超越传统与数据驱动模型极限的全美小时级洪水模拟
Hourly U.S.-wide flood simulation beyond the limits of traditional and data-driven models
- The Pennsylvania State University（宾夕法尼亚州立大学）
- Scripps Institution of Oceanography, University of California San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出多时间尺度物理嵌入学习模型dHBV2.0MTS-MC，覆盖全美80万河段，超越业务化NWM3.0和AI模型，显著提升小时级洪水模拟精度与稀有洪水预测能力。
中文摘要 AI 辅助
随着日益严重的洪水灾害可在风暴发生后数小时内袭击未设水文站的河段，小时级全网络模拟已成为关键的社会基础设施。本文展示了一种多时间尺度物理嵌入学习模型，其性能优于美国业务化系统，并在洪峰预测上超越基于人工智能的系统。该模型覆盖美国本土超过80万个河段，将2,831个水文站的每小时纳什-苏特克利夫效率中位数从业务化国家水模型3.0版的0.461提升至0.683，并将洪峰时间误差从7-8小时缩小至4.5-6小时。dHBV2.0MTS-MC捕获的≥50年一遇洪水比NWM3.0多33%，比业务化LSTM基线多159%。与近期AI模型相比，整体小时级技能相当，而稀有洪水准确性显著更高，≥100年一遇洪水的相对洪峰量级误差降低了34%。该模型结合长期水文背景、短期冲击和超渗产流机制，以解析日尺度图上不可见的极端小时级洪峰。模型为每个河段生成过程模型参数和小时流量，以7.2平方公里中位数分辨率无缝覆盖整个大陆。作为下一代国家水模型的候选方案，该模型为国家尺度洪水预测设立了新的业务化精度水平。
英文摘要
As increasingly-damaging floods can strike within hours of a storm and in ungauged reaches, hourly network-wide simulation has become critical societal infrastructure. Here we demonstrate a multi-timescale physics-embedded learning model which outperforms the United States' operational system and surpasses AI-based systems at flood peaks. Covering more than 800,000 river reaches of the conterminous U.S., the model elevates median hourly Nash-Sutcliffe efficiency at 2,831 gauges to 0.683 from 0.461 for the operational National Water Model v3.0, and narrows flood-peak timing errors from 7-8 hours to 4.5-6 hours. dHBV2.0MTS-MC captures 33% more >=50-year floods than NWM3.0 and 159% more than an operational LSTM baseline. Against recent AI models, overall hourly skill is comparable while rare-flood accuracy is distinctly higher, with relative peak-magnitude error reduced by 34% for >=100-year floods. It combines long-term hydrologic context, short-term shocks, and infiltration excess to resolve extraordinary hourly peaks not visible on a daily plot. Process-model parameters and hourly discharge are produced for every reach, seamlessly covering the continent at 7.2 km2 median resolution. This candidate for the next-generation National Water Model sets a new operational accuracy level for national-scale flood prediction.
