Yang et al. (2026) Trigger thresholds and drivers of meteorological to agricultural drought propagation in the Hanjiang basin under climate change

Identification

• Journal: Journal of Water and Climate Change
• Year: 2026
• Date: 2026-02-01
• Authors: Kuan Yang, Zhongmin Liang, Yiming Hu, Jun Wang, Binquan Li
• DOI: 10.2166/wcc.2026.233

Research Groups

• College of Hydrology and Water Resources, Hohai University, Nanjing, China

Short Summary

This study quantifies how climate change alters meteorological drought trigger thresholds for agricultural drought in the Hanjiang basin, finding that milder meteorological deficits will increasingly trigger agricultural drought by 2100, with temperature dominating annual shifts and precipitation/evaporation driving seasonal changes.

Objective

• To investigate how severity-dependent, time-varying trigger thresholds linking meteorological to agricultural drought evolve under climate change in the middle–upper Hanjiang River Basin, and to identify their hydroclimatic drivers.

Study Configuration

• Spatial Scale: Upper and Middle Hanjiang River Basin (UMHRB), 142,000 km²
• Temporal Scale: Historical (1980–2020) and future projections (2021–2100), divided into P2 (2021–2060) and P3 (2061–2100) for climate scenarios.

Methodology and Data

• Models used:
  • Variable Infiltration Capacity (VIC) model (version 4.2.d)
  • Copula functions (Gaussian, t, Frank, Gumbel, Joe, Clayton families)
  • Bayesian conditional probability
  • Bias correction methods: Quantile Mapping-Robust Quantile (QM-RQUANT) for precipitation, Quantile Mapping-Distribution (QM-DIST) for temperature.
  • R² decomposition methods for driver attribution: Lindeman–Merenda–Gold (LMG) metric, Proportional Marginal Variance Decomposition (PMVD), Relative Weight Analysis (RWA).
• Data sources:
  • Observational hydrometeorological data: Monthly averaged runoff (Huangzhuang hydrological station, 2006–2020), Daily precipitation, maximum/minimum temperature, and wind speed (161 national meteorological stations, China's Daily Meteorological Elements Dataset V3.0, 1979–2020).
  • Topographic and land surface data: Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Model (DEM), Harmonized World Soil Database (HWSD v2.0), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1.
  • Climate projection data: NASA NEX-GDDP-CMIP6 dataset (8 Global Climate Models) for Shared Socioeconomic Pathways (SSP) 2-4.5 and 5-8.5 scenarios.
  • Drought indices: 3-month Standardized Precipitation Index (SPI-3) for meteorological drought, 1-month Standardized Soil Moisture Index (SSMI-1) for agricultural drought.
  • Climate oscillation index: Multivariate ENSO Index (MEI v2).

Main Results

• The VIC model demonstrated robust hydrological simulation performance with Nash–Sutcliffe Efficiency (NSE) values of 0.86 during calibration (2006–2014) and 0.73 during validation (2015–2020).
• Bias correction of CMIP6 data using Quantile Mapping significantly improved accuracy, with the Multi-Model Ensemble (MME) outperforming individual GCMs, reducing Root Mean Square Error (RMSE) by 10–80% for precipitation and 27% for temperature extremes.
• Agricultural Drought Trigger Thresholds (ADTTs) are projected to universally increase on an annual scale under both SSP2-4.5 and SSP5-8.5 scenarios, indicating that milder meteorological droughts will trigger agricultural droughts in the future.
• Under SSP2-4.5, moderate drought thresholds are projected to increase by 24% (2021–2060) and 41% (2061–2100), while under SSP5-8.5, they increase by 1.8% and 29% respectively. By 2100, a one-class weaker meteorological drought (SPI-3 ≈ −1.0) could trigger agricultural drought.
• The gap between severity thresholds is projected to narrow, particularly under SSP5-8.5, suggesting a higher probability of rapid escalation from mild to extreme agricultural drought events.
• Spatially, annual impacted areas are projected to expand by 22% (SSP2-4.5) and 18% (SSP5-8.5) during 2021–2060, with the Nanyang region identified as a vulnerability hotspot.
• Temperature is identified as the dominant driver for annual ADTT variations, while precipitation and evaporation govern most seasonal responses, particularly in summer.
• A negative correlation exists between ADTT and the Multivariate ENSO Index (MEI v2) in spring and summer, strengthening with increasing drought severity, whereas positive correlations are observed in autumn and winter.

Contributions

• Establishes an integrated probabilistic-hydrological framework to quantify severity-dependent, time-varying agricultural drought trigger thresholds (ADTTs) and their hydroclimatic drivers under climate change scenarios.
• Provides dynamic, driver-specific ADTTs as actionable metrics for early warning systems, crop insurance triggers, and adaptive water allocation strategies.
• Offers a transferable framework applicable to other monsoon-influenced basins.
• Informs the management of national inter-basin water transfer schemes traversing the Hanjiang River Basin.

Funding

• National Natural Science Foundation of China (42371045 and 42471049).

Citation

@article{Yang2026Trigger,
  author = {Yang, Kuan and Liang, Zhongmin and Hu, Yiming and Wang, Jun and Li, Binquan},
  title = {Trigger thresholds and drivers of meteorological to agricultural drought propagation in the Hanjiang basin under climate change},
  journal = {Journal of Water and Climate Change},
  year = {2026},
  doi = {10.2166/wcc.2026.233},
  url = {https://doi.org/10.2166/wcc.2026.233}
}