Climate change is expected to alter the environmental suitability of land use and land cover (LULC) classes globally. In this study, we investigated the potential impacts of climate change on the environmental suitability of the most representative LULC classes in the southern Brazilian semiarid region. We employed the Random Forest algorithm trained with climatic, soil, and topographic data to project future LULC suitability under the Representative Concentration Pathway RCP 2.6 (optimistic) and 8.5 (pessimistic) scenarios. The climate data included the mean annual air temperature and precipitation from the WorldClim2 platform for historical (1970–2000) and future (2061–2080) scenarios. Soil data were obtained from the SoilGrids 2.1 digital soil mapping platform, while topographic data were produced by NASA’s Shuttle Radar Topography Mission (SRTM). Our model achieved an overall accuracy of 60%. Under the worst-case scenario (RCP 8.5), croplands may lose approximately 8% of their suitable area, while pastures are expected to expand by up to 30%. Areas suitable for savannas are expected to increase under both RCP scenarios, potentially expanding into lands historically occupied by forests, grasslands, and eucalyptus plantations. These projected changes may lead to biodiversity loss and socioeconomic disruptions in the study area.

Brazil has the most populous and biodiverse semi-arid region in the world (Brazilian Semi-arid - SAB). However, in recent decades, clusters of desertification have emerged, a problem that could intensify from climate change. The objective of this study was to elaborate on the spatial distribution of areas susceptible to climatic desertification in the SAB, considering future climate change scenarios. Understanding this dynamic is essential for SAB's agri-environmental management. Aridity indices and proposition of climate classes for current condition (1970-2000) and future scenarios (2061-2080) of the Intergovernmental Panel on Climate Change (IPCC) were prepared, considering scenarios from Shared Socioeconomic Pathways: Optimistic (SSP 126) and pessimists (SSP 585). The results indicate that by the end of the century, the climate in the SAB should become significantly drier (Kruskal-Wallis = p-value < 0.05), with an intensification of the aridity index in SSP 585. In the scenarios, the expansion of more arid areas over humid climates could reach 56,500 km² (10%) in SSP 126 and 140,400 km² (24%) in SSP 585. Consequently, areas with high (622,400 km² to 706,300 km²) and very high (622,400 km² to 706,300 km²) are expected to expand. 4,400 to 21,700 km²) susceptibility to climate desertification in the SAB, respectively in scenarios SSPs 126 and 585. Confirming these projections would imply socioeconomic and ecological risks in the SAB.

Brazil has the most populous and biodiverse semi-arid region in the world (Brazilian Semi-arid - SAB). However, in recent decades, clusters of desertification have emerged, a problem that could intensify from climate change. The objective of this study was to elaborate on the spatial distribution of areas susceptible to climatic desertification in the SAB, considering future climate change scenarios. Understanding this dynamic is essential for SAB's agri-environmental management. Aridity indices and proposition of climate classes for current condition (1970-2000) and future scenarios (2061-2080) of the Intergovernmental Panel on Climate Change (IPCC) were prepared, considering scenarios from Shared Socioeconomic Pathways: Optimistic (SSP 126) and pessimists (SSP 585). The results indicate that by the end of the century, the climate in the SAB should become significantly drier (Kruskal-Wallis = p-value < 0.05), with an intensification of the aridity index in SSP 585. In the scenarios, the expansion of more arid areas over humid climates could reach 56,500 km² (10%) in SSP 126 and 140,400 km² (24%) in SSP 585. Consequently, areas with high (622,400 km² to 706,300 km²) and very high (622,400 km² to 706,300 km²) are expected to expand. 4,400 to 21,700 km²) susceptibility to climate desertification in the SAB, respectively in scenarios SSPs 126 and 585. Confirming these projections would imply socioeconomic and ecological risks in the SAB.

