Rescaled range/vector autoregressive-based changing characteristics of dry season streamflow in the Yujiang River Basin, Southern China

• Published in: Stochastic environmental research and risk assessment 2025, Vol.39 (1), p.403-421
• Main Authors: Dong, Xu, Li, Xungui, Liu, Yiling
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Climate change; Computational Intelligence; Computer Science; Decision making; Dry season; Earth and Environmental Science; Earth Sciences; Environment; Evaporation; Function analysis; Global climate; Impulse response; Long term memory; Math. Appl. in Environmental Science; Meteorological data; Original Paper; Physics; Precipitation; Probability Theory and Stochastic Processes; Regression analysis; Regression models; Resource management; Response functions; River basins; Rivers; Statistics for Engineering; Stream discharge; Stream flow; Trends; Waste Water Technology; Water Management; Water policy; Water Pollution Control; Water resources management
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-024-02870-5

In response to global climate change and intensified human activities, hydrological conditions around the world have experienced significant shifts, particularly evident in the complex and unpredictable variations in dry season streamflow (DSS). Understanding the characteristics of these changes and their relationship with meteorological factors is crucial for regional water resource management and ecological protection. This study conducts a systematic analysis of DSS changes and their correlation with meteorological factors, utilizing daily streamflow and meteorological data over 1965 to 2014 from the Guigang Station in the Yujiang River Basin (YRB). The study employs the Mann-Kendall trend test, rescaled analysis, and vector autoregressive (VAR) model to identify trends and driving factors of DSS across various time scales. The results indicate a significant upward trend in both series of the average DSS and the minimum 90-day flow, with pronounced long-term memory characteristics. Precipitation and evaporation emerge as the primary meteorological factors affecting DSS with a positive and negative correlation with DSS, respectively. Granger causality tests and impulse response function analysis reveal a significant bidirectional causal relationship between DSS and the primary meteorological factors. Variance decomposition analysis highlights the substantial contributions of precipitation and sunshine to DSS changes. These findings offer a scientific foundation for water resource management and policy-making in the YRB, emphasizing the need to address volatility and extreme variations in DSS as a response to the challenges posed by climate change and human activities. This study’s key innovation lies in the application of the VAR model system to reveal the bidirectional causal relationships between DSS and meteorological factors in the YRB for the first time. This approach addresses the limitations of traditional regression analysis in studying multivariable dynamic relationships. It offers new insights into the long-term changes in DSS and its driving mechanisms, providing a scientific basis for water resource management and decision-making in the YRB.