Abstract. This paper presents the development of the European Flood Alert System (EFAS), which aims at increasing preparedness for floods in trans-national European river basins by providing local water authorities with mediumrange and probabilistic flood forecasting information 3 to 10 days in advance. The EFAS research project started in 2003 with the development of a prototype at the European Commission Joint Research Centre (JRC), in close collaboration with the national hydrological and meteorological services. The prototype covers the whole of Europe on a 5 km grid. In parallel, different high-resolution data sets have been collected for the Elbe and Danube river basins, allowing the potential of the system under optimum conditions and on a higher resolution to be assessed. Flood warning lead-times of 3-10 days are achieved through the incorporation of medium-range weather forecasts from the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF), comprising a full set of 51 probabilistic forecasts from the Ensemble Prediction System (EPS) provided by ECMWF. The ensemble of different hydrographs is analysed and combined to produce early flood warning information, which is disseminated to the hydrological services that have agreed to participate in the development of the system. In Part 1 of this paper, the scientific approach adopted in the development of the system is presented. The rational of the project, the systems set-up, its underlying components, basic principles and products are described. In Part 2, results of a detailed statistical analysis of the performance of the system are shown, with regard to both probabilistic and deterministic forecasts.

Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of populations affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System (GloFAS), which has been set up to provide an overview on upcoming floods in large world river basins. GloFAS is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the earth.

The political pressure on the scientific community to provide medium to long term flood forecasts has increased in the light of recent flooding events in Europe. Such demands can be met by a system consisting of three different model components (weather forecast, rainfall runoff forecast and flood inundation forecast) which are all liable to considerable uncertainty in the input, output and model parameters. Thus, an understanding of cascaded uncertainties is a necessary requirement to provide robust predictions. In this paper, 10-day ahead rainfall forecasts, consisting of one deterministic, one control and 50 ensemble forecasts, are fed into a rainfallrunoff model (LisFlood) for which parameter uncertainty is represented by six different parameter sets identified through a Generalised Likelihood Uncertainty Estimation (GLUE) analysis and functional hydrograph classification. The runoff of these 52 * 6 realisations form the input to a flood inundation model (LisFlood-FP) which acknowledges uncertainty by utilising ten different sets of roughness coefficients identified using the same GLUE methodology. Likelihood measures for each parameter set computed on historical data are used to give uncertain predictions of flow hydrographs as well as spatial inundation extent. This analysis demonstrates that a full uncertainty analysis of such an integrated system is limited mainly by computer power as well as by how well the rainfall predictions represent potential future conditions. However, these restrictions may be overcome or lessened in the future and this paper establishes a computationally feasible methodological approach to the uncertainty cascade problem.

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Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth

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