In this work, the use of Tropical Rainfall Measuring Mission (TRMM) rainfall data and the Standardized Precipitation Index (SPI) for monitoring spatial and temporal drought variabilities in the Upper São Francisco River basin is investigated. Thus, the spatiotemporal behavior of droughts and cluster regions with similar behaviors is identified. As a result, the joint analysis of clusters, dendrograms, and the spatial distribution of SPI values proved to be a powerful tool in identifying homogeneous regions. The results showed that the northeast region of the basin has the lowest rainfall indices and the southwest region has the highest rainfall depths, and that the region has well-defined dry and rainy seasons from June to August and November to January, respectively. An analysis of the drought and rain conditions showed that the studied region was homogeneous and well-distributed; however, the quantity of extreme and severe drought events in short-, medium- and long-term analysis was higher than that expected in regions with high rainfall depths, particularly in the south/southwest and southeast areas. Thus, an alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).
Trend analysis is an important issue for the decision-making processes. Thus, trends of rainfall, consecutive dry days (CDD), and consecutive wet days (CWD) in the Upper São Francisco River basin, Brazil, using daily rainfall data from the Tropical Rainfall Measuring Mission (TRMM) for recent 18 years, were analyzed. Instead of analyzing the trend of one average time series for one specific confidence level, a spatiotemporal analysis over the entire area with 169 continuous time series is done by applying the nonparametric Mann-Kendall and Sen tests for simultaneously 13 confidence levels and a new integrated confidence classification is proposed. The results show that the rainfall has increased during the less rainy periods (from June to October) and has decreased in the rainy periods (from November to May), with the highest and lowest confidence levels, respectively. An analysis of CDD and CWD shows that the number of CDD has decreased, while the number of CWD has increased, which revealed that the dry periods are more frequently interrupted for the period studied.
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