Abstract
In this study, statistical linkages between autumn streamflow in the northeast United States and preceding summer sea surface temperatures are developed to establish predictive potential for climateβinformed seasonal streamflow forecasts in this region. Predictor regions with physically plausible teleconnections to local streamflow are identified and evaluated in a multivariate and nonlinear framework using local regression techniques. Three such regions are identified, located in the Bering Sea, the tropical Pacific just west of Mexico, and the tropical Atlantic off the coast of Africa. Asymmetries in each region's univariate local regression result are apparent, and bivariate local regressions are used to attribute these asymmetries to interactions with physical mechanisms associated with the other two regions, and possibly other unaccounted for climatic predictors. A bivariate model including the tropical Pacific and tropical Atlantic regions yields the strongest local regression result, explaining 0.68 of the interannual streamflow variability. An analogous multivariate linear regression analysis is only able to explain 0.20 of the streamflow variability and thus the use of nonlinear methods' results in a marked improvement in streamflow simulation capability. Crossβvalidation considerably weakens the streamflow forecasts using this model; however, forecast skill may improve with a longer period of record or the inclusion of additional predictors. Copyright Β© 2010 Royal Meteorological Society