STATISTICAL PREDICTION OF EXTREME RAINFALL SEASON IN COMAHUE

GONZÁLEZ, MARCELA HEBE; DOMINGUEZ DIANA

mendoza

Congreso; congremet XI; 2012

The Comahue region is located in that area, between 38ºS and 43ºS over the Andes mountain. Two important rivers - Limay and Neuquen- run in this area. The Alicura, Piedra del Aguila, Pichi Picun Leufu and El Chocón hydroelectric dams are on these rivers and their functioning are mainly affect by rainfall variability. Therefore, this paper tries to detect possible predictors for rainy season. General features of rainy season with excess or deficits are analyzed using standardized six-months precipitation index (SPI) in Limay and Neuquen River basins. Results indicate that most of dry and wet periods persist less than three months in both basins. There is a tendency for wet (dry) periods to take place in warm (cold) phase on ENSO years in both basins. Rainfall in both basins, have an important annual cycle with its maximum in winter. Therefore the SPI9, defined as SPI for since April to September, was used to detect possible atmospheric causes for an extreme rainfall season in Limay basin. The wet and dry period were classified in severe, moderate and slight taking into account the value of SPI9. In order to establish the existence of previous circulation patterns associated with interannual SPI9 variability, the composite fields of sea sea surface temperature, geopotential height at different levels, low level winds and precipitable water of severe and moderate wet and dry years were compared. The possible predictors were carefully selected according the significance and physical reasoning. The result showed that rainfall is related to El Niño- Southern Oscillation (ENSO) phenomenon and circulation over the Pacific Ocean. The difference circulation variables composites in April, let define some predictors and generate a prediction scheme, using multiple linear regressions, which showed that 46% of the SPI variance can be explained by this model. The scheme was validated by using a cross-validation method, and significant correlation (0,68) is detected between observed and forecast SPI. In order to convert the individual estimations in a probabilistic forecast, the accumulated frequencies are calculated for the observed and forecast SPI9 values and the empiric probability functions are drawn. A chi square test is used and the empiric probability functions are significantly similar at the 95% confidence level. A polynomial model is used and it little improved the linear one, explaining the 49% of the SPI variance. The analysis shows that circulation indicators are useful to predict winter rainfall behavior.