HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
<p>Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/359/2023/essd-15-359-2023.pdf |
| _version_ | 1797946955128635392 |
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| author | H. Zhang M. Luo M. Luo Y. Zhao L. Lin E. Ge Y. Yang G. Ning J. Cong Z. Zeng K. Gui J. Li T. O. Chan X. Li S. Wu P. Wang X. Wang |
| author_facet | H. Zhang M. Luo M. Luo Y. Zhao L. Lin E. Ge Y. Yang G. Ning J. Cong Z. Zeng K. Gui J. Li T. O. Chan X. Li S. Wu P. Wang X. Wang |
| author_sort | H. Zhang |
| collection | DOAJ |
| description | <p>Human-perceived thermal comfort (known as human-perceived temperature)
measures the combined effects of multiple meteorological factors (e.g.,
temperature, humidity, and wind speed) 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 generated the first high spatial resolution (1 km) dataset
of monthly human thermal index collection (HiTIC-Monthly) over China during
2003–2020. In this collection, 12 commonly used thermal indices were
generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm
from multi-source data, including land surface temperature, topography, land
cover, population density, and impervious surface fraction. Their accuracies
were comprehensively assessed based on the observations at 2419 weather
stations across the mainland of China. The results show that our dataset has
desirable accuracies, with the mean <span class="inline-formula"><i>R</i><sup>2</sup></span>, root mean square error, and mean
absolute error of 0.996, 0.693 <span class="inline-formula"><sup>∘</sup></span>C, and 0.512 <span class="inline-formula"><sup>∘</sup></span>C,
respectively, by averaging the 12 indices. Moreover, the data exhibit high
agreements with the observations across spatial and temporal dimensions,
demonstrating the broad applicability of our dataset. A 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) studies. Further
investigation reveals that nearly all thermal indices exhibit increasing
trends in most parts of China during 2003–2020. The increase is especially
significant in North China, Southwest China, the Tibetan Plateau, and parts
of Northwest China, during spring and summer. The HiTIC-Monthly dataset is
publicly available from Zenodo at <a href="https://doi.org/10.5281/zenodo.6895533">https://doi.org/10.5281/zenodo.6895533</a> (Zhang et al., 2022a).</p> |
| first_indexed | 2024-04-10T21:19:09Z |
| format | Article |
| id | doaj.art-fb1eadf21643406c9863abbf1c3078b3 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-10T21:19:09Z |
| publishDate | 2023-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-fb1eadf21643406c9863abbf1c3078b32023-01-20T09:15:14ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162023-01-011535938110.5194/essd-15-359-2023HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020H. Zhang0M. Luo1M. Luo2Y. Zhao3L. Lin4E. Ge5Y. Yang6G. Ning7J. Cong8Z. Zeng9K. Gui10J. Li11T. O. Chan12X. Li13S. Wu14P. Wang15X. Wang16School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaInstitute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, ChinaSchool of Management, Guangdong University of Technology, Guangzhou 510520, ChinaDalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5T 3M7, CanadaSchool of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaInstitute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, ChinaTianjin Municipal Meteorological Observatory, Tianjin 300074, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaCollege of Resources and Environment, Fujian Agriculture and Forest University, Fuzhou 35002, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, ChinaSchool of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510006, China<p>Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) 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 generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean <span class="inline-formula"><i>R</i><sup>2</sup></span>, root mean square error, and mean absolute error of 0.996, 0.693 <span class="inline-formula"><sup>∘</sup></span>C, and 0.512 <span class="inline-formula"><sup>∘</sup></span>C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A 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) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at <a href="https://doi.org/10.5281/zenodo.6895533">https://doi.org/10.5281/zenodo.6895533</a> (Zhang et al., 2022a).</p>https://essd.copernicus.org/articles/15/359/2023/essd-15-359-2023.pdf |
| spellingShingle | H. Zhang M. Luo M. Luo Y. Zhao L. Lin E. Ge Y. Yang G. Ning J. Cong Z. Zeng K. Gui J. Li T. O. Chan X. Li S. Wu P. Wang X. Wang HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 Earth System Science Data |
| title | HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 |
| title_full | HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 |
| title_fullStr | HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 |
| title_full_unstemmed | HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 |
| title_short | HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020 |
| title_sort | hitic monthly a monthly high spatial resolution 1 thinsp km human thermal index collection over china during 2003 2020 |
| url | https://essd.copernicus.org/articles/15/359/2023/essd-15-359-2023.pdf |
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