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Amazon forests are the largest forests in the tropics and play a fundamental role for regional and global ecosystem service provision. However, they are under threat primarily from deforestation. Amazonia’s carbon balance trend reflects the condition of its forests. There are different approaches to estimate large-scale carbon balances, including top-down (e.g., CO2 atmospheric measurements combined with atmospheric transport information) and bottom-up (e.g., land use and cover change (LUCC) data based on remote sensing methods). It is important to understand their similarities and differences. Here we provide bottom-up LUCC estimates and determine to what extent they are consistent with recent top-down flux estimates during 2010 to 2018 for the Brazilian Amazon. We combine LUCC datasets resulting in annual LUCC maps from 2010 to 2018 with emissions and removals for each LUCC, and compare the resulting CO2 estimates with top-down estimates based on atmospheric measurements. We take into account forest carbon stock maps for estimating loss processes, and carbon uptake of regenerating and mature forests. In the bottom-up approach total CO2 emissions (2010 to 2018), deforestation and degradation are the largest contributing processes accounting for 58% (4.3 PgCO2) and 37% (2.7 PgCO2) respectively. Looking at the total carbon uptake, primary forests play a dominant role accounting for 79% (−5.9 PgCO2) and secondary forest growth for 17% (−1.2 PgCO2). Overall, according to our bottom-up estimates the Brazilian Amazon is a carbon sink until 2014 and a source from 2015 to 2018. In contrast according to the top-down approach the Brazilian Amazon is a source during the entire period. Both approaches estimate largest emissions in 2016. During the period where flux signs are the same (20152018) top-down estimates are approximately 3 times larger in 20152016 than bottom-up estimates while in 20172018 there is closer agreement. There is some agreement between the approachesnotably that the Brazilian Amazon has been a source during 20152018 however there are also disagreements. Generally, emissions estimated by the bottom-up approach tend to be lower. Understanding the differences will help improve both approaches and our understanding of the Amazon carbon cycle under human pressure and climate change.

Climate change can modify the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In Brazil, an example of a location with a high occurrence of landslides and with few studies on the subject is the municipality of Cunha, located in the State of São Paulo. In this way, the main aims of this study are twofold: 1) to identify and analyze areas susceptible to landslides and rainfall-induced landslide hazard in the municipality of Cunha; and 2) by using climate projections from the regional model Eta-HadGEM2-ES under RCP 4.5, to predict new extreme events and possible new landslides in this municipality, from 2024 to 2040. The susceptibility map has been prepared using selected physical-biotic environment elements whereas the hazard map has been produced using precipitation data. Using Eta-HadGEM2-ES climate projections under RCP 4.5, for precipitation events, results project the occurrence of 49 days with landslides for the period between 2024 and 2040. The outcomes of this study are considered of extreme relevance for the local community, public authorities, and decision-makers, to raise awareness regarding future urban planning strategies to prevent and mitigate the effects of catastrophes arising from natural hazard-induced disasters.

Most of the world’s nations (around 130) have committed to reaching net-zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net-zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature-based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost-effective climate mitigation measure for Brazil, whereas relying heavily on costly and not-mature-yet engineered solutions will jeopardise Brazil’s chances of achieving its net-zero pledge by mid-century. We show that the full implementation of Brazil’s Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short-term climate targets up to 2030. However, it would reduce the gap to net-zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large-scale ecosystem restoration, would reduce this gap by 62% by mid-century, keeping Brazil on a clear path towards net-zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net-zero pledge, we show that the more mitigation measures from the land-use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short- and long-term climate targets, and to build climate resilience while curbing biodiversity loss.

