Origin and variability of statistical dependencies between peak, volume, and duration of rainfall-driven flood events

L Rahimi,C De Michele,C Deidda

doi:10.1038/s41598-021-84664-1

Abstract

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”.

Highlights

Floods are among the most common and impactful natural events
We have investigated the impact of the criterion of temporal independence of flood events on the estimation of the pairwise dependence
We have considered the impact of the threshold, chosen for the identification of flood events, on the estimation of the pairwise dependence, both in terms of Kendall’s tau and Spearman’s rho

Summary

Introduction

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. 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. Karmakar and Simonovic[15] analyzed the pairwise dependence between peak, volume, and duration, of flood events identified using different discharge thresholds, using the streamflow data of Red River at Grand Forks, North Dakota (USA). Time series of discharge recorded in the continental United States, and bootstrap algorithms They found that the pairwise dependence can be reproduced using a univariate bootstrapping of discharge without recurring to multivariate techniques. They concluded that the pairwise dependence can be explained as the result of summing independent random variables over random durations

Methods

Results

Conclusion