Search

Flooding is a natural phenomenon, an intrinsic part of the natural cycle that serves important ecological functions. However, in highly anthropogenic-developed landscapes they cause serious consequences for human lives, societies in general, and their economy. Therefore comprehensive disaster risk reduction policies have been promoted in the last decade including actions on the development of early warning systems at local as well as regional scale. This report provides a brief global overview on the occurrences and damages resulting from riverine floods over the past decades. The first part of the report then summarises European policies put in place to deal with flooding in the different phases of the disaster management cycle addressing the prevention, preparedness, response, and recovery phase. This is followed by a description of the development of flood early warning capabilities at European scale, how such a system fits into the responsibility chain between national services and EU civil protection and what the potential financial benefit of flood early warning systems in Europe amounts to. The second part of the report addresses the gaps in flood early warning systems in Africa and presents a description of the African Flood Forecasting System (AFFS), which has been built in analogy to EFAS, but which is still in experimental stage, JRC.H.7-Climate Risk Management

Highlights • First continent-scale assimilation of surface water extent into hydrological model. • Improvements in flood peaks timing and volume for 60% of validated gauges. • Daily surface water extent provide promising opportunities for ungauged regions., In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE’, NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.