A Novel Modeling Framework for Computationally Efficient and Accurate Real‐Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so‐called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high‐fidelity hydrologic and hydraulic models should be increasingly adopted for real‐time and ensemble flood forecasting.Key PointsA surrogate model must be built over various forcing and flow conditions and it does not need to be rebuilt at each time stepModel parameter specification for data assimilation can significantly improve forecasting performance and reduce uncertainty boundsNo substantial differences in results exists between single and dual EnKFs, but the latter better simulates flood peaksPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154302/1/wrcr24506_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154302/2/wrcr24506.pd

Breaking Down the Computational Barriers to Real‐Time Urban Flood Forecasting

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