Zhang et al. (2025) Assessing the impacts of climate change and land use/land cover data characteristics on streamflow using the SWAT model in the Upper Han River Basin

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-09-09
• Authors: Longhui Zhang, Chao Deng, Jia Wei, Jiacheng Zou
• DOI: 10.1016/j.ejrh.2025.102764

Research Groups

• College of Hydrology and Water Resources, Hohai University, Nanjing, China
• Department of Land Surveying and Geo-informatics, Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, Hong Kong
• Hydrology and Water Resources Monitoring Center of Lower Ganjiang River, Yichun, China

Short Summary

This study assesses the impact of various land use/land cover (LULC) data characteristics and future climate change on streamflow in the Upper Han River Basin using the SWAT model, finding that LULC resolution significantly affects model performance and projecting a future increase in annual streamflow with notable seasonal shifts.

Objective

• To quantify the influence of multi-resolution and multi-source land use/land cover (LULC) datasets on SWAT model streamflow simulation accuracy.
• To project future streamflow changes in the Upper Han River Basin under CMIP6 Shared Socio-economic Pathway (SSP) climate change scenarios.

Study Configuration

• Spatial Scale: Upper Han River Basin (UHRB), central China, covering an area of 159,000 km² (106.0°E-110.5°E, 31.5°N-34.5°N).
• Temporal Scale:
  • Historical baseline: 1990–2017
  • Model warm-up: 2008–2010
  • Model calibration: 2011–2015
  • Model validation: 2016–2017
  • Future projections: 2040–2100 (Near future: 2040–2070; Far future: 2071–2100)

Methodology and Data

• Models used:
  • Soil and Water Assessment Tool (SWAT) hydrological model.
  • SWAT Calibration and Uncertainty Procedures (SWAT-CUP) with the Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm for calibration, validation, sensitivity, and uncertainty analyses.
• Data sources:
  • Digital Elevation Model (DEM): SRTM3 version 4.1, 90 m resolution (Geospatial Data Cloud site, Chinese Academy of Sciences).
  • Soil Data: Harmonized World Soil Database (HWSD) v1.2, 1 km × 1 km resolution (FAO-90 classification); Soil Water Characteristics (SPAW) software used for hydraulic parameters.
  • Land Use/Land Cover (LULC): Seven datasets with spatial resolutions from 10 m to 1000 m.
    • GRDC 1000 m, MODIS 500 m, ESA_CCI 300 m, CGLS 100 m, GLC 30 m, ESRI 10 m, FROM 10 m.
    • Reference years: Majority 2010, some high-resolution 2017.
  • Meteorological Data (Historical): China Meteorological Assimilation Driving Datasets (CMADS) v1.1, 0.25° × 0.25° spatial resolution, daily data (precipitation, temperature, relative humidity, solar radiation, wind speed) from 2008–2017.
  • Streamflow Data (Observed): Daily observed streamflow from Ankang hydrological station (ID: 61801300) from 2008–2017 (Hydrological Yearbooks of the Hanjiang River Basin).
  • Future Climate Projections: Climate Change for East Asia with Bias Corrected UNet Dataset (CLIMEA-BCUD), 0.1° horizontal resolution, daily data (2-m air temperature, precipitation, 10-m wind speed, downward longwave radiation, downward shortwave radiation, 2-m relative humidity, 2-m specific humidity). Combines 19 CMIP6 models and Multi-Source Weather dataset, bias-corrected and downscaled using the BC-NET method. Scenarios: SSP1–2.6 and SSP5–8.5 for 2015–2100.

Main Results

• LULC Impact on SWAT Performance: The number of Hydrological Response Units (HRUs) varied significantly with LULC resolution and source, but did not directly correlate with streamflow simulation accuracy. The FROM 10 m LULC dataset yielded the most stable daily streamflow performance (Nash-Sutcliffe Efficiency (NSE) = 0.68), while the CGLS 100 m dataset showed the highest monthly skill (NSE = 0.87). SWAT model performance does not consistently improve with higher spatial resolution LULC data.
• Sensitive Parameters: Moist soil bulk density (SOLBD), Base flow alpha factor (ALPHABF), Soil hydraulic conductivity in the main channel (SOL_K), SCS runoff curve number (CN2), and Threshold depth of water in the shallow aquifer required for return flow (GWQMN) were identified as the most sensitive parameters, showing robustness across different LULC resolutions.
• Future Streamflow Projections (using FROM 10 m LULC):
  • Annual Mean Streamflow: The average annual streamflow in the UHRB is projected to increase by 28.8 % under SSP1–2.6 and 18.1 % under SSP5–8.5 compared to the baseline (503 m³/s). The far future (2071–2100) shows more pronounced increases (47.1 % for SSP1–2.6, 32.0 % for SSP5–8.5) than the near future (2040–2070) (11.8 % for SSP1–2.6, 4.4 % for SSP5–8.5).
  • Monthly Streamflow: Significant seasonal shifts are projected. In the near future, May shows the largest increase (up to 64.3 %), while July experiences the largest reduction (up to 32.6 %). In the far future, February shows the largest increase (up to 154.9 %), with July still experiencing reductions (up to 22.4 %).
  • Seasonal Streamflow Proportion: Summer streamflow proportion is projected to decrease significantly from 44 % to 34–35 %, while spring streamflow proportion increases from 18 % to 26 %. Winter streamflow proportion also increases from 7 % to 11–12 %, with autumn showing a slight decrease of 3 %.

Contributions

• Provides a comprehensive evaluation of the impact of multiple LULC datasets with varying spatial resolutions (10 m to 1000 m) and sources on SWAT model streamflow simulation accuracy, offering guidance for optimal LULC input selection.
• Quantifies the combined effects of LULC data characteristics and future climate change (CMIP6 SSP scenarios) on streamflow in the Upper Han River Basin, a critical temperate monsoon region.
• Identifies the most stable LULC datasets for daily (FROM 10 m) and monthly (CGLS 100 m) streamflow simulations in the study area.
• Presents detailed projections of annual, monthly, and seasonal streamflow changes under SSP1–2.6 and SSP5–8.5 scenarios, highlighting significant increases in overall annual flow but critical seasonal redistributions (e.g., decreased summer flow, increased spring/winter flow).
• Emphasizes the importance of considering LULC data characteristics and climate drivers for accurate hydrological modeling and sustainable water resource management, particularly for addressing future flood risks and potential dry season water scarcity.
• Utilizes regionally downscaled CMIP6 products (CLIMEA-BCUD) to provide higher spatial resolution and more accurate representation of regional climatic and hydrological conditions, addressing a gap in previous studies.

Funding

• National Key Research and Development Program (2022YFC3202802)
• National Natural Science Foundation of China (42307117)
• Hong Kong Scholars Program (XJ2024046)

Citation

@article{Zhang2025Assessing,
  author = {Zhang, Longhui and Deng, Chao and Wei, Jia and Zou, Jiacheng},
  title = {Assessing the impacts of climate change and land use/land cover data characteristics on streamflow using the SWAT model in the Upper Han River Basin},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102764},
  url = {https://doi.org/10.1016/j.ejrh.2025.102764}
}