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
Global climate change has brought significant challenges to human society and natural systems (Arias et al., 2021; Haines and Ebi, 2019) by inducing higher air temperature and more frequent extreme weather and climate events around the world (Arias et al., 2021; Schwingshackl et al., 2021). Heat-related disasters, e.g., heatwaves, droughts, and wildfires, are occurring more frequently and becoming more intense (Tong et al., 2021; Arias et al., 2021; Luo et al., 2022), exacerbating the thermal environment and threatening the tolerance limits of humans, animals, and plants (Raymond et al., 2020). Substantial warming and increasing extreme weather and climate events aggravate human thermal comfort and increase the exposures to uncomfortable thermal environments (Brimicombe et al., 2021), thus posing adverse impacts on public health, socio-economy, and agricultural productivities (Budhathoki and Zander, 2019; Moda et al., 2019; Tuholske et al., 2021; Sun et al., 2019; Zhao et al., 2017).
The thermal stress that human beings actually perceive is not only related to air temperature, but also jointly influenced by other environmental variables such as humidity, wind, and/or direct sunlight (Mistry, 2020; Djongyang et al., 2010). These variables alter the heat balance that maintains the core temperature of human bodies by influencing the heat exchange (e.g., radiation, convection, conduction, and evaporation) between humans and the surrounding environment (Periard et al., 2021; Stolwijk, 1975). High atmospheric humidity can exacerbate the thermal stress on human bodies by reducing evaporation from the skin through sweating when the air temperature is high (Li et al., 2018; Rogers et al., 2021; Luo and Lau, 2021). Furthermore, abnormal weather with a combination of extremely high air temperature, humidity, and/or wind can reduce labor capacity and human performance (Roghanchi and Kocsis, 2018; Lazaro and Momayez, 2020; Enander and Hygge, 1990), leading to temperature-related discomfort, stress, morbidity, and even death (Di Napoli et al., 2018; Kuchcik, 2021; Nastos and Matzarakis, 2011), particularly during heatwaves. For example, in the summer of 2017, 2018, and 2019, there were 1489, 1700, and 161 heatwave-related deaths, respectively, in the United Kingdom (Rustemeyer and Howells, 2021). Additionally, vulnerable groups including children, the elderly, chronic patients, and poor communities are at higher risk of being affected by thermal stress (Patz et al., 2005; Wang et al., 2019), which is likely to be further exacerbated as global population aging and climate warming (United Nations, 2017).
The changes and impacts of human thermal stress have attracted increasing
attention in recent years (Schwingshackl et al., 2021; Krzysztof et al.,
2021; Li et al., 2018; Rahman et al., 2022; Ren et al., 2022; Luo and Lau,
2021). For instance, Szer et al. (2022) estimated the impact of
heat stress on construction workers based on the Universal Thermal Climate
Index (UTCI). Ren et al. (2022) and Luo and Lau (2021)
quantified the contribution of urbanization and climate change to urban
human thermal comfort in China. Schwingshackl et al. (2021) assessed
the future severity and trend of global heat stress based on Coupled Model
Intercomparison Project phase 6 (CMIP6). These studies were mainly based on
meteorological stations or coarse-gridded data. However, the meteorological
stations are sparsely distributed (Peng et al., 2019), particularly in
undeveloped and mountainous areas, which cannot reveal continuously spatial
distributions of air temperature and thermal stress conditions (He et
al., 2022). Additionally, existing low spatial resolution image products
(Mistry, 2020; Di Napoli et al., 2020) cannot be applied to
fine-scale studies because they cannot provide information with spatial
details and variations. However, the changes in human thermal stress at a
fine scale (e.g.,
Although extensive studies have been conducted to generate high-resolution
land surface temperature (LST) (such as the Land Surface Temperature in
China LSTC; Zhao et al., 2020 and the global seamless land surface
temperature dataset, Zhang et al., 2022b; Hong et al., 2022), or near
surface air temperatures (SAT) products (such as ERA5, Copernicus Climate Change Service, 2017,
TerraClimate, Abatzoglou et al., 2018, and
GPRChinaTemp1km, He et al., 2022), human thermal stress datasets were
generally produced at low-resolution levels, such as ERA5-HEAT (Di
Napoli et al., 2020), HDI_0p25_1970_2018 (hereafter, HDI) (Mistry, 2020), and HiTiSEA
(Yan et al., 2021). ERA5-HEAT was derived from ERA5
and includes two global hourly human thermal stress indices (UTCI and mean
