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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 centres are increasingly using the meteorological output from these 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. Currently, operational systems 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.

International audience; Increased automation and use of computer-aided decision support systems are attractive options for hydrologic forecasting agencies faced with growing product complexity and institutional resourcing pressures. Although the hydrologic literature has been nearly silent on the roles of expertise and automation in forecasting, other disciplines such as meteorology have had decades of open discussion on the topic. To address the lack of dialogue in hydrology on automation, this article seeks to contextualize relevant findings from similar disciplines, including meteorology, psychology, decision support systems, and interface design. We predict which aspects of operational hydrology have the greatest chance for successfully increasing automation in the near future. Some applications have employed higher levels of automation, notably flash flood forecasting which requires rapid response times, and extended prediction which requires heavy emphasis on uncertainty quantification. Short-range flood forecasting may be more challenging to automate and traditionally has been less automated than other types of forecasts, partly because of existing practices of interfacing with meteorologists and water system operators, and the difficulties in modeling human impacts on the water cycle. Overall, we suggest that the design of computer-aided decision support systems for forecasting systems should consider three factors: (1) processes change under automation and people may require new roles; (2) automation changes the way people behave, sometimes negatively; and (3) people may not have accurate perceptions of the quality of the automated guidance. Seven lessons learned from automation in meteorology are highlighted and translated into a hydrologic forecasting context, leading to a set of recommendations for how to make best use of expertise in increasingly automated systems.