Geodetic landmarks (GLs) are essential for obtaining precise height, horizontal coordinates, and the Earth’s gravity field. This study aims to assess the soil susceptibility of GLs for past, present, and future scenarios, considering the projected anthropogenic effect from Coupled Model Intercomparison Project, Version 6 (CMIP6). Therefore, the soil loss estimations were made for all the GLs in the southern region of Santa Catarina state in Brazil using the Revised Soil Loss Equation (RUSLE). Our results showed average soil loss decreasing from 1985 to 2020. There was an increase in GLs in the null class (soil loss = 0 t/ha/year), mainly caused by urban growth. A decrease occurred in the low (0 t/ha/year < soil loss ≤10 t/ha/year), very severe (soil loss >200 t/ha/year), severe (50 t/ha/year < soil loss ≤200 t/ha/year), and moderate classes (10 t/ha/year < soil loss ≤50 t/ha/year). In addition, most future scenarios projected an increase in soil loss susceptibility, which also increased the GLs’ susceptibility to soil loss from 2020 to 2100, albeit with lower values than the historical series. The soil loss remained stable in ssp126, slightly increased in ssp245 and ssp370, and increased in ssp585. The future scenarios only take into account changes in rainfall. Thus, the land cover change forecast would also be necessary for better analysis for future studies. Therefore, climate simulations can be used to understand the effects of climate change on soil erosion to support decision-making regarding GLs maintenance and the construction of new ones.

Little is known about the impact of hypoxia and anoxia during mycelial growth on tolerance to different stress conditions of developing fungal conidia. Conidia of the insect-pathogenic fungus Metarhizium robertsii were produced on potato dextrose agar (PDA) medium under normoxia (control = normal oxygen concentrations), continuous hypoxia, and transient anoxia, as well as minimal medium under normoxia. The tolerance of the conidia produced under these different conditions was evaluated in relation to wet heat (heat stress), menadione (oxidative stress), potassium chloride (osmotic stress), UV radiation, and 4-nitroquinoline-1-oxide (=4-NQO genotoxic stress). Growth under hypoxic condition induced higher conidial tolerance of M. robertsii to menadione, KCl, and UV radiation. Transient anoxic condition induced higher conidial tolerance to KCl and UV radiation. Nutritional stress (i.e., minimal medium) induced higher conidial tolerance to heat, menadione, KCl, and UV radiation. However, neither of these treatments induced higher tolerance to 4-NQO. The gene hsp30 and hsp101 encoding a heat shock protein was upregulated under anoxic condition. In conclusion, growth under hypoxia and anoxia produced conidia with higher stress tolerances than conidia produced in normoxic condition. The nutritive stress generated by minimal medium, however, induced much higher stress tolerances. This condition also caused the highest level of gene expression in the hsp30 and hsp101 genes. Thus, the conidia produced under nutritive stress, hypoxia, and anoxia had greater adaptation to stress.

The Telescope Array (TA) detector located in the southwestern desert of the State of Utah is the largest ultra-high energy cosmic ray experiment in the Northern Hemisphere. The TA detector has been a unique facility for studying downward TGFs and has reported several discoveries over the past few years that allowed us to better understand the Terrestrial Gamma-ray Flashes (TGF) phenomena. The surface detector array part of the TA experiment, used in these observations, is composed of 507 scintillator detectors on a 1.2 km square grid covering a 700-kilometer square area, 1400 m above sea level. New instrumentations have been added at the Telescope array site to study the optical emission of lightning in association with TGFs. In this work, we present optical emission observations of lighting discharges in association with multiple downward-directed TGFs as observed at the Telescope Array detector. Results from this study allow us to further the understanding of the initiation and propagation mechanism of TGFs. Moreover, they enhance our capability to compare satellite optical emissions of upward-directed TGFs with those of downward-directed TGFs.

The Telescope Array (TA) detector located in the southwestern desert of the State of Utah is the largest ultra-high energy cosmic ray experiment in the Northern Hemisphere. The TA detector has been a unique facility for studying downward TGFs and has reported several discoveries over the past few years that allowed us to better understand the Terrestrial Gamma-ray Flashes (TGF) phenomena. The surface detector array part of the TA experiment, used in these observations, is composed of 507 scintillator detectors on a 1.2 km square grid covering a 700-kilometer square area, 1400 m above sea level. New instrumentations have been added at the Telescope array site to study the optical emission of lightning in association with TGFs. In this work, we present optical emission observations of lighting discharges in association with multiple downward-directed TGFs as observed at the Telescope Array detector. Results from this study allow us to further the understanding of the initiation and propagation mechanism of TGFs. Moreover, they enhance our capability to compare satellite optical emissions of upward-directed TGFs with those of downward-directed TGFs.