radiant temperature (MRT)) from January 1979 to the present (Di
Napoli et al., 2020). The HDI dataset was generated using 3 h climate
variables of the global land data assimilation system (GLDAS), and it
contains 10 daily indices with a spatial resolution of
Various indices have been proposed to measure human thermal stress, but there is no universal thermal stress index that works in all climate zones (Schwingshackl et al., 2021; Brake and Bates, 2002; Roghanchi and Kocsis, 2018; Luo and Lau, 2021). Existing human thermal stress indices considered different climate conditions, direct or indirect exposures to weather elements, human metabolism, and the local working environment (Di Napoli et al., 2020), which were designed to evaluate or quantify the comprehensive environmental pressure of meteorological factors (e.g., temperature, humidity, wind) on human bodies (Epstein and Moran, 2006). These indices are based on the thermal exchange between the human and surrounding environments or empirical relationships gained by studying human responses to various environmental factors, varying in complexity, applicability, and capacity (Staiger et al., 2019). For example, the heat index (HI) is used for meteorological service (NWS, 2011); wet-bulb temperature (WBT) is used to measure the upper physiological limit of human beings (Raymond et al., 2020); physiologically equivalent temperature (PET) and UTCI are used to estimate human thermal comfort (Varentsov et al., 2020). Therefore, a high-resolution dataset that contains different commonly used human thermal stress indices is urgently called for in global and regional studies, particularly for those with complex climate conditions (e.g., China).
China has been threatened by deteriorating thermal environments under global climate change and rapid local urbanization over the past decades (Ren et al., 2022; Luo and Lau, 2019). The changes and characteristics of human thermal stress across China have attracted extensive attention in recent years (Yan, 2013; Tian et al., 2022; Li et al., 2022). Wang et al. (2021) found that the frequency of extreme human-perceived temperature events increases in summer and decreases in winter in most urban agglomerations (UAs) of China. Li et al. (2022) showed that the frequency of thermal discomfort days in China exhibits a significant increasing trend from 1961 to 2014, and there will be more threats from thermal discomfort in the future. Therefore, a long-term and high-resolution dataset with multiple human thermal stress indices in China is of great importance for investigating detailed spatial and temporal variations of human thermal stress across the country. Such a dataset has the potential to (1) assess population exposure to extreme thermal conditions and heat-related health risks, (2) reveal the spatiotemporal evolution of human thermal stress and its influence on public health, tourism, industries, military, epidemiology, and biometeorology at a fine scale, and (3) provide policymakers with data in manipulating targeted strategies to mitigate heat stress and protect vulnerable people.
In this study, we produced a high-resolution (
Daily mean surface air temperature, relative humidity, and wind speed
recorded at the 2419 weather stations across China
(Fig. 1) during 2003–2020 were collected from the
China Meteorological Data Service Center (CMDC) at
Gridded datasets used in this study.
Spatial distribution of meteorological stations in the mainland of China, with color shadings indicating the elevation in meters.
Equations of the human thermal indices for each station.
SAT is observed air temperature (
Human thermal stress is related to temperature, topography, land cover,
population density, surface water, and vegetation (Wang et al., 2020;
Rahman et al., 2022; Krzysztof et al., 2021). In this study, eight variables
reflecting the changes and spatial distribution characteristics of
temperature were used to predict human thermal indices
(Table 1) in addition to the meteorological
variables. As LST is one of the most essential parameters for predicting
human thermal indices, the seamless LST dataset created by Zhang et al. (2022b) was introduced into our model training. This LST dataset used a
spatiotemporal gap-filling algorithm to fill the missing or invalid value
caused by clouds in the Moderate Resolution Imaging Spectroradiometer
(MODIS) LST dataset (MOD11A1 and MYD11A1). It includes daily mid-daytime
(13:30) and mid-nighttime (01:30) LST with 1 km spatial resolution. The mean
root mean squared errors (RMSEs) of daytime and nighttime LST are 1.88 and 1.33
Scatter plots of predictions versus observations of the 12
human thermal indices over the mainland of China during 2003–2020.
