In contrast to the basic assumption of a homogeneous population underlying common approaches to flood frequency analysis, flood events often arise from different runoff-generating processes. In many large river basins, the diversity of these processes within tributary basins and the superposition of their flood waves increase the complexity of statistical flood modeling. Under these circumstances, the allocation of the most effective flood protection measures requires a spatially explicit analysis of flood-generating processes and the determination of the probability of downstream flood scenarios. For large basins, flood scenarios are often derived using individual historical floods along with model-based simulations. We, instead, performed hydrograph-based flood-type classification and volume-based runoff analyses for the Upper Danube River to estimate the contributions of subbasins to floods at downstream locations. Using this information, we generated long synthetic samples of peak-volume-pairs to apply a multivariate statistical flood-frequency model that yields a conditional probability of a flood peak given the peaks in tributary stations. The results show that only certain combinations of flood types may result in extreme peaks downstream of confluences. They also highlight the need to distinguish runoff-generation mechanisms for the larger floods from ones that drive smaller, more frequent events. Through an example with the Rhine River, we demonstrate how the statistical model can be generalized for complex river networks featuring several tributary confluences. Finally, design floods for different scenarios of flood-type combinations and assigned probabilities are derived, an approach that can be used to possible climate impacts to flood frequency.

中文翻译：

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考虑支流影响和洪水类型的大流域多变量洪水频率分析

与基于洪水频率分析常用方法的同质人口的基本假设相反，洪水事件通常来自不同的径流生成过程。在许多大型流域，支流流域内这些过程的多样性及其洪水波的叠加增加了统计洪水建模的复杂性。在这种情况下，最有效的防洪措施的分配需要对洪水产生过程进行空间明确的分析，并确定下游洪水情景的概率。对于大型流域，洪水情景通常是使用单个历史洪水以及基于模型的模拟得出的。相反，我们 对多瑙河上游进行了基于水位线的洪水类型分类和基于体积的径流分析，以估计子流域对下游位置洪水的贡献。使用这些信息，我们生成了峰体积对的长合成样本，以应用多元统计洪水频率模型，该模型产生给定支流站峰的洪水峰的条件概率。结果表明，只有某些洪水类型组合可能会导致汇流下游出现极端峰值。他们还强调需要将较大洪水的径流生成机制与驱动较小、更频繁事件的径流生成机制区分开来。通过莱茵河的一个例子，我们展示了如何将统计模型推广到具有多个支流汇流点的复杂河流网络。