Long-Lead Seasonal Prediction of Streamflow over the Upper Colorado River Basin: The Role of the Pacific Sea Surface Temperature and Beyond

We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ∼0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ∼0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ∼0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.