A Streamflow Forecast Model for Central Arizona

Abstract

A spring-runoff forecast model for central Arizona was developed based on multiple discriminant analysis. More than 6500 potential predictor variables were analyzed, including local precipitation and temperature variables, as well as global sea level pressure variables. The forecast model was evaluated on nine years exclusive of the years on which the model was based. Forecasts are provided in the form of a cumulative distribution function (cdf) of the expected runoff, based on analogs. A ranked probability score to evaluate forecast skill for the cdf forecasts was developed. Ranked probability skill scores ranged from 25% to 45%. Local and global forecast models were developed and compared to the combined data source model. The global forecast model was equivalent in skill to the local forecast model. The combined model exhibited a marked improvement in skill over either the local or global models. Three recurrent patterns in the predictor variables used by the forecast model are analyzed in some depth. Above-normal pressure at Raoul Island northeast of New Zealand 14 to 18 months prior to the event forecast was found to be associated with above-normal runoff. A westward shift of the Bermuda high, as evidenced by the pressure change at Charleston, South Carolina, from December to August of the preceding year, was found to be associated with above-normal runoff. Above-normal pressure at Port Moresby, New Guinea coupled with below-normal pressure at San Diego, California, the month prior to the forecast, was found to be associated with above-normal runoff.