Improving streamflow forecast lead time using oceanic-atmospheric oscillations for Kaidu River Basin, Xinjiang, China

• Main Authors: Kalra, Ajay, Li, Lanhai, Li, Xuemei, Ahmad, Sajjad
• Format: Article in Journal/Newspaper
• Language: English
• Published: Digital Scholarship@UNLV 2012
• Subjects: China – Kaidu River Watershed; Global warming; Ocean-atmosphere interaction; Streamflow – Forecasting; Climate; Environmental Engineering; Environmental Sciences; Fresh Water Studies; Meteorology; Water Resource Management; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/fac_articles/85

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 due to heavy rains during the snowmelt season. An accurate estimation of streamflow is important for water resources planning and management; therefore, this study focuses on improving the streamflow forecast for Kaidu River Basin, situated at the north fringe of Yanqi basin on the south slope of Tianshan Mountains in Xinjiang, China. The interannual and decadal scale oceanic-atmospheric oscillations — Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Niño—Southern Oscillations (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 to 2006. A data-driven model, Least Square Support Vector Machine (LS-SVM), 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 to 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 LS-SVM streamflow predictions outperform the predictions obtained from the most widely used feed-forward back propagation models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The current research contributed in improving the streamflow forecast lead time, and identified a coupled climate signal within the ...