Brazil has the world's most populous semi-arid region and climate change represents significant ecological and socioeconomic challenges for this area. To better understand the impact of these changes, it is crucial to analyze the dynamics of climate variables and evapotranspiration (ETo), a critical climate variable. This study aimed to model ETo rates considering climate change scenarios in the Brazilian Semi-arid region (BSR). The modeling was based on tests of five machine learning algorithms: Bayesian Regularized Neural Networks (BRNN), Cubist, Earth, Linear Regression (LM), and Random Forest (RF). A dataset with 20 covariates was used to represent the current scenario. In the future prediction, covariates from two shared socio-economic pathways were used (SSPs 126 and 585). The best statistical performance was achieved by Cubist (R² = 0.98 and RMSE = 0.08 mm day-¹ in the holdout-test). The current daily average ETo is 4.77 mm day-¹, while in future scenarios, it can increase by 3.56% in SSP 126 and 15.51% in SSP 585. ETo rates are expected to expand territorially; ranges from > 0.60 mm day-¹ should increase 8% in SSP 126 and 40% in SSP 585. The applied model suggests that ETo may increase in future scenarios in the BSR, which could affect biodiversity levels and intensify social conflicts.Keywords: Machine Learning; Spatial Prediction; Cubist; Semi-arid Zone; Climate changes. Mapeamento dos Efeitos das Mudanças Climáticas na Evapotranspiração de Referência em Cenários Futuros no Semiárido Brasileiro - América do Sul RESUMOO Brasil possui a região semiárida mais populosa do mundo e as mudanças climáticas impõem desafios ecológicos e socioeconômicos significativos para esta área. Para entender melhor o impacto dessas mudanças, é crucial analisar a dinâmica das variáveis climáticas e a evapotranspiração (ETo), uma importante variável do clima. Este estudo teve como objetivo modelar taxas de ETo considerando cenários de mudanças climáticas no Semiárido Brasileiro (BSR). A modelagem foi baseada em testes de cinco algoritmos de aprendizado de máquina: Bayesian Regularized Neural Networks (BRNN), Cubist, Earth, Linear Regression (LM) e Random Forest (RF). Um conjunto de dados com 20 covariáveis foi usado para representar o cenário atual. Na predição futura, foram usadas covariáveis de dados dos Caminhos Socioeconômicos Compartilhados (SSPs 126 e 585). Na modelagem, o melhor desempenho estatístico foi com modelo Cubist (R² = 0,98 e RMSE = 0,08 mm dia-¹ no holdout-test). A média diária atual de ETo é de 4,77 mm dia-¹, enquanto em cenários futuros a média pode aumentar 3,56% no SSP 126 e 15,51% no SSP 585. As taxas de ETo devem se expandir territorialmente; intervalos de > 0,60 mm dia-¹ devem expandir 8% no SSP 126 e 40% no SSP 585. O modelo aplicado sugere que a ETo pode aumentar em cenários futuros na BSR, o que pode afetar os níveis de biodiversidade e intensificar os conflitos sociais.Palavras-Chave: Aprendizado de máquina; Predição espacial; Cubist; Zona Semiárida; Mudanças climáticas.

In Brazil there are six well-defined biomes and the Caatinga represents 9.92% of the total area. This biome is exclusively Brazilian and very rich in biodiversity. Because it has low resistance to human interference is necessary to know the important factors in monitoring the biome. Vegetation coverage and climate are two of these factors, as they indicate the intensity of human activity and the wear caused with time. Normalized Difference Vegetation Index (NDVI) has been recently explored in the description of tree phenology. In this study we sought to associate NDVI/Landsat values to a climatic variable (phenomena El-Nino and La-Nina - NINONINA) in order to describe the phenological behavior of the Caatinga in the Parque Nacional da Serra das Capivaras in the state of Piauí/Brazil. A time series analysis was carried out, describing the intrinsic parameters of the series (Seasonality and Trends), the forecast of NDVI values using SARIMA model and co-integration between NDVI and NINONINA series. The results showed that the NDVI series presents seasonally, but does not exhibit a trend. The forecasting process presenting relatively low error at a 95% confidence interval. Finally, it was observed that the NDVI series is co-integrated with the NINONINA series.

Mapping activities for the implementation of basic and executive projects for electric power transmission lines systematically involves topographic surveys. This study presents the results of a comparative study on the influence of the density of aerial survey points, LiDAR (Light Detection and Ranging), for the calculation of digital terrain models (DTMs) in typical areas of the northeastern region of Brazil, in the context of modeling ground for planning electricity transmission lines. The study area is the region covered by the 230 kV Ibicoara/Brumado transmission line, located in the state of Bahia/Brazil.. In part of this region, elevations of the mountains of the Western Edge of Chapada Diamantina predominate. Therefore, points with varying densities were used to interpolate DTMs in different environments. Point cloud classification (Airborne LiDAR) was performed using the TerraScan program using semi-automatic classification methods, followed by manual refinements. To evaluate the terrain models obtained with different point densities, four areas were selected under transmission line: Area 1 (24.4/m2), Area 2 (46.0/m2), Area 3 (33.7/m2) e Area 4 (29.7/m2). The Airborne LiDAR survey was performed with an Optech ALTM Pegasus HD500 sensor, calibrated to obtain a density of 15 points/m2. The results showed that, depending on the nature of the vegetation cover, less dense laser surveys do not offer enough quality to generate DTMs. However, in some cases, when the vegetation is denser and the terrain is not flat, the quality of the DTM decreases as the density of the points decreases. The study shows that the survey density must be suitable for the region to be analyzed. An important guideline would be to repeat this study in other areas with different coverage and relief variations, in order to create specifications to serve as a basis for planning LiDAR surveys on other transmission lines.