Xu et al. (2026) Quantitative assessment of the reservoir-induced impact on multivariate flood risk via the nonstationary Vine Copula model

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-01-25
• Authors: Pengcheng Xu, Zhilang Zhang, Dong Wang, Vijay P. Singh, Gengxi Zhang, Xiaolei Fu, Huanyu Yang
• DOI: 10.1016/j.jhydrol.2026.134997

Research Groups

• College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, PR China
• Yangtze Institute for Conservation and Development, Hohai University, Nanjing, PR China
• Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China
• Department of Biological and Agricultural Engineering, Zachry Department of Civil & Environmental Engineering, Texas A & M University, College Station, USA
• National Water and Energy Center, UAE University, Al Ain, United Arab Emirates

Short Summary

This study proposes a nonstationary Dynamic Vine Copula (DVC) framework to quantify the impact of human activities and climate change on multivariate flood risk in the Yellow River Basin. The research finds that reservoir operations are the strongest driver of flood risk alterations, causing a significant decrease in the angles of flood peak and flood volume.

Objective

• To develop and apply a nonstationary Dynamic Vine Copula (DVC) framework to quantify and attribute the impacts of human activities (urbanization, reservoir operation) and climate change on multivariate flood occurrence likelihood and risk in the Yellow River Basin.

Study Configuration

• Spatial Scale: Yellow River Basin (YRB)
• Temporal Scale: Recent decades; based on daily mean streamflow data.

Methodology and Data

• Models used: Dynamic Vine Copula (DVC) framework, nonstationary univariate distributions, time-varying copula parameters. Comparative analysis was performed using Peaks-Over-Threshold (POT)-derived characteristics (PDC) and Annual Maximum Series (AMS)-derived characteristics (ADC).
• Data sources: Daily mean streamflow.

Main Results

• A nonstationary Dynamic Vine Copula (DVC) framework was successfully developed, integrating nonstationary univariate distributions with time-varying copula parameters.
• The nonstationary DVC model based on Annual Maximum Series (ADC) provided more robust and interpretable insights for nonstationary hydrological event analysis compared to Peaks-Over-Threshold (PDC).
• Reservoir operations were identified as having the strongest statistical association with flood risk alterations in recent decades.
• Reservoir operations caused a decrease of 2.5° to 31.9° in the angles of flood peak and flood volume, respectively, as indicated by regression lines.
• The interval time in PDC signaled additional nonstationary influences.

Contributions

• Introduces a novel nonstationary Dynamic Vine Copula (DVC) framework for multivariate flood risk assessment under changing conditions.
• Enables dynamic quantification of changing dependencies among flood characteristics and systematic attribution of contributions from multiple covariates (human activities and climate change).
• Provides a more robust and interpretable approach for nonstationary hydrological event analysis, particularly highlighting the effectiveness of ADC-based DVC.
• Offers a new tool for disentangling and quantifying the impacts of human and climate drivers in large river basins.

Funding

Not specified in the provided text.

Citation

@article{Xu2026Quantitative,
  author = {Xu, Pengcheng and Zhang, Zhilang and Wang, Dong and Singh, Vijay P. and Zhang, Gengxi and Fu, Xiaolei and Yang, Huanyu},
  title = {Quantitative assessment of the reservoir-induced impact on multivariate flood risk via the nonstationary Vine Copula model},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.134997},
  url = {https://doi.org/10.1016/j.jhydrol.2026.134997}
}