Xiao-ya et al. (2025) Quantifying Individual Contribution of Human Activities on Long-Term Streamflow Change in a Large Climate-Sensitive River Basin

Identification

• Journal: Water Resources Management
• Year: 2025
• Date: 2025-12-29
• Authors: Wang Xiao-ya, Fengping Li, Guangxin Zhang, Y. Jun Xu, Yanfeng Wu, Fan Liu, Hongze Wang
• DOI: 10.1007/s11269-025-04394-1

Research Groups

• Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, Jilin, China
• Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, Jilin, China
• College of New Energy and Environment, Jilin University, Changchun, Jilin, China
• School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, Hubei, China
• Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, China
• School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
• School of Geography and Tourism, Chongqing Normal University, Chongqing, China
• Songliao River Water Resources Commission of Ministry of Water Resources, Changchun, Jilin, China

Short Summary

This study developed a Variable Infiltration Capacity (VIC) model-based framework to quantify the individual contributions of climate change and human activities to long-term streamflow changes in the Songhua River Basin, finding that water withdrawal was the dominant driver, explaining 48.30% of streamflow changes between 1994 and 2017.

Objective

• To analyze the spatiotemporal characteristics of climate variables and streamflow across the Songhua River Basin (SRB) and its sub-basins.
• To establish a Variable Infiltration Capacity (VIC) model-based hydrological modeling framework that isolates the individual contributions of human activities.
• To quantify the distinct contributions of climate change, land-use changes, and direct water withdrawals to observed streamflow changes across the basin.

Study Configuration

• Spatial Scale: Songhua River Basin (SRB) in Northeast China, covering a drainage area of 556,800 km². The basin was divided into three sub-basins: Nenjiang River Basin (NRB), Second Songhua River Basin (SSRB), and Mainstream Songhua River Basin (MSRB).
• Temporal Scale: 1960 to 2016 (57 years). The study period was divided into three subperiods: sp1 (1960–1979), sp2 (1980–1993), and sp3 (1994–2016).

Methodology and Data

• Models used:
  • Variable Infiltration Capacity (VIC) model for daily streamflow simulation.
  • Mann-Kendall (M-K) trend test for trend analysis of streamflow and meteorological factors.
  • Spearman correlation coefficient for assessing relationships between meteorological variables and streamflow.
• Data sources:
  • Monthly climate data (precipitation, maximum temperature, minimum temperature, wind speed) from 1960 to 2016: China Meteorological Data Network (CMDN).
  • Soil texture data: Food and Agriculture Organization (FAO).
  • Vegetation cover data (1 km resolution): Global land cover dataset from the University of Maryland.
  • Elevation data (90 m resolution): Geospatial Data Cloud.
  • Monthly streamflow data from 1960 to 2016: 5 hydrologic stations in SRB.
  • Water resources data: Water Resources Bulletins in China.

Main Results

• Streamflow in the SRB and its sub-basins exhibited significant decreasing trends from 1960 to 2016, with variation coefficients ranging from 0.37 to 0.57.
• Annual precipitation showed an overall decreasing trend across 37% of the basin, while maximum and minimum temperatures increased by approximately 0.03 °C/a and 0.035 °C/a, respectively, across most of the basin. Wind speed declined in 67% of the basin.
• Precipitation showed strong positive correlations with streamflow in MSRB, NRB, and SRB (r > 0.7), with the strongest in SRB (r values of 0.92, 0.79, and 0.80 for the three subperiods). Annual average temperature consistently showed negative correlations with streamflow.
• The VIC model demonstrated robust performance in streamflow simulation, with Nash-Sutcliffe efficiency (Ce) exceeding 0.6 and correlation coefficients (r) above 0.8 at most stations during calibration and validation.
• From sp1 (1960–1979) to sp2 (1980–1993), human activities were the predominant driver of streamflow changes, explaining 49.86% to 85.85% of the total changes, with land surface changes contributing an average of 42.43%. Water withdrawals accounted for 16.65% to 41.94% across sub-basins.
• From sp2 (1980–1993) to sp3 (1994–2016), human activities remained the primary driver (74.33% to 86.45% of changes), but water withdrawals became the dominant factor, contributing an average of 48.30%. Land use changes accounted for 23.82% to 43.75%.
• Irrigation consumption significantly surged from 9.02% to 25.03% of the streamflow reduction, highlighting its increasing impact.

Contributions

• Developed an enhanced attribution framework that explicitly quantifies the individual contributions of direct human water withdrawals, separating them from land-use changes and climatic factors, addressing a gap in previous research.
• Provided a comprehensive long-term analysis (1960–2016) of streamflow drivers in the Songhua River Basin, offering insights into dynamics over an extended period.
• Highlighted the increasing and dominant impact of human water consumption, particularly irrigation, on hydrological regimes in a large agricultural river basin.
• Demonstrated a transferable technical approach for quantifying individual contributions of human activities to streamflow dynamics, applicable to other river basins globally.

Funding

• Strategic Priority Research Program of the Chinese Academy of Sciences, China (XDA28020501)
• National Natural Science Foundation of China (No. 41701020)

Citation

@article{Xiaoya2025Quantifying,
  author = {Xiao-ya, Wang and Li, Fengping and Zhang, Guangxin and Xu, Y. Jun and Wu, Yanfeng and Liu, Fan and Wang, Hongze},
  title = {Quantifying Individual Contribution of Human Activities on Long-Term Streamflow Change in a Large Climate-Sensitive River Basin},
  journal = {Water Resources Management},
  year = {2025},
  doi = {10.1007/s11269-025-04394-1},
  url = {https://doi.org/10.1007/s11269-025-04394-1}
}