Tropical forests play an important role as a reservoir of carbon and biodiversity, specifically forests in the Brazilian Amazon. However, the last decades have been marked by important changes in the Amazon, particularly those associated with climatic extremes. Quantifying the variability of rainfall patterns, hence, is essential for understanding changes and impacts of climate upon this ecosystem. The aim of this study was to analyse spatiotemporal trends in rainfall along the Brazilian Legal Amazon between 1998 and 2015. For this purpose, rainfall data derived from the Tropical Rainfall Measuring Mission satellite (TRMM) and nonparametric statistical methods, such as Mann–Kendall and Sen’s Slope, were used. Through this approach, some patterns were identified. No evidence of significant rainfall trends (p ≤ 0.05) for annual or monthly (except for September, which showed a significant negative trend) averages was found. However, significant monthly negative rainfall anomalies were found in 1998, 2005, 2010, and 2015, and positive in 1999, 2000, 2004, 2009, and 2013. The annual pixel-by-pixel analysis showed that 92.3% of the Brazilian Amazon had no rainfall trend during the period analysed, 4.2% had significant negative trends (p ≤ 0.05), and another 3.5% had significant positive trends (p ≤ 0.05). Despite no clear temporal rainfall trends for most of the Amazon had negative trends for September, corresponding to the peak of dry season in the majority of the region, and negative rainfall anomalies found in 22% of the years analysed, which indicate that water-dependent ecological processes may be negatively affected. Moreover, these processes may be under increased risk of disruption resulting from other drought-related events, such as wildfires, which are expect to be intensified by rainfall reduction during the Amazonian dry season.

Tropical forests provide essential environmental services to human well-being. In the world, Brazil has the largest continuous area of these forests. However, in the state of Maranhão, in the eastern Amazon, only 24% of the original forest cover remains. We integrated and analyzed active fires, burned area, land use and land cover, rainfall, and surface temperature datasets to understand forest fragmentation and forest fire dynamics from a remote sensing approach. We found that forest cover in the Maranhão Amazon region had a net reduction of 31,302 km² between 1985 and 2017, with 63% of losses occurring in forest core areas. Forest edges extent was reduced by 38%, while the size of isolated forest patches increased by 239%. Forest fires impacted, on average, around 1031 ± 695 km² year⁻¹ of forest edges between 2003 and 2017, the equivalent of 60% of the total burned forest in this period. Our results demonstrated that forest fragmentation is an important factor controlling temporal and spatial variability of forest fires in the eastern Amazon region. Thus, both directly and indirectly, forest fragmentation can compromise biodiversity and carbon stocks in this Amazon region.

Resumo A zona de transição Amazônia-Cerrado é caracterizada pela alta diversidade de ecossistemas, biodiversidade e condições climáticas. Este trabalho tem como objetivo investigar evidências de mudanças climáticas na precipitação e temperatura no Estado do Maranhão. Os dados de normais climatológicas desde 1977 até 2014 provenientes de 12 estações meteorológicas do Instituto Nacional de Meteorologia, distribuídas no Estado foram utilizados para construir a série temporal de dados climáticos. Os dados foram divididos em dois grupos referentes às estações climáticas, seca e chuvosa. O teste de Mann-Kendall foi aplicado para detecção de tendências de aumento ou decréscimo, tanto na série temporal completa, quanto nos dados referentes aos períodos seco e chuvoso. Os resultados evidenciaram uma forte elevação na temperatura do ar em todas estações do Estado. No bioma amazônico em relação a precipitação, as estações de Bacabal e Zé Doca registraram tendências de aumento e diminuição, respectivamente. No bioma cerrado, as estações de Carolina e Colinas apresentaram tendências de aumento e diminuição, respectivamente. Os resultados apresentados neste trabalho mostraram que as mudanças climáticas ocorrem diferentemente no Estado do Maranhão, não necessariamente influenciada pelo bioma

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.