Yang et al. (2025) Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties

Identification

• Journal: Environmental Modelling & Software
• Year: 2025
• Date: 2025-12-05
• Authors: Xuesong Yang, Bin Xu, Huili Wang, Xiaolin Qin, Xinrong Wang, Zichen Ren, Yao Yao, Siying Zhou, Yao Liu, Ping‐Chen Chang
• DOI: 10.1016/j.envsoft.2025.106818

Research Groups

• College of Hydrology and Water Resources, Hohai University, Nanjing, China
• National Key Laboratory of Water Disaster Prevention, Nanjing, China
• Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China

Short Summary

This study developed a hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in complex reservoir–lake systems, demonstrating its effectiveness in the Chaohu Lake Basin by identifying dominant risk sources and quantifying their impact on lake water levels.

Objective

• To develop and apply a hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems, specifically addressing multisource uncertainties and facilitating refined risk prediction and diagnosis.

Study Configuration

• Spatial Scale: Chaohu Lake Basin, China
• Temporal Scale: Not explicitly defined in the provided text.

Methodology and Data

• Models used: Vine copula, Monte Carlo methods, Bayesian network
• Data sources: Probabilistic characteristics of risk sources (specific sources not detailed in the provided text).

Main Results

• The vine copula effectively elucidates both intervariable correlations and single variable characteristics within the complex flood control system.
• The lateral inflow volume of the lake and the external river water levels are identified as the dominant risk sources.
• When the maximum water level of the lake increases from 9.5 m to 11.5 m, the posterior probability of dominant risk sources exceeding the design value at 20 % increases by 46.12 % and 32.22 %, respectively.

Contributions

• Developed an innovative and comprehensive risk analysis framework integrating stochastic simulation (vine copula and Monte Carlo) and Bayesian networks for complex reservoir–lake systems.
• Provides a refined approach for risk prediction and diagnosis by effectively modeling multisource uncertainties and intervariable correlations.
• Identifies and quantifies the impact of dominant risk sources in complex hydrological systems.

Funding

• Not explicitly defined in the provided text.

Citation

@article{Yang2025Hybrid,
  author = {Yang, Xuesong and Xu, Bin and Wang, Huili and Qin, Xiaolin and Wang, Xinrong and Ren, Zichen and Yao, Yao and Zhou, Siying and Liu, Yao and Chang, Ping‐Chen},
  title = {Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties},
  journal = {Environmental Modelling & Software},
  year = {2025},
  doi = {10.1016/j.envsoft.2025.106818},
  url = {https://doi.org/10.1016/j.envsoft.2025.106818}
}