Publicações

O acesso a novas fontes de energia, principalmente renováveis, requer níveis específicos de dados para sua classificação correta. As estações de coleta capturam informações em um espaço curto de tempo com uma precisão estável, executando o tratamento das informações coletadas. O controle de qualidade é um aspecto principal em qualquer sistema de tratamento de dados, para garantir confiabilidade e permitir a comparação com outras amostras. O Instituto Nacional de Pesquisas Espaciais (INPE), com base nesses conceitos, implementou a rede SONDA para prover dados meteorológicos e solarimétricos com a finalidade de suporte ao setor de energia no Brasil. Este trabalho consiste na continuação da implementação do algoritmo de validação das informações anemométricas e solarimétricas coletadas na rede SONDA, consistindo em adaptar, em seu último estágio, um modelo de céu claro proposto por Muhammad Iqbal (IQBAL, Muhammad, An Introduction to Solar Radiation, Academic Press, Canadá, 1983). Para dar continuidade a este projeto de Iniciação Científica estão programadas as atividades: Revisão bibliográfica e escolha dos principais modelos de céu claro; reestruturação do algoritmo implementando novos testes de qualidade para o aumento de confiabilidade das análises; Validação de modelos computacionais utilizados no levantamento destes recursos; implementação do modelo de céu claro baseado no proposto por Muhammad Iqbal, e agregá-lo ao conjunto de validação de dados solarimétricos na rede SONDA.

Este trabalho, iniciado em março de 2019, teve como objetivo a continuidade ao estudo de métodos de tratamento de dados meteorológicos para disponibilização de séries temporais que possam contribuir para o atendimento da demanda de informações confiáveis relativos ao recurso solar para o setor energético nacional. Mais especificamente, a avaliação de métodos computacionais para geração de séries temporais de dados de irradiação solar com resolução temporal minuto a minuto, utilizando modelos computacionais baseados em imagens de satélite com resolução temporal horária e dados observados em superfície para fase de treinamento do algoritmo. Até o presente momento deste trabalho, foram realizados estudos voltados às metodologias de aprendizado de máquina, incluindo métodos de redes neurais e técnicas de agrupamento de dados em linguagem Python. Também foi desempenhada a etapa de organização das bases de dados observados e modelados (Atlas Brasileiro de Energia Solar) que serão utilizadas pelos algoritmos nas etapas seguintes. Para continuidade deste projeto de Iniciação Científica, estão programadas as seguintes atividades: Desenvolvimento de algoritmos para simulação da irradiação solar em resolução temporal maior aos dados de entrada organizados na última etapa concluída, implementação dos códigos desenvolvidos e validação dos resultados produzidos, tomando como base a distribuição de frequência dos dados observados.

Background: Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from diferent sources, but they are unstandardized and not always available to the scientifc community. Considering the Brazil‑ ian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground biomass (AGB). Nevertheless, the combination of limited forest inventory data and diferent remote sensing products has resulted in signifcant diferences in the spatial distribution of AGB estimates. This study evaluates the spatial coverage of AGB data (forest inventory plots, AGB maps and remote sensing products) in undisturbed forests in the Brazilian Amazon. Additionally, we analyze the interconnection between these data and AGB stakeholders producing the information. Specifcally, we provide the frst benchmark of the existing feld plots in terms of their size, frequency, and spatial distribution. Results: We synthesized the coverage of forest inventory plots, AGB maps and airborne light detection and rang‑ ing (LiDAR) transects of the Brazilian Amazon. Although several extensive forest inventories have been implemented, these AGB data cover a small fraction of this region (e.g., central Amazon remains largely uncovered). Although the use of new technology such as airborne LiDAR cover a signifcant extension of AGB surveys, these data and forest plots represent only 1% of the entire forest area of the Brazilian Amazon. Conclusions: Considering that several institutions involved in forest inventories of the Brazilian Amazon have difer‑ ent goals, protocols, and time frames for forest surveys, forest inventory data of the Brazilian Amazon remain unstand‑ ardized. Research funding agencies have a very important role in establishing a clear sharing policy to make data free and open as well as in harmonizing the collection procedure. Nevertheless, the use of old and new forest inventory plots combined with airborne LiDAR data and satellite images will likely reduce the uncertainty of the AGB distribu‑ tion of the Brazilian Amazon.

