A high-spatial-resolution dataset of human thermal stress indices over South and East Asia

• Published in: Scientific data Vol. 8; no. 1; pp. 229 - 14
• Main Authors: Yan, Yechao, Xu, Yangyang, Yue, Shuping
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 704/106/35; 704/106/694/2739; Asia; Asia, Eastern; Computer applications; Data Descriptor; Heat-Shock Response; Hot Temperature; Humanities and Social Sciences; Humans; multidisciplinary; Public health; Science; Science (multidisciplinary); Spatial Analysis; Surface water; Temperature effects; Tourism; Asia, Eastern; Asia; East Asia
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-021-01010-w

Thermal stress poses a major public health threat in a warming world, especially to disadvantaged communities. At the population group level, human thermal stress is heavily affected by landscape heterogeneities such as terrain, surface water, and vegetation. High-spatial-resolution thermal-stress indices, containing more detailed spatial information, are greatly needed to characterize the spatial pattern of thermal stress to enable a better understanding of its impacts on public health, tourism, and study and work performance. Here, we present a 0.1° × 0.1° gridded dataset of multiple thermal stress indices derived from the newly available ECMWF ERA5-Land and ERA5 reanalysis products over South and East Asia from 1981 to 2019. This high-spatial-resolution database of human thermal stress indices over South and East Asia (HiTiSEA), which contains the daily mean, maximum, and minimum values of UTCI, MRT, and eight other widely adopted indices, is suitable for both indoor and outdoor applications and allows researchers and practitioners to investigate the spatial and temporal evolution of human thermal stress and its impacts on densely populated regions over South and East Asia at a finer scale. Measurement(s) thermal stress Technology Type(s) computational modeling technique Factor Type(s) temporal interval • geographic location Sample Characteristic - Environment climate system Sample Characteristic - Location South Asia • East Asia Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.15149010