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We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (20402059 and 20802099) relative to the reference period (19952014) under four Shared Socioeconomic Pathways (SSPs, SSP12.6, SSP24.5, SSP37.0 and SSP58.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andesa result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP12.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

The objective of this study was to evaluate variations in the composition and abundance of the phytoplankton community after an exceptional harmful bloom of Akashiwo sanguinea that occurred in Todos os Santos Bay (BTS) in early March, 2007. Samples were collected every ten days, between April, 2007 and March, 2008, from the estuarine gradient of the Paraguacu River to BTS. The physical and chemical variables were measured in situ using a multiparameter sensor. Water samples were collected for analysis of the dissolved inorganic nutrient concentrations and for the study of composition and abundance of the microphytoplankton. Overall, 135 taxa were identified, with a higher richness of diatoms. The total cell density ranged from 2.92 x 10(3) to 1.16 x 10(7) (5.47 x 10(5) +/- 1.69 x 10(6)) cells L-1, with higher values in the freshwater zone than in the marine area. Five species showed peaks of abundance throughout the study, forming small blooms. Four of these blooms occurred in the rainy season, formed by the species Guinardia striata (April, 2007), Scrippsiella cf. acuminata (August, 2007), Euglena gracilis (August, 2007), and Skeletonema cf. costatum (September, 2007), while a new bloom of the species Akashiwo sanguinea occurred during the dry season (December, 2007). The environment was typically oligotrophic, with low spatiotemporal variation in the concentrations of dissolved nutrients. Even so, we observed short-term variations in the structure and composition of the phytoplankton community, demonstrated by rapid bloom events, followed by an increase in the total abundance of microphytoplankton, especially during the rainy season. The blooms did not cause any notable changes in the water column and did not present any harmful effects on the system.

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the worlds fast moving agricultural frontiers for the year 2018.

Objectives: Crime counts are sensitive to granularity choice. There is an increasing interest in analyzing crime at very fine granularities, such as street segments, with one of the reasons being that coarse granularities mask hot spots of crime. However, if granularities are too fine, counts may become unstable and unrepresentative. In this paper, we develop a method for determining a granularity that provides a compromise between these two criteria. Methods: Our method starts by estimating internal uniformity and robustness to error for different granularities, then deciding on the granularity offering the best balance between the two. Internal uniformity is measured as the proportion of areal units that pass a test of complete spatial randomness for their internal crime distribution. Robustness to error is measured based on the average of the estimated coefficient of variation for each crime count. Results: Our method was tested for burglaries, robberies and homicides in the city of Belo Horizonte, Brazil. Estimated optimal granularities were coarser than street segments but finer than neighborhoods. The proportion of units concentrating 50% of all crime was between 11% and 23%. Conclusions: By balancing internal uniformity and robustness to error, our method is capable of producing more reliable crime maps. Our methodology shows that finer is not necessarily better in the micro-analysis of crime, and that units coarser than street segments might be better for this type of study. Finally, the observed crime clustering in our study was less intense than the expected from the law of crime concentration.

Standardized crime rates (e.g., homicides per 100,000 people) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.

By analyzing 6,480 tree leaf samples from 57 sites within Brazilian biomes, we considered whether vegetation types in terrestrial ecosystems reflect biogeochemical diversity and whether they fit into a leaf economics spectrum (LES). To achieve this, we investigated the relations among leaf carbon (C) and nitrogen (N) concentrations, their isotope natural abundance and C:N ratio. In addition, we tested their correlations with mean annual temperature (MAT) and precipitation (MAP), as climatic factors. We found consistent differences in the C and N concentrations and their isotopic composition among the vegetation types. MAP is the main climatic driver of changes in N, C:N ratio, δ15N, and δ13C, correlating negatively with N and positively with C:N ratio. These relations show that these biomes follow an LES. The Caatinga had the highest δ15N values, suggesting that N residence time in soil is longer due to low leaching and plant uptake. We observed that MAP is not the only factor influencing δ13C values in different biomes; instead canopy effect probably explains the highest values observed in the Cerrado. Our results reinforce earlier findings that life diversity in the tropics reflects biogeochemistry diversity and leaf δ15N opens the possibility for investigating plant trade-offs dictated by the LES. Finally, we expect our findings to contribute to a better understanding of the tropics in global climate models.

Amazon tropical forests and the semiarid Northeast Brazil (NEB) region have registered very severe droughts during the last two decades, with a frequency that may have exceeded natural climate variability. Severe droughts impact the physiological response of Amazon forests, decreasing the availability to absorb atmospheric CO2, as well as biodiversity and increasing risk of fires. Droughts on this region also affect population by isolating them due to anomalous low river levels. Impacts of droughts over NEB region are related to water and energy security and subsistence agriculture. Most drought episodes over Amazonia and NEB are associated with El Niño (EN) events, anomalous warming over the Tropical North Atlantic (TNA), and even an overlapping among them. However, not all the dry episodes showed a large-scale pattern linked to a canonical EN event or warm TNA episodes. For instance, dry episodes linked to EN events present distinct spatial patterns of precipitation anomalies depending on EN type (Central-Pacific vs. Eastern-Pacific EN), and NEB region experienced a severe drought in 2012 that is not attributed to EN or warm TNA events. Even in the case of the strong EN in 2015/16, some regional impacts have not been explained by EN contribution. This paper discusses the effects of CP and EP EN events, and the role of warm TNA events on tropical Walker and Hadley circulation leading to drought over Amazonia and NEB regions.