No Brasil e no mundo, as bacias de drenagem são afetadas não apenas pelas mudanças ambientais diretas, mas também por influências combinadas de várias atividades humanas e pelos impactos da pluviosidade, acarretando em mudanças na vazão e propriedades físico-químicas dos rios (OMETTO; MARTINELLI, 2008; COSTA; MEDEIROS, 2021). O rio Paraíba do Meio, situado no Nordeste do Brasil, vem sofrendo transformações significativas devido à ação humana que acaba afetando os volumes de vazão, a sua biodiversidade e os seus usos potenciais (COSTA; MEDEIROS, 2021). Assim, o objetivo do presente trabalho é o de analisar a variabilidade dos níveis de precipitação e vazão no rio Paraíba do Meio (AL) para os anos hidrológicos de 2016 e 2017. A bacia do rio Paraíba do Meio percorre entre os estados brasileiros de Alagoas e Pernambuco, com área total aproximada de 3.127,83 km², cujo rio principal possui 171,98 km de extensão. Os dados de vazão e precipitação pluvial foram obtidos através do portal HidroWeb, da Agência Nacional de Águas e Saneamento Básico ANA. Foi realizada uma correlação entre a vazão e precipitação do rio Paraíba do Meio para os anos de 2016 e 2017, por meio do cálculo obtido através do coeficiente de correlação de Pearson. Em 2016, a precipitação total anual foi de 921,4 mm, abaixo da última normal climatológica de 1991-2020 do INMET para a região (1808,1 mm). Os meses mais chuvosos do ano foram abril (151,4 mm e 13,23 m3s-1 de vazão) e maio (172,5 mm e 18,18 m3s-1 de vazão) e os menos chuvosos foram setembro (0 mm e 4,08 m3s-1 de vazão) e outubro (0,8 mm e 1,89 m3s-1 de vazão). Em 2017, o total anual da precipitação foi de 1866,9 mm, próximo à normal climatológica de 1991-2020. Houveram meses com totais atípicos, como maio (637,7 mm e 165 m3s-1 de vazão), junho (343,8 mm e 105,51 m3s-1 de vazão) e julho (406,3 mm e 104,90 m3s-1 de vazão). Em 2016, o coeficiente r foi de 0,85, já em 2017, o coeficiente de correlação foi de 0,95, demonstrando uma forte proximidade entre as duas variáveis. Conclui-se que a precipitação pluvial influenciou diretamente na vazão do rio Paraíba do Meio para os anos de 2016 e 2017, com valores de correlação elevados, principalmente no ano de 2017, uma vez que, a variação da precipitação neste ano foi maior devido aos altos volumes de chuvas ocorridos no período chuvoso.

The Brazilian Amazon is a hotspot of deforestation and forest degradation caused by logging, fire, and deforestation-associated fragmentation and edge effects. While the extent of deforestation and associated carbon losses are relatively well known, the quantification of the carbon losses caused by degradation and the carbon gained by recovery of degraded forest ranges widely and is difficult to quantify accurately regionally. We analyzed forest changes and associated carbon gains and losses by using repeated randomized airborne lidar surveys for 2016 and 2017-2018 over 99 different transects, totaling 48,279 ha of forest throughout the Brazilian Arc of Deforestation. We directly measured changes in canopy height between the two airborne lidar campaigns, and gridded the surveyed area into 50 x 50 m cells to estimate aboveground carbon using a previously calibrated model based on the top of canopy height. We classified every 0.25 ha cell into one of the seven forest transition classes: deforestation, forest fires, logging, windthrows, other disturbances, forest growth, and no change. We found that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7% respectively. By extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through deforestation, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 20.8 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. Our regional detailed quantification of carbon changes highlights that degradation is a major driver of the carbon budget of the Amazon tropical forest.

Forest degradation is a major issue and a key component of tropical forests and carbon emissions. In this study, we use repeated Airborne Laser Scanning (ALS) data to quantify carbon losses in degraded forests due to logging at the Mato Grosso state rainforests. We identified logged areas using Planet Norway’s International Climate and Forests Initiative (NICFI) satellite imagery and estimated aboveground carbon density (ACD) and changes (ΔACD) using canopy structure derived from ALS data acquired before and after the logging. Logging caused carbon losses between 16-35% of the original ACD, but also as high as 89% in heavily disturbed areas. Our findings bring estimates to limited sites, so we recommend caution on using them for estimates of carbon loss elsewhere. Spatialized and continuous estimates should be explored in future studies connecting ALS estimates with other optical and SAR remote sensing datasets.

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.