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Forest degradation is widespread around the world, due to multiple factors such as unsustainable logging, agriculture, invasive species, fire, fuelwood gathering, and livestock grazing. In the Brazilian Amazon forest degradation from August 2006 to July 2016 reached 1,1 869 800 ha. The processes of forest degradation are still poorly understood, being a missing component in anthropogenic CO(2)emission estimates in tropical forests. In this work, we analyzed temporal trajectories of forest degradation from August 2006 to July 2016 in the Brazilian Amazon and assessed their impact on the regional carbon balance. We combined the degradation process with deforestation-related processes (clear-cut deforestation and secondary vegetation dynamics), using the spatially-explicit INPE-EM carbon emission model. The trajectory analysis showed that 13% of the degraded area ended up being cleared and converted in the period and 61% of the total degraded area experienced only one event of degradation throughout the whole period. Net emissions added up to 5.4 GtCO(2), considering the emissions from forest degradation and deforestation, absorption from degraded forest recovery, and secondary vegetation dynamics. The results show an increase in the contribution of forest degradation to net emissions towards the end of the period, related to the decrease in clear-cut deforestation rates, decoupled from the forest degradation rates. The analysis also indicates that the regeneration of degraded forests absorbed 1.8 GtCO2 from August 2006 and July 2016-a component typically overlooked in the regional carbon balance.

The Brazilian Amazon has the highest concentration of indigenous peoples in the world. Recently, the Brazilian government sent a bill to Congress to regulate commercial mining in indigenous lands. This work analyzes the risks of the proposed mining bill to Amazonian indigenous peoples and their lands. To evaluate the possible impact of the new mining bill, we consider all mining license requests registered in Brazils National Mining Agency that overlap indigenous lands as potential mining areas in the future. The existing mining requests cover 176 000 km2 of indigenous lands, a factor 3000 more than the area of current illegal mining. Considering only these existing requests, about 15% of the total area of ILs in the region could be directly affected by mining if the bill is approved. Ethnic groups like Yudj´a, Kayapo, Apalaí, Wayana, and Katuena may have between ´ 47% and 87% of their lands impacted. Gold mining, which has previously shown to cause mercury contamination, death of indigenous people due to diseases, and biodiversity degradation, accounts for 64% of the requested areas. We conclude that the proposed bill is a significant threat to Amazonian indigenous peoples, further exposing indigenous peoples to rural violence, contamination by toxic pollutants, and contagious diseases. The obligation of the government is to enforce existing laws and regulations that put indigenous rights and livelihoods above economic consideration and not to reduce such protections.

Several studies have presented morphometric analysis as the key to understanding hydrological behavior or support tool for the assessment of natural resources and environmental risks in a river basin. The Rio Grande Basin has great socioeconomic and environmental relevance for Brazil; however, its morphometric characterization is not widely known. Therefore, we present the morphometric characteristics of the Rio Grande Basin (BHRG), from the geometric, relief, and drainage variables. Data processing was carried out using the Geographic Information System (GIS), from the Digital Elevation Model (DEM). The results indicate that the BHRG is of the ffth order, presenting a dendritic drainage pattern, low susceptibility to fooding under normal precipitation conditions, sinuous watercourses, and an undulating relief. In addition, the BHRG is permeable, poorly drained, and displays low capacity to generate and maintain drainage tracts. The fndings can contribute to a better understanding of hydrological behavior on a river basin and support integrated water resource plans.

Several studies show the effects of lake breezes on cloudiness over natural lakes and large rivers, but only few contain information regarding large flooded areas of hydroelectric dams. Most Brazilian hydropower plants have large water reservoirs that may induce significant changes in the local environment. In this work, we describe the prevailing breeze mechanism in a Brazilian tropical hydropower reservoir to assess its impacts on local cloudiness and incoming surface solar irradiation. GOES-16 visible imagery, ISCCP database products, and ground measurement sites operated by INMET and LABREN/INPE provided data for the statistical analysis. We evaluate the cloudiness frequency assuming two distinct perspectives: spatial distribution by comparing cloudiness over the water surface and areas nearby its shores, and time analysis by comparing cloudiness prior and after reservoir completion. We also evaluated the solar irradiance enhancement over the water surface compared to the border and land areas surrounding the hydropower reservoir. The results pointed out daily average cloudiness increases moving away from the reservoir in any of the four cardinal directions. When looking at the afternoon-only cloudiness (14 h to 16 h local time), 4% fewer clouds were observed over the flooded area during summer (DJF). This difference reaches 8% during autumn (MAM) and spring (SON). Consequently, the irradiance enhancement at the water surface compared to external areas was around 1.75% for daily average and 4.59% for the afternoon-only average. Our results suggest that floating solar PV power plants in hydropower reservoirs can be an excellent option to integrate both renewable energy resources into a hybrid power generation due to the high solar irradiance in Brazilian territory combined with the prevailing breeze mechanism in large tropical water reservoirs.

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha-1 and a bias of 0.43 Mg ha-1.

