Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.Plain Language SummaryCurrently, we cannot forecast flooding depths and extent in real‐time at a high level of detail in urban areas. This is the result of two key issues: detailed and accurate flood modeling requires a lot of computing power for large areas such as a city, and uncertainty in precipitation forecasts is high. We present an innovative flood forecasting method that resolves flood characteristics with enough detail to inform emergency response efforts such as timely road closures and evacuation. This is achieved by performing complex analysis of information on flooding impacts well before a future storm event, which subsequently allows much faster predictions when flooding actually happens. This approach completely changes the demand for required resources, replacing the nearly impossible burden of computation in real‐time with the easy problem of data storage, feasible even with a low‐end computer. Example results for Hurricane Harvey flooding in Houston, TX, show that predictions of both flood hazard and uncertainty work well over different areas of the city. This approach has the potential to provide timely and detailed information for emergency response efforts to help save lives and reduce other negative impacts during major flood events and other natural hazards.Key PointsThere is presently no means to forecast urban flooding at high resolution due to prohibitive computational demands and data uncertaintiesProposed framework combines high‐fidelity modeling and probabilistic learning to forecast flood attributes with uncertainty in real‐timeThe framework can be extended to other real‐time hazard forecasting, requiring high‐fidelity simulations of extreme computational demandPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/1/2021GL093585-sup-0001-Supporting_Information_SI-S01.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/2/grl63104_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170850/3/grl63104.pd
