Xiang et al. (2025) Unraveling the nonstationary effect of climate change and urbanization on summer drought-heatwave coupling degree in Beijing-Tianjin-Hebei Area, China

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-11-24
• Authors: Xiaohua Xiang, Yuyang Wei, Xiaoling Wu, Jiabo Lu, Wenbin Wang, Yongxuan Li
• DOI: 10.1016/j.ejrh.2025.102944

Research Groups

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

Short Summary

This study investigates the nonstationary coupling between summer drought and heatwaves in the Beijing-Tianjin-Hebei (BTH) area from 1970 to 2018, revealing a progressively intensifying relationship driven by the combined effects of climate change and urbanization. A novel Conditional Risk Sensitivity Indicator (CRSI)-based Sensitivity Enhancement Factor (SEF) is introduced to quantitatively attribute these impacts.

Objective

• To unravel the nonstationary effect of climate change and urbanization on the summer drought-heatwave coupling degree in the Beijing-Tianjin-Hebei (BTH) Area, China, by developing a dynamic Copula model incorporating large-scale oscillation indexes and urbanization factors as parameter covariates.

Study Configuration

• Spatial Scale: Beijing-Tianjin-Hebei (BTH) urban agglomeration area, China (approximately 218,000 km²), covering 70 meteorological stations.
• Temporal Scale: 49 years (1970–2018) for meteorological and climate data; PISA data interpolated for the same period.

Methodology and Data

• Models used:
  • Dynamic Copula models (Gaussian, Frank, Rotated Clayton, Rotated Gumbel at 90°) with time-varying parameters.
  • Maximum Likelihood (ML) estimation for parameter fitting.
  • Likelihood Ratio (LR) tests, Kolmogorov-Smirnov (K-S) test, and Akaike Information Criterion (AIC) for model selection and goodness-of-fit.
  • Standardized Precipitation Index (SPI) calculated using a gamma distribution.
  • Conditional Risk Sensitivity Indicator (CRSI) and Sensitivity Enhancement Factor (SEF) for attribution.
  • Multivariate Mann-Kendall test for trend analysis.
• Data sources:
  • Daily precipitation and maximum temperature records (1970–2018) from 70 gauging stations in BTH, sourced from the China Meteorological Administration’s National Meteorological Information Center.
  • Large-scale climate indices (Ni˜no 3.4 (NINO), North Atlantic Oscillation (NAO), Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO)) as summer averages (1970–2018) from the National Oceanic and Atmospheric Administration (NOAA).
  • Percentage of Impervious Surface Area (PISA) data (30-m resolution) as a proxy for urbanization, sourced from Liu et al. (2020) and interpolated for 1970–2018.

Main Results

• The 3-month Standardized Precipitation Index (SPI3) was identified as the optimal timescale for representing precipitation deficits, showing significant negative correlations with summer hot extremes (HD and HWDmax) across most BTH stations.
• Nonstationary Copula models were selected as the best-fitted models for 64.28% of stations for SPI3-HD dependence and 55.71% for SPI3-HWDmax dependence, outperforming stationary models and detecting trends missed by the non-parametric Mann-Kendall test.
• The conditional risk of hot extremes (HD or HWDmax exceeding the 85th percentile given a 10th percentile SPI3) showed an increasing trend from 1979 to 2016, with nonstationary models indicating higher risks than stationary models.
• Covariate analysis revealed that increasing Ni˜no 3.4 index (El Ni˜no events) and urbanization (PISA) intensified the summer precipitation deficit-temperature coupling degree, indicated by negative coefficients in the Copula parameter structures (e.g., θCPISA = −0.68 for station 54511).
• The CRSI-based Sensitivity Enhancement Factor (SEF) analysis showed that climate change factors enhanced the summer precipitation deficit-temperature coupling degree by 9–250% for stations primarily driven by climate. For stations under combined effects, the coupling was intensified at all locations.
• Comparative analysis of SEF values indicated that climate change factors generally contributed more significantly to intensifying summer precipitation deficit-temperature coupling than urbanization, except for a few stations near Tianjin Binhai New Area where urbanization played a dominant role for SPI3-HD.

Contributions

• Developed a dynamic Copula model that integrates climate change and urbanization factors as covariates to analyze the nonstationary coupling between summer drought and heatwaves.
• Introduced a novel Conditional Risk Sensitivity Indicator (CRSI)-based Sensitivity Enhancement Factor (SEF) to quantitatively attribute the relative contributions of climate change and urbanization to changes in compound drought-heatwave risk.
• Demonstrated that parametric nonstationary Copula models can detect significant trends in dependency structures that conventional non-parametric methods (e.g., Mann-Kendall test) may miss.
• Provided mechanistic insights into how urbanization interacts synergistically with large-scale climate processes to amplify regional climatic extremes, rather than acting in isolation.

Funding

• National Key R&D Programme of China (2024YFC3211400, 2023YFC3006501).

Citation

@article{Xiang2025Unraveling,
  author = {Xiang, Xiaohua and Wei, Yuyang and Wu, Xiaoling and Lu, Jiabo and Wang, Wenbin and Li, Yongxuan},
  title = {Unraveling the nonstationary effect of climate change and urbanization on summer drought-heatwave coupling degree in Beijing-Tianjin-Hebei Area, China},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102944},
  url = {https://doi.org/10.1016/j.ejrh.2025.102944}
}