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Peer-reviewedCC BYInternational Journal of Applied Earth Observations and Geoinformation

High-resolution mapping of urban wet-bulb temperature and its intra-urban variability: A London case study

  • Zitong Wen1
  • Lu Zhuo2
  • Jiao Wang3
  • Zeyi Jiang4
  • Jingxuan Zhu5
  • Ming Zhong6
  • Dawei Han1
6 affiliations
  1. 1Department of Civil Engineering, University of Bristol, Bristol BS8 1TR, UK
  2. 2School of Earth and Environmental Sciences, Cardiff University, Cardiff CF10 3AT, UK; Corresponding author.
  3. 3Department of Land, Air and Water Resources, University of California Davis, Davis, CA 95616, USA
  4. 4School of Mechanical, Aerospace and Civil Engineering, University of Sheffield, Sheffield S1 4DT, UK
  5. 5School of Geography, Nanjing Normal University, Nanjing 210023, China
  6. 6School of Geography and Planning, Sun Yat-Sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
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Abstract

Most extreme-heat studies map high-resolution near-surface dry-bulb air temperature ( T a ), yet wet-bulb temperature ( T w ) fields that can capture the combined effects of T a and humidity to represent human heat stress remain limited. This data gap constrains investigation of intra-urban heat variability and the timing of peak heat stress. Here, based on the Rankine-Kirchhoff approximations of Romps, this study innovatively generates hourly 1-km T w by integrating crowdsourced station observations with remote sensing and reanalysis data in machine learning based downscaling frameworks. Taking London during the summers of 2019–2022 as a case study, the tuned RF-based downscaling approach achieved the best performance ( MAE = 0.65 °C, RMSE = 0.93 °C, R 2 = 0.91) among all models. The resulting T w patterns diverge from those commonly reported for T a : blue infrastructure shows no consistent cooling effect, whereas vegetated areas cool T w increasingly as tree cover rises, pointing to tree-dominated green infrastructure as a promising humid-heat countermeasure. Results further show that daily maximum T w occurs 0.7 h earlier than daily maximum T a in London, opposite to findings elsewhere, implying that climate type and coastal influence modulate the timing of peak T w , which may affect the attribution of excess nighttime mortality. Besides, a systematic global assessment of the mechanisms controlling T w extremes is also required in the future.

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Cite

BibTeXRIS
BibTeX
@article{wen2026highresolution,
  title = {{High-resolution mapping of urban wet-bulb temperature and its intra-urban variability: A London case study}},
  author = {Zitong Wen and Lu Zhuo and Jiao Wang and Zeyi Jiang and Jingxuan Zhu and Ming Zhong and Dawei Han},
  journal = {International Journal of Applied Earth Observations and Geoinformation},
  year = {2026},
  month = oct,
  doi = {10.1016/j.jag.2026.105649},
  url = {https://doi.org/10.1016/j.jag.2026.105649},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - High-resolution mapping of urban wet-bulb temperature and its intra-urban variability: A London case study
AU  - Zitong Wen
AU  - Lu Zhuo
AU  - Jiao Wang
AU  - Zeyi Jiang
AU  - Jingxuan Zhu
AU  - Ming Zhong
AU  - Dawei Han
PY  - 2026
DA  - 2026/10/31
JO  - International Journal of Applied Earth Observations and Geoinformation
DO  - 10.1016/j.jag.2026.105649
UR  - https://doi.org/10.1016/j.jag.2026.105649
AB  - Most extreme-heat studies map high-resolution near-surface dry-bulb air temperature ( T a ), yet wet-bulb temperature ( T w ) fields that can capture the combined effects of T a and humidity to represent human heat stress remain limited. This data gap constrains investigation of intra-urban heat variability and the timing of peak heat stress. Here, based on the Rankine-Kirchhoff approximations of Romps, this study innovatively generates hourly 1-km T w by integrating crowdsourced station observations with remote sensing and reanalysis data in machine learning based downscaling frameworks. Taking London during the summers of 2019–2022 as a case study, the tuned RF-based downscaling approach achieved the best performance ( MAE = 0.65 °C, RMSE = 0.93 °C, R 2 = 0.91) among all models. The resulting T w patterns diverge from those commonly reported for T a : blue infrastructure shows no consistent cooling effect, whereas vegetated areas cool T w increasingly as tree cover rises, pointing to tree-dominated green infrastructure as a promising humid-heat countermeasure. Results further show that daily maximum T w occurs 0.7 h earlier than daily maximum T a in London, opposite to findings elsewhere, implying that climate type and coastal influence modulate the timing of peak T w , which may affect the attribution of excess nighttime mortality. Besides, a systematic global assessment of the mechanisms controlling T w extremes is also required in the future.
SN  - 1569-8432
SN  - 1872-826X
ER  -