Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (V_C). Moreover, land surface temperature (LST) and actual evapotranspiration (ET_a) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ET_a. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and V_C values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 °C, raising ET_a rates (~4.7 mm day⁻¹). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.

The objective of this study was to evaluate the annual standardized precipitation index (SPI) obtained from the DrinC software based on multivariate analysis in the identification of rainfall and drought extremes in the State of Alagoas and its relationship with El Niño–Southern Oscillation. Monthly rainfall data from 1960 to 2016 from National Water Agency were analysed. Annual SPI (SPI‐12) has been designed for comparison with ENSO phases via Oceanic Niño Index for 3.4 region and in identifying climate extremes in the State of Alagoas. The principal component analysis and cluster analysis techniques were applied to the rainfall series of SPI‐12. Extreme events were identified in both rainy and drought periods according to SPI‐12, and were associated with the ENSO phases (El Niño, La Niña, and Neutral). The first four principal components explained 46.68% of the variance. Our findings are crucial for agriculture and civil defence since northeastern Brazil has several areas of risk and social vulnerability.

Brazilian biomes are home to a significant portion of the world’s biodiversity, with a total of 14% of existing species and still concentrate 20% of the world’s water resources. However, changes in biomes have a direct impact on rainfall patterns and water recycling. Based on this, the objective was to evaluate the variability of rainfall in the four existing biomes in the Northeast Brazil (NEB) and their interaction with the ENSO climate variability mode and regional scale meteorological systems via CHELSA product. For this, monthly rainfall data were used from 1979 to 2013, with a spatial resolution of 1 km × 1 km of the CHELSA product, and seasonal and annual rainfall patterns were extracted via boxplot. It was found that the rainy season in the Amazon, Caatinga and Cerrado biomes occurred between January and April, with varying intensities, except for the Atlantic Forest. Such seasonality patterns are associated with the NEB meteorological systems, with emphasis on ITCZ (all Biomes), UTCV (Amazon, Caatinga and Cerrado), Frontal Systems (extreme south of Caatinga, Cerrado and Atlantic Forest) and EWD/TWD in the (Atlantic Forest). In the inter-annual scale, the remarkable influence of ENSO was verified, mainly in the years 1983, 1985, 1989, 1993, 1998, 2009 and 2012. It is noteworthy that 1985 was the wettest year of the period, with a surplus in all biomes, while the driest year differs between the Amazon (1983), Atlantic Forest and Caatinga (1993) and Cerrado (2012) biomes. The study via orbital product in NEB showed that anthropogenic processes and natural variability interfere with the forms of rain interception in the biomes and hence in rainfall patterns and water recycling in NEB.