Xu et al. (2026) The interaction between vegetation greenness and hydro-climatic factors in China

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-01-13
• Authors: Yan Xu, Junjie Liao, Qing Liu, Yuchen Li, Jiatian Pi, Changqing Ke, Jianli Chen, Hang Pan, Lixin Wang, Liyin He, Ling Yao, C. Zhou
• DOI: 10.1016/j.jhydrol.2026.134956

Research Groups

• Jiangsu Provincial Key Laboratory for Advanced Remote Sensing and Geographic Information Technology, Frontiers Science Center for Critical Earth Material Cycling, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
• Department of Land Surveying and Geo-Informatics and Research Institute for Land and Space, The Hong Kong Polytechnic University, Hung Hom, Kowloong, China
• Jiangsu Maritime Institute, Nanjing, China
• Department of Earth Sciences, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA
• Nicholas School of the Environment, Duke University, Durham 27708 NC, USA
• College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
• State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Short Summary

This study investigated the bidirectional interactions between vegetation greenness and key hydro-climatic factors across China from 2002 to 2021, revealing that temperature is the primary driver of vegetation growth, while vegetation significantly influences terrestrial water storage and precipitation.

Objective

• To analyze the bidirectional relationships between vegetation greenness and key hydro-climatic variables (temperature, terrestrial water storage, soil moisture, precipitation, and solar radiation) across China from 2002 to 2021.

Study Configuration

• Spatial Scale: China (grid cells)
• Temporal Scale: 2002 to 2021 (20 years)

Methodology and Data

• Models used: Nonlinear Granger causality tests
• Data sources: Vegetation greenness, temperature, terrestrial water storage, soil moisture, precipitation, and solar radiation data (specific datasets not detailed in the provided text, but typically derived from satellite observations, reanalysis, or ground measurements).

Main Results

• Temperature was the dominant Granger-causal factor influencing vegetation growth, affecting 43.49 % of grid cells.
• Other factors influencing vegetation growth included terrestrial water storage (16.49 %), soil moisture (11.44 %), precipitation (10.92 %), and solar radiation (3.11 %).
• Vegetation exerted the strongest feedback on terrestrial water storage (31.18 %) and precipitation (28.77 %).
• Weaker vegetation feedback effects were observed on soil moisture (20.68 %), solar radiation (9.67 %), and temperature (3.79 %).
• Bidirectional Granger causality was identified in 33.57 %–46.77 % of the assessed areas.
• Grasslands exhibited the highest proportion of significant causal relationships.

Contributions

• Improved understanding of the complex interactions between vegetation and hydro-climatic factors across China.
• Provided valuable insights to guide the refinement and optimization of ecological and hydrological models.

Funding

• Not specified in the provided text.

Citation

@article{Xu2026interaction,
  author = {Xu, Yan and Liao, Junjie and Liu, Qing and Li, Yuchen and Pi, Jiatian and Ke, Changqing and Chen, Jianli and Pan, Hang and Wang, Lixin and He, Liyin and Yao, Ling and Zhou, C.},
  title = {The interaction between vegetation greenness and hydro-climatic factors in China},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.134956},
  url = {https://doi.org/10.1016/j.jhydrol.2026.134956}
}