1,3,7,8,10,12,13,14,15,16,17,1- 1Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
- 2Woods Institute for the Environment, Stanford University, Stanford, CA, USA
- 3School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA
- 4Discovery Partners Institute, University of Illinois System, Chicago, IL, USA
- 5School of Arts, Media and Engineering, Arizona State University, Tempe, AZ, USA
- 6School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- 7Chair of Environmental Meteorology, University of Freiburg, Freiburg im Breisgau, Germany
- 8Democritus University of Thrace, Komotini, Greece
- 9School of Art and Architecture, University of Kent, Canterbury, UK
- 10Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA
- 11School of Sustainability, Arizona State University, Tempe, AZ, USA
- 12State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
- 13Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, China
- 14Department of Earth and Environment, Boston University, Boston, MA, USA
- 15Department of Mechanical Engineering, Boston University, Boston, MA, USA
- 16Laboratory of Urban and Environmental Systems, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- 17Department of Engineering, University of Perugia, Perugia, Italy
About two hundred thermal indicators exist and yield divergent assessments of heat stress impacts and mitigation. Thus, examining how these indicators respond to various meteorological variables and exploring the implications for their practical use is imperative. Using a correlation analysis, we cluster common indicators into three types: 1) human energy budget models, 2) integrated weather indices, and 3) thermal perception indicators. Distinct extreme hot conditions are identified differently by the various clusters of indicators: human energy budget models are more responsive to micro-scale variation in wind and radiation; while integrated weather indices mainly capture synoptic moist heat extremes. These biophysical indicators also do not concur with a metamodel of thermal perception, developed here using a meta-analysis of coefficients in existing thermal sensation vote equations. The developed thermal perception metamodel is more sensitive to radiation fluxes than other thermal stress indicators. It implies that humans’ thermal sensation may underestimate humid heat stress at nighttime, which can pose a significant risk to human health in hot, humid cities such as Chennai (India) and Dakar (Senegal) and across the Global South. These findings deepen our understanding of heat stress variability on humans and provide a framework for selecting suitable indicators in future applications.
How to cite: Huang, X., Kong, Q., Wang, Z.-H., Li, P., Middel, A., Matzarakis, A., Nikolopoulou, M., Huber, M., Vanos, J., Song, J., Li, D., Manoli, G., Pisello, A. L., and Bou-Zeid, E.: The diverging predictions of extreme heat risk indicators, 12th International Conference on Urban Climate, Rotterdam, The Netherlands, 7–11 Jul 2025, ICUC12-483, https://doi.org/10.5194/icuc12-483, 2025.
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