In the past years, the analysis of compound events has emerged as an essential step to enhance our knowledge of and response to multi-hazard high-impact events that occur simultaneously or sequentially, causing interconnected or aggravated impacts. Compound events involve two (or more) events happening together. These can be independent events (in which the outcome of one event has no effect on the probability of the other), or dependent events (when the outcome of one event affects the probability of another). Compound weather, climate or hydrological events refer to combinations of multiple drivers or hazards that may lead to large impacts and disasters. These events can be related to extreme conditions (e.g. storms, heatwaves, floods and droughts), or to combinations of events that are not themselves extremes but lead to an extreme event or significant impact when combined.
In this Short Course, we will introduce compound events, their types (preconditioned, multivariate, temporally compounding, and spatially compounding events), and the methods used to detect and characterize them. We will highlight the advantages and limitations of statistical methods (regression, multivariate statistics, and classification), empirical approaches based on large datasets, high-dimension approaches such as copulas, and complex network-based techniques that help to identify non-trivial spatio-temporal patterns of extreme events.
The Short Course will focus on sharing experience from a wide range of applications worldwide, state-of-the-art methodological approaches, open access code and datasets, and will allow participants to discuss their own challenges in detecting, characterizing and assessing the risk of compound events in diverse contexts (climate, atmospheric, hydrologic, ocean and natural hazards sciences).