Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

Wei Zhang,Weilin Chen,Ming Luo,Vittal Hari,Si Gao,Abdou Khouakhi

doi:10.3389/feart.2021.673495

Abstract

Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.

Highlights

Extreme weather and climate events can have devastating consequences on human societies and the environment (Troy et al, 2015; Zscheischler et al, 2020b)
The compound storm surge and heavy rainfall events are associated with tropical cyclones, atmospheric rivers (Lin et al, 2010a), medicanes (Amores et al, 2020; Davolio et al, 2020; Zhang et al, 2020), and extreme extratropical cyclones (Danard et al, 2004; Colle et al, 2015; Mäll et al, 2017; Lin et al, 2019)
We have summarized compound flooding events that include storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding

Summary

INTRODUCTION

Extreme weather and climate events can have devastating consequences on human societies and the environment (Troy et al, 2015; Zscheischler et al, 2020b). The compound storm surge and heavy rainfall events are associated with tropical cyclones, atmospheric rivers (Lin et al, 2010a), medicanes (Amores et al, 2020; Davolio et al, 2020; Zhang et al, 2020), and extreme extratropical cyclones (Danard et al, 2004; Colle et al, 2015; Mäll et al, 2017; Lin et al, 2019) This type of compound has been reported in many parts of the world including the Netherlands (van den Hurk et al, 2015; Ridder et al, 2018), in coastal and estuarine regions of Australia (Wu et al, 2018), Morocco (Zellou and Rahali, 2019), the United States (Lin et al, 2010b; Gori et al, 2020a), China (Xu et al, 2018; Fang et al, 2021), Britain (Svensson and Jones, 2002, 2004), and Europe in general (Bevacqua et al, 2019).

Statistical methods

CONCLUSION AND DISCUSSION