Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station, Heihe River Basin, China

• Main Author: Chenglong ZHANG,Mo LI,Ping GUO
• Format: Article
• Language: English
• Published: Higher Education Press 2017-03-01
• Series: Frontiers of Agricultural Science and Engineering
• Subjects: Monte Carlo|nonstationary|trend detection|streamflow prediction|decomposition and ensemble|Yingluoxia
• Online Access: http://academic.hep.com.cn/fase/fileup/2095-7505/PDF/1475041576014-2033495797.pdf

Investigating long-term variation and prediction of streamflow are critical to regional water resource management and planning. Under the continuous influence of climate change and human activity, the trends of hydrologic time series are nonstationary, and consequently the established methods for hydrological frequency analysis are no longer applicable. Five methods, including the linear regression, nonlinear regression, change point analysis, wavelet analysis and Hilbert-Huang transformation, were first selected to detect and identify the deterministic and stochastic components of streamflow. The results indicated there was a significant long-term increasing trend. To test the applicability of these five methods, a comprehensive weighted index was then used to assess their performance. This index showed that the linear regression was the best method. Secondly, using the normality test for stochastic components separated by the linear regression method, a normal distribution requirement was satisfied. Next, the Monte Carlo stochastic simulation technique was used to simulate these stochastic components with normal distribution, and thus a new ensemble hydrological time series was obtained by combining the corresponding deterministic components. Finally, according to these outcomes, the streamflow at different frequencies in 2020 was predicted.