Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2407-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022
Zhiwei Yang
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Jian Peng
CORRESPONDING AUTHOR
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Yanxu Liu
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Song Jiang
Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61820, USA
Xueyan Cheng
Faculty of Environment and Natural Resources, University of Freiburg, 79106 Freiburg im Breisgau, Germany
Xuebang Liu
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Jianquan Dong
School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Tiantian Hua
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Xiaoyu Yu
Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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EGUsphere, https://doi.org/10.5194/egusphere-2025-5597, https://doi.org/10.5194/egusphere-2025-5597, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Regional coupled human–natural systems models are essential for regional sustainability. We developed a new model, CHANS-SD-YRB, using System Dynamics for the Yellow River Basin in China, which faces severe human-water conflicts. The model links 10 components, including Population, Economy, Energy, Food, Water, Sediment, Land, Carbon, and Climate to simulate basin’s key human-natural interactions. The model is applicable for sustainable development through scenario analyses and predictions.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
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To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
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We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
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Short summary
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global...
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