Enabling real-time high-resolution flood forecasting for the entire state of Berlin through multi-GPU accelerated physics-based modeling

Urban areas are increasingly experiencing more frequent and intense pluvial flooding due to the combined effects of climate change and rapid urbanization-a trend expected to continue in the coming decades. This highlights the growing need for effective flood forecasting and disaster management systems. While recent advances in GPU computing have made high-resolution hydrodynamic modeling feasible at the urban scale, operational use remains limited, particularly for large domains where single-GPU processing falls short in terms of memory and performance. This study demonstrates the capabilities of the hydrodynamic model RIM2D (Rapid Inundation Model 2D), enhanced with multi-GPU processing, to perform highresolution pluvial flood simulations across large urban domains such as the whole state of Berlin (891.8 km2) within operationally relevant timeframes. We evaluate RIM2D’s performance across spatial resolutions of 2, 5, and 10 m using GPU configurations ranging from 1 to 8 units. Two flood scenarios are analyzed: the real-world pluvial flood of June 2017 and a standardized 100-year return period (HQ100) event used for official hazard mapping. Results show that RIM2D can deliver detailed flood extents, flow characteristics, and impact estimates for the 48 h 2017 event in 8 min at 10 m resolution, 34 min at 5 m, and approximately 5.5 h at 2 m using 8 A100 GPUs-fast enough to be integrated into realtime early warning systems. Multi-GPU processing proves essential not only for enabling high-resolution simulations (e.g., dx= 2 m or finer), but also for making simulations at resolutions finer than 5 m computationally feasible for flood forecasting and early warning applications. Additionally, we find that beyond 4 GPUs, runtime improvements become marginal for 5 and 10 m resolutions, and similarly, more than 6 GPUs offer limited benefit at dx= 2 m resolution, illustrating the balance between computational nodes of the used GPUs and number of raster cells of the model. Moreover, simulations at a finer dx= 1 m resolution demand more than 8 GPUs to be run. Overall, this work demonstrates that large-scale, high-resolution flood simulations can now be executed rapidly enough to support operational early warning and impact-based forecasting. With models like RIM2D and the continued advancement of GPU hardware, the integration of detailed, real-time flood forecasting into urban flood risk management is both technically feasible and urgently needed.