Emissions of nitrous oxide (N2O) over croplands are a major source of greenhouse gases to the atmosphere. The precise accounting of sources of N2O is essential to national and global budgets, as well as the understanding of the spatial and temporal relationships with environmental variables such as rainfall, air and soil temperature, and soil moisture. The objective of this work was to investigate the temporal correlations of N2O fluxes with soil and air temperatures, as well as soil moisture. N2O fluxes were measured over four biofuel crops in Central Illinois during their establishment phase. Measurements were carried out from 2009 to 2011 using a trace gas analyzer (TGA) with tunable laser technology. Measurements of concentrations of N2O and CO(2)were taken at the center of four plots of maize/soybean rotation, miscanthus (Miscanthus x giganteus), switchgrass (Panicum virgatum) and a mixture of native prairie plants. Cumulative fluxes indicate an average emission of nitrogen via N2O fluxes on the order of 1.5 kg N ha(-1) year(-1), in agreement with chamber measurements previously reported for the site. N2O fluxes were associated with peaks in soil and air temperature, and soil moisture, particularly during spring and winter thaws. Cross-wavelet analysis was used to investigate the correlation between N2O fluxes and those variables. Results indicate that N2O fluxes and meteorological variables have significant covariance in time scales ranging from 4 to 32 days. In addition, temporal delays of 1-8 days were found in those relationships. Cross-wavelet patterns were similar when relating N2O fluxes with soil temperature, air temperature and soil moisture. The temporal patterns of fluxes and environmental variables reported here support the modeling of emissions and highlight the importance of considering the timing of fluxes in relation to trends in meteorological variables.

The importance of greenhouse gas inventories cannot be overstated: the process of producing inventories informs strategies that governments will use to meet emissions reduction targets. The Intergovernmental Panel on Climate Change (IPCC) leads an effort to develop and refine internationally agreed upon methodologies for calculating and reporting greenhouse gas emissions and removals. We argue that these guidelines are not equipped to handle the task of developing national greenhouse gas inventories for most countries. Inventory guidelines are vital to implementing climate action, and we highlight opportunities to improve their timeliness and accuracy. Such reforms should provide the means to better understand and advance the progress countries are making toward their Paris commitments. Now is the time to consider challenges posed by the current process to develop the guidelines, and to avail the policy community of recent major advances in quantitative and expert synthesis to overhaul the process and thereby better equip multi-national efforts to limit climate change.

Biological nitrogen fixation (BNF) supports terrestrial primary productivity and plays key roles in mediating human-induced changes in global nitrogen (N) and carbon cycling. However, there are still critical uncertainties in our understanding of the amount of BNF occurring across terrestrial ecosystems, and of how terrestrial BNF will respond to global change. We synthesized BNF data from Latin America, a region reported to sustain some of the highest BNF rates on Earth, but that is underrepresented in previous data syntheses. We used metaanalysis and modeling approaches to estimate BNF rates across Latin America’s major biomes and to evaluate the potential effects of increased N deposition and land-use change on these rates. Unmanaged tropical and subtropical moist forests sustained observed and predicted total BNF rates of 10 ± 1 and 14 ± 1 kg N ha−1 y−1 , respectively, supporting the hypothesis that these forests sustain lower BNF rates than previously thought. Free-living BNF accounted for two-thirds of the total BNF in these forests. Despite an average 30% reduction of free-living BNF in response to experimental N-addition, our results suggest free-living BNF rate responses to current and projected N deposition across tropical and subtropical moist forests are small. In contrast, the conversion of unmanaged ecosystems to crop and pasture lands increased BNF rates across all terrestrial biomes, mostly in savannas, grasslands, and dry forests, increasing BNF rates 2-fold. The information obtained here provides a more comprehensive understanding of BNF patterns for Latin Americ.

The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.

In this paper, we evaluate temperature and precipitation trends over South America (SA), simulated by the regional Eta model with 20-km horizontal resolution nested to three global models: The Canadian Earth System Model Second Generation (CanESM2-ES), The Met Office Hadley Centre Global Environmental Model – Earth System (HadGEM2-ES) and the Model for Interdisciplinary Research on Climate version 5 (MIROC5). The simulated trends are compared to observed trends (OBS) using six (four) extreme temperature (precipitation) indices recommended by the Expert Team on Climate Change Detection and Indices. In both simulations and OBS, extreme temperature trends over Amazon (AMZ) and Northeast Brazil (NEB) are higher than over South East SA (SESA) and West SA (WSA). Also, trends based on maximum temperatures are, in general, smaller than those based on minimum temperatures. The three downscaling simulations reproduce these features. The temperature simulations of the Eta-CanESM2 (Eta-MIROC5) show the largest (smallest) trends among the three simulations. The Eta-MIROC5, performs better than the other two simulations, reproducing the signals and the magnitudes of the extreme temperature trends, and some cooling trends over SESA, as shown in the observation. Precipitation trends from the three simulations do not compare well with observations, such as temperature trends. However, Eta-MIROC5 presents the best simulations, showing increase in the magnitude of the precipitation extremes over most of SA. The results from the three simulations presented here will be further used for the assessment of climate change projections in the SA region.

We apply different methods for detection of extreme phenomena (EP) in air-turbulent time series measured in the nocturnal boundary layer above the Amazon forest. The methods used were: (a) a Morlet complex wavelet transform, which is often used in analysis of non-linear application processes. Through the use of the wavelet, it is possible to observe a phase singularity that involves a strong interaction between an extensive range of scales; (b) recurrence plot tests, which were used to identify a sudden change between different stable atmospheric states. (c) statistical analysis of early-warning signals, which verify simultaneous increases in the autocorrelation function and in the variance in the state variable; and (d) analysis of wind speed versus turbulent kinetic energy to identify different turbulent regimes in the stable boundary layer. We found it is adequate to use a threshold to classify the cases of strong turbulence regime, as a result of the occurrence of EP in the tropical atmosphere. All methods used corroborate and indicate synergy between events that culminate in what we classify as EP of the stable boundary layer above the tropical forest.