相关实验视频
Updated: Jan 18, 2026

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.4K
一个多场景框架,用于量化洪水危险和暴露,以计算全球洪水模型中排水驱动的不确定性
Jayesh Parmar1, Subhankar Karmakar2
1Environmental Science and Engineering Department, Indian Institute of Technology Bombay, Mumbai, 400076, India.
The Science of the total environment
|September 10, 2025
概括
这项研究引入了一个新的框架,通过使用多个下水场景来减少大规模洪水危险评估中的不确定性. 最好的情景使用ERA5-Reanalysis流水,改善洪水风险预测,以便更好地做出决策.
科学领域:
- 地球和环境科学 地球和环境科学
- 水文学的水文学
- 气候科学 气候科学
背景情况:
- 由于人口和洪水事件的增加,大规模洪水危险评估 (LSFHA) 是至关重要的.
- 全球洪水模型 (GFMs) 对LSFHA至关重要,但对冲流输入不确定性敏感.
- 现有的方法很难量化洪水危险和暴露的全部范围.
研究的目的:
- 为全球洪水模型 (GFMs) 开发一个多场景框架,以应对排水驱动的不确定性.
- 为了捕捉一系列可信的洪水危险和暴露场景,从乐观到保守.
- 为了确定最有效和经过验证的排水强迫,用于最佳情景模拟.
主要方法:
- 在多个场景框架内,将多种不同的流水强迫集成到CaMa-Flood GFM中.
- 根据模拟的洪水深度和模型效率定义了乐观,保守和最佳情景.
- 使用参数和非参数分布进行回归期洪水深度频率分析.
主要成果:
- ERA5-Reanalysis流水成为最有效的强迫,在~50%的测量站实现了Nash-Sutcliffe效率>0.5.
- 对每100年发生一次洪水的危险分析表明,印度约48%的土地面积 (约160万平方公里) 处于灾难性危险类.
- 暴露分析显示,印度约有6.9亿人面临严重的洪水风险.
结论:
- 开发的框架有效量化了洪水危险和暴露不确定性,这些不确定性源于流水输入.
- 该研究提供了一个可扩展的,开源的工具,用于全国范围的,风险知情的洪水管理决策.
- 该框架在印度得到了验证,它解决了对即时和长期洪水风险管理的关键不确定性.
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