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Approximately 2.5 × 106 square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year−1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year−1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities.

The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the countrys precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0 (Formula presented.) × 1.0 (Formula presented.) and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year (Formula presented.) in the extreme south to 1750 mm year (Formula presented.) in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year (Formula presented.), within the expected average, compared to observed values of 7501500 mm year (Formula presented.) (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved.

The 2030 Agenda framework represents an opportunity for the co-development of local indicators and shares the same principles of cross-sectoral integration and universality as the waterenergyfood nexus. This research aimed to develop a quantitative-collaborative methodology for constructing indicators and indexes for the sustainability of the Cerrado and Caatinga Brazilian biomes, considering the connection and synergies between the Nexus perspective and the 2030 Agendas SDGtarget framework. The study was applied in 2,512 municipalities that make up the total of the mentioned biomes. The methodology consists of four major blocks: i) identification and construction of indicators based on the association of them with one or more 2030 Agendas targets, in a collaborative way from expert groups; ii) generation of weights by an indicator considering leverage, according to the number of target assignments, and importance, attributed by experts for each indicator to the target; iii) generation of sustainable development goal indexes, considering the performance of the SDGs; Nexus performance, considering different weights between the SDGs; Nexus synergistic, the standard deviation between the SDGs; and Nexus overall, a combination of the last two; and iv) sensitivity analysis for performance indexes considering the assignment of zero values to the governance indicators. In general, the regions of MATOPIBA, northern and western of Cerrado and the Caatinga, are, on average, the areas of greatest vulnerability. The targets that respond to this inequality are related to eradicating hunger, sustainable and climate-resilient production systems, universal access to water, water quality, efficiency of multiple water uses, access to energy, and the participation of renewable energies. We found that in areas of better average nexus performance, there is also greater inequality between nexus dimensions; therefore, the Nexus overall index is penalized. The federal units and areas with the worst Nexus dimension performances and with insufficient governance measures are north and northwest of Bahia and municipalities bordering the State of Tocantinsfood; Maranhão and Piauí, northeast of Mato Grosso, northwest of Goiás, Rio Grande do Norte and Paraíbawater; and Maranhão, extreme southeast of Pará, and more sparsely in Paraíbaenergy.

Invasive earthworms are threatening soil biodiversity and ecosystem functioning in formerly earthworm-free boreal and temperate forests. Although exotic earthworms are also found in tropical forests, they have received less attention from soil scientists. Here, we summarize data on earthworm populations in the native Brazilian Atlantic Forest (one of the world’s 25 biodiversity hotspots) and present a case study on three forests at different regeneration stages, aiming to identify the patterns of exotic species distribution and also the possible consequences of invasive earthworms for soil ecosystem services and macrofauna communities. We found that exotic earthworms, mainly Pontoscolex corethrurus and pheretimoid species (Amynthas corticis and A. gracilis), dominated the earthworm fauna in the native Brazilian Atlantic Forest, while native earthworms were less abundant. Furthermore, we observed that exotic earthworms are probably threatening soil macroinvertebrates, especially detritivores and predators, due to the high soil bioturbation. The widespread occurrence of invasive earthworms in this biome raises concern regarding possible competition with the native soil fauna, including native earthworms, which could dramatically impact ecosystem services linked to soil, especially those related to climate regulation and water storage.

Climate crisis requires intergenerational groups’ participation to reduce their vulnerability and to promote disaster risk mitigation public policies. This paper analyzes what and how social innovations can nurture risk mitigation public policies to enhance flood resilience in small cities. A mixed-methods approach, which considered a survey with 231 participants (54% women, 45% men, and 1% non-identified) and the facilitation of a seminar with participatory methodologies, was implemented between August and October 2021, engaging municipal secretariats, civil defense, an NGO, schools, and residents of São Luiz do Paraitinga, Brazil. Most participants (205 out of 231 surveyed respondents) indicated that floods are the main hazards, and 90% stated that municipal authorities should lead prevention measures prioritizing ten social innovation actions to avoid and reduce the disaster risks. The survey’s results were shared during a seminar that included serious gaming activities to identify, discuss and formulate implementation pathways (how, when, with whom, with what resources) for the prioritized social innovation actions. Among the measures are prevention and evacuation plans, which were significantly correlated (0.749), as well as natural areas restoration and conservation plans, risk areas tours, and children and youth engagement in disaster risk reduction.

