Publicações

The presence of an aerosol layer in the upper troposphere/lower stratosphere (UT/ LS) in South America was identified with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2). This layer, which we shall refer to as the South American tropopause aerosol layer (SATAL), was identified over the Amazon basin at altitudes between 11 and 14 km. It exhibits a seasonal behavior similar to the Asian tropopause aerosol layer (ATAL) and the North American tropopause aerosol layer (NATAL). The SATAL is observed from October to March, coinciding with the presence of the South American monsoon. It forms first in the eastern Amazon basin in October, then moves to the southern Amazon, where it weakens in DecemberJanuary and finally dissipates in FebruaryMarch. We hypothesize that two main factors influence the SATAL formation in the UT/LS: 1) the source of aerosols from Africa and 2) the updraft mass flux from deep convective systems during the active phase of the South American monsoon system that transports aerosols to the UT/LS. Further satellite observations of aerosols and field campaigns are needed to provide useful information to find the origin and composition of the aerosols in the UT/LS during the South American monsoon.

A Bacia do Rio Paraíba do Sul é um importante depósito de sedimentos, e tal fato, somado a sua localização estratégica, transforma o Vale do Paraíba em uma importante fonte do mineral não-metálico. A região conta com normativos acerca da extração mineral bastante específicos e restritivos, o que leva os empreendimentos minerários a envidarem grandes esforços para o cumprimento das questões legais. Assim, o presente estudo tem como objetivo realizar uma análise espacial das cavas de mineração de areia no município de Caçapava, Estado de São Paulo, quanto às legislações ambientais. O método utilizado consistiu na identificação e caracterização das áreas de mineração quanto às questões legais utilizando ferramentas de análise espacial baseadas em distância e intersecção. Como resultados, pode-se verificar a presença de áreas com desconformidades no que se refere à distância mínima de áreas urbanizadas, incidência em áreas de proteção ambiental e zonas de várzea. Tais fatos, somados aos já conhecidos impactos ambientais causados pela atividade de mineração, agravam os riscos aos corpos dágua e sua vizinhança. Conclui-se que o método proposto é uma importante ferramenta para o monitoramento constante dessas áreas e que pode auxiliar órgãos ambientais fiscalizadores a tomadas de decisão mais assertivas.

Severe floods and landslides in Eastern Northeast Brazil in May 2022 led to severe impacts with human losses and material damage. These disasters were a direct consequence of extremely heavy rainfall days. A rapid attribution study was performed to assess the role of anthropogenic climate change in 7 and 15-day mean rainfall over this region. A dense network of 389 weather stations was analysed resulting in 79 selected stations containing consistent and spatially well-distributed data over the study region with records starting in the 1970s. Daily rainfall from a multi-model ensemble of climate simulations were also examined to investigate the role of climate change in modifying the likelihood of such extreme events over the studied region. However, such an analysis was hindered by the fact that most investigated models have deficiencies in representing convection associated with warm rains, which are key for the manifestation of such extreme events associated with Easterly Wave Disturbances. From the observational analysis, both 7 and 15-day mean events in 2022 were found to be exceptionally rare, with an approximately 1-in-500 and 1-in-1000 chance of happening in any year in today’s climate, respectively. Even though both events were located far outside the previously observed records, because of the short observational record and associated uncertainties it was not possible to quantify how much climate change made these events more likely to happen. The performed analysis also revealed that global warming increased the intensity of such extreme rainfall: rainfall events as rare as those investigated here occurring in a 1.2 °C cooler climate would have been approximately a fifth less intense. Combining observations with the physical understanding of the climate system, this study showed that human-induced climate change is, at least in part, responsible for the increase in likelihood and intensity of heavy rainfall events as observed in May 2022. Besides, the extreme nature, as a result of such events, made it so extraordinary that population exposure and vulnerability was identified as the main driver for the observed impacts, although long-term impacts and recovery will likely be mediated by socio-economic, demographic and governance factors.

The enormous soil carbon pool stored by mangroves depends upon microbial respiratory activities and enzymes associated with carbon breakdown in soils. Our hypothesis is that increased microbial activity leads to elevated carbon breakdown. To prove this, we measured microbial respiratory and enzyme activity for nitrogen, phosphorus, and carbon cycle in soil from three coastal ecosystems: mangroves, restinga, and Atlantic forest. The multivariate ordination analysis showed that each treatment had distinct soil microbial activity according to the soil layer and seasons. Our results suggest that the Atlantic forest ecosystem had the highest respiration activity (i.e., CO2-C release), dehydrogenase, phosphatase, protease, and urease activities, while mangroves and sandy coastal plains (called restinga) had lower respiration and enzyme activity. Therefore, soil microbial respiratory activity variation was influenced by soil microbial activity in the most superficial layer in coastal soil ecosystems. The agreement between dehydrogenase activity and CO2-C respiration measurements suggests that microbial activity is an efficient indicator of carbon breakdown. Our findings also indicate that the vegetation type in the different ecosystems contribute to stimulate the soil microbiota increasing both its microbial activity and carbon storage. These issues should be considered for the conservation policies to promote effective protection of vital coastal ecosystems in Brazil. Understanding the coastal ecosystem-induced nutrient shifts in microbial communities is important because they can lead to lagged and multiplicative effects on carbon storage. Taken together, our results illustrate the identification of keystone (organic carbon and microbial activity) as an indicator to offer evidence-providing tools to achieve more ecologically efficient managing practices.

