Wang et al. (2025) Runoff Estimation in the Upper Yangtze River Basin Based on CMIP6 and WRF-Hydro Model

Identification

• Journal: Water
• Year: 2025
• Date: 2025-10-30
• Authors: Peng Wang, Jun Zhou, Xue Ke, Zeqiang Chen
• DOI: 10.3390/w17213104

Research Groups

• College of Information Science and Engineering, Wuchang Shouyi University, Wuhan, China
• National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China

Short Summary

This study integrates CMIP6 climate model data with the WRF-Hydro model to project future runoff changes in the Upper Yangtze River Basin under SSP2-4.5 and SSP5-8.5 scenarios, finding significant warming, modest precipitation increases with seasonal redistribution, and a substantial increase in extreme flood risks, particularly from the Jialing River.

Objective

• To systematically assess the effects of climate change on runoff projections in the Upper Yangtze River Basin by integrating bias-corrected CMIP6 climate model data with the WRF-Hydro model.

Study Configuration

• Spatial Scale: Upper Yangtze River Basin (approximately 1 million square kilometers), with WRF-Hydro simulations at 10 km (Noah-MP) and 500 m (hydrological module) resolutions.
• Temporal Scale: Historical baseline period (1981–2010) and future period (2021–2050). Data resolutions include daily, 5-day, and monthly averages.

Methodology and Data

• Models used:
  • CMIP6 (Coupled Model Intercomparison Project Phase 6) multi-model ensemble (MME) including AWI-ESM-1-RecoM, GFDL-ESM4, MPI-ESM1-2-HR, and MRI-ESM2-0.
  • WRF-Hydro v5.2 (Weather Research and Forecasting Hydrological modeling system) with Noah-MP land surface model.
  • Bias correction methods: Local Intensity Correction (LOCI) for precipitation and Daily Bias Correction (DBC) for both precipitation and temperature.
• Data sources:
  • Meteorological observation data: Daily climate dataset from the National Meteorological Science Data Center of China (824 stations, 284 selected for 1981–2010).
  • China Meteorological Forcing Dataset (CMFD): From the National Qinghai–Tibet Plateau Scientific Data Center (daily, downscaled to 0.25° resolution).
  • CMIP6 historical (1981–2010) and future (SSP2-4.5, SSP5-8.5 for 2021–2050) scenario data: Earth System Grid Federation (ESGF).
  • Hydrological observation data: Yangtze River Basin Hydrological Annual Report and "River and Water Information Report" website (daily discharge from 6 control stations: Pingshan, Gaochang, Beibei, Wulong, Cuntan, Yichang).
  • Static geographical data: WRF Preprocessing System (WPS) default global static geographical database, HydroSHEDS dataset for DEM.

Main Results

• CMIP6 Bias Correction: Raw CMIP6 data significantly overestimated precipitation (e.g., >1000 mm bias in Hengduan Mountains) and underestimated temperature (e.g., 4–8 °C bias in Sichuan Basin) compared to CMFD. After correction, LOCI reduced precipitation MAE from 35 mm to 10 mm and RMSE from 40 mm to 17 mm. DBC reduced temperature MAE from 1.6 °C to 0.8 °C and RMSE from 2.0 °C to 1.0 °C.
• WRF-Hydro Calibration: A two-stage "independent sub-basin calibration + mainstem calibration" strategy significantly improved runoff simulation accuracy. At Yichang station, NSE increased from 0.72 to 0.85, RSR decreased from 0.53 to 0.39, and PBIAS approached zero. Validation at Cuntan station (2009–2018) showed robust performance with daily NSE of 0.82, 5-day NSE of 0.86, and monthly NSE of 0.91.
• Future Climate Projections (2021–2050):
  • Precipitation: Annual mean precipitation is projected to increase by 5.4% (SSP2-4.5) to 6.1% (SSP5-8.5). Seasonal redistribution shows the most pronounced decrease in spring precipitation (approx. -150 mm) and increases in autumn. Spatially, increases are concentrated in the Sichuan Basin and eastern basin.
  • Temperature: Annual mean temperature is projected to rise by 1.0 °C (SSP2-4.5) to 1.2 °C (SSP5-8.5). Winter warming is most significant (+1.8–2.0 °C). Spatially, warming is strongest in the Sichuan Basin and Jinsha River Basin.
• Future Runoff Projections (2021–2050):
  • Annual Discharge: No statistically significant trend in annual mean discharge was detected under either scenario.
  • Seasonal Discharge: Jinsha River's flood-season discharge decreases by 4.2% under SSP5-8.5 compared to SSP2-4.5, while Jialing and Wujiang Rivers show slight increases. Non-flood-season discharge for the Upper Yangtze River Basin increases by 2.0%.
  • Extreme Flood Events: The frequency of extreme flood events (inflows > 50,000 m³/s) into the Three Gorges Reservoir increases dramatically under SSP5-8.5 (25 days, including 17 days in 2050 alone) compared to SSP2-4.5 (4 days). The Jialing River is identified as the dominant source for most extreme flood events, contributing 60.0% to 99.1% of flood peaks.

Contributions

• Proposed and validated a bias correction scheme for CMIP6 data based on rainfall threshold adjustment, improving accuracy for regional hydrological applications.
• Developed and calibrated a large-scale WRF-Hydro model for the Upper Yangtze River Basin using a novel two-stage sub-basin calibration strategy, significantly enhancing simulation accuracy compared to basin-wide methods.
• Provided comprehensive projections of future climate and runoff changes, including extreme flood risks, under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios for the Upper Yangtze River Basin, highlighting the increasing role of the Jialing River in flood generation.
• Offered scientific support for water resource management, flood control scheduling, and adaptive reservoir regulation at the Three Gorges Reservoir under future climate change.

Funding

This research received no external funding.

Citation

@article{Wang2025Runoff,
  author = {Wang, Peng and Zhou, Jun and Ke, Xue and Chen, Zeqiang},
  title = {Runoff Estimation in the Upper Yangtze River Basin Based on CMIP6 and WRF-Hydro Model},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w17213104},
  url = {https://doi.org/10.3390/w17213104}
}