Análise de secas no Estado do Rio Grande do Norte baseada em índice climático.

Abstract

Drought is a climatic phenomenon that affects numerous regions around the world, associated with water scarcity for prolonged periods, representing one of the most complex and least understood natural hazards, assessing the risks caused by drought is difficult as there is no globally accepted method to measure and qualify their impacts. The effects of drought can be more drastic in regions susceptible to water scarcity, such as semiarid regions, where the state of Rio Grande do Norte is inserted, for which it has been suffering from impacts caused by the deficit of precipitation for several consecutive years. Therefore, this work aims to analyze drought events in the state of Rio Grande do Norte from 1950 to 2018 based on data from the SPEI drought index. In this study, the SPEI index was used in different time scales, together with the multivariate statistics and forecasting model. Multivariate statistics were applied in order to define homogeneous drought regions. The results show the efficiency of the cluster analysis when identifying regions with similar drought characteristics, where five groups were determined divided by micro-regions, the SPEI-3 values reflect complex variations in drought conditions according to their classification. The highest concentration of drought, according to its duration, occurred in group G4 with the greatest presence of moderate drought, whereas severe drought was identified in groups G1 and G5 and the extreme in groups G3 and G5, with greater intensity, however with a shorter duration, so G4 was considered the driest group and G1 the most humid. It was also possible to see the distribution of hydrological drought for the two groups mentioned above as the most humid and driest, where G1 stood out for having a higher frequency of wet periods, whereas G4 showed a greater evolution for dry episodes, increasing the number of episodes of hydrological drought. Through the forecast made for groups G1 and G4, it was shown to be efficient, since both groups had significant r2 values, and G4 had the best value (0.9061), the predicted series followed the same pattern both of the validated and the observed, thus considering the ARIMA model satisfactory for the groups analyzed, however, adjustments must be made to minimize errors and improve the quality of the results, as it cannot be guaranteed that there are ideal forecasting models for this variable.