Rahimi L, Deidda C, De Michele C
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy.
Sci Rep. 2021 Mar 4;11(1):5182. doi: 10.1038/s41598-021-84664-1.
Floods are among the most common and impactful natural events. The hazard of a flood event depends on its peak (Q), volume (V) and duration (D), which are interconnected to each other. Here, we used a worldwide dataset of daily discharge, two statistics (Kendall's tau and Spearman's rho) and a conceptual hydrological rainfall-runoff model as model-dependent realism, to investigate the factors controlling and the origin of the dependence between each couple of flood characteristics, with the focus to rainfall-driven events. From the statistical analysis of worldwide dataset, we found that the catchment area is ineffective in controlling the dependence between Q and V, while the dependencies between Q and D, and V and D show an increasing behavior with the catchment area. From the modeling activity, on the U.S. subdataset, we obtained that the conceptual hydrological model is able to represent the observed dependencies between each couple of variables for rainfall-driven flood events, and for such events, the pairwise dependence of each couple is not causal, is of spurious kind, coming from the "Principle of Common Cause".
洪水是最常见且影响重大的自然事件之一。洪水事件的危险程度取决于其峰值（Q）、流量（V）和持续时间（D），而这三者相互关联。在此，我们使用了一个全球日流量数据集、两种统计方法（肯德尔秩相关系数和斯皮尔曼等级相关系数）以及一个概念性水文降雨径流模型作为模型依赖现实主义，来研究控制每对洪水特征之间相关性的因素及其成因，重点关注降雨驱动的事件。通过对全球数据集的统计分析，我们发现集水面积对控制Q和V之间的相关性无效，而Q与D以及V与D之间的相关性则随集水面积的增加而增强。从对美国子数据集的建模活动中，我们得出概念性水文模型能够表示降雨驱动洪水事件中各变量对之间观察到的相关性，并且对于此类事件，每对变量之间的成对相关性并非因果关系，而是虚假的，源于“共同原因原则”。
