AI 中文总结
研究针对洪水淹没建模难题，提出Inunda这一GPU原生、支持智能体、可微的求解器，用质量守恒局部惯性方案求解二维浅水方程，通过案例研究展示其在洪水后报、概率性预报及火灾后应用中的优势，结合物理水力学与可微编程优势。
AI 中文摘要
预测洪水去向及深度在高分辨率和大区域上计算成本高昂，传统方法昂贵，纯数据驱动方法缺乏物理保障且泛化性差。我们提出Inunda，一种GPU原生洪水淹没模型，求解二维浅水方程。它采用质量守恒局部惯性方案，在单个GPU上几分钟内就能在数百万个单元格上运行多日事件。由于每个算子都与自动求导兼容，求解器可微，能通过反向模式自动微分估计模型参数。我们通过三个案例研究展示Inunda。对2017年德克萨斯州哈里斯县飓风哈维的后报，Inunda与已建立的洪水模型相比具有竞争力；对2025年7月德克萨斯州中部山洪暴发，它能产生概率性洪水预报；对里奥鲁伊多索烧伤疤痕的火灾后山洪暴发应用，可微校准能恢复饱和水力传导率。Inunda为实际洪水建模提供了一个开放的端到端管道，将基于物理的水力学的准确性与现代可微编程的校准和耦合优势结合起来。
英文摘要
Predicting where floodwater goes and how deep it gets, at high resolution and across large domains, remains computationally expensive with conventional hydraulic solvers, while purely data-driven surrogates are fast but lack physical guarantees and generalize poorly beyond their training events. We present Inunda, a GPU-native flood inundation model that solves the two-dimensional shallow water equations. Inunda uses a mass-conservative local-inertial scheme and runs multi-day events over millions of cells in minutes on a single GPU. Because every operator is autograd-compatible, the solver is differentiable by construction: model parameters can be estimated by gradient descent against gage observations through reverse-mode automatic differentiation of the full simulation. We demonstrate Inunda on three case studies. For a hindcast of Hurricane Harvey (2017) in Harris County, Texas, Inunda matches surveyed high-water marks to a mean absolute error of 0.67 m, competitive with or better than a suite of established flood models, and reaches a median gage water-level Nash Sutcliffe efficiency of +0.72, more than double the +0.31 of the operational National Water Model v3.0. For the July 2025 Central Texas flash flood, Inunda is driven by an 18-member 1-km convection-allowing precipitation ensemble to produce probabilistic flood forecasts whose skill improves systematically as lead time to the crest shortens. For a post-fire flash-flood application in the Rio Ruidoso burn scar, differentiable calibration recovers the saturated hydraulic conductivity as a spatially explicit field at the model's own resolution and traces its multi-year post-fire recovery. Inunda provides an open, end-to-end pipeline for real-event flood modeling that couples the accuracy of physics-based hydraulics with the calibration and coupling advantages of modern differentiable programming.
