The January 2005 flood event in the Eden catchment (UK) has focused considerable research effort towards strengthening and extending operational flood forecasting in the region. The Eden catchment has become a key study site within the remit of phase two of the Flood Risk Management Research Consortium. This paper presents a synthesis of results incorporating model uncertainty analysis, computationally efficient real-time data assimilation/forecasting algorithms, two-dimensional (2D) inundation modelling, and data visualization for decision support. The emphasis here is on methods of presenting information from a new generation of probabilistic flood forecasting models. Using Environment Agency rain and river-level gauge data, a data-based mechanistic model is identified and incorporated into a modified Kalman Filter (KF) data assimilation algorithm designed for real-time flood forecasting applications. The KF process generates forecasts within a probabilistic framework. A simulation of the 6-h ahead forecast for river levels at Sheepmount (Carlisle) covering the January 2005 flood event is presented together with methods of visualizing the associated uncertainty. These methods are then coupled to the 2D hydrodynamic LISFLOOD-FP model to produce real-time flood inundation maps. The value of incorporating probabilistic information is emphasized.