Gao et al. (2025) Climate change and human activities amplify runoff variability risks in lower reaches of large rivers

Identification

• Journal: Communications Earth & Environment
• Year: 2025
• Date: 2025-10-07
• Authors: Ju Gao, Chunhui Li, Xiong Zhou, Yujun Yi, Xuan Wang, Qiang Liu
• DOI: 10.1038/s43247-025-02759-3

Research Groups

• Key Lab of Water and Sediment Science of Ministry of Education, School of Environment, Beijing Normal University, Beijing, China
• State Key Laboratory of Wetland Conservation and Restoration, School of Environment, Beijing Normal University, Beijing, China

Short Summary

This study developed a three-tiered attribution framework to analyze spatiotemporal runoff variations and their drivers in the Yellow River Basin (1952–2021), revealing that cumulative climatic and, predominantly, human activities amplify runoff variability risks in downstream regions with significant seasonal fluctuations.

Objective

• Analyze the annual and seasonal runoff trend characteristics (1952–2021) across upstream and downstream regions of the Yellow River Basin.
• Quantify the relative contributions of climate change and human activities to runoff variations in each sub-catchment.
• Reveal the interaction mechanisms and key driving factors of runoff variations between upstream and downstream areas within the basin under the impacts of climate change and human activities.

Study Configuration

• Spatial Scale: Yellow River Basin, divided into twenty sub-catchments.
• Temporal Scale: 1952–2021 (70 years), analyzed in two main periods (Period I: 1952–1986; Period II: 1987–2021) and four sub-periods (Period 1: 1952–1969; Period 2: 1970–1986; Period 3: 1987–2003; Period 4: 2004–2021).

Methodology and Data

• Models used: Three-tiered attribution framework, linear regression, multi-year average differentiation analysis, Budyko water-energy balance curve approach (Schreiber, Ol'dekop, Budyko, Turc non-parametric curves), Spearman correlation, Peter-Clark Momentary Conditional Independence Plus (PCMCI+) causal discovery algorithm (including PC1 algorithm and MCI test), hierarchical clustering analysis.
• Data sources: Monthly flow data from 20 hydrological stations (1952–2021) from the Hydrology Bureau of the Yellow River Conservancy Commission; monthly average air temperature, precipitation, and potential evapotranspiration data (1 km resolution, 1952–2021) from the National Tibetan Plateau Data Center (TPDC); land use data (1 km resolution, 1980, 1990, 2005, 2015) from TPDC; GIS population distribution dataset for six national censuses from the Scientific Data Bank; ASTER GDEM V3 digital elevation model (DEM) dataset (30 m resolution) from the Geospatial Data Cloud.

Main Results

• Runoff reduction intensifies from upstream to downstream in the Yellow River Basin (1952–2021), with significant decreasing trends observed at 13 downstream sites (e.g., H20: –16.93 m³ s⁻¹ yr⁻¹).
• Human activities are the dominant driver of runoff changes, contributing 15.2% to 92.9% of runoff reduction, while climate change contributes from -8.6% to 7.1% (relative to total reduction at H20).
• From Period 1 to Period 3, the basin-wide runoff contribution decreased by 538.19 m³ s⁻¹ and 586.35 m³ s⁻¹, with human activities accounting for 81.1% and 70% of these reductions, respectively. From Period 3 to Period 4, runoff increased by 252.94 m³ s⁻¹, primarily due to climate change (138.7% contribution, human activities -38.7%).
• Runoff changes are generally more pronounced in summer and autumn, predominantly driven by climate change, while spring and winter changes are largely governed by human disturbances, especially reservoir regulation.
• Key drivers of runoff changes include precipitation variability, declining glacial meltwater, reservoir construction, agricultural production, and socio-economic development.
• Strong mutual causal relationships exist between upstream and downstream river runoff, with causal strength weakening with increasing distance between stations. Downstream water demands can exert a "bottom-up" feedback, influencing upstream runoff contributions.
• Glaciers, lakes, and reservoirs' storage capacities cause lagged effects of climate change and human activities on runoff variations and serve as critical drivers for inter-basin water resource regulation.

Contributions

• Proposes a novel three-tiered attribution framework to comprehensively investigate spatiotemporal patterns, underlying drivers, and upstream-downstream relationships of runoff variations in large river basins.
• Quantifies the longitudinal cumulative effects of climatic and anthropogenic factors, demonstrating their amplification of runoff variation risks in downstream regions with pronounced seasonal fluctuations.
• Elucidates the interaction mechanisms and key driving factors of runoff variations between upstream and downstream areas, including the identification of "bottom-up" feedback from downstream water demand influencing upstream contributions.
• Highlights the critical role of natural and artificial water storage capacities (glaciers, lakes, reservoirs) in mediating lagged effects of climate change and human activities on runoff, and in facilitating inter-basin water resource regulation.
• Provides a comprehensive understanding of multi-scale hydrological variability drivers and spatial interaction mechanisms, offering essential insights for advancing adaptive river basin management and sustainable water resources allocation globally.

Funding

• Joint Funds of the National Natural Science Foundation of China (Grant No. U2243236)
• Major Science and Technology Innovation Demonstration Projects in Inner Mongolia Autonomous Region of China (Grant No. 2025ZDSF0010)
• National Science Fund for Distinguished Young Scholars (52025092)
• Qinghai Haidong Urban-Rural Eco-Development Project of ADB (L3443-PRC-HD-CB-CS4)

Citation

@article{Gao2025Climate,
  author = {Gao, Ju and Li, Chunhui and Zhou, Xiong and Yi, Yujun and Wang, Xuan and Liu, Qiang},
  title = {Climate change and human activities amplify runoff variability risks in lower reaches of large rivers},
  journal = {Communications Earth & Environment},
  year = {2025},
  doi = {10.1038/s43247-025-02759-3},
  url = {https://doi.org/10.1038/s43247-025-02759-3}
}