Interdependence of Extreme Floods

极端洪水的相互依存性

• 批准号: 324225691
• 负责人: Professor Dr.-Ing. András Bárdossy
• 金额: --
• 依托单位: Lehrstuhl für Hydrologie und Geohydrologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/324225691?language=en
• 关键词: Interdependence; Extreme; Floods

This research proposal deals with the spatial aspects of large scale floods. Instead of the traditional investigation of events using a selected set of extreme events, a methodology to derive common occurrences from time series properties of observed discharges is suggested. The essential properties can be derived using Fourier and Haar transformations which allow a description of the dependence corresponding to different time scales. Using these techniques critical time scales can be identified and their contribution to floods can be quantified. Complex high dimensional dependence structures cannot be detected using pairwise investigations. New techniques based on spectral properties and topological structures will be used to detect such dependences and to quantify their role. Note that these dependences may act differently, in some cases their consequence is a higher in others a lower large scale flood risk. Observed rainfall, series and output of meteorological models will be compared to detect differences in spatial dependence and spatial structure. This way a meteorologically plausible explanation of simultaneous flood occurrences will be possible. In order to assess probabilities of simultaneously occurring extreme discharges at different locations stochastic discharge and rainfall generators will be developed. Two different approaches – a traditional generator based on pairwise dependences and one reflecting high order dependence will be developed. For the rainfall generator spatial properties of the meteorological model output will also be reflected. The two types of models will be compared with respect to their areal behavior. Outputs of the rainfall generator will be used as input for hydrological models to obtain simulated discharge series. These will then be compared to the observed series and to the series obtained from the discharge generating model and both the plausibility of the models and the role of the meteorological factors and surface properties can be better understood.

这项研究建议涉及大规模洪水的空间方面。而不是传统的调查使用一组选定的极端事件的事件，一种方法来获得常见的发生从观测到的放电的时间序列特性的建议。的基本属性可以使用傅立叶变换和Haar变换，允许对应于不同的时间尺度的依赖性的描述。利用这些技术，可以确定关键的时间尺度，并量化其对洪水的贡献。复杂的高维依赖结构不能使用成对调查来检测。基于光谱特性和拓扑结构的新技术将被用来检测这种依赖关系，并量化它们的作用。请注意，这些依赖性可能会有不同的作用，在某些情况下，它们的后果是较高的，在其他情况下，较低的大规模洪水风险。将比较观测到的降雨量、系列和气象模型的输出，以发现空间依赖性和空间结构方面的差异。这样，从气象学上对同时发生的洪水做出合理的解释就成为可能。为了评估在不同地点同时发生极端放电的概率，将开发随机放电和降雨发生器。两种不同的方法-一个传统的发电机的基础上成对的依赖和一个反映高阶依赖将开发。对于降雨发生器，还将反映气象模型输出的空间特性。这两种类型的模型将比较其面积行为。降雨发生器的输出将用作水文模型的输入，以获得模拟的流量系列。然后将这些与观测到的系列和从放电生成模型获得的系列进行比较，可以更好地理解模型的可扩展性以及气象因素和表面特性的作用。