Trend analysis of droughts and their geospatial and temporal variability assists decision-making about water resources management around the world and decreases the harmful effects of drought that affect the entire population. This work aimed to analyze short-, medium-and long-term droughts and their trends in the Brazilian state of Paraíba from 1998 to 2015 using Tropical Rainfall Measuring Mission (TRMM) data and applying the Mann-Kendall test and Sen's slope estimator method, based on the standardized precipitation index (SPI). TRMM data were validated by comparison with data from 267 rain gauges in the region, which showed the consistency of the satellite data. Therefore, 187 monthly TRMM rainfall time series were used, each with 216 months. The series were equally distributed over the entire study area. At the significance level of 0.01, a new geospatial classification of drought severity is proposed, through which it is possible to determine exactly which types of drought events affected or did not affect a given region based on the SPI and the trend of the analyzed SPI time series, which shows the situation of drought risk analysis. The results of the comparison between long-and short-term droughts indicate that the wettest regions of the state of Paraíba are strongly affected by extreme drought events and show trends with increasingly negative slopes. In this way, the proposed geospatial classification is proved to be a useful tool because it provides information about the current drought situation of a given region, simultaneously showing the trend slope with respect to short-, medium-and long-term droughts.

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).

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In Paraíba state, precipitation is strongly affected by several climate systems, such as trade winds, the intertropical convergence zone (ITCZ), easterly wave disturbances (EWDs), and the South Atlantic subtropical high. Accordingly, the objective of this study was to analyze the spatiotemporal variability in precipitation to identify homogeneous trends of that variable and the effects of climate systems in Paraíba state by cluster analysis. The precipitation data used in this study derive from the Tropical Rainfall Measuring Mission (TRMM) satellite for the period from January 1998 to December 2015, and hierarchical clustering was used to classify the sites into different groups with similar trends. The findings show an uneven spatiotemporal precipitation distribution in all mesoregions of the state and considerable monthly precipitation variation in space. The estimated precipitation depth was highest in coastal regions and in high-altitude areas due to orographic precipitation. In general, the precipitation over Paraíba is characterized by strong gradients in the coastal zone towards the continent (Agreste, Borborema, and Sertão mesoregions) and from north to south due to the physiography of the region and the effects of climate systems with different time scales. Finally, the proposed clustering method using TRMM data was effective in characterizing climatic systems.

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.