Time-Varying Effects of Precipitation and Temperature on Daily Streamflow Determined Using the DCC Model
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 10
Abstract
Existing nonlinear time series methods are mostly used to capture the conditional variance or volatility of hydrometeorological factors. Insufficient attention has been paid to the time-varying variance-covariance and dynamic conditional correlation among multivariate hydrometeorological time series. This paper introduces a type of multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, named the dynamic conditional correlation (DCC) model, to characterize the time-varying relationship between rainfall (P), temperature (T), and streamflow (Q). For this purpose, the Yellow River basin is used as an example, and three association models are established, including the bivariate relationships of P-Q, T-Q, and the three-variable relationship of P&T-Q (rainfall and temperature jointly on streamflow). The results of the univariate generalized autoregressive conditional heteroscedasticity (GARCH) model show that the volatility of the temperature series is weaker than that of rainfall and streamflow. The conditional variance of the streamflow and temperature series has long-term persistence, while that of rainfall has short-term persistence, but the temperature has the lowest degree of variance memory. The bivariate DCC model shows that the streamflow is more sensitive to precipitation changes than temperature, and both rainfall-streamflow (P-Q) and temperature-streamflow (T-Q) associations have stronger long-term persistence and covariance memory. The P&T-Q correlation shows that the covariance of rainfall and temperature has stronger long-term persistence than streamflow fluctuations. In addition, the effectiveness of the P&T-Q relationship model is also stronger than that of the P-Q and T-Q relationship models. DCC, considering the combined effects of rainfall and temperature, can better represent the impact of environmental changes on streamflow than a single variable.
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Data Availability Statement
All data and methods code are available from the corresponding author upon request.
Acknowledgments
This work is financially supported by the National Natural Science Foundation of China (Grant No. 52079110), and the Natural Science Foundation of Jiangsu Province, China (BK20220590).
Author contributions: Huimin Wang: conceptualization, methodology, software, formal analysis, and writing - original draft. Songbai Song: writing - review & editing, project administration, and funding acquisition. Gengxi Zhang: writing - review & editing. Olusola O. Ayantobo: writing - review & editing.
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Journal of Water Resources Planning and Management
Volume 149 • Issue 10 • October 2023
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© 2023 American Society of Civil Engineers.
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Received: Nov 20, 2022
Accepted: May 17, 2023
Published online: Jul 26, 2023
Published in print: Oct 1, 2023
Discussion open until: Dec 26, 2023
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