Luo et al. (2026) Evolution and Prediction of Meteorological Elements in the Luanhe River Basin Based on Machine Learning and CMIP6

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Identification

• Journal: Journal of Hydrometeorology
• Year: 2026
• Date: 2026-07-28
• Authors: Zhuoran Luo, Xianzhi Wang, Yitong Li, Jiahong Liu, Litao Huo, Ruitao Jia, Li Zhang
• DOI: 10.1175/jhm-d-26-0020.1

Research Groups

• Institute of Hydrology and Water Resources, Hebei University
• Beijing Climate Center, China Meteorological Administration
• Department of Earth System Science, Tsinghua University

Short Summary

This study analyzes the spatiotemporal evolution characteristics of key meteorological elements in the Luanhe River basin from 1980 to 2023 and predicts their trends from 2025 to 2049 using machine learning and CMIP6 data.

Objective

• Investigate the long-term changes in temperature and precipitation in the Luanhe River basin and predict future tendencies under different greenhouse gas emission scenarios.

Study Configuration

• Spatial Scale: The study focuses on the Luanhe River basin, a critical water source for the Beijing–Tianjin–Hebei region.
• Temporal Scale: The study covers a period of 43 years (1980-2023) and predicts future trends from 2025 to 2049.

Methodology and Data

• Models used: Machine learning algorithms and CMIP6 climate models
• Data sources: Observation data from meteorological and hydrological stations, CMIP6 data

Main Results

• The annual average temperature in the Luanhe River basin has shown a fluctuating upward trend of 0.33°C every 10 years from 1980 to 2023.
• The average precipitation increases by 23.73 mm every 10 years during the same period.
• Under the SSP585 scenario, the predicted annual average temperature is 10.15°C and the annual cumulative precipitation is 712.06 mm.

Contributions

This study provides a comprehensive understanding of the long-term changes in temperature and precipitation in the Luanhe River basin and offers valuable insights for policymakers to design better water management and climate adaptation strategies.

Funding

• National Natural Science Foundation of China (Grant No. 51879215)
• Hebei Provincial Key Research and Development Program (Grant No. 1921021)

Citation

@article{Luo2026Evolution,
  author = {Luo, Zhuoran and Wang, Xianzhi and Li, Yitong and Liu, Jiahong and Huo, Litao and Jia, Ruitao and Zhang, Li},
  title = {Evolution and Prediction of Meteorological Elements in the Luanhe River Basin Based on Machine Learning and CMIP6},
  journal = {Journal of Hydrometeorology},
  year = {2026},
  doi = {10.1175/jhm-d-26-0020.1},
  url = {https://doi.org/10.1175/jhm-d-26-0020.1}
}