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

Abstract

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time series are nonstationary, and consequently the established methods for hydrological frequency analysis are no longer applicable.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.

Keywords

Monte Carlo / nonstationary / trend detection / streamflow prediction / decomposition and ensemble / Yingluoxia

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Acknowledgements

The research was supported by the National Natural Science Foundation of China (51439006, 91425302) and the Governmental Public Research Funds for Projects of Ministry of Water Resources (201501017).

Compliance with ethics guidelines

Chenglong Zhang, Mo Li, and Ping Guo declare that they have no conflict of interest or financial conflicts to disclose.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2016. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)