Publicações

Mangrove distribution maps are used for a variety of applications, ranging from estimates of mangrove extent, defor-estation rates, quantify carbon stocks, to modelling response to climate change. There are multiple mangrove distribu-tion datasets, which were derived from different remote sensing data and classification methods, and so there are some discrepancies among these datasets, especially with respect to the locations of their range limits. We investigate the latitudinal discrepancies in poleward mangrove range limits represented by these datasets and how these differences translate climatologically considering factors known to control mangrove distributions. We compare four widely used global mangrove distribution maps -the World Atlas of Mangroves, the World Atlas of Mangroves 2, the Global Distri-bution of Mangroves, the Global Mangrove Watch. We examine differences in climate among 21 range limit positions by analysing a set of bioclimatic variables that have been commonly related to the distribution of mangroves. Global mangrove maps show important discrepancies in the position of poleward range limits. Latitudinal differences be-tween mangrove range limits in the datasets exceed 5 degrees, 7 degrees and 10 degrees in western North America, western Australia and northern West Africa, respectively. In some range limit areas, such as Japan, discrepancies in the position of mangrove range limits in different datasets correspond to differences exceeding 600 mm in annual precipitation and > 10 degrees C in the minimum temperature of the coldest month. We conclude that dissimilarities in mapping mangrove range limits in different parts of the world can jeopardise inferences of climatic thresholds. We expect that global mapping efforts should prioritise the position of range limits with greater accuracy, ideally combining data from field-based surveys and very high-resolution remote sensing data. An accurate representation of range limits will contribute to better predicting mangrove range dynamics and shifts in response to climate change.

Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grossos total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions.

This study investigates a summer heat wave (HW) associated with downslope winds (DWs) affecting the central region of the state of Rio Grande do Sul (RS), Brazil. The temporal evolution of both phenomena is analyzed in the atmospheric boundary layer (ABL) using a combination of micrometeorological and rawinsonde data. For spatial characterization, ERA-5 reanalysis data are used. The HW covered a large area in southern Brazil, Argentina, and Paraguay. The main features of the HW were locally enhanced in the central region of RS by the development of DWs. The establishment of DWs near the surface depends on the dynamics of the ABL and local topographic features. The results showed that DWs that occurred during the HW contributed to the extreme temperatures and were associated with strong northerly winds, low relative humidity, and a drop in the dew points. Together, these extreme events influenced the turbulent and mean flow patterns of the ABL. The increase in turbulent activity associated with the warming of the ABL favored enhanced growth of morning ABL, while at night the simultaneous effects of radiative and turbulence cooling inhibited the formation of a strong stable ABL. The analysis highlights the complex interplay of synoptic and local factors associated with DWs and HW.

Amazon forests are being degraded by myriad anthropogenic disturbances, altering ecosystem and climate function. We analyzed the effects of a range of land-use and climate-change disturbances on fine-scale canopy structure using a large database of profiling canopy lidar collected from disturbed and mature Amazon forest plots. At most of the disturbed sites, surveys were conducted 1030 years after disturbance, with many exhibiting signs of recovery. Structural impacts differed in magnitude more than in character among disturbance types, producing a gradient of impacts. Structural changes were highly coordinated in a manner consistent across disturbance types, indicating commonalities in regeneration pathways. At the most severely affected site burned igapó (seasonally flooded forest) no signs of canopy regeneration were observed, indicating a sustained alteration of microclimates and consequently greater vulnerability to transitioning to a more open-canopy, savanna-like state. Notably, disturbances rarely shifted forests beyond the natural background of structural variation within mature plots, highlighting the similarities between anthropogenic and natural disturbance regimes, and indicating a degree of resilience among Amazon forests. Studying diverse disturbance types within an integrated analytical framework builds capacity to predict the risk of degradation-driven forest transitions.

In face of complex socio-environmental issues experienced in different social-ecological systems, we ask if comprehensive lessons could be learned from cases of community self-organization that were successful in solving collective problems at the local level. Considering that the trajectory of each community is unique and self-organization develops in distinctive settings, we sought to identify the common elements shared by six case studies in Brazil and investigate how they interact (i.e., if they generate feedback that amplifies self-organization), synthesizing the lessons drawn from each case so they may be applied to other contexts. In all cases, community self-organization provided good conditions to overcome crisis and led to desirable changes regarding the problem in question. We explored the underlying mechanisms of successful community self-organization from a social-ecological and community resilience standpoint and identified six elements in common: ability and/or willingness to find opportunities in crisis; partnerships with external actors; human and social capital within the community; generation of income opportunities and/or guarantee of rights; existence of spaces that favor social interaction; and agency oriented to collective mobilization and problem solving. Elements were interconnected and often reinforced one another, generating amplifying feedback, which is seen in the repetition and improvement of practices and attitudes over time and space. Agency was a prominent catalyst for self-organization by generating amplifying feedback that positively affected other elements; it was an element of the feedback chain reinforced by the benefits reaped at the individual level. When collective interests prevailed over individual ones, it was less likely to generate feedback that inhibited self-organization. We argued that ordinary relationships related to different cultural practices and livelihoods were important exercises of collective action that provided communities with a repertoire of responses that could be activated in times of crisis, thus enhancing their capacity to self-organize.

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.