HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020

Abstract

Abstract. 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 R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘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 https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003–2020

“…It also uses the Gradient-based One-Side Sampling (GOSS) to select important categorical features and reduce the dimensionality of the problem. Its high accuracy and stability have been substantiated in building prediction models for both classification and regression tasks of geophysical variables 49 – 51 .…”

A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020

“…LightGBM consumes much less memory and improves training efficiency by more than 20 times while maintaining nearly the same accuracy 48 . Due to its superior degree of generalizability, extremely fast processing speed, and high accuracy, LightGBM has been widely applied in different fields (e.g., climatology, air pollution, social psychology) 47 , 62 – 67 . As shown in Fig.…”

“…Zhang et al . 47 have generated a 1-km-resolution dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. However, this monthly dataset is not capable of detecting extreme weather events such as heatwaves and cold spells, which are usually defined based on daily temperatures.…”

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

“…The urban thermal environment has become a significant concern in light of global warming's impact on human settlements. Many people in the world are exposed to extreme heat [1]. The urban heat island effect, which occurs as a result of rapid urbanization and expansion, is a growing issue in the Yangtze River Delta region [2].…”

“…Taking Nanjing as an example, this study analyses the scale effect of relationship between landscape indices and land surface temperature and find the optimal window sizes. The content includes the following: (1) This paper ses the relationship between landscape indices and land surface temperature by using the Pearson correlation coefficient. (2) This paper quantifies the law of the scale effect of correlation of landscape indices and land surface temperature.…”