Abstract
Extreme events, such as drought, heat wave, cold wave, flood, and extreme rainfall, have received increasing attention in recent decades due to their wide impacts on society and ecosystems. Meanwhile, the compound extremes (i.e., the simultaneous or sequential occurrence of multiple extremes at single or multiple locations) may exert even larger impacts on society or the environment. Thus, the past decade has witnessed an increasing interest in compound extremes. In this study, we review different approaches for the statistical characterization and modeling of compound extremes in hydroclimatology, including the empirical approach, multivariate distribution, the indicator approach, quantile regression, and the Markov Chain model. The limitation in the data availability to represent extremes and lack of flexibility in modeling asymmetric/tail dependences of multiple variables/events are among the challenges in the statistical characterization and modeling of compound extremes. Major future research endeavors include probing compound extremes through both observations with improved data availability (and statistical model development) and model simulations with improved representation of the physical processes to mitigate the impacts of compound extremes.
Highlights
The climate system has been changing significantly as exhibited by global warning, which is expected to intensify the hydrologic cycle due to the involvement of certain temperature dependence processes
The extreme value theory (EVT) constitutes the basis for statistical modeling of univariate extremes in this regard, which can generally be achieved with the probability distribution of individual extremes, such as generalized extreme value (GEV) distribution or generalized Pareto distribution (GPD) based on the annual maxima or peak over threshold [22,23]
The empirical counting approach and indicator approach have been commonly used to characterize the variability of compound extremes
Summary
The climate system has been changing significantly as exhibited by global warning, which is expected to intensify (and accelerate) the hydrologic cycle due to the involvement of certain temperature dependence processes. Coastal flooding may be caused by large waves combined with a high sea level and its multivariate distribution has been employed to study the compound extreme of wave height and water level at different coastline stretches around the globe [40,41,42,43,44,45,46]. These studies advanced our understanding of compound extremes and how to enhance the capacity to cope with the adverse impacts of these climate anomalies.
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