Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

Accurate water level forecasts during flood events are crucial to mitigate the loss of human lives and economic damages. However, the accuracy of flood models can be affected by various factors, including the complexity of the terrain geometry and bathymetry, imperfect physics as well as uncertainties in the inflows and parameters. This paper describes a practical implementation of an ensemble Kalman filter (EnKF) based data assimilation system that is aimed towards enhancing the forecasting skill of flood models. The system was implemented and tested with a real world dam break flood, based on the experimentally scaled Toce River valley flood that occurred on July 8th, 1996. Water depth data are available for assimilation from a network of 21 sensors distributed across the domain. Our results demonstrate that assimilating data into the flood model significantly improves the model prediction by up to 90% after assimilation and 60% during forecasting. Assimilating the data more frequently significantly enhances the system performances. Estimating the two-dimensional Manning coefficient together with the model’s dynamical variables (water depth and velocities) further improves the model prediction skill. Overall, our results suggest that assimilating data into the flood model, while jointly inferring the state and (poorly known) parameters, using an EnKF may provide an efficient framework for developing an operational flood forecasting system.