Zhao et al. (2026) Assessment of potential drought hazard in Gansu Province under climate change

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-01-05
• Authors: Sha Zhao, Haoyan Zhang, Yanzhe Sun, Yansheng Liu, Yaowen Xie
• DOI: 10.1016/j.ejrh.2025.103093

Research Groups

• College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
• Qinghai-Tibet Plateau Human Environment Data Intelligence Laboratory, Lanzhou University, Lanzhou 730000, China
• Center for Remote Sensing of Ecological Environments in Cold and Arid Regions, Lanzhou University, Lanzhou 730000, China

Short Summary

This study assesses future drought evolution and ecosystem responses in Gansu Province under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios, proposing a novel drought hazard index (DHI) that integrates instantaneous development and recovery speeds (IDS and IRS). Findings indicate intensifying droughts, accelerated IRS, and varied ecosystem sensitivities, with northwestern Gansu facing increased hazard.

Objective

• To determine if Gansu Province will exhibit a trend toward increased dryness or wetness in the future.
• To analyze how drought duration, occurrence frequency, and the instantaneous development speed (IDS) and instantaneous recovery speed (IRS) of drought will change under future climate change.
• To identify the differences in IDS and IRS among various ecosystem types and their responses to climatic factors.
• To evaluate potential drought hazard by incorporating dynamic drought characteristics.

Study Configuration

• Spatial Scale: Gansu Province, Northwestern China, spanning approximately 1000 kilometers from east to west.
• Temporal Scale: Historical (1980–2014), near-future (2020–2060), and far-future (2061–2100).

Methodology and Data

• Models used:
  • Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble (8 models).
  • Standardized Precipitation Evapotranspiration Index (SPEI) at a 3-month scale.
  • Hargreaves method for Potential Evapotranspiration (PET) calculation.
  • Theil-Sen median and Mann-Kendall trend analysis.
  • Run theory approach for drought event determination.
  • Drought Hazard Index (DHI) developed using Principal Component Analysis (PCA) and entropy weighting method for indicator weights.
• Data sources:
  • China Meteorological Forcing Dataset (CMFD) for daily precipitation (1979–2018).
  • Daily minimum and maximum temperature dataset (CDTA) for China (1979–2018, 0.1° spatial resolution).
  • Landsat imagery-derived 2009 land cover dataset for historical land use.
  • Normalized Difference Vegetation Index (NDVI) and soil moisture for DHI validation.
  • CMIP6 global gridded land cover data (850–2100) for future land cover.

Main Results

• Droughts are projected to intensify in Gansu Province, with the Standardized Precipitation Evapotranspiration Index (SPEI) declining by 0.015 per decade under SSP5-8.5, similar to historical trends, while SSP2-4.5 shows a statistically insignificant decline of -0.002 per decade.
• Spatially, drought intensification is most pronounced in northwestern Gansu, with a weakening trend towards the southeast.
• Drought frequency and duration are expected to increase in the future under both SSP2-4.5 and SSP5-8.5 scenarios compared to historical conditions, with northwestern Gansu experiencing the most frequent and prolonged events.
• The instantaneous development speed (IDS) and instantaneous recovery speed (IRS) of droughts are generally projected to increase in the far-future, particularly under SSP5-8.5, with IDS increasing by 10.720% and IRS by 14.124% in the far-future compared to historical conditions under SSP5-8.5.
• Ecosystem-specific responses show that forests and croplands exhibit relatively lower drought intensity, shorter durations, and higher IDS and IRS, indicating faster recovery. In contrast, grasslands, urban areas, and bare lands experience more frequent and prolonged droughts with slower IRS.
• The IRS of vegetation shows particularly high sensitivity to variations in precipitation and temperature, responding more significantly to climatic factors than IDS. Bare land shows weaker sensitivity to climatic factors.
• The newly developed Drought Hazard Index (DHI) indicates that drought hazard levels will further increase in the northwestern arid and semi-arid areas, with their spatial extent expanding under SSP2-4.5. Central areas show a decreasing trend, while the relatively low hazard in the southwestern region is gradually increasing. The DHI is strongly and significantly negatively correlated with soil moisture and NDVI, confirming its reliability.

Contributions

• Developed a novel Drought Hazard Index (DHI) that integrates dynamic drought evolution characteristics, specifically Instantaneous Development Speed (IDS) and Instantaneous Recovery Speed (IRS), alongside traditional static metrics (frequency, duration, intensity).
• Provided a comprehensive assessment of future drought trends, frequency, duration, and dynamic evolution speeds (IDS and IRS) across different ecosystems in Gansu Province under CMIP6 SSP2-4.5 and SSP5-8.5 scenarios.
• Quantified the varying sensitivities of different ecosystem types (forest, cropland, grassland, urban, bare land) to drought dynamics and their responses to climatic factors.
• Emphasized the critical importance of incorporating dynamic drought characteristics into hazard assessments to inform more effective and ecosystem-specific drought management and adaptation strategies.

Funding

• Qinghai-Tibet Plateau Human Environment Data Intelligence Laboratory Project: "Intelligent Interpretation of Human Environment Entities on the Qinghai-Tibet Plateau"
• Gansu Provincial Department of Natural Resources Science and Technology Innovation Project: "Research on the Evolution and Protection and Governance of Ecosystems in Gansu Province" [202401]

Citation

@article{Zhao2026Assessment,
  author = {Zhao, Sha and Zhang, Haoyan and Sun, Yanzhe and Liu, Yansheng and Xie, Yaowen},
  title = {Assessment of potential drought hazard in Gansu Province under climate change},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2025.103093},
  url = {https://doi.org/10.1016/j.ejrh.2025.103093}
}