AI-driven flood digital twins demand fast hydrodynamic surrogates for ensemble forecasting and observation assimilation. Yet even GPU-accelerated two-dimensional shallow water equation (SWE) solvers still require $\sim 55$ minutes per $96$-hour run on a $\sim 4.2$-million-active-cell metropolitan basin (the Des~Plaines River basin at $30\,\mathrm{m}$ resolution), making such workloads prohibitive at native resolution. We present the Conditional Latent Dynamics Network (CLDNet): a low-dimensional latent neural ODE driven by rainfall, paired with a coordinate-based decoder conditioned on static terrain (elevation, slope, Manning roughness) that reconstructs depth and discharge at arbitrary query points. Pointwise decoding decouples memory from grid size and handles irregular watersheds natively, enabling metropolitan-scale training on a single compute node and direct queries at exact gauge coordinates without raster snapping. We evaluate CLDNet on a synthetic $250{,}000$-cell Texas benchmark and on a new Des~Plaines case study of $114$ real-rainfall Stage~IV storms whose reference simulator we validate against United States Geological Survey (USGS) gauges at the April~2013 flood-of-record (Nash--Sutcliffe efficiency $0.57$--$0.94$ on mean-recentered water-surface elevation). CLDNet roughly halves the relative root-mean-squared error of an unconditional baseline, outperforms regular-grid VAE--ConvLSTM and FNO baselines on the Texas benchmark (both presuppose a Cartesian grid and do not apply to the irregular Des~Plaines watershed), reaches a critical success index of $\approx 86\%$ at the $0.5\,\mathrm{m}$ inundation threshold, and produces a full $96$-hour basin-wide forecast in $\sim 29$ seconds -- a $\sim 115\times$ speedup.

翻译：AI驱动洪水数字孪生需要快速的水动力学替代模型，以实现集合预报和观测同化。然而，即使是GPU加速的二维浅水方程求解器，在拥有约420万个活动单元的都市流域（Des Plaines河流域，分辨率30米）上，完成一次96小时模拟仍需约55分钟，这使得在原始分辨率下开展此类计算任务变得不可行。我们提出条件潜动力学网络（CLDNet）：一种由降雨驱动的低维潜神经ODE模型，配合基于坐标的解码器——该解码器以静态地形（高程、坡度、曼宁糙率）为条件，可在任意查询点重构水深和流量。逐点解码将内存需求与网格规模解耦，并天然适配不规则流域，从而能在单台计算节点上完成都市级训练，并直接查询精确测站坐标，无需栅格重采样。我们在包含25万个单元的综合得克萨斯基准测试集以及一个新的Des Plaines案例研究（涵盖114场基于Stage-IV实测数据的真实降雨事件，其参考模拟器已通过2013年4月创纪录洪水期间的美国地质调查局（USGS）测站数据验证，平均归零化水面高程的纳什-萨特克里夫效率为0.57–0.94）上评估了CLDNet。CLDNet将无条件基准模型的相对均方根误差大致减半，在得克萨斯基准测试中优于规则网格的VAE-ConvLSTM和FNO基准模型（两者均预设笛卡尔网格，且不适用于不规则Des Plaines流域），在0.5米淹没阈值下的临界成功指数达到约86%，并在约29秒内生成完整的96小时全流域预报——加速比达约115倍。