Purpose: This study aims to exploit the abundance of solar energy resources for socioeconomic development in the semi -arid Northeastern Brazil as a potent adaptation tool to global climate change. It points out a set of conjuncture factors that allow us to foresee a new paradigm of sustainable development for the region by transforming the suns radiant energy into electricity through distributed photovoltaic generation. The new paradigm, as presented in this essay, has the transformative potential to free the region from past regional development dogma, which was dependent on the scarce water resource, and the marginal and predatory use of its Caatinga Biome. Design/methodology/approach: The research uses a pre ante design, following the procedures of scenario building, as an adaptation mechanism to climate change in the sector of energy generation and socioeconomic inclusion. Findings: The scenarios of socioeconomic resilience to climate change based on the abundance of solar radiation, rather than the scarcity of water, demonstrates its potential as a global adaptation paradigm to climate change. Research limitations/implications: The developments proposed are dependent on federal legislation changes, allowing the small producer to be remunerated by the energy produced. Practical implications: The proposed smart grid photovoltaic generation program increases the country’s resiliency to the effect of droughts and climate change. Social implications: As proposed, the program allows for the reversion of a pattern of long term poverty in semi-arid Northeast Brazil. Originality/value: The exploitation of the characteristics of abundance of the semiarid climate, i.e. its very condition of semi-aridity with abundant solar radiation, is itself an advantage factor toward adaption to unforeseen drought events. Extensive previous research has focused on weighting and monitoring drought i.e. the paradigm of scarcity. The interplay between exploiting Northeast Brazils abundant factors and climate change adaptation, especially at the small farmer levels constitutes a discovery never before contemplated.

Earlier studies have demonstrated that the Northeastern Brazilian region (NEB) has the most abundant solar energy resource of the Brazilian territory due to its geographical location, and its environmental characteristics. However, the spatial and seasonal variability was little evaluated. This work investigated the surface solar irradiation variability and trends based on a statistical analysis of the ground data acquired in automated weather stations operated by the Brazilian Institute of Meteorology. A data quality control procedure was accomplished according to WMO criteria to avoid suspicious data. The results pointed out a remarkable variability in seasonal and annual scales. The cluster analysis provided five regional patterns presenting quite interesting temporal complementary regimes for the incoming solar irradiation. The surface solar irradiation achieves the highest values in the Southeastern area of NEB and the lowest in the Western area during the austral summer. The annual and seasonal trends of the surface solar irradiation were investigated using the Mann-Kendall method and the Sen technique. The surface solar irradiation in the Southeastern area of NEB is decreasing by 50 Wh/m2 /year since 2008. On the other hand, it is increasing around 40 Wh/m2 /year in the semi-arid area at the same timeframe.

Forest degradation from either natural or anthropogenic drivers involves processes that change the capacity of the ecosystem to provide services. In Brazil, estimates of carbon emissions do not currently take into account emissions from forest degradation caused by fire or by selective logging. Here, we present a methodology to estimate aboveground biomass in forest degradedareas, that can be accounted to estimate carbon emissions. We explored a multi-scale and temporal approach involving Airborne Laser Scanning (ALS) and orbital images from Landsat 8 Operational Land Imager (OLI) sensor to estimate the aboveground biomass. Cross-validation results showed that 49% of the variation in biomass could be explained using this approach, with an estimation error 58 Mg ha(-1) (49.08%). Due to the difficulty in measuring biomass in tropical forests, the proposed methodology can be an alternative in future works to estimate aboveground biomass in order to improve the estimates of carbon emissions by the governmental organizations.

The joint and relative effects of future land-use and climate change on fire occurrence in the Amazon, as well its seasonal variation, are still poorly understood, despite its recognized importance. Using the maximum entropy method (MaxEnt), we combined regional land-use projections and climatic data from the CMIP5 multimodel ensemble to investigate the monthly probability of fire occurrence in the mid (2041-2070) and late (2071-2100) 21st century in the Brazilian Amazon. We found striking spatial variation in the fire relative probability (FRP) change along the months, with October showing the highest overall change. Considering climate only, the area with FRP >= 0.3 (a threshold chosen based on the literature) in October increases 6.9% by 2071-2100 compared to the baseline period under the representative concentration pathway (RCP) 4.5 and 27.7% under the RCP 8.5. The best-case land-use scenario (“Sustainability”) alone causes a 10.6% increase in the area with FRP >= 0.3, while the worse-case land-use scenario (“Fragmentation”) causes a 73.2% increase. The optimistic climate-land-use projection (Sustainability and RCP 4.5) causes a 21.3% increase in the area with FRP >= 0.3 in October by 2071-2100 compared to the baseline period. In contrast, the most pessimistic climate-land-use projection (Fragmentation and RCP 8.5) causes a widespread increase in FRP (113.5% increase in the area with FRP >= 0.3), and prolongs the fire season, displacing its peak. Combining the Sustainability land-use and RCP 8.5 scenarios causes a 39.1% increase in the area with FRP >= 0.3. We conclude that avoiding the regress on land-use governance in the Brazilian Amazon (i.e., decrease in the extension and level of conservation of the protected areas, reduced environmental laws enforcement, extensive road paving, and increased deforestation) would substantially mitigate the effects of climate change on fire probability, even under the most pessimistic RCP 8.5 scenario.

Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, primarily owing to the rising atmospheric carbon dioxide (CO 2 ) concentration. Soil phosphorus impoverishment in parts of the Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model ensembles-for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO2 enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus availability reduces the projected CO2-induced biomass carbon growth by about 50% to 79 +/- 63 g C m(-2) yr(-1) over 15 years compared to estimates from carbon and carbