Multihazard Scenarios for Analysis of Compound Extreme Events

Compound extremes correspond to events with multiple concurrent or consecutive drivers (e.g., ocean and fluvial flooding, drought, and heat waves) leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we review the existing multivariate design and hazard scenario concepts and introduce a novel copula‐based weighted average threshold scenario for an expected event with multiple drivers. The model can be used for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. The proposed model offers uncertainty ranges of most likely compound hazards using Bayesian inference. We show that the uncertainty ranges of design quantiles might be large and may differ significantly from one copula model to the other. We also demonstrate that the choice of marginal and copula functions may profoundly impact the multihazard design values. A robust analysis should account for these uncertainties within and between multivariate models that translate into multihazard design quantiles. Plain Language Summary Compound extremes correspond to events with multiple concurrent or consecutive drivers, leading to substantial impacts such as infrastructure failure. Hurricane Harvey, with more than 100 fatalities, is an example of concurrent hazards (extreme precipitation and storm surge); and recent mudslide in California, with a death toll of 20 people in Montecito, CA, is an example of consecutive hazards (significant precipitation a few weeks after the Thomas wildfire). In many risk assessment and design applications, however, multihazard scenarios of extremes and compound events are ignored. In this paper, we present a general framework for obtaining multihazard design and risk assessment scenarios and their corresponding likelihoods. This framework also quantifies the underlying uncertainties of multihazard scenarios and employs an ensemble of univariate and multivariate models for robust risk assessment. Key Points We present a framework for multivariate analysis of natural hazards driven by multiple forcings The choice of marginal probability distribution and copula can significantly influence design and hazard scenarios Bayesian approach for parameter estimation illuminates the uncertainties of different multihazard scenarios