A statistical approach for reconstructing natural streamflow series based on streamflow variation identification

Abstract

Natural streamflow reconstruction is highly significant to assess long-term trends, variability, and pattern of streamflow, and is critical for addressing implications of climate change for adaptive water resources management. This study proposed a simple statistical approach named NSR-SVI (natural streamflow reconstruction based on streamflow variation identification). As a hybrid model coupling Pettitt's test method with an iterative algorithm and iterative cumulative sum of squares algorithm, it can determine the reconstructed components and implement the recombination depending only on the information of change points in observed annual streamflow records. Results showed that NSR-SVI is suitable for reconstructing natural series and can provide the stable streamflow processes under different human influences to better serve the hydrologic design of water resource engineering. Also, the proposed approach combining the cumulative streamflow curve provides an innovative way to investigate the attributions of streamflow variation, and the performance has been verified by comparing with the relevant results in nearby basin. HIGHLIGHTS A statistical approach is proposed to improve the accuracy of hydrological variation detection, and further reconstruct natural streamflow only depending on the variation information of streamflow.; The hybrid method has better performance on detecting the multiple change points in mean and variance.; The proposed approach combining the cumulative curve provides a way to investigate the attribution of streamflow variation.;

Keywords

• icss algorithm
• natural streamflow reconstruction
• over-whitening
• pettitt's test
• streamflow variation