发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
C-STRIDE是一种观测驱动的AI数字孪生，利用稀疏水位计记录、地形和降雨数据，快速生成流域尺度水深图并预测未来一天洪水，误差降低约40%，速度提升150倍。
AI 中文摘要
应急管理人员需要知道洪水在何处、有多深，以及在未来数小时内整个流域的洪水将如何变化。然而，在洪水期间，实时测量仅来自少数几个水位计，而高分辨率水动力模型因成本过高，无法在每次新数据到达时重新运行，也无法作为大型集合运行。我们提出了C-STRIDE，一种观测驱动的AI数字孪生，它将来自少数水位计的短记录，连同地形和降雨数据，转化为流域尺度的水深图，并将这些预测延长至未来一天。该模型基于经过校准的二维水动力模型的模拟结果进行训练，无需单独的数据同化步骤。在芝加哥附近的德斯普兰斯河流域，六个水位计为覆盖420万个30米网格单元的预测提供信息。地形对预测的改善最为显著，降雨则防止误差在更长预测时限内增长，两者结合相比仅使用水位计记录可将误差减少约40%。当未来降雨已知时，提前一天的误差保持在约15%附近，而若无降雨数据则误差接近40%。当使用真实而非模拟的水位计记录时，模型无需重新训练即可将预测向六个水位计中三个的观测水文过程线靠拢，并且其运行速度比水动力模型快约150倍。这些结果表明，稀疏水位计、地形和降雨可以结合起来，实现快速、持续更新的洪水预测，这是迈向运行性洪水数字孪生的一步，但仍需使用实时数据和降雨预报进行测试。
英文摘要
Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come from only a handful of stream gauges, and high-resolution hydrodynamic models are too costly to rerun each time new data arrive or to run as large ensembles. We present C-STRIDE, an observation-driven AI digital twin that turns short records from a few stream gauges, together with terrain and rainfall, into basin-wide maps of water depth and extends these predictions up to a day ahead. It is trained on simulations from a calibrated two-dimensional hydrodynamic model and needs no separate data-assimilation step. In the Des Plaines River basin near Chicago, six gauges inform predictions over 4.2 million 30-m grid cells. Terrain improves the predictions most, rainfall keeps errors from growing over longer horizons, and together they reduce errors by about 40% compared with gauge records alone. When future rainfall is known, errors remain near 15% one day ahead, compared with nearly 40% without rainfall. Given real instead of simulated gauge records, the model shifts its predictions toward the observed hydrographs at three of six gauges without retraining, and it runs about 150 times faster than the hydrodynamic model. These results show how sparse gauges, terrain, and rainfall can be combined into fast, continuously updated flood predictions, a step toward operational flood digital twins that still requires testing with real-time data and rainfall forecasts.
