Jiao et al. (2025) Spatial-temporal pattern and attribution factors of Yellow River’s streamflow seasonality and inter-annual variability

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-12-01
• Authors: Chentai Jiao, Shuai Wang, Xutong Wu, Luying Cheng, Bojie Fu
• DOI: 10.1016/j.ejrh.2025.103019

Research Groups

• State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction, Faculty of Geographical Science, Beijing Normal University, Beijing, China
• State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

Short Summary

This study quantifies the spatial-temporal patterns and attribution factors of streamflow seasonality and inter-annual variability in the Yellow River Basin since 1960. It reveals a 29% decline in seasonal variability and a sharp post-2000s intensification of inter-annual variability (weighted coefficient of variation reaching 0.24), primarily driven by anthropogenic water extraction rather than climate.

Objective

• To investigate the intra-annual and inter-annual streamflow variability in the Yellow River Basin and its attribution factors (climatic and anthropogenic).

Study Configuration

• Spatial Scale: Yellow River Basin (YRB) in northern China, covering an area of 7.7 × 10^5 km^2, analyzed at eight mainstream gauge stations (Tangnaihai, Lanzhou, Toudaoguai, Longmen, Sanmenxia, Xiaolangdi, Huayuankou, and Lijin).
• Temporal Scale: Streamflow variability analyzed from 1960 to 2023, with data records spanning November 1959 to December 2023 for streamflow and climate, and varying periods for anthropogenic data (e.g., 1998–2023 for annual water consumption/reservoir storage).

Methodology and Data

• Models used:
  • Seasonal-Trend decomposition based on LOESS (STL) for intra-annual variability.
  • Central Time of Discharge (CTQ) for intra-annual streamflow distribution.
  • Weighted Coefficient of Variation (WCV) within 5-year sliding windows for inter-annual variability.
  • Segment linear regression for trend analysis.
  • Two-way Analysis of Variance (ANOVA) for climatic attribution.
  • Scenario reconstruction (reservoir-free, consumption-free, natural conditions) for anthropogenic attribution.
  • Pixel-based random forest regression model for extending climate datasets.
• Data sources:
  • Monthly streamflow records from eight mainstream gauges, obtained from the Yellow River Conservation Commission (YRCC).
  • Gridded monthly climate data (precipitation and actual evapotranspiration) from a composite of China Meteorological Forcing Dataset (CMFD), ERA5-Land, TerraClimate, GLDAS2, and a global actual evapotranspiration dataset.
  • Annual and monthly water consumption and net reservoir storage data from YRCC (Annual Water Resource Bulletins, YRCC’s streamflow statistics, YRCC’s monthly water allocation scheme).

Main Results

• Seasonal streamflow variability in the Yellow River Basin has continuously declined by 29% since the 1960s (e.g., at Huayuankou station, comparing 1960–1969 to 2014–2023). This reduction is accompanied by a shift in intra-annual flow distribution towards the dry season (decreasing CTQ values).
• Inter-annual streamflow variability underwent a distinct regime shift around 2009, transitioning from a declining trend to a sharp increase. The weighted coefficient of variation (WCV) reached 0.24 in the most recent decade, exceeding the average since 1960.
• Climatic factors (precipitation and evapotranspiration) contribute to streamflow variability but are not the dominant drivers, especially outside the source region, where their average contribution is less than 30% in areas with intensive water use or strong reservoir regulation.
• Anthropogenic activities have profoundly modified streamflow variability:
  • Reservoir operations reduced intra-annual variability by 27.6–44.2% and inter-annual variability by 24.4–38.9%, also shifting the Central Time of Discharge (CTQ) by 0.70–1.55 months towards the dry season downstream of Lanzhou.
  • Water consumption substantially amplified inter-annual variability, accounting for up to 48% in the middle reaches and reaching a 185% increase in the lower reaches since 1998.
• Reservoir regulation is insufficient to counteract the amplifying effect of water consumption on inter-annual variability, leading to an overall anthropogenic contribution to inter-annual variability that intensifies downstream (from -17.8% at Lanzhou to +118% at Lijin).

Contributions

• Developed and applied an integrated diagnostic framework combining STL decomposition, Central Time of Discharge (CTQ), and Weighted Coefficient of Variation (WCV) to comprehensively characterize intra-annual and inter-annual streamflow variability.
• Provided a systematic and spatially differentiated assessment of streamflow variability and its attribution factors across the entire Yellow River Basin over six decades.
• Quantified the distinct and coupled roles of climatic and anthropogenic factors, demonstrating that anthropogenic water extraction is the primary driver of the recent intensification in inter-annual variability, while reservoir operations moderate seasonality.
• Highlighted a critical paradox where human regulation has amplified inter-annual variability, threatening water supply reliability and socio-ecological resilience, despite a recent recovery in annual discharge.
• Emphasized the urgent need to integrate streamflow variability considerations into hydrological forecasting and water resource management strategies, particularly in arid and semi-arid basins.

Funding

• National Natural Science Foundation of China (grant no. U2243601, 42201306)
• Fundamental Research Funds for the Central Universities

Citation

@article{Jiao2025Spatialtemporal,
  author = {Jiao, Chentai and Wang, Shuai and Wu, Xutong and Cheng, Luying and Fu, Bojie},
  title = {Spatial-temporal pattern and attribution factors of Yellow River’s streamflow seasonality and inter-annual variability},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.103019},
  url = {https://doi.org/10.1016/j.ejrh.2025.103019}
}