Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

• Main Authors: Carrier, Christopher Allen, Kalra, Ajay, Ahmad, Sajjad
• Format: Article in Journal/Newspaper
• Language: English
• Published: Digital Scholarship@UNLV 2013
• Subjects: Climate; Climatology; Forecast; Oscillations; Reconstruction; Streamflow; Streamflow—Forecasting; Support vector machines; SVM; Tree-rings; Water; Water > Management; Atmospheric Sciences; Civil and Environmental Engineering; Environmental Engineering; Environmental Sciences; Oceanography and Atmospheric Sciences and Meteorology; Water Resource Management; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/fac_articles/364

In water stressed regions, water managers are exploring new horizons that would help in long-range streamflow forecasts. Oceanic-atmospheric oscillations have been shown to influence streamflow variability. In this study, long-lead time streamflow forecasts are made using a multiclass kernel-based data-driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic-atmospheric oscillations included the El Niño-Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one-year to five-year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one- to five-year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long-term water resources planning and management.