Scaling Approach to Hydrologic Extremes and Their Dependence on Climate and Basin Characteristics

水文极端事件的标度方法及其对气候和流域特征的依赖性

• 批准号: 9612531
• 负责人: Rafael Bras
• 金额: $ 27.82万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9612531&HistoricalAwards=false
• 关键词: Scaling; Approach; Hydrologic; Extremes; Their

9612531 Bras The prevalent approaches to estimating extreme precipitation and flood relationships are empirical curve fitting to historical data and numerical simulation also based on empirical models. Both methods become inaccurate if very rare events or the impact of climatic variations and human interventions on such extremes are estimated. The source of the inaccuracy is that, in the absence of a theoretical or physical basis, extrapolation far beyond the range of the historical data becomes highly uncertain. A large number of these empirical relations are of the power-type that reflect the scale-invariant properties of the observed hydrologic event. This project will use a scaling or fractal approach to the problem of extrapolation from limited data of hydrologic extremes. A new class of rainfall and width functions models will be developed which posses the simple- and multi-scaling properties observed in nature for these quantities and that will be validated through extensive data analysis. These models will combine two basic approaches, the multi-scaling and the pulse superposition. The final phase of the project will include the parameterization of rainfall and basin response in terms of climate and catchment characteristics, using them to assess the sensitivity of peak precipitation and flood events to both natural changes in the climate, basin characteristics, and human intervention. Improving the understanding of the rainfall-flood relationship is of great benefit to areas and people at risk and will advance the state-of-the-art. ***

布拉斯估算极端降水和洪水关系的普遍方法是对历史数据的经验曲线拟合和也基于经验模型的数值模拟。 如果估计非常罕见的事件或气候变化和人类干预对这种极端事件的影响，这两种方法都不准确。 不准确的原因是，在缺乏理论或物理基础的情况下，远远超出历史数据范围的外推法变得非常不确定。 大量的这些经验关系是幂型的，反映了所观察到的水文事件的尺度不变特性。 该项目将使用比例或分形方法来解决从水文极端情况的有限数据进行外推的问题。 将开发一类新的降雨量和宽度函数模型，这些模型将模拟自然界中观察到的这些量的简单和多尺度特性，并将通过广泛的数据分析加以验证。 这些模型将结合联合收割机两种基本方法，多尺度和脉冲叠加。 该项目的最后阶段将包括根据气候和集水特征确定降雨量和流域响应的参数，利用这些参数评估降雨高峰和洪水事件对气候自然变化、流域特征和人类干预的敏感性。 提高对灾害与洪水关系的认识对处于危险中的地区和人民有很大的好处，并将促进最先进的技术。