Publicações

The native vegetation removal (NVR) of Caatinga Brazilian biome has been mapped by Prodes Project coordinated by National Institute for Spatial Research (INPE) between 2000 to 2023 year. Prodes is considered an official government product for achieving Brazilian and international targets for the conservation of natural vegetation. Therefore, Prodes is committed to achieving the highest possible accuracy associated with the history of mapping already carried out, by visual interpretation of satellite images and the use of past mapping masks techniques. Mapping NVR in Caatinga biome is a major challenge compared to biomes where the tree canopy with native vegetation prevails. Caatinga is one of the least protected of Brazilian biomes and is in one of the most pressured semi-arid regions on the planet. This work aims to present the NVR in Caatinga biome from Prodes data, in terms of its trajectory and main geographical patterns, concerning the municipalities, and areas that are the focus of monitoring, conservation or restoration. According to Prodes results, by 2023, the cumulative NVR in the entire Caatinga reached 43,05%; in 2000, this figure was 29,54%. The major NVR occurred in the easternmost areas of Caatinga and regions of Serrinha and Feira de Santana in Bahia State. Also, there are areas with considerable NVR further inside the biome, such as, Irecê and Guanambi region in Bahia, west of Pernambuco and Paraíba States and South and North of Ceará State. Areas prioritized for conservation actions showed high NVR values, indicating the urgent need for implementation of such actions. The fact that Desertification Cores showed low NVR values is important, and these regions should be closely monitored to prevent uncontrolled NVR rates. The two regions with high anthropic pressure or speculation showed contrasting behaviors; while the Araripe Gypsum Pole exhibited higher NVR levels compared to the control area, the São Francisco River integration project (PISF) so far does not appear significantly different from the rest of the biome. Overall, this study clearly demonstrates the potential of monitoring the Caatinga through Prodes in understanding and integrating the various factors influencing changes in the biome’s native vegetation cover. Future approaches should move beyond descriptive studies to increasingly contribute to the territorial management of this important Brazilian region.

Climate change is among the principal threats to global terrestrial biodiversity, especially to megadiverse ecosystems such as the Amazon rainforest. In this study, we investigate how it could affect an iconic forest speciesBertholletia excelsa(the Brazil nut) which has values in multiple dimensions in an Amazonian context. We used an ensemble from various distribution modeling methods designed for four different climate scenarios from CMIP6 by the end of the century. Then, we simulate how spatial dynamics under climate change, including explicitly dispersal events, can affect the persistence, colonization, and potential extinction of Bertholletia excelsa in the future. Our results show that by the end of the century there would be a generalized loss of suitability on the Amazon biome, regardless of the climate scenario evaluated, which could promote a significant loss (up to 94%) of the area available for the species via extinction. Our results also show that, in the future, the species would colonize higher altitudes in search of favorable conditions for its survival. Finally, we detected that areas that had previously become unsuitable because of climate change would have favorable conditions by the end of the century. Such an outcome could be useful in fostering an active restoration agenda that can mitigate the negative effects of climate change on species in this study.

Pantanal fires have a significant impact on the environment. Anthropogenic emissions of residual gases have changed the tropospheric composition in this region due to burning. This study aims to analyze the spatial patterns of atmospheric pollutants (including carbon monoxide (CO), nitrogen dioxide (NO2), black carbon (BC), organic carbon (OC) and sulfur dioxide (SO2) and aerosol optical depth, along with fire outbreaks across the Pantanal biome from 2016 to 2021. The data collected is based on remote sensing data. The fire outbreaks peaked pollutant concentrations reached their highest between June to November, particularly during the drier months of August to October. This increase was even greater during the last three years (2019-2021), especially in 2020, when the average CO, NO2, SO2, BC, and OC concentrations increased by 29%, 31%, 50%, 52%, and 50%, respectively. The rainfall values do not justify the increase in the number of fire outbreaks between 2019 and 2021, indicating that the rise is likely due to increased burning. In 2021, the average monthly rainfall was 48% greater than that in 2016-2020 but it had the highest FRP value and the second highest fire outbreak number and pollutant concentration. The 2020 year experienced a record number of fire outbreaks and the highest levels of pollutants in the atmosphere in the region for this period.

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazils vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors.

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (19842017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.

