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.

建立了一种快速、稳定、准确、分辨率无关和几何自适应的洪水建模和预测框架，包括多卫星观测和水动力建模模块，以及基于几何自适应的物理信息神经求解器（GeoPINS），并通过与传统水动力学和基于合成孔径雷达的洪水深度数据的比较验证模型的性能。