter
updating scheme to provide an updated probability density of the model state.
In this paper the implementation of the EnKF in the combined 1D-2D model is
discussed and is demonstrated on a flood model application in Bangladesh.