ABSTRACT
ABSTRACT: The aim of this study is to identify support tools for the regional monitoring of drought conditions over Southern South America (SSA). The Standardized Precipitation Index (SPI) was used to characterize rainfall variabilities through the 1961-2008 period. The SPI was calculated at two time scales (3 and 12 months), which represents short-and long-term droughts, respectively. Rotated principal components analysis was used to identify spatially homogeneous regions with different precipitation temporal variabilities. We obtained two different sets of seven homogeneous and climatically consistent regions for the respective two time scales (SPI3, SPI12). In order to identify the months or seasons that are prone to drought, we calculated the regional drought frequency at both time scales. This analysis showed that regional drought occurrences possess the same probability in all months of the year and seasons. Then, a continuous drought monitoring system should be performed in SSA, taking into account the different physical forcings that triggers drought conditions. Some of these forcings are the El Niño and La Niña events, one of the key factors that influence the interannual variability of precipitation over SSA. In order to identify the regional influence of these events on the SPI time series, we calculated lagged correlations between the Oceanic Niño Index, a well known ENSO indicator, and regional rainfall series. We found coherent and significant SPI responses to ENSO phases in most of the regions considered for the respective SPI time scales. However, there were some divergent responses in some regions, and for instance the North West region did not show any relationship with the considered ENSO index for SPI. The precipitation response to La Niña events is characterized with regional deficits, identified with negative values of the SPI during the end of La Niña year and the year after. During El Niño events the precipitation response is reversed and more intense than during La Niña ones. This signal has some regional differences over precipitation regarding its magnitude and timing, and the quantification of these features provided critical baseline information for the water resources and agricultural sectors and for its use in seasonal drought forecasts.
