CONICET | Buscador de Institutos y Recursos Humanos

This paper attempts to design statistical models to forecast annual precipitation in the Neuquen andLimay river basins in the Comahue region of Argentina. These forecasts are especially useful as they areused to better organize the operation of hydro-electric dams, the agriculture in irrigated valleys and thesafety of the population. In this work, multiple linear regression statistical models are built to forecastmean annual rainfall over the two river basins. Since the maximum precipitation occurs in the winter(June?August), forecasting models have been developed for the beginning of March and for thebeginning of June, just before the rainy season starts. The results show that the sea-surface temperaturesof the Indian and Pacific oceans are good predictors for March models and explain 42.8% of theprecipitation index variance. The efficiency of the models increases in June, adding more predictorsrelated to the autumn circulation.