compound extreme events

What is it about?

Based on ideas from event coincidence analysis (ECA), we propose a network analysis method to study compound extreme events at different geographical locations. Integrating network modeling into statistical correlation research allows us to analyze potential risk enhancement relationship and trigger causal relationship between these events. In this approach, we consider different geographical locations as nodes and construct a directed edge from node i to node j when event A at location i occurs synchronously before event B at location j. Precursor coincidence analysis quantifies the risk enhancement relationship between two types of extreme events, while trigger coincidence analysis quantifies the trigger causal relationship between two types of extreme events. A directed weighted network can be constructed based on statistical correlations between these events at different geographical locations. Further analysis of network topology characteristics extends traditional ECA in method and application. Herein, we construct the precursor functional network and the trigger functional network of high voice traffic and high data throughput to analyze potential risk enhancement and trigger causal relationships between these events at different base stations within a communication system.

Featured Image

Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

Compound extreme events are multiple extreme events that occur simultaneously or sequentially. Unlike single extreme events such as drought, high temperatures, and rainstorms, the impact of compound extreme events is often not a simple superposition of their individual effects. As the frequency and impact of extreme events change, the evolution of compound extreme events shows a more complex trend. The existing literature on the application of ECA mainly focuses on two aspects: (1) applying ECA to measure the synchronization between single extreme events at different geographical locations and then constructing functional networks and (2) applying ECA to measure the statistical correlation between compound extreme events at the same geographical location. There is still a lack of network analysis methods for composite extreme events. In this study, based on the idea of ECA, we propose a network analysis method for studying compound extreme events at different geographical locations. We have incorporated network modeling into the research of statistical correlations between compound extreme events. Through the further analysis of network topology characteristics, we have extended traditional ECA at the methodological and application levels.

Perspectives