Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Abstract

Copula functions have been widely used in stochastic simulation and prediction of streamflow. However, existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months. To address this limitation, this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations. This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas. The up-to-down sequential method, which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach, was used to determine the structures of multivariate D-vine copulas. The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station, the inflow control station of the Longyangxia Reservoir in the Yellow River Basin. The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow. This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.