Ocorrências sucessivas de inundações na cidade de São Paulo têm gerado resultados catastróficos causando diversos desafios sociais em uma larga escala de tempo. A bacia do Rio Tamanduateí, foi palco do surgimento e colonização da cidade de São Paulo. Características da evolução da cidade, denotam tratar-se de uma sociedade que se desenvolveu tendendo ao viés tecnológico, em se tratando de respostas a eventos de inundação, resolvendo seus problemas através de estruturas de sistemas de proteção ao longo da história, objetivando a utilização de importantes áreas de planícies de inundação para se estabelecer. Neste sentido, através de tentativas constantes de moldar o sistema fluvial ao assentamento humano, toliu o rio de suas características meândricas naturais, o que levou a complexas interações e respostas entre processos sociais e hidrológicos. A complexidade dos sistemas na cidade de São Paulo, se encontra na interação que combina dados quantitativos da hidrologia com dados qualitativos sociais, gerando uma dinâmica acoplada através do tempo. A sociedade ao longo dos anos respondeu com ações de regulação e controle do rio, contribuindo para o reforço ou o balanço de processos catastróficos de inundação. Essas dinâmicas de resposta consideram interações através de efeitos de macro escala específicos, tais como, efeito dique, efeito reservatório, efeito vivendo com inundações, efeito adaptação, efeito mudança de valores, efeito chamada, efeito colapso do sistema, efeito paradoxo do desenvolvimento seguro, efeito bloqueio e ainda efeito guerra de diques. Este estudo emprega uma abordagem interdisciplinar para elucidar os processos sócio-hidrológicos na bacia do Tamanduateí. A investigação iniciou com uma pesquisa historiográfica detalhada, georreferenciando e contextualizando dados históricos para identificar variáveis influentes e fenômenos sistêmicos. Adotamos o ambiente de acoplamento GIS como ferramenta de análise espacial, integrando dados sociais e geográficos para examinar padrões em macro escala e dinâmicas sociais emergentes. A teoria de sistemas serviu como base teórica, orientando a integração e interpretação de dados. O estudo propõe um modelo dinâmico de interação humano-água, utilizando modelos conceituais e espaciais integrados. Como resultado final deste estudo, obteve-se relevantes contribuições para o entendimento das variáveis analisadas e interadas aos processos complexos da bacia, trazendo resultados de análise sistêmica contundentes que traduzem perspectivas das duas principais disciplinas, social e hidrológica na bacia do Tamanduateí e ao longo do tempo, O estudo alcança ampliar o ferramental de análises para compreensão de fenômenos complexos servindo à futuro suporte a decisão.

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced.

The Madeira River, a major tributary of the Amazon River, often undergoes severe flood anddrought conditions. This study seeks to investigate the climate processes associated with the opposingextreme precipitation events in the Madeira River basin and to relate them to river discharge variabilitybased on a flood awareness dataset. Despite the uncertainty in the observational datasets, the annualprecipitation cycle exhibits a rainy season from November to March. A significant result is the highcorrelation between the rainy season variability in the Madeira River basin and the sea surfacetemperature (SST) anomalies in the tropical North Atlantic Ocean and the southwestern South Atlantic
Ocean. This result indicates that improving the Atlantic SST representation in climate modeling allowsfor capturing extreme precipitation events in the region. In addition to this impact, certain Madeira Rivertributaries present significant climate trends. The river discharge variability reveals an increase inhydrological extremes in recent years in the upper sector, but more significantly, in the lower basin,where it has reduced by more than 400 m3/s per decade. These findings highlight the need to improve insitu data and climate and hydrological modeling, with a focus on describing the intense climate variabilityand trends in river discharges.

This article provides an overview of the evolving agenda surrounding the fourth SustainableDevelopment Goal (SDG 4), dedicated to education. The authors examine the transformation of itsguiding principles via the introduction of new priorities, benchmarks and modes of governance. Drawingon theoretical and methodological insights from political sociology of global education, and employing acombination of ethnographic methods, they highlight key moments which illustrate the dynamic nature ofglobal coordination efforts for SDG 4. The article examines the reform process of the Global EducationCooperation Mechanism (GCM), alongside the adoption of a renewed thematic agenda, ranging from theGlobal Education Meetings in 2020 and 2021 to the Transforming Education Summit in 2022. Theirfindings underscore the predominant influence of multilateral agencies and donors over Member States,driven by a preference for a multistakeholder approach and a pragmatic vision of education. Key trendsidentified include a focus on basic learning, digital literacy and education financing, highlighting thecurrent trajectory of global education governance.

A avaliação dos riscos de impactos das mudanças climáticas associada à gestão de resíduos é crucial devido às suas implicações econômicas, sociais e ambientais, especialmente no setor da construção civil, que contribui para a economia e geração de empregos, mas também é um importante poluidor. Este trabalho visou avaliar indicadores de resíduos sólidos urbanos e de construção civil para análise de riscos relacionados a chuvas intensas, aplicando a metodologia da plataforma AdaptaBrasil. Utilizando 29 indicadores nas dimensões de exposição, vulnerabilidade e ameaça climática, os resultados mostraram que a maioria dos municípios apresenta risco baixo (57%) ou médio (41%) e baixa ameaça climática (75%). No entanto, 37,4% dos municípios são altamente vulneráveis, especialmente nas regiões Nordeste e Sudeste. A sensibilidade é alta nas regiões Sul, Sudeste e Nordeste, enquanto 50% dos municípios têm capacidade adaptativa muito baixa. Sugere-se aumentar a capacidade adaptativa, reduzir a sensibilidade e melhorar a gestão de resíduos para minimizar a vulnerabilidade. Melhorar a gestão institucional, incentivar a reciclagem e o comprometimento municipal com dados sobre resíduos pode facilitar análises futuras e auxiliar na busca de recursos estaduais e federais.

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazils history. However, the regions high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3−), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearmans correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3−, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3− concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system.