A Stepwise and Dynamic C-Vine Copula–Based Approach for Nonstationary Monthly Streamflow Forecasts
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 1
Abstract
In recent years, the stationary assumption of hydroclimatic variables as well as their temporal and spatial dependence structure have been challenged due to anthropogenic and climate changes. This study developed a stepwise and dynamic C-vine copula–based conditional model (SDCVC) to incorporate the nonstationarity into a monthly streamflow prediction, which depicted the predictor–predictand association on the basis of monthly streamflow and rainfall series of upstream and downstream stations in the Yangtze River basin of China. The model consists of (1) nonstationary modeling of the margins and nonstationary temporal and spatial dependence structure by the generalized additive models with location, scale, and shape (GAMLSS) and C-vine copula incorporating climate-related indexes as covariates during training time frames; and (2) a four-dimensional C-vine copula–based conditional quantile function to generate the simulated series during validation time frames. Three kinds of nonstationary models corresponding to different degrees were investigated to show the impact of nonstationarity on the streamflow forecasting. The proposed SDCVC model considering highest degree of nonstationarity outperformed the other two nonstationary models in terms of the performance metrics, because the SDCVC model not only described the dynamic change of time-varying connections between parameter and the large-scale climate forcings, but the stepwise strategy, by selecting the optimum time horizon, helped increase forecasting accuracy. Furthermore, the SDCVC model is superior, to some extent, to classical data-driven approaches [support vector regression (SVR) and adaptive-network-based fuzzy inference system (ANFIS)] in terms of the performance metrics.
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Data Availability Statement
All data that support the findings of this study are available from corresponding author upon reasonable request, including the original streamflow and rainfall data from selected gauges, and the associated code implemented in the R environment to run the dynamic copula-based models (SDCVC models).
Acknowledgments
Streamflow data are from the Bureau of Hydrology, Ministry of Water Resources, China. Because of national security issues, the data cannot be released. Rainfall data to support this paper are from the National Meteorological Information Center, China Meteorological Administration. This study was supported by the Open Fund of the Key Laboratory of Water Science and Engineering, Ministry of Water Resources (2021100108), the National Key Research and Development Program of China (2017YFC1502704), and the Jiangsu Graduate Research and Practice Innovation Program (KYCX21_0059).
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Journal of Hydrologic Engineering
Volume 27 • Issue 1 • January 2022
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© 2021 American Society of Civil Engineers.
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Received: Jun 21, 2021
Accepted: Sep 1, 2021
Published online: Nov 3, 2021
Published in print: Jan 1, 2022
Discussion open until: Apr 3, 2022
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