Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

• Main Authors: Kalra, Ajay, Ahmad, Sajjad
• Format: Article in Journal/Newspaper
• Language: English
• Published: Digital Scholarship@UNLV 2009
• Subjects: Atlantic Multidecadal Oscillation (AMO); Artificial neural network (ANN); El Nino–Southern Oscillations (ENSO); Long-range weather forecasting; Neural networks (Computer science); North America – Colorado River Watershed; North Atlantic Oscillation (NAO); Ocean-atmosphere interaction; Ocean-atmospheric oscillations; PDO; Streamflow – Forecasting; Support Vector Machines (SVM); Environmental Engineering; Environmental Sciences; Fresh Water Studies; Meteorology; Water Resource Management; Nash; Pacific; Sutcliffe; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/fac_articles/100; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1099&context=fac_articles

We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino–Southern Oscillations (ENSO) for a period of 1906–2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906–1991) and tested with 10 years of data (1992–2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression.