  Large-scale hydrodynamic models generally rely on fixed-resolution spatial
grids and model parameters as well as incurring a high computational cost. This
limits their ability to accurately forecast flood crests and issue
time-critical hazard warnings. In this work, we build a fast, stable, accurate,
resolution-invariant, and geometry-adaptative flood modeling and forecasting
framework that can perform at large scales, namely FloodCast. The framework
comprises two main modules: multi-satellite observation and hydrodynamic
modeling. In the multi-satellite observation module, a real-time unsupervised
change detection method and a rainfall processing and analysis tool are
proposed to harness the full potential of multi-satellite observations in
large-scale flood prediction. In the hydrodynamic modeling module, a
geometry-adaptive physics-informed neural solver (GeoPINS) is introduced,
benefiting from the absence of a requirement for training data in
physics-informed neural networks and featuring a fast, accurate, and
resolution-invariant architecture with Fourier neural operators. GeoPINS
demonstrates impressive performance on popular PDEs across regular and
irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence
GeoPINS model to handle long-term temporal series and extensive spatial domains
in large-scale flood modeling. Next, we establish a benchmark dataset in the
2022 Pakistan flood to assess various flood prediction methods. Finally, we
validate the model in three dimensions - flood inundation range, depth, and
transferability of spatiotemporal downscaling. Traditional hydrodynamics and
sequence-to-sequence GeoPINS exhibit exceptional agreement during high water
levels, while comparative assessments with SAR-based flood depth data show that
sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with
smaller prediction errors.
