Yang et al. (2025) Crucial drivers and interaction mechanisms of ecosystem water use efficiency in the Yellow River Basin, China

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-12-17
• Authors: Shengjie Yang, Liang Zhong, Jianlong Li, Shuai Song, Yunqiao Zhou, Liangyun Sheng, Zhengguo Sun
• DOI: 10.1016/j.ejrh.2025.103042

Research Groups

• Department of Ecology, School of Life Sciences, Nanjing University, Nanjing, China
• State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
• Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing, China
• School of Marxism, Nanjing University, Nanjing, China
• College of Agro-Grassland Science, Nanjing Agricultural University, Nanjing, China

Short Summary

This study analyzed the spatiotemporal trends and complex interaction mechanisms of ecosystem water use efficiency (WUE) in the Yellow River Basin (YRB) from 2001 to 2020, revealing an overall increase in WUE driven by synergistic and antagonistic effects among key factors like water conditions, leaf area index, human activities, radiation-temperature, and geographic environment.

Objective

• To analyze the spatial and temporal changes in ecosystem water use efficiency (WUE) in the Yellow River Basin (YRB) from 2001 to 2020.
• To identify the key drivers among potential factors influencing WUE in the YRB ecosystem.
• To quantify the interaction mechanisms, including direct, indirect, and total effects, through which the main driving forces influence WUE, specifically analyzing antagonistic and synergistic relationships.

Study Configuration

• Spatial Scale: Yellow River Basin (YRB), China, covering approximately 795,000 square kilometers. All spatial data were resampled to a uniform spatial resolution of 5 kilometers.
• Temporal Scale: 2001 to 2020 (20 years). Daily data were aggregated to annual totals.

Methodology and Data

• Models used: Ordinary Least Squares (OLS) regression, Sen’s slope estimator (SSE), Mann-Kendall (MK) trend test, Random Forest (RF) analysis, Partial Least Square-Structural Equation Modeling (PLS-SEM).
• Data sources: Multi-source spatial data, including satellite, observation, and reanalysis products.
  • WUE components: Gross primary productivity (GPP, gC⋅m⁻²⋅d⁻¹) and Evapotranspiration (ET, mm⋅d⁻¹).
  • Climate factors: Maximum temperature (Tmx, °C), Minimum temperature (Tmn, °C), Average temperature (Tmp, °C), Precipitation (Pre, 0.1 mm), Solar radiation (SR, W⋅m⁻²), Soil water content (SW, mm), Wind speed (WS, m⋅s⁻¹).
  • Human activity factors: Human footprint (HFP), Population density (PD, People⋅km⁻²), Night lights (NL, DN value), Gross domestic product (GDP, million USD).
  • Geographic factors: Elevation (m), Slope (Degree), Terrain niche index (TNI), Relief degree of land surface (RDLS).
  • Vegetation factor: Leaf area index (LAI).
  • Land use: Land use data (30 m resolution).

Main Results

• Ecosystem WUE in the YRB showed an overall upward trend from 2001 to 2020, with an OLS slope of 0.02 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹ and a Sen’s slope of 0.128 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹.
• The average WUE exhibited a spatial pattern of lower basin > middle basin > upper basin.
• Annual WUE change rates by vegetation type were: cropland (0.037 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹) > forest (0.027 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹) > grassland (0.015 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹) > shrubland (0.006 gC⋅m⁻²⋅mm⁻¹⋅yr⁻¹).
• Key drivers of WUE identified were Leaf Area Index (LAI), Night Lights (NL), Soil Water (SW), Elevation, Relief Degree of Land Surface (RDLS), Minimum Temperature (Tmn), Precipitation (Pre), Average Temperature (Tmp), and Solar Radiation (SR).
• The five-year average effect intensities of driving forces on WUE ranked as: water conditions (0.646) > leaf area index (0.506) > human activities (0.235) > radiation–temperature (0.115) > geographic environment (0.070).
• Radiation-temperature had a positive direct effect on WUE, but negative indirect effects antagonized this contribution, resulting in a net negative total effect (e.g., -0.212 in 2020).
• Water conditions and human activities showed synergistic effects that reinforced their positive impacts on WUE. Human activities consistently had positive direct, total, and indirect effects on WUE.
• The geographic environment exhibited mainly negative direct effects, partially offset by positive indirect consequences, leading to a predominantly negative total effect (except for 2001).

Contributions

• Deepens the understanding of drivers influencing WUE changes in the Yellow River Basin by explicitly analyzing complex antagonistic and synergistic interaction mechanisms between direct and indirect effects of driving forces.
• Provides valuable implications for improving WUE management by demonstrating that strategically leveraging mediating effects can effectively enhance the sustainability of ecosystem WUE.
• Suggests that management authorities can use water conditions as a mediating variable to promote WUE sustainability and mitigate negative effects of other variables through targeted regulation of mediating variables.

Funding

• National Natural Science Foundation of China (No. 42371485)
• National Key Research and Development Program of China (No. 2018YFD0800201)
• Asia-Pacific Network for Global Change Research (No. ARCP2015-03CMY-Li)

Citation

@article{Yang2025Crucial,
  author = {Yang, Shengjie and Zhong, Liang and Li, Jianlong and Song, Shuai and Zhou, Yunqiao and Sheng, Liangyun and Sun, Zhengguo},
  title = {Crucial drivers and interaction mechanisms of ecosystem water use efficiency in the Yellow River Basin, China},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.103042},
  url = {https://doi.org/10.1016/j.ejrh.2025.103042}
}