Data assimilation in the MIKE 11 Flood Forecasting system using Kalman filtering

A procedure is presented for assimilation of water levels and fluxes in the MIKE 11 Flood Forecasting (FF) system. The procedure implemented is based on the ensemble Kalman filter that provides a cost-effective and efficient updating and uncertainty propagation scheme for real-time applications. Up to the time of forecast, the model is updated according to the Kalman filter algorithm using the available measurements. In forecast mode, the Kalman filter provides an ensemble forecast that is used for estimation of water levels and fluxes in the river system and the associated uncertainties. A test example is presented where the MIKE 11 FF system is applied for flood forecasting in the Piedmont region in the northwestern part of Italy. Application of the ensemble Kalman filter significantly improves the forecast skills as compared to forecasting without data assimilation.