摘要
This study focuses on improving the spring-summer streamflow forecast lead time using large scale climate patterns. An artificial intelligence type data-driven model, Support Vector Machine (SVM), was developed incorporating oceanic-atmospheric oscillations to increase the forecast lead time. The application of SVM model is tested on three unimpaired gages in the North Platte River Basin. Seasonal averages of oceanic-atmospheric indices for the period of 1940-2007 are used to generate spring-summer streamflow volumes with 3-, 6- and 9-month lead times. The results reveal a strong association between coupled indices compared to their individual effects. The best streamflow estimates are obtained at 6-month compared to 3-month and 9-month lead times. The proposed modeling technique is expected to provide useful information to water managers and help in better managing the water resources and the operation of water systems.