Early flood warning and real-time monitoring systems play a key role in flood risk reduction and disaster response decisions. Global-scale flood forecasting and satellite-based flood detection systems are currently operating, however their reliability for decision-making applications needs to be assessed. In this study, we performed comparative evaluations of several operational global flood forecasting and flood detection systems, using 10 major flood events recorded over 2012-2014. Specifically, we evaluated the spatial extent and temporal characteristics of flood detections from the Global Flood Detection System (GFDS) and the Global Flood Awareness System (GloFAS). Furthermore, we compared the GFDS flood maps with those from NASA's two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results reveal that: (1) general agreement was found between the GFDS and MODIS flood detection systems, (2) large differences exist in the spatio-temporal characteristics of the GFDS detections and GloFAS forecasts, and (3) the quantitative validation of global flood disasters in data-sparse regions is highly challenging. OPEN ACCESSRemote Sens. 2015, 7 15703Overall, satellite remote sensing provides useful near real-time flood information that can be useful for risk management. We highlight the known limitations of global flood detection and forecasting systems, and propose ways forward to improve the reliability of large-scale flood monitoring tools.

The study presents the development of flood warning decision support products based on ensemble forecasts in the European Flood Alert System (EFAS). EFAS aims to extend the lead time of flood forecasts to 3-10 days in transnational river basins and complement Member States' activities on flood forecasting. Weather forecasts are used as input to a hydrological model and simulated discharges are evaluated for exceedances of flood thresholds. Products were developed in collaboration with users for a concise and useful visualization of probabilistic results. Forecasts of flood events observed in the Danube river basin in 2005 illustrate the analysis.

The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts.

Abstract.A staggered approach to flash flood forecasting is developed within the IMPRINTS project (FP7-ENV-2008-1-226555). Instead of a single solution system, a chain of different models and input data is being proposed that act in sequence and provide decision makers with information of increasing accuracy in localization and magnitude as the events approach. The first system in the chain is developed by adapting methodologies of the European Flood Alert System (EFAS) to forecast flash floods and has the potential to provide early indication for probability of flash floods at the European scale. The last system in the chain is an adaptation of the data based mechanistic model (DBM) to probabilistic numerical weather predictions (NWP) and observed rainfall, with the capability to forecast river levels up to 12 h ahead. The potential of both systems to provide complementary information is illustrated for a flash flood event occurred on 2 November 2008 in the Cévennes region in France. Results show that the uncertainty in meteorological forecasts largely affects the outcomes. However, at an early stage, uncertain results are still valuable to decision makers, as they raise preparedness towards prompt actions to be taken.

Ensemble prediction system (EPS) meteorological forecasts are increasingly being used as input to hydrological models in order to extend flood-warning lead-times and to improve forecasts and the knowledge of their uncertainty. Probabilistic skill scores classically used in meteorology are usually also utilized for assessing the quality of hydrological ensemble forecasts. However, the different river discharge magnitudes can make difficult the interpretation and comparisons of these scores, as it is for the continuous rank probability score (CRPS). In this letter, a novel 'Reduction' CRPS (RCRPS), which takes into account the different river discharge magnitudes, is proposed, and its usefulness is exhibited.