STFS-urban: Spatio-temporal flood simulation model for urban areas.

Amidst escalating urbanization and increasing extreme climatic events, strengthening flood resilience strategies in global cities has become imperative. This study introduces an innovative spatiotemporal urban flood simulation model that seamlessly integrates diverse refined and multi-spatiotemporal scales, ranging from 7.5 to 60 min and 100-2000 m, respectively. The model comprises multi-scale radar rainfall inversion (MRI), fine-grained coupled flood simulation model (FGCFS), and transformer-CNN flood prediction (TCFP) modules. Employing the Nanjing urban area as a case study, the model's efficacy is subjected to rigorous assessment. The advantages derived from integrated refinement coupling and boundary conditions through FGCFS and TCFP are accentuated. Impressively, the results underscore the robust performance of radar rainfall inversion across most scales, revealing a correlation coefficient surpassing 0.8 and a root-mean-square error of under 5.2 mm. FGCFS achieves optimal simulated water depth changes at 7.5 min × 500 m resolution, with the Nash efficiency coefficient exceeding 0.69 (0.94 at YS observation point and 0.89 at SXM observation point), alongside percentage deviations below 12.89 (3.59 at SXM observation point and 2.42 at XJL observation point). TCFP's learning proficiency is showcased through error convergence to 0.002 m after twenty iterations, particularly suitable for resolutions below 4 m. Notably, both FGCFS and TCFP demonstrate efficient utilization of resources, enabling streamlined simulations across varying data resolutions. Consequently, our study propels a sophisticated framework harmonizing multi-scale data integration, refinement coupling, and dynamic allocation. Our work extends beyond practical solutions, offering a glimpse into the future of flood simulation modeling, and reaffirming its pivotal role within the realm of environmental research and management.