Land use and climate change are expected to significantly alter hydrology and consequently electricity production in countries extremely dependent on their water resources, such as Brazil. Therefore, we used the large-scale hydrological model Soil and Water Assessment Tool (SWAT), which we integrated with climate change and land use scenarios for the Tocantins-Araguaia Watershed (TAW) with a focus on energy production at the Tucuruí Hydroelectric Plant (THP) in the southeastern Amazon. We used daily precipitation and temperature data from two General Circulation Models (GCM), HadGEM2-ES and MIROC5 with moderate (+4.5 W/m2 in the year 2100 relative to pre-industrial levels) and severe (+8.5 W/m2) radiative forcing from carbon dioxide emissions in the atmosphere (Representative Concentration Pathways). For the land use and land cover change (LULCC) scenario, we replaced forest areas only with pasture, then with agriculture, then with reforestation vegetation, and finally with regenerated forest. Each LULCC period was coupled with the highest impact climate scenario found for TAW (MIROC5-RCP 8.5); thus, we investigated five scenarios and their impacts on hydropower production and revenue in THP. Our results highlight that the TAW will face a large water reduction by the end of the century, which in all scenarios will strongly impact the basin’s energy production and hydro revenue. Reductions of up to 74% in annual flow and 63% in electricity generation are expected for the most pessimistic scenario (L8.5), triggering a 135% deficit per year in THP revenue. Although some land use change scenarios partially minimize the climate-driven flow decrease trend in the period of higher precipitation, there is still a dramatic reduction in flow during the dry season, thus exacerbating seasonal and inter-annual variability.

This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2008 up to 2020 of precipitation data from the main airport stations in Rio de Janeiro, Brazil, and atmospheric discharges from the surrounding area of around 150 km are investigated. The Weather Research and Forecasting (WRF) model was used to numerically simulate atmospheric conditions for every day in February, as it is the month with the greatest daily rate of atmospheric discharge for the data period. The p-value hypothesis test (with α=0.05) was applied to each grid point of the numerically predicted variables (defined as an independent attribute) to find those most associated with convective events using the output of the 3-D WRF grid. This one identified 36 attributes (or predictors) that were used as input in the machine learning algorithms’ training-test process in this study. Several cross-validation training and testing experiments were carried out using the nine-selected categorical machine learning algorithms and the 36 defined predictors. After applying the boosting technique to the nine previously trained-tested algorithms, the results of the 24-h predictions of convective occurrences were deemed satisfactory. The RandomForest method produced the best results, with statistics values close to perfection, such as POD = 1.00, FAR = 0.02, and CSI = 0.98. The 24-h hindcast utilizing the nine algorithms for the 28 days of February 2019 was very encouraging because it was able to almost recreate the maturation phase of events and their eventual failures were noted during the formation and dissipation phases. The best and worst 24-h hindcast had POD = 0.97 and 0.88, FAR = 0.02 and 0.12, and CSI = 0.94 and 0.78, respectively.

Conservation status assessments are one of the key scientific measures to inform solutions aiming at minimizing the biodiversity crisis of the Anthropocene, being the IUCN’s Red List approach the most widely used and accepted tool for species-level assessment. However, Red Lists are time-consuming because they necessarily rely on expert evaluation. Thus, the development of automated tools, able to handle the huge increasingly amount of data in less time, are useful for guiding conservation actions. In this sense, the aim of this research is to evaluate an automated preliminary conservation status assessment for all known endemic flora from the megadiverse Brazil. Vascular taxa were derived from the Brazilian Flora 2020 project and occurrences were retrieved from GBIF and SpeciesLink. Data was filtered and then evaluated with the aid of ConR package and high resolution nationwide vegetation maps. The current approach estimates that 28.78 % (22.7243.75 %) of the endemic flora is threatened by extinction, with different patterns between the six Brazilian ecoregions: from 30.75 % (Amazonia’s lower) to 100 % (Pantanal’s best/upper). The high variation in the estimate is because more than a fifth of the vascular flora is poorly known (<3 records). Accuracy assessment reveals a good agreement between the reference from the Brazilian Flora 2020 project and a binary classification (threatened/not- or near-threatened), with an overall accuracy of 61.67 % (58.8464.47 %), but with limited capacity to distinguish between the IUCN’s threat levels (Critically Endangered, Endangered and Vulnerable to extinction). Bromeliaceae and Orchidaceae are the families with higher estimated number, proportion in relation to the whole family and uncertainty of estimated threatened species, reflecting that herb is the life form with far more threatened species, followed by shrubs. The presented approach can serve as rapid assessment tool for optimizing efforts and to identify potentially threatened species and hotspots of species that could be considered for timely and effective conservation actions. As a practical recommendation, botanists could prioritize the assessment of the species indicated here as threatened, as there is a high chance of confirming this status, and efforts can be properly addressed for their conservation. Moreover, the automated approach can be useful for monitoring the progress of conservation targets. Finally, further scientific research is urgent to fill the knowledge gap on the Brazilian flora aiming at its conservation.

