Zhang et al. (2025) Spatiotemporal evolution characteristics and driving forces of natural lakes in Qingtongxia irrigation area, China

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-11-19
• Authors: Yiran Zhang, Xin Tong, Asaad Y. Shamseldin, Limin Duan, Le Ye, Shuo Lun, Hong Fang, He Meng, Tingxi Liu
• DOI: 10.1016/j.ejrh.2025.102937

Research Groups

• State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Inner Mongolia Agricultural University, Hohhot 010018, China
• Inner Mongolia Key Laboratory of Ecohydrology and High-Efficient Utilization of Water Resources, College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
• Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot 010018, China
• Department of Civil and Environmental Engineering, The University of Auckland, Auckland 1010, New Zealand
• Hydrology and Water Resources Sub-Center, Tongliao 028006, China

Short Summary

This study investigated the spatiotemporal evolution and driving forces of natural lakes in the Qingtongxia Irrigation Area, China, from 1984 to 2022, revealing an average annual lake surface area expansion of 2.10 km² predominantly driven by human activities in low-lying lakes and hydro-meteorological conditions in wetland lakes.

Objective

• To accurately identify and map natural lakes using remote sensing techniques.
• To quantify their long-term spatiotemporal dynamics and shoreline changes.
• To evaluate the relative contributions of meteorological, hydrological, and anthropogenic factors using a data-driven attribution framework.

Study Configuration

• Spatial Scale: Qingtongxia Irrigation Area (approximately 16800 km²) in the Ningxia Hui Autonomous Region of Northwest China, focusing on three primary natural lakes: Xinghai Lake, Sand Lake, and Yuehai Lake.
• Temporal Scale: 1984 to 2022 (39 years).

Methodology and Data

• Models used:
  • Hybrid index rule set (NDWI, EVI, MNDWI, NDVI) for lake boundary delineation.
  • Spatial quadrant-based orientation method for spatial differentiation analysis.
  • Correlation analysis and partial differential decomposition for quantitative attribution.
  • Multiple regression analysis to determine sensitivity coefficients.
  • ArcGIS 10.5 for spatial analysis and projection (Albers Conical Equal Area coordinate system).
  • Fragstats 4.2.1 for shoreline landscape indicators (circularity ratio (Cr), shoreline development coefficient (DL), mean fractal dimension index (FRAC-mean)).
  • Google Earth Engine (GEE) for satellite image processing.
  • CFmask algorithm for cloud and cloud shadow masking.
  • MATLAB for detrending data.
• Data sources:
  • Satellite Imagery: Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) Collection 1 Tier 1 images (30 m spatial resolution, 16 d temporal resolution) from 1984 to 2022, acquired during late March to early April and late October to early November.
  • Digital Elevation Data: SRTM (for terrain shadow removal).
  • Validation Data: High-resolution historical imagery from Google Earth Pro.
  • Hydrological Data (1984–2020): Precipitation (P), temperature (T), pan evaporation (ETpan) from the National Meteorological Science Data Center; Groundwater depth (GD) from the Ningxia Water Resources Bulletin and Yellow River Hydrological Yearbook.
  • Socioeconomic Data (1984–2022): Irrigation Water Diversion Volume (IWDV), Irrigation Drainage Volume (IDV), Gross Domestic Product (GDP), Cultivated Area (CA), and Dryland Cultivated Area (DCA) from the Ningxia Statistical Yearbook, China Economic and Social Big Data Research Platform, and Ningxia Hui Autonomous Region Statistics Bureau.

Main Results

• Natural lake mapping achieved an overall accuracy of 94.5 % and a Kappa coefficient of 0.92, with an average relative error of 2.83 % compared to Google Earth images.
• The total surface area of natural lakes expanded at an average rate of 2.10 km² per year from 1984 to 2022, fluctuating between a minimum of 8.64 km² (1991) and a maximum of 115.91 km² (2010).
• Lake evolution showed two stages: substantial fluctuations (1984–2000) and a gradual upward trend with greater stability (2001–2022).
• Lake expansion predominantly occurred in the north-northeast (Quadrant I), east-northeast (Quadrant II), and east-southeast (Quadrant III) directions.
• Shoreline morphology of Xinghai Lake remained stable, while Sand Lake exhibited diverse and complex shoreline features, and Yuehai Lake had a more regular shape.
• Driving factors varied by lake type:
  • Human activities contributed 59.77 % and 66.42 % to the expansion of plain low-lying lakes (Xinghai Lake and Sand Lake, respectively).
  • Hydro-meteorological conditions accounted for 73.10 % of the expansion in plain wetland lakes (Yuehai Lake).
• "Other variables" (e.g., regional reservoir construction, lakeshore protection) exerted the strongest positive influence on lake surface area increases.
• Irrigation water diversion from the Yellow River also positively affected lake expansion.
• Temperature reduced lake surface area growth (relative impact rates: Xinghai Lake 11.25 %, Sand Lake 19.92 %, Yuehai Lake 6.67 %).
• Precipitation contributed marginally (0.01 % for Sand Lake, 1.54 % for Yuehai Lake), and increased evaporation negatively affected Xinghai Lake (17.97 %) and Yuehai Lake (25.95 %).

Contributions

• Developed a robust and scalable framework for long-term monitoring of natural lake dynamics in arid irrigated regions, addressing a key research gap for small, fragmented lakes.
• Advanced the understanding of lake evolution under combined natural and anthropogenic influences, demonstrating spatially differentiated responses.
• Provided critical insights for water resource management and ecological restoration in irrigation regions globally.
• Highlighted a shift towards a more balanced eco-hydrological regime since 2000, influenced by ecological restoration policies.
• Offered transferable insights for water resource management in other irrigated and semi-arid regions worldwide.

Funding

• National Key Research and Development Program of China (Grant Nos. 2021YFC3201200 and 2023YFC3206501)
• Yellow River Water Science Research Joint Fund (Grant No. U2243234)
• Science and Technology Plan Project of Inner Mongolia Autonomous Region (2025YFHH0170, 2025KYPT0099 and 2020ZD0009)
• Inner Mongolia Natural Science Foundation (2024QN05019)
• Inner Mongolia Agricultural University Basic Research Project (Grant No. BR251403 and BR251018)
• Ministry of Education Innovative Research Team (IRT_17R60)
• Ministry of Science and Technology Innovative Team in Priority Areas (2015RA4013)
• First-class Academic Subjects Special Research Project of the Education Department of Inner Mongolia Autonomous Region (YLXKZX-NND-010)
• State Key Laboratory of Water Engineering Ecology and Environment in Arid Area of Inner Mongolia Agricultural University (SQ2024SKL08048)

Citation

@article{Zhang2025Spatiotemporal,
  author = {Zhang, Yiran and Tong, Xin and Shamseldin, Asaad Y. and Duan, Limin and Ye, Le and Lun, Shuo and Fang, Hong and Meng, He and Liu, Tingxi},
  title = {Spatiotemporal evolution characteristics and driving forces of natural lakes in Qingtongxia irrigation area, China},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102937},
  url = {https://doi.org/10.1016/j.ejrh.2025.102937}
}