Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Weather and Climate Extremes |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212094725000507 |
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| author | J. Pérez-Aracil C. Peláez-Rodríguez Ronan McAdam Antonello Squintu Cosmin M. Marina Eugenio Lorente-Ramos Niklas Luther Verónica Torralba Enrico Scoccimarro Leone Cavicchia Matteo Giuliani Eduardo Zorita Felicitas Hansen David Barriopedro Ricardo García-Herrera Pedro A. Gutiérrez Jürg Luterbacher Elena Xoplaki Andrea Castelletti S. Salcedo-Sanz |
| author_facet | J. Pérez-Aracil C. Peláez-Rodríguez Ronan McAdam Antonello Squintu Cosmin M. Marina Eugenio Lorente-Ramos Niklas Luther Verónica Torralba Enrico Scoccimarro Leone Cavicchia Matteo Giuliani Eduardo Zorita Felicitas Hansen David Barriopedro Ricardo García-Herrera Pedro A. Gutiérrez Jürg Luterbacher Elena Xoplaki Andrea Castelletti S. Salcedo-Sanz |
| author_sort | J. Pérez-Aracil |
| collection | DOAJ |
| description | 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 named Spatio-Temporal Cluster-Optimized Feature Selection (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 grid cells 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. |
| format | Article |
| id | doaj-art-7b78cd39de08462c95ed3f9f328b5cd1 |
| institution | Kabale University |
| issn | 2212-0947 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Weather and Climate Extremes |
| spelling | doaj-art-7b78cd39de08462c95ed3f9f328b5cd12025-08-20T04:03:22ZengElsevierWeather and Climate Extremes2212-09472025-09-014910079210.1016/j.wace.2025.100792Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detectionJ. Pérez-Aracil0C. Peláez-Rodríguez1Ronan McAdam2Antonello Squintu3Cosmin M. Marina4Eugenio Lorente-Ramos5Niklas Luther6Verónica Torralba7Enrico Scoccimarro8Leone Cavicchia9Matteo Giuliani10Eduardo Zorita11Felicitas Hansen12David Barriopedro13Ricardo García-Herrera14Pedro A. Gutiérrez15Jürg Luterbacher16Elena Xoplaki17Andrea Castelletti18S. Salcedo-Sanz19Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Spain; Programa de doctorado en Computación Avanzada, Energía y Plasmas, Universidad de Córdoba, Córdoba, Spain; Corresponding author.Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, SpainCMCC Foundation - Euro-Mediterranean Center on Climate Change, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, ItalyDepartment of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, SpainDepartment of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, SpainDepartment of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Giessen, GermanyBarcelona Supercomputing Center (BSC), Barcelona, SpainCMCC Foundation - Euro-Mediterranean Center on Climate Change, ItalyCMCC Foundation - Euro-Mediterranean Center on Climate Change, ItalyDepartment of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, ItalyHelmholtz-Zentrum Hereon, Hamburg, GermanyHelmholtz-Zentrum Hereon, Hamburg, GermanyInstituto de Geociencias (IGEO), Consejo Superior de Investigaciones Científicas–Universidad Complutense de Madrid, Madrid, SpainDepartamento de Física de la Tierra y Astrofísica, Facultad de Ciencias Físicas, UCM, Madrid, SpainDepartamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Córdoba, Córdoba, SpainDepartment of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Giessen, GermanyCMCC Foundation - Euro-Mediterranean Center on Climate Change, Italy; Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Giessen, GermanyDepartment of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; RFF-CMCC European Institute on Economics and the Environment, Euro-Mediterranean Center on Climate Change, Milano, ItalyDepartment of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, SpainHeatwaves (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 named Spatio-Temporal Cluster-Optimized Feature Selection (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 grid cells 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.http://www.sciencedirect.com/science/article/pii/S2212094725000507HeatwavesSpatio-temporal optimizationLarge-scale driversCluster-based feature selectionMulti-method ensemblesOptimization |
| spellingShingle | J. Pérez-Aracil C. Peláez-Rodríguez Ronan McAdam Antonello Squintu Cosmin M. Marina Eugenio Lorente-Ramos Niklas Luther Verónica Torralba Enrico Scoccimarro Leone Cavicchia Matteo Giuliani Eduardo Zorita Felicitas Hansen David Barriopedro Ricardo García-Herrera Pedro A. Gutiérrez Jürg Luterbacher Elena Xoplaki Andrea Castelletti S. Salcedo-Sanz Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection Weather and Climate Extremes Heatwaves Spatio-temporal optimization Large-scale drivers Cluster-based feature selection Multi-method ensembles Optimization |
| title | Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection |
| title_full | Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection |
| title_fullStr | Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection |
| title_full_unstemmed | Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection |
| title_short | Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection |
| title_sort | identifying key drivers of heatwaves a novel spatio temporal framework for extreme event detection |
| topic | Heatwaves Spatio-temporal optimization Large-scale drivers Cluster-based feature selection Multi-method ensembles Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2212094725000507 |
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