Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

TL;DR

This paper introduces two complex network techniques for analyzing spatio-temporal systems: one for detecting regime transitions via recurrence networks, and another for predicting extreme events using spatial network divergence.

Contribution

It presents novel methods for transforming time series into recurrence networks and constructing spatial networks from extreme event data, enhancing analysis of complex systems.

Findings

Recurrence networks effectively identify regime transitions.

Spatial network divergence aids in predicting extreme rainfall events.

The methods show promise for future complex systems analysis.

Abstract

We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.