Preprints
https://doi.org/10.5194/essd-2022-257
https://doi.org/10.5194/essd-2022-257
 
11 Aug 2022
11 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

HiTIC-Monthly: A High Spatial Resolution (1 km×1 km) Monthly Human Thermal Index Collection over China from 2003 to 2020

Hui Zhang1, Ming Luo1,2, Yongquan Zhao3, Lijie Lin4, Erjia Ge5, Yuanjian Yang6, Guicai Ning1,2, Jing Cong7, Zhaoliang Zeng8, Ke Gui9, Jing Li10, Ting On Chen1, Xiang Li1, Sijia Wu1, Peng Wang1, and Xiaoyu Wang1 Hui Zhang et al.
  • 1School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
  • 2Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 3School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
  • 4School of Management, Guangdong University of Technology, Guangzhou 510520, China
  • 5Dalla Lana School of Public Hea lth, University of Toronto, Toronto, Ontario M5T 3M7, Canada
  • 6School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 7Tianjin Municipal Meteorological Observatory, Tianjin 300074, China
  • 8State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 9State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 10Collegee of Resources and Environment, Fujian Agriculture and Forest University, Fuzhou 35002, China

Abstract. Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Hui Zhang et al.

Status: open (until 06 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-257', Minyan Wang, 23 Aug 2022 reply
  • RC2: 'Comment on essd-2022-257', Anonymous Referee #2, 28 Aug 2022 reply

Hui Zhang et al.

Data sets

HiTIC-Monthly: A High Spatial Resolution (1 km×1 km) Monthly Human Thermal Index Collection over China from 2003 to 2020 Hui Zhang, Ming Luo, Yongquan Zhao, et al. https://zenodo.org/record/6895533

Hui Zhang et al.

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Short summary
We generate the first high spatial resolution (1 km×1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) in China over 2003~2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning from multi-source data. The dataset has a high accuracy of R2=0.996, RMSE=0.69 °C, and MAE=0.512 °C, and can describe explicit spatial variations for potential fine-scale studies. The dataset is publicly available via https://zenodo.org/record/6895533.