Deforestation rates in the Amazon have markedly increased in the last few years, affecting non-protected and protected areas (PAs). Brazil is a hotspot of Protected Area Downgrading, Downsizing, and Degazettement (PADDD) events, with most events associated with infrastructure projects. Despite the threats dams impose on PAs, there is a knowledge gap in assessing deforestation in PAs around large dams in the Amazon. This study investigates how deforestation affects Biodiversity Protection Areas (BioPAs) and Indigenous Lands around the Jirau and Santo Antônio (JSA) dams (Madeira River, Rondônia) and Belo Monte dam (Xingu River, Pará) in the Brazilian Amazon. We compared clear-cutting between PAs and control areas and the annual rates of forest change between pre-dam and post-dam periods. We discussed deforestation-related factors (e.g., PADDD events and the presence of management plans or councils). Our results show an increase in deforestation after the operation of the dams when environmental control from licensing agencies decreases and other political and economic factors are in practice. Indigenous Lands experienced a significant increase in deforestation around the Belo Monte dam, which is associated with the demarcation process and land conflicts. Surrounding the JSA dams, sustainable use BioPAs showed high deforestation rates, and 27 PADDD events were reported, four directly related to dams. In addition to dams, deforestation was associated with the crisis of Brazilian democracy and the weakening of environmental policies. In conclusion, the weak environmental control from environmental licensing agencies during dam operation and PADDD events have contributed to increased deforestation rates and additional stresses in the Amazon.

The lack of a solid methodology defining urban and non-urban areas has hindered accurately estimating the Surface Urban Heat Island (SUHI). This study addresses this issue by using the official national urban areas limit together with a surrounding areas classification to define three different reference classes: the urban adjacent (Ua), the future urban adjacent (FUa), and the peri-urban (PUa), consequently providing a more accurate SUHI estimation on the nine northeastern Brazilian capitals. The land surface temperature was obtained in this study using the Sentinel-3 satellite data for 2019 and 2020. Subsequently, the maximum and average SUHI and the complementary indexes, specifically the Urban Thermal Field Variation Index (UTFVI) and the Thermal Discomfort Index (TDI), were calculated. The UTFVI expresses how harmful the eco-environmental spaces are, with a very strong SUHI for three capitals. In addition, the TDI, with values between 24.6-28.8 degrees C, expresses the population’s thermal comfort, with six capitals showing a very hot TDI. These findings highlight the need for strategies to mitigate the effects of the SUHI and ensure the population’s thermal comfort. Therefore, this study provides a better SUHI understanding and comparison for the Brazilian northeastern region, which has diverse areas, populations, and demographic variations.

The Fluxpart partitioning model was employed to assess transpiration, photosynthesis and evaporation, respiration based exclusively through high-frequency eddy covariance (EC) data. The model was implemented across three sites with two vegetation cover conditions (dense and sparse) during the drought episode between 2012 and 2015 in the Caatinga biome in Northeast Brazil. Fluxpart, an open-source Python software, partitions data by analyzing the flux variance similarity (FVS) relationship and conducting correlation analyses of EC data. The main contribution to the evapotranspiration (ET) process was transpiration (T), representing about 64 and 67% of the amount ET in areas with dense and sparse vegetation cover, respectively. When the Caatinga biome is in a good state of conservation, it behaves as a carbon sink; the net ecosystem exchange average was -6 g C m – 2 day – 1 (gram of carbon per square meter daily) for dense vegetation and 3 g C m – 2 day – 1 for sparse vegetation. Through ET, vapor pressure deficit (VPD), and gross primary production (GPP) via Fluxpart, an assessment was made concerning the underlying water use efficiency (uWUE) to understand the nonlinear effects of VPD on the carbon-water coupling at the ecosystem scale. The overvalues of 60 g C hPa 0.5 kg -1 H 2 O -1 (grams of carbon times square root of hectopascals per kilogram of water) prevailing in the rainy season and below 20 g C hPa 0.5 kg – 1 H 2 O -1 were mostly in the dry season. uWUE was an important indicator of the ability of the Caatinga biome to optimize water loss and carbon gain according to the water available in the soil-plant-atmosphere system to withstand water stress, especially at dense vegetation covers.

