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Short-term drought forecasting can be aided with an understanding of the likelihood of dry periods persisting from one season to the next. This research examines drought persistence in the Southeastern United States by identifying spatial patterns of seasonal drought frequency and persistence, using logistic regression to calculate the odds and probability of drought persisting from one season to the next, and examining the effects of El Nino-Southern Oscillation (ENSO) drought persistence in the Southeast. The 3-month climate division-scale Standardized Precipitation Index (SPI) data from 1895 to 2011 is used to examine meteorological drought. Logistic regression is well-suited to examining a binary independent variable (drought or no drought) and also circumvents many of the assumptions that limit linear regression. Results show generally weak seasonal drought persistence throughout the region. However, we do find that some areas in the Southeast United States, like North-Central Alabama are more prone to drought and drought persistence than others. Logistic regression model outcome shows that the probability of spring drought varies as a strong function of winter SPI in the central Southeast United States region. While areas in the western portion of the study region, including Texas and Oklahoma are more prone to summer-to-fall drought persistence, as the probability of fall drought is strongly related to summer SPI. Overall we conclude that seasonal drought forecasts are difficult in the Southeast United States because of infrequent drought persistence. However, the logistic regression model does provide an accurate method for probabilistic seasonal drought forecasts in the region. |
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