Automatic mapping of urban heat islands based on high-resolution climate simulations with application to French cities
Abstract
The identification of rural reference areas often requires predefined information on regional environmental characteristics or established climatic classifications, which may add subjectivity to studies and cannot be reproduced in new areas. This study presents a novel methodology that uses kilometric-scale climate simulations to systematically identify suitable rural reference areas for cities and estimate the associated urban heat island (UHI) intensity at the grid cell level. The method in question, called MOD_Mask, integrates the classification of ECOCLIMAP land cover, derived from high-resolution CORINE Land Cover data, to extract urban areas without prior regional knowledge. The method was tested across metropolitan France, including cities of various sizes, from the densely populated Paris region to the medium-sized city of Dijon. Data from local weather stations in these two cities was used to evaluate the automated UHI mapping method. MOD_Mask successfully identified and mapped areas most exposed to UHI effects with high precision, helping to investigate population exposure and its effects within 12 French cities. This methodology has significant potential for future climate projections and scenario analyses, providing valuable insights into important climate indicators. It provides spatial UHI diagnostics to help decision-makers understand UHI conditions and develop climate adaptation strategies. The framework can also be applied to other regions and expanded to include more climate variables.
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BibTeX
@article{quenum2026automatic,
title = {{Automatic mapping of urban heat islands based on high-resolution climate simulations with application to French cities}},
author = {Gandome Mayeul Léger Davy Quenum and Aude Lemonsu and Léa Corneille and Elizabeth Harader-Coustau and Cécile Caillaud and Samuel Somot and Diego Monteiro and Tiago Machado and Shan Li and Mathis Chevé and Yves Richard},
journal = {Urban Climate},
year = {2026},
month = oct,
doi = {10.1016/j.uclim.2026.103117},
url = {https://ube.hal.science/hal-05747460v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Automatic mapping of urban heat islands based on high-resolution climate simulations with application to French cities
AU - Gandome Mayeul Léger Davy Quenum
AU - Aude Lemonsu
AU - Léa Corneille
AU - Elizabeth Harader-Coustau
AU - Cécile Caillaud
AU - Samuel Somot
AU - Diego Monteiro
AU - Tiago Machado
AU - Shan Li
AU - Mathis Chevé
AU - Yves Richard
PY - 2026
DA - 2026/10/09
JO - Urban Climate
DO - 10.1016/j.uclim.2026.103117
UR - https://ube.hal.science/hal-05747460v1
AB - The identification of rural reference areas often requires predefined information on regional environmental characteristics or established climatic classifications, which may add subjectivity to studies and cannot be reproduced in new areas. This study presents a novel methodology that uses kilometric-scale climate simulations to systematically identify suitable rural reference areas for cities and estimate the associated urban heat island (UHI) intensity at the grid cell level. The method in question, called MOD_Mask, integrates the classification of ECOCLIMAP land cover, derived from high-resolution CORINE Land Cover data, to extract urban areas without prior regional knowledge. The method was tested across metropolitan France, including cities of various sizes, from the densely populated Paris region to the medium-sized city of Dijon. Data from local weather stations in these two cities was used to evaluate the automated UHI mapping method. MOD_Mask successfully identified and mapped areas most exposed to UHI effects with high precision, helping to investigate population exposure and its effects within 12 French cities. This methodology has significant potential for future climate projections and scenario analyses, providing valuable insights into important climate indicators. It provides spatial UHI diagnostics to help decision-makers understand UHI conditions and develop climate adaptation strategies. The framework can also be applied to other regions and expanded to include more climate variables.
SN - 2212-0955
ER -