Measuring socioeconomic indices at the scale of regions or countries is required in various contexts, in particular to inform public policies. The use of Deep Learning (DL) and Earth Observation (EO) data is becoming increasingly common to estimate specific variables like societal wealth. This paper presents an end-to-end framework DeepWealth that calculates such a wealth index using open-source EO data and DL. We use a multidisciplinary approach incorporating satellite imagery, socio-economic data, and DL models. We demonstrate the effectiveness and generalizability of DeepWealth by training it on 24 African countries and deploying it in Madagascar, Brazil and Japan. Our results show that DeepWealth provides accurate and stable wealth index estimates with an R2 of 0.69. It empowers computer-literate users skilled in Python and R to estimate and visualize well-being-related data. This open-source framework follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, providing data, source code, metadata, and training checkpoints with its source code made available on Zenodo and GitHub. In this manner, we provide a DL framework that is reproducible and replicable.

Agriculture is the worlds main economic activity. According to the Intergovernmental Panel on Climate Change, this activity is expected to be impacted by drought. In the Northeast region of Brazil (NEB), most agricultural activity is carried out by small rural communities. Local socio-economic data were analyzed using multivariate statistical techniques in this study to determine agricultural sensitivity to drought events (SeA) and agricultural vulnerability to drought extremes (VaED). The climate data used to develop the risk factor (Rdrought) were the drought indicator with the Standard Precipitation Index (SPI) and the average number of drought disasters from 1991 to 2012. Conditional probability theory was applied to determine agricultural vulnerability to drought extremes (VaED). Characterization of the risk of agricultural drought using the proposed methodology showed that the rainy season presents high risk values in the central region, covering areas of the states of Ceará, Piauí, Pernambuco and Rio Grande do Norte, as well as all areas of the semi-arid region. The risk ranged from high to medium. The results also indicated that part of the south of Bahia and the west of Pernambuco have areas of extreme agro-climatic sensitivity. Consequently, these states have an extreme degree of climate vulnerability during the regions rainy season.

A compreensão da microfísica de nuvens associada a tempestades severas com grande incidência de descargas atmosféricas pode servir como base científica para o desenvolvimento de sistemas de alerta para a proteção de vidas e equipamentos expostos a céu aberto. Neste trabalho, foram avaliadas as variáveis polarimétricas de um radar meteorológico durante tempestades com elevada ocorrência de descargas atmosféricas em áreas do Leste da Amazônia, com o intuito de subsidiar melhorias em um projeto de alerta de descargas atmosféricas. O estudo foi realizado para eventos com registros acima de 13.000 descargas/dia na região de interesse. Os resultados mostraram que regiões com alta atividade elétrica apresentaram maior produção de cristais de gelo e maior disponibilidade de gotas super-resfriadas em níveis mais altos das nuvens, mecanismos indispensáveis no processo de separação de cargas, cujo principal fator que favoreceu este comportamento foram os movimentos verticais mais intensos dentro das nuvens. Nas áreas de menor atividade elétrica, as frequências dos valores negativos das variáveis polarimétricas foram menores, indicativo de campo elétrico menos intensos, e consequente menor geração de descargas atmosféricas. Desta forma, os resultados sugerem que a inclusão dos dados do radar no sistema de alerta de descargas atmosféricas apresenta potencial para gerar informações mais precisas.

The increase in the spatial resolution of satellite imagery and the greater distribution of data have enabled the use of remote sensing for urban studies. However, there is still a lack of databases and cartographic publications for cities in the Brazilian Amazon. Thus, the following questions guided this work: what is the state-of-the-art in remote sensing to identify urban patterns in Brazil? Do these urban patterns equally cover all Brazilian regions? How to identify urban patterns in different contexts, such as the Amazon urban region? To this end, we conducted a review of publications that have identified urban patterns in Brazilian cities and put forward an identification model. For the most part, we observed that the works found analyzed small areas in São Paulo cities, using visual interpretation in private access and high spatial resolution imagery. To cover this gap, we propose a framework to identify Urban and Socio-Environmental Patterns based on remote sensing data, Voluntary Geographic Information data and census data. Although the framework can be applied in all regions of Brazil, the focus of the classification model is on Amazonian cities.

The municipality of Ubatuba, São Paulo State, Southeast Brazil, is known for its strong orographic rainfall as a result of moisture fronts in the Atlantic Ocean, morphological structure, and abrupt relief of Serra do Mar. These conditioning combinations reinforce the occurrence of natural disasters such as runoff, floods, and landslides. There are several indices to assess flood hazard with different individual characteristics, but not an integrated index, based on traditional approaches. We used the compactness coefficient, circularity ratio, sinuosity index, drainage density, and curve number to propose an integrated index based in a multi-criteria assessment. Hence, the aim of this study was to propose and discuss an integrated index for morphometric and land use and land cover analysis of the Ubatuba municipality watersheds based on a decision matrix. Overall, the integrated multi-criteria index was efficient in discriminating hazard areas to flooding on the north coast of São Paulo, with 82.35% of the watersheds studied being identified with moderate hazard. The use of an integrated multi-criteria index using relief, LULC and soil type, an empirical parameter used in the determination of direct surface runoff from excess rainfall, and hydrography features helps in the interpretation of local hazard to flooding and contributes to urban planning, flood warning systems, and water resources management.