Improving Streamflow Forecast Lead Time Using Oceanic-Atmospheric Oscillations for Kaidu River Basin, Xinjiang, China
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 8
Abstract
Increasing global warming has led to the incremental retreat of glaciers, which in turn affects the water supply of the rivers dependent on glacier melts. This is further affected by the increases in flooding that is attributable to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management. Therefore, this paper focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of the Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations, i.e., Pacific decadal oscillation (PDO), North Atlantic oscillation (NAO), Atlantic multidecadal oscillation (AMO), and El Niño–southern oscillation (ENSO), are used to generate streamflow volumes for the peak season (April–October) and the water year, which is from October of the previous year to September of the current year for a period from 1955–2006. A data-driven model, least-square support vector machine (LSSVM), was developed that incorporated oceanic atmospheric oscillations to increase the streamflow lead time. Based on performance measures, predicted streamflow volumes are in agreement with the measured volumes. Sensitivity analyses, performed to evaluate the effect of individual and coupled oscillations, revealed a stronger presence of coupled PDO, NAO, and ENSO indices within the basin. The AMO index shows a pronounced effect when individually compared with the other oscillation modes. Additionally, model-forecasted streamflow is better than that for climatology. Overall, very good streamflow predictions are obtained using the SVM modeling approach. Furthermore, the LSSVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back-propagation models, artificial neural network, and multiple linear regression. The current paper contributes in improving the streamflow forecast lead time, and identified a coupled climate signal within the basin. The increased lead time can provide useful information to water managers in improving the planning and management of water resources within the Kaidu River Basin.
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Acknowledgments
This paper is supported by the State Key Basic Research and Development Program of China (No. 2010CB951002), a Chinese Academy of Sciences visiting professorship for senior international scientists (Grant No. 2011T2Z40), and the Natural Sciences Foundation of China (No. 40871027).
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Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 8 • August 2013
Pages: 1031 - 1040
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© 2013 American Society of Civil Engineers.
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Received: Nov 15, 2011
Accepted: Aug 9, 2012
Published online: Aug 23, 2012
Discussion open until: Jan 23, 2013
Published in print: Aug 1, 2013
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