Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central Texas

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize system benefits. In this study, streamflow autocorrelation and large-scale climate information are used to generate probabilistic streamflow forecasts for the Lower Colorado River system in central Texas. A number of potential predictors are evaluated for forecasting flows in various seasons, including large-scale climate indices related to the El Nino/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and others. Results indicate that, of the predictors evaluated, only hydrologic persistence and Pacific Ocean sea surface temperature patterns associated with ENSO and PDO provide forecasts which are statistically better than climatology. An ordinal polytomous logistic regression approach is proposed as a means of incorporating multiple predictor variables into a probabilistic forecast model. Forecast performance is assessed through a cross-validation procedure, using distribution-oriented metrics, and implications for decision making are discussed.