Information about lightning activity and its parameters is necessary to design and evaluate the lightning protection of an electrical power system. This information can be obtained from ground-based lightning detection networks that provide information on cloud-to-ground lightning strikes with a location accuracy of few hundred meters. Recently, the first satellite-based lightning optical detectors are operating continuously from geostationary orbits. These imagers observe the luminosity escaping from clouds to detect and locate total lightning activity with a spatial accuracy of several kilometers. This allows delineating the initiation and propagation (sometimes over tens to hundreds of kilometers before striking the ground) not observable by the ground-based networks. In this paper, we explore the use of this new technology for lightning protection in power systems. We focus on tall objects such as wind turbines and overhead transmission lines. We show how the optical detections allow identifying lightning flashes that likely produce continuing currents. This provides additional information for the identification of dangerous events and also can be used to estimate the number of upward-flashes from tall objects triggered by a nearby flash. The analysis of a transmission line shows the concentration of faults in the areas of high total lightning flash density. We found regional variations of the optical energy of the flashes along the line.

This study applied regionalization techniques on future climate change scenarios for the precipitation over the Xingu River Basin (XRB) considering the 20212080 horizon, in order to assess impacts on the monthly flow rates and possible consequences for electricity generation at the Belo Monte Hydroelectric Power Plant (BMHPP). This is the fourth largest hydroelectric power plant in the world, with a generating capacity of 11,233 MW, and is located in the Brazilian Amazon. Two representative concentration pathways (RCP 4.5 and RCP 8.5) and an ensemble comprising four general circulation models (CanESM2, CNRM-CM5, MPI-ESM-LR and NORESM1-M) were used. The projections based on both scenarios indicated a considerable decrease in precipitation during the rainy season and a slight increase during the dry season relative to the reference period (19812010). According to the results, a reduction in the flow rates in Altamira and in the overall potential for power generation in the BMHPP are also to be expected in both analyzed periods (20212050 and 20512180). The RCP 4.5 scenario resulted in milder decreases in those variables than the RCP 8.5. Conforming to our findings, a reduction of 21.3% in the annual power generation at the BMHPP is expected until 2080, with a corresponding use of 38.8% of the maximum potential of the facility. These results highlight the need for investments in other renewable energy sources (e.g., wind and solar) in order to compensate for the upcoming losses in the BMHPP production.

Brazil is one of the most vulnerable regions to extreme climate events, especially in recent decades, where these events posed a substantial threat to the socio-ecological system. This work underpins the provision of actionable information for society’s response to climate variability and change. It provides a comprehensive assessment of the skill of the state-of-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) models in simulating regional climate variability over Brazil during the present-day period. Different statistical analyses were employed to identify systematic biases and to choose the best subset of models to reduce uncertainties. The results show that models perform better for winter than summer precipitation, consistent with previous results in the literature. In both seasons, the worst performances were found for Northeast Brazil. Results also show that the models present deficiencies in simulating temperature over Amazonian regions. A good overall performance for precipitation and temperature in the La Plata Basin was found, in agreement with previous studies. Finally, the models with the highest ability in simulating monthly rainfall, aggregating all five Brazilian regions, were HadGEM3-GC31-MM, ACCESS-ESM1-5, IPSL-CM6A-LR, IPSL-CM6A-LR-INCA, and INM-CM4-8, while for monthly temperatures, they were CMCC-ESM2, CMCC-CM2-SR5, MRI-ESM2-0, BCC-ESM1, and HadGEM3-GC31-MM. The application of these results spans both past and possible future climates, supporting climate impact studies and providing information to climate policy and adaptation activities.