Interdependence of Extreme Floods

这项研究方案涉及大规模洪水的空间方面。与传统的使用选定的一组极端事件来调查事件不同，提出了一种从观测到的放电的时间序列特性得出常见事件的方法。可以使用傅里叶变换和哈尔变换来导出基本性质，这允许描述对应于不同时间尺度的相关性。利用这些技术，可以确定关键的时间尺度，并可以量化它们对洪灾的贡献。复杂的高维依赖结构不能用成对研究来检测。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，这些依赖关系的作用可能不同，在某些情况下，它们的后果是较高的，而在另一些情况下，则是较低的大规模洪水风险。将对观测到的降雨量、系列和气象模型的输出进行比较，以发现空间相关性和空间结构的差异。这样，就有可能对同时发生的洪水做出气象学上合理的解释。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。将开发两种不同的方法--基于成对依赖的传统生成器和反映高阶依赖的生成器。对于降雨产生器，还将反映气象模型输出的空间特性。这两种类型的模型将从它们的区域行为方面进行比较。降雨产生器的输出将作为水文模型的输入，以获得模拟流量序列。然后将这些序列与观测序列和从放电产生模型获得的序列进行比较，可以更好地理解模型的可信程度以及气象因素和地表属性的作用。

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.