Akademska digitalna zbirka SLovenije - logo
E-resources
Full text
Peer reviewed
  • A Framework of Dependence M...
    Wang, Xu; Shen, Yong‐Ming

    Water resources research, August 2023, 2023-08-00, 20230801, Volume: 59, Issue: 8
    Journal Article

    The coincidence and superposition of flood processes from different rivers and regions tend to form compound flood events, determined by spatial relationship between diverse flood processes that cannot be accurately depicted and evaluated by existing dependence analysis methods. A framework, integrating multi‐dimensional vine copula model and dependence evaluation system, was developed with a testing‐oriented application to explore underlying dependence between two kinds of extreme runoff series (peak discharge and flood volume) extracted from the identified compound flood events in the upper reaches of the Yangtze River. Multi‐dimensional regular vine (R‐vine) copula models were established to depict the complex and diverse dependence, and corresponding vine structure was specified by the vine structure array that can reflect the sequence of tributaries flowing into the main stream and the spatial locations of different hydrometric stations. Dependence magnitude and association status were calculated and compared according to the optimal R‐vine copula models and information theory. Comparison with existing methods demonstrated that dependence evaluation system could reflect nonlinear and local dependence characteristics and eliminate the effect of extreme runoff series from other hydrometric station on the dependence. The association status between different extreme runoff series of the upper Yangtze River and its tributaries were diverse in view of the impact of tributary flood inflow. The proposed framework can be regarded as an effective way for dependence modeling and evaluation of compound floods, thus providing a scientific reference for the risk analysis of water resources systems. Key Points Vine structure is specified by the sequence of tributaries flowing into main stream and spatial locations of hydrological stations Dependence evaluation system determines dependence magnitude and association status through R‐statistic and interaction gain Extreme runoff series of the same hydrometric station show distinct association status