UrbanFloodBench: Bridging AI and hydrology through benchmarking of coupled 1D–2D urban flood surrogate models

Physics-based hydrodynamic models are the standard approach for urban flood simulation but remain computationally prohibitive for real-time forecasting. Although machine learning surrogate models offer a promising alternative, most existing approaches rely on private or simplified datasets, limiting reproducibility and rigorous comparison, particularly for coupled 1D--2D flood systems. To address this gap, we introduce UrbanFloodBench, the first open benchmark for coupled 1D--2D urban flood surrogate modelling. UrbanFloodBench combines a multi-catchment dataset, a variance-aware evaluation framework, and a reproducible large-scale AI Kaggle challenge. The benchmark involves 264 teams and 3,799 submissions, producing the first large-scale empirical comparison of AI methods for coupled urban flood emulation. Results show that multiple modelling approaches can achieve strong performance, while the 1D drainage component remains the primary unresolved challenge. UrbanFloodBench establishes a reproducible foundation for advancing AI-based surrogate modelling for urban flood prediction and enabling consistent future evaluation of data-driven flood forecasting methodologies.