Seasonal probabilistic precipitation prediction in Comahue region (Argentina) using statistical techniques

Abstract

This work proposes a probabilistic forecast of seasonal precipitation in the basins of the Limay, Neuquén, and Negro rivers in the north of Argentine Patagonia. The Comahue region is particularly important because part of the country’s hydroelectric energy is generated there. The amount of winter precipitation modulates the flow of rivers, and therefore, prior knowledge of possible precipitation thresholds is very useful for decision-makers. Ensembles made up of statistical models that explain more than 50% of the precipitation were used and were generated with multiple techniques such as linear regression, generalized additive models, support vector regression, and artificial neural networks. The result showed that the forecasts are better in Limay and Neuquén river basins in winter than in Negro River basin. Brier Skill Score values indicate that the probabilistic forecast is better than the climatology in winter, in Neuquén and Limay basins for below and above normal categories.