Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centers are increasingly using their meteorological output to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data, and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large‐scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state‐of‐the‐art operational large‐scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Operational systems currently have the capability to produce coarse‐scale discharge forecasts in the medium‐range and disseminate forecasts and, in some cases, early warning products in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale alongside a move towards multi‐model forecasts and grand ensemble techniques, responding to the requirement of developing multi‐hazard early warning systems for disaster risk reduction. WIREs Water 2016, 3:391–418. doi: 10.1002/wat2.1137This article is categorized under: Science of Water > Water Extremes

[1] Hydrodynamic modeling of large remote forested floodplains, such as the Amazon, is hindered by the vegetation signal contained within Digital Elevation Models (DEMs) such as the Shuttle Radar Topography Mission (SRTM). Not removing the vegetation signal causes DEMs to be overelevated preventing the correct simulation of overbank inundation. Previous efforts to remove this vegetation signal have either not accounted for its spatial variability or relied upon single assumed error values. As a possible solution, a systematic approach to removing the vegetation signal which accounts for spatial variability using recently published estimates of global vegetation heights is proposed. The proposed approach is applied to a well-studied reach of the Amazon floodplain where previous hydrodynamic model applications were affected by the SRTM vegetation signal. Greatest improvements to hydrodynamic model accuracy were obtained by subtracting 50–60% of the vegetation height from the SRTM. The vegetation signal removal procedure improved the RMSE (Root-Mean-Square Error) accuracy of the hydrodynamic model than when using the original SRTM in three ways: (1) seasonal floodplain water elevation predictions against TOPEX/Poseidon observations improved from 6.61 to 1.84 m; (2) high water inundation extent prediction accuracy improved from 0.52 to 0.07 against a JERS (Japanese Earth Resources Satellite) observation; (3) low water inundation extent accuracy against a JERS observation improved from 0.22 to 0.12. The simple data requirements of this vegetation removal method enable it to be applied to any remote floodplain for which hydrodynamic model accuracy is hindered by vegetation present in the DEM.