Presentation Information
[ACG64-P01]Re-evaluating Empirical WBGT Estimation Formulas under Recent Climate Change in Japan
*Ken Noguchi1, KOJI DAIRAKU2, Guo Qiang2,3 (1.College of Engineering Systems, University of Tsukuba, 2.Institute of Systems and Information Engineering, University of Tsukuba, 3.Tsukuba Institute for Advanced Research (TIAR), University of Tsukuba)
Keywords:
climate change,heat stress,WBGT,heat illness
Wet Bulb Globe Temperature (WBGT) is widely used to evaluate heat stress risk. In Japan, hourly WBGT has been reported at 840 locations; however, in 2025, the number of stations with direct WBGT measurements was limited to 47, and only 11 stations had measured WBGT from 2010 to 2024. Other stations have relied on the WBGT estimation formula developed by Ono and Tonouchi (2014), which has good correlation with measured WBGT data. However, the validation was conducted using data from 2007 to 2009; thus, its validity has not yet been confirmed under current severe conditions and may lead to misjudgments of heat stress.
In this study, we compare observed WBGT data with estimated values using two models: a Japanese empirical model (Ono and Tonouchi, 2014) and a physics-based model (Liljegren et al., 2008). Comparing these models with observational data from 2010 to 2024, we examine the magnitude and characteristics of discrepancies between them and explore their spatiotemporal variability. Furthermore, we investigate the relationship between their bias and meteorological factors.
Our results would support a more robust assessment of heat stress and help prevent heat-related illnesses under climate change.
In this study, we compare observed WBGT data with estimated values using two models: a Japanese empirical model (Ono and Tonouchi, 2014) and a physics-based model (Liljegren et al., 2008). Comparing these models with observational data from 2010 to 2024, we examine the magnitude and characteristics of discrepancies between them and explore their spatiotemporal variability. Furthermore, we investigate the relationship between their bias and meteorological factors.
Our results would support a more robust assessment of heat stress and help prevent heat-related illnesses under climate change.