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A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

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  • Huawei Li

    (Sino-Canada Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, China
    State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Guohe Huang

    (Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK S4S 7H9, Canada)

  • Yongping Li

    (State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
    Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, SK S4S 7H9, Canada)

  • Jie Sun

    (School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Pangpang Gao

    (Sino-Canada Energy and Environmental Research Center, North China Electric Power University, Beijing 102206, China)

Abstract

In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.

Suggested Citation

  • Huawei Li & Guohe Huang & Yongping Li & Jie Sun & Pangpang Gao, 2021. "A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4627-:d:540537
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