GPU-accelerated city-scale urban flood forecasting for real-time decision-making

Abstract High-intensity rainfall flooding is an escalating global urban hazard, with exposure growing as cities expand and climate change intensifies. Increasing short-duration extremes are driving more frequent, severe flooding, raising damages and disproportionately impacting vulnerable communities. These trends highlight the need for flood modeling approaches that are both high-resolution and computationally efficient to support real-time forecasting and operational decision-making. This study evaluates SynxFlow, a GPU-accelerated hydrodynamic model designed to deliver rapid, neighborhood-scale forecasts. Using gridded precipitation fields, SynxFlow simulated flood extent, depth, and velocity at fine spatial resolution across Cook County, Chicago, achieving short runtimes suitable for operational use. Validation against satellite-derived flood observations for a major storm event showed strong agreement, while a conventional CPU-based workflow substantially underestimated inundation. Overall, GPU-enabled hydrodynamic modeling can deliver accurate, near-real-time flood intelligence to strengthen warning systems, support equitable emergency response, and guide resilience investments.