Liu et al. (2026) Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-03
• Authors: Shuyou Liu, Jun Xia, Guodong Yin
• DOI: 10.1016/j.jhydrol.2026.135074

Research Groups

• State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, China
• Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan 430072, China
• Department of Civil Engineering, The University of Hong Kong, Hong Kong, China

Short Summary

This study developed a novel framework using Bayesian Model Averaging and Copula methods to project future population and economic exposure to compound precipitation and wind extremes (CPWEs). It found that CPWE intensity and exposure risk are projected to increase, particularly around mid-century, with high-risk areas persisting in the North China Plain and southeastern coast under climate change scenarios.

Objective

• To establish a novel framework for assessing intensity risk and projecting future exposure to compound precipitation and wind extremes (CPWEs), addressing challenges posed by complex social-meteorological factor interaction and uncertainty of General Circulation Model (GCM) outputs.

Study Configuration

• Spatial Scale: Regional (e.g., North China Plain and southeastern coast).
• Temporal Scale: Historical period to future projections (mid-century and beyond), under Shared Socioeconomic Pathway (SSP) scenarios SSP245 and SSP585.

Methodology and Data

• Models used: Bayesian Model Averaging (BMA) for GCM output ensemble, Copula methods for CPWE selection and intensity assessment.
• Data sources: General Circulation Model (GCM) outputs, specifically from CMIP6.

Main Results

• The BMA ensemble demonstrated superior accuracy and robustness in simulating precipitation and windspeed compared to individual GCMs.
• The intensity of future CPWEs is projected to increase, characterized by enhanced variability and spatial heterogeneity, with a significantly larger proportion of regions showing upward trends under both SSP245 and SSP585 scenarios, especially under the latter.
• High exposure risk persists in the North China Plain and southeastern coast, with most regions experiencing increased risk relative to the historical period, peaking around mid-century.

Contributions

• Establishment of a novel CPWE risk assessment framework integrating Bayesian Model Averaging for GCM output ensemble and Copula methods for CPWE selection and intensity assessment.
• Development of a comprehensive exposure index to evaluate future population and economic exposure to CPWEs.
• Provides critical insights into the spatiotemporal patterns of future CPWEs, supporting the development of effective early warning systems and climate resilience strategies.

Funding

• Not explicitly mentioned in the provided text.

Citation

@article{Liu2026Projecting,
  author = {Liu, Shuyou and Xia, Jun and Yin, Guodong},
  title = {Projecting future exposure to compound precipitation and wind extremes using Copula methods with Bayesian model averaging},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135074},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135074}
}