The main objective of this research was to verify the long-term rainfall behavior in a location in the Brazilian semiarid region. Century-long monthly precipitation data (1911 to 2017) provided by DNOCS/SUDENE were used in the investigation. These data were used to determine descriptive statistics at monthly, annual, decadal and climatic scales. Next, nonparametric statistical tests were applied: Mann-Kendall (trend) and Kruskal-Wallis (multiple comparisons), both with a statistical confidence level of 95%. It was observed that 67.8% of annual rainfall (661.4 mm) is concentrated in three months (Feb-Mar-Apr) and that there was no significant tendency of monthly and consequently, annual rainfall. The 1930s, 1950s, 1990s, and 2010s were the driest years in the series, showing that the current dry period (2012-2016) is not unexceptional. Although significant trends were observed at 1% rainfall increase between 1930-1980 and 1% rainfall decrease from 1960-2000, when the full series was analyzed there was no significant trend. For climatologies the differences were significant at 1% when comparing Climate_2 (1958-1987), which was the wettest, with Climate_1 (1928-1957) and Climate_3 (1988-2017). It is concluded that the current climate and decade have had significantly less rain, but they are not unique since other decades and/or past climate series have been drier. Keywords: Rainfall, Brazilian Semiarid, Tendencies, Water Resources.

Since the early 2000s, Brazil has been one of the world’s leading grain producers, with agribusiness accounting for around 28% of the Brazilian GDP in 2021. Substantial investments in research, coupled with the expansion of arable areas, owed to the advent of new agriculture frontiers, led the country to become the world’s greatest producer of soybean. One of the newest agricultural frontiers to be emerging in Brazil is the one known as SEALBA, an acronym that refers to the three Brazilian states whose areas it is comprised of—Sergipe, Alagoas, and Bahia—all located in the Northeast region of the country. It is an extensive area with a favorable climate for the production of grains, including soybeans, with a rainy season that takes place in autumn/winter, unlike the Brazilian regions that are currently the main producers of these kinds of crops, in which the rainfall regime has the wet period concentrated in spring/summer. Considering that precipitation is the main determinant climatic factor for crops, the scarcity of weather stations in the SEALBA region poses an obstacle to an accurate evaluation of the actual feasibility of the region to a given crop. Therefore, the aim of this work was to carry out an assessment of the performance of four different precipitation databases of alternative sources to observations: two from gridded analyses, MERGE and CHIRPS, and the other two from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them to observational records from stations along the region. The analysis was based on a comparison with data from seven weather stations located in SEALBA, in the period 2001–2020, through three dexterity indices: the mean absolute error (MAE), the root mean squared errors (RMSE), and the coefficient of Pearson’s correlation (r), showing that the gridded analyzes performed better than the reanalyses, with MERGE showing the highest correlations and the lowest errors (global average r between stations of 0.96, followed by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm and ERA5 with 71.4 mm). Since all databases provide up-to-date data, our findings indicate that, for any research that needs a complete daily precipitation dataset for the SEALBA region, preference should be given to use the data in the following order of priority: MERGE, CHIRPS, ERA5Land, and ERA5.

The São Francisco River basin is one of the largest in the Brazilian territory. This basin has enormous economic, social and cultural importance for the country. Its water is used for human and animal supply, irrigation and energy production. This basin is located in an area with different climatic characteristics (humid and semiarid) and studies related to precipitation are very important in this region. In this scenario, the objective of this investigation is to present an assessment of rainfall estimated through the Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) product compared with rain gauges over the São Francisco river basin in Brazil. For that, a period from of 20 years and 18 surface weather stations were used to evaluate the product. Based on different evaluation techniques, the study found that the IMERG is appropriate to represent precipitation over the basin. According to the results, the performance of the IMERG product depends on the location where the rain occurs. The bias ranged from −1.67 to 0.34 mm, the RMSE ranged from 5.36 to 10.36 mm and the values of the correlation coefficients between the daily data from the IMERG and rain gauge ranged from 0.28 to 0.61. The results obtained by Student t-test, density curves and regression analysis, in general, show that the IMERG is able to satisfactorily represent rain gauge data. The exception is the eastern portion of the basin, where the product, on average, underestimates the precipitation (p-value < 0.05) and presents the worst statistical metrics.

Various studies have identified that between 2012 and 2017, Brazil’s semiarid region suffered severe drought. However, few studies have analyzed whether this drought also affected the eastern coastal region of Northeast Brazil (ENEB). Therefore, the objective of this work is to identify rainfall anomalies in these regions and verify the hydrometeorological impact on reservoirs in the 2012-2017 interval. For this purpose, we used precipitation data and atmospheric variables in the period from 1981 to 2017 to investigate the rainy season and associated dynamic patterns, as well as the consequences of these mechanisms on the variation of the water parameters of important reservoirs. The results indicated that rain events in the ENEB during 2012-2017 presented similar climatological behavior, without the characteristic of a drought event as observed in the semiarid region. The meteorological analyses showed that the combination of convergence with moisture over the Atlantic Ocean possibly favored greater frequency of shallow convective rainfall in ENEB, an important factor to explain the absence of generalized negative anomalies in the region. As a consequence, the reservoirs did not suffer from water collapse, unlike in the semiarid region.

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.