Reactive nitrogen has both positive and negative effects on environment and human health. The use of nitrogen fertilization enabled raising crops and livestock to feed an increasing world population, but at the same time resulted in a succession of unwanted impacts occurring through air, soil, and water, with detrimental consequences for all mankind. Nitrogen pollution is one of Latin America’s most widespread and challenging environmental problems and is caused due to excess of nitrogen emissions from productive activities, especially agriculture, to attend a growing demand for food and energy. Here, we develop a conceptual framework of nitrogen emissions in Latin America to better understand the complexity of nitrogen dynamics in the region, the diverse drivers, and potential harms to environment and human health, as well as to support the formulation of adequate mechanisms to deal with the adverse impacts while increasing the benefits. Thus, our objective was to generate a functional tool not just for nitrogen scientists, but also for decision and policy makers. Using Brazil as an example, our main finding was that the increase in N emissions is due to demand drivers (demand for food, energy, and housing, connected with socioeconomic factors), while preparing and implementing a successful response to solve the N pollution problem depends entirely on the structural drivers (political and institutional factors, as governance issues). We highlighted the crucial role of political decision and institutional forcefulness in designing and implementing suitable policy instruments to handle the duality of nitrogen use. This finding prompts us to rethink about the (often non-existent) policy responses and the challenge for Latin American countries to deal with the nitrogen dilemma, too much or too little of a very important nutrient.

Meeting Brazil’s ambitious national commitments on both climate change mitigation and biodiversity conservation depends on securing its reserves of forest carbon and biodiversity. Brazil’s ‘Forest Code’ is a key tool to reconcile environmental preservation and agricultural production; it limits deforestation and requires forest restoration in illegally deforested areas. However, not all provisions of the law’s 2012 revision have yet been implemented and some are facing new challenges. Using modelled land use change projections for the whole of the country, we show that full implementation and enforcement of the law has the potential to contribute to conserving biodiversity. Biodiversity outcomes will be especially positive if (i) deforested areas are restored in ways that support recolonization by native species and (ii) additional measures are implemented to protect native vegetation in areas like Caatinga dry forests and Cerrado savannas, which may experience added pressure displaced from other regions by Forest Code implementation.

The use of stable isotopes of hydrogen and oxygen is a tool widely used to trace water paths along the hydrological cycle, providing support for understanding climatic conditions in different spatial scales. One of the main synoptic scale events acting in southeastern Brazil is the South Atlantic Convergence Zone (SACZ), which causes a large amount of precipitation from southern Amazonia to southeastern Brazil during the southern summer. In order to determine the isotopic composition of precipitation during the action of SACZ in São Francisco Xavier in southeastern Brazil, information from the Weather Forecasting and Climate Studies Center of the National Institute for Space Research (CPTEC) was used regarding SACZ performance days, the retrograde trajectories of the HYSPLIT model, and images from the GOES-16 satellite, in addition to the non-parametric statistical tests by Spearman and Kruskal-Wallis. A high frequency of air mass trajectories from the Amazon to southeastern Brazil was observed when the SACZ was operating. During the SACZ events, the average isotopic composition of precipitation was more depleted, with a δ18O of −9.9 (±2.1), a δ2H of −69.3 (±17.9), and d-excess of 10.1 (±4.0). When disregarding the SACZ performance, the annual isotopic composition can present an enrichment of 1.0 for δ18O and 8.8 for the δ2H. The long-term monitoring of trends in the isotopic composition of precipitation during the SACZ events can assist in indicating the evapotranspiration contribution of the Amazon rainforest to the water supply of southeastern Brazil.

The use of stable isotopes of hydrogen and oxygen is a tool widely used to trace water paths along the hydrological cycle, providing support for understanding climatic conditions in different spatial scales. One of the main synoptic scale events acting in southeastern Brazil is the South Atlantic Convergence Zone (SACZ), which causes a large amount of precipitation from southern Amazonia to southeastern Brazil during the southern summer. In order to determine the isotopic composition of precipitation during the action of SACZ in São Francisco Xavier in southeastern Brazil, information from the Weather Forecasting and Climate Studies Center of the National Institute for Space Research (CPTEC) was used regarding SACZ performance days, the retrograde trajectories of the HYSPLIT model, and images from the GOES-16 satellite, in addition to the non-parametric statistical tests by Spearman and Kruskal-Wallis. A high frequency of air mass trajectories from the Amazon to southeastern Brazil was observed when the SACZ was operating. During the SACZ events, the average isotopic composition of precipitation was more depleted, with a δ18O of −9.9 (±2.1), a δ2H of −69.3 (±17.9), and d-excess of 10.1 (±4.0). When disregarding the SACZ performance, the annual isotopic composition can present an enrichment of 1.0 for δ18O and 8.8 for the δ2H. The long-term monitoring of trends in the isotopic composition of precipitation during the SACZ events can assist in indicating the evapotranspiration contribution of the Amazon rainforest to the water supply of southeastern Brazil.