物理嵌入式学习揭示了全球不同的水文反应模式和趋势
Haoyu Ji1, Yalan Song1, Tadd Bindas1
1Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Nature communications
|October 15, 2025
概括
全球水模型 (GWMs) 现在可以更好地捕捉水文变化,使用一种新的物理嵌入式,大数据训练的方法. 这揭示了水分和基流的重大变化,影响了全球的洪水风险和供水.
科学领域:
- 水文学的水文学
- 气候科学 气候科学
- 环境建模环境建模
背景情况:
- 全球水模型 (GWMs) 由于有限的数据同化能力,难以表示动态的水文系统反应.
- 准确跟踪水资源的快速变化需要捕捉特征性水文反应模式 (签名) 的模型.
研究的目的:
- 为改进水文签名分析引入一种新的,高分辨率的,嵌入物理,大数据训练模型.
- 评估水平衡成分的全球变化及其生态和管理影响.
主要方法:
- 开发一个嵌入物理,大数据训练模型,集成高分辨率数据.
- 长期水平衡的现实表示,包括绿色-蓝色-水分区和基流比率.
- 分析了20年来水文反应模式的变化.
主要成果:
- 在过去二十年中,在全球范围内观察到水分区和基流比率的广泛变化超过20%.
- 确定了这些变化对洪水风险增加 (北部中度),供水紧张 (亚热带地区) 和淡水投入减少 (欧洲) 的贡献.
- 在模拟每月和每天的排水反应,包括弹性和闪性,特别是在干旱/半干旱地区,证明了卓越的准确性.
结论:
- 下一代模型提供了比目前的操作系统更精确的模拟.
- 突出地区因供水变化和气候敏感性而面临管理挑战.
- 这种先进的工具为全球水资源管理提供了季节性水资源可用性的可靠预测.
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