In addition to SAT, the calculation of human thermal indices used in this
study is described in Table 2. These indices are
first calculated based on SAT (also simply denoted as
The Light Gradient Boosting Machine (LightGBM) algorithm was employed to predict human thermal indices during 2003–2020. LightGBM is one of the gradient boosting decision tree (GBDT) algorithms developed by Microsoft Research (Ke et al., 2017). This algorithm has become a very popular nonlinear machine learning algorithm due to its superior performance in machine learning competitions and efficiency (Candido et al., 2021). Its performance has been evaluated and shows desirable results in different applications, such as evapotranspiration estimation (Fan et al., 2019), land cover classification (Candido et al., 2021; Mccarty et al., 2020), air quality prediction (Su, 2020; Zeng et al., 2021; Tian et al., 2021), subsurface temperature reconstruction (Su et al., 2021), and above-ground biomass estimation (Tamiminia et al., 2021).
Overall prediction accuracies of the 12 human thermal
indices over the mainland of China during 2003–2020.
Overall prediction accuracies of the 12 human thermal indices over the mainland of China during 2003–2020.
Furthermore, LightGBM adopts the Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) algorithms to improve the training speed (Su et al., 2021). Here, GOSS is used to select data instances with larger gradients and to exclude a considerable proportion of small gradient data instances (Ke et al., 2017), and EFB is used to merge features (Ke et al., 2017). Compared with traditional GBDT algorithms including eXtreme gradient boosting (XGBoost) and Stochastic Gradient Boosting (SGB), LightGBM effectively decreases the training time without reducing the accuracy (Los et al., 2021; Ke et al., 2017).
We used the Python package
Four statistic metrics – namely, determination coefficient (
Spatial distribution of
The prediction accuracies of the 12 human thermal indices were evaluated
based on the validation data introduced in Sect. 3.2. All predicted human
thermal indices exhibit high accuracies. Figure 2
shows the scatter plots of the observed versus the predicted values of the
12 human thermal indices. As the figure displays, the data points of all
indices are concentrated around the corresponding
As Fig. 4 but for MAE.
As Fig. 4 but for RMSE.
The spatial distributions of
As Fig. 4 but for bias.
Annual prediction accuracies of the 12 human thermal
indices over the mainland of China during 2003–2020:
The annual accuracies regarding RMSE, MAE, and bias of the 12 human thermal indices
during 2003–2020 are shown in Fig. 8. RMSEs and
MAEs of all indices in nearly all years are less than 1.0
Spatial distributions of the monthly mean ET over the mainland of China in 2020.
More than half of the national population in China lives in cities,
particularly in UAs (i.e., also known as city clusters). Here we assessed
the prediction accuracies in 20 major UAs in China, which hold 62.83 % and
80.57 % of the total population and gross domestic product (GDP) of the
country (Fang and Yu, 2016). These accuracy assessments are presented in Tables S1–S4 in the Supplement. As shown in Table S1, all UAs have
The abovementioned assessments show that our model based on LightGBM can yield high-accuracy predictions at both national and local scales. Therefore, this model is employed to generate a high-resolution human thermal index collection at a monthly scale over China (HiTIC-Monthly) during 2003–2020. By taking monthly ET in 2020 as an example, we examined the monthly evolution of spatial patterns of the HiTIC-Monthly dataset in this subsection.
Figure 9 shows the monthly distribution of the predicted ET in 2020, which exhibits obvious seasonality with higher temperatures in summer and lower in winter. The temperature shows a significant zonal difference with colder temperatures in northern than in southern China. The temperature has a close relationship with topography and decreases with elevation, varying from plateaus to plains. The Qinghai–Tibet Plateau (TP) has the lowest temperature, while southern China, the Sichuan Basin, and the Gobi regions in Northwest China witness the highest temperatures. The distribution of temperature exhibits different patterns among the four seasons, especially between winter (e.g., January) and summer (e.g., July). In winter, the temperature increases from northern to southern areas and is the coldest in Northeast and Northwest China and the warmest on the island of Hainan. In the summer, the hottest temperature appears in the Tarim and Junggar basins of Xinjiang. The NCP region also has a high temperature in summer, which might be related to local urbanization (Liu et al., 2008) and irrigation (Kang and Eltahir, 2018).
Spatial distributions of the 12 human thermal indices over the mainland of China in July 2020.
The spatial variations of the predicted human thermal indices in summer (which is often characterized by severe heat stress) are examined in Fig. 10 by taking July 2020 as an example. As it shows, the 12 indices exhibit similar distribution patterns. There are significant differences in temperature among Northwest, northeastern, and southeastern China. Generally, the temperature decreases from the southeast to the northwest, and the southeast and northwest parts have the highest and lowest temperatures, respectively.
HMI exhibits the highest temperature while NET shows the lowest in July 2020. The dominant modes of these indices are further examined by applying the empirical orthogonal function (EOF) analysis (Figs. S10–S13). As Fig. S10 shows, the leading EOF (EOF1) of all 12 thermal indices exhibit highly consistent spatial distribution with higher values in the northern region and lower values in the south. Their temporal variations are also similar to each other (Fig. S11). The second and third EOF modes (EOF2 and EOF3) are also similar among different thermal indices (except EOF3 of NET, Figs. S11–S13). These results demonstrate the desirable quality of our products.
Temporal changes of the 12 annually averaged human thermal indices over the mainland of China during 2003–2020. The line illustrates the linear trend, the number in the square bracket means the corresponding trend per decade, and the asterisk next to the number indicates that the trends are significant at the 0.05 level.
The yearly evolutions of the annual mean human thermal indices during
2003–2020 are displayed in Fig. 11. Despite the
interannual fluctuation in the time series, all indices exhibit upward
trends except for NET and WCT, of which the decreasing trends are mainly
affected by the recovering wind speed in the recent decade (Zeng
et al., 2019). The fastest warming appears in HMI (0.303
Spatial distributions of the linear trends (unit:
Furthermore, different indices have different degrees of increasing trends. HMI has the largest increasing magnitude (Fig. 12h), and ET is seen with relatively slight increases across China (Fig. 12f). The trends of NET and WCT have similar spatial distribution patterns, with large proportions having cooling trends since 2003 (Fig. 12j and l). Most parts of Xinjiang, northeastern and southern China have obvious decreasing trends, and the Inner Mongolia Plateau (IMP), NCP, eastern TP, YRD, and YGP have slightly increasing trends.
The temporal trends of the human thermal indices in different seasons were
also examined (Fig. 13). The fastest warming
tendency is observed in the spring season. The rising trends of spring HMI,
HI, MDI, AT
Temporal trends of the 12 annually and seasonally averaged human thermal indices over the mainland of China during 2003–2020. The number means linear trend per decade. The asterisk indicates that the trends are significant at the 0.05 level.
Comparison of the spatial patterns among
HDI_0p25_1970_2018 (HDI),
HiTiSEA, and HiTIC-Monthly for AT
Figure S6 maps the spatial patterns of the trends of summer mean human
thermal indices over the mainland of China during 2003–2020. All indices
show warming trends in most parts of China, particularly in NCP and TP. As
one of the most densely populated regions in China, the prominent increases
in thermal indices in NCP indicate that the local has been experiencing
increasing threats of intensifying heat stress. Among the 12 indices,
AT
The spatial distributions of the changing trends in winter across the mainland of China during 2003–2020 are depicted in Fig. S7. The trend patterns in winter are similar to that in summer to some degree. The warming trends are concentrated in Southwest China, most parts of Northwest China, and parts of East China (e.g., YRD). The cooling trends are located in TP, parts of Northeast and South China. The cooling tendencies are especially significant in Northeast China and most parts of Northwest and South China (Fig. S7j and m). Parts of central China are seen with even stronger cooling thermal comfort.
In the spring, increases in all thermal indices are observed in most parts of China (Fig. S8), particularly in northern regions, such as central Inner Mongolia, parts of NCP, and Northeast China, while parts of southern China have slight decreases. These decreases are noticeable in NET and WCT (Fig. S8j and m). In contrast to spring, the autumn season is observed with decreased thermal temperature in the north and increases in the south (e.g., Southwest China, Fig. S9).
Comparisons of the four thermal index datasets.
We compared our HiTIC-Monthly with two existing datasets, i.e., HDI
(Mistry, 2020) and HiTiSEA (Yan et al., 2021),
which have coarser spatial resolutions of
There are 12 commonly used human thermal indices in the HiTIC-Monthly
dataset produced in this study. Nine of these indices were computed from
temperature and humidity (or water vapor) and the other three (i.e.,
AT
Since LST is the most important variable for predicting the 11 human thermal indices, the uncertainty in the LST dataset may influence the accuracy of the human thermal indices. The LST variable in our prediction is collected from a global seamless 1 km resolution daily LST dataset (Zhang et al., 2022b). This dataset was generated based on spatiotemporal gap-filling algorithms and the MODIS LST data. It may overestimate LST in some cases because the LST under cloudy weather was filled based on the data in clear sky conditions (Zhang et al., 2022b). A high-quality LST dataset would further improve the prediction accuracy of the human thermal indices.
The human thermal indices dataset is at a monthly scale, but the temporal resolution may not be sufficient for the research of extreme weather events (e.g., heatwaves and cold spells) and related environmental health (e.g., heat-related mortality). A daily high-resolution human thermal index collection (HiTIC-Daily) will be produced and released in our future studies. In the current study, we provided the first national-level dataset over the mainland of China with multiple high-resolution human thermal indices in a monthly interval, which shows high prediction accuracies in all climate regimes across China. A global dataset of multiple human thermal indices dataset is also expected in the near future.
The high spatial resolution monthly human thermal index collection
(HiTIC-Monthly) generated in this study is freely available to the public in
network common data form (NetCDF) from Zenodo at
A long-term and high-resolution dataset of multiple human thermal indices is
of great significance for monitoring detailed spatiotemporal changes of
human thermal stress in different climate regions across China and assessing
the health risks of people exposed to extreme heat at a fine scale. However,
the current datasets of human thermal indices (e.g., HDI and HiTiSEA) only
have coarse spatial resolutions (
The HiTIC-Monthly dataset was produced by LightGBM based on multi-source data,
including MODIS LST, DEM, land cover, population density, and impervious
surface fraction. This dataset shows a desirable performance, with mean
The supplement related to this article is available online at:
ML and YZ conceptualized and designed the study. HZ collected the data, conducted the analyses, and wrote the first draft of the paper. All authors discussed the results and edited the paper.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported by the National Natural Science Foundation of China (grant no. 41871029), the Guangdong Basic and Applied Basic Research Foundation (grant no. 2019A1515011025), the National Youth Talent Support Program of China, the Pearl River Talent Recruitment Program of Guangdong Province (grant no. 2017GC010634), and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (grant no. 311021008). The authors are grateful to the editor and two reviewers, whose comments and suggestions have significantly improved the quality of our manuscript.
This research has been supported by the National Natural Science Foundation of China (grant no. 41871029), the National Key Research and Development Program of China (grant no. 2019YFC1510400), the Natural Science Foundation of Guangdong Province (grant no. 2019A1515011025), and the Guangdong Provincial Pearl River Talents Program (grant no. 2017GC010634).
This paper was edited by Qingxiang Li and reviewed by Minyan Wang and one anonymous referee.