Identifying Key Drivers of Heatwaves: A Novel Spatio-Temporal Framework for Extreme Event Detection

TL;DR

This paper introduces a novel spatio-temporal framework combining clustering and evolutionary algorithms to identify key drivers of heatwaves, improving understanding and prediction of these extreme events.

Contribution

The work presents a new method (STCO-FS) for identifying immediate heatwave drivers using clustering and ensemble evolutionary algorithms on spatio-temporal data.

Findings

Effectively identified significant variables influencing heatwaves in Italy.

Reduced dimensionality by grouping similar geographical nodes.

Enhanced understanding of heatwave drivers and their predictability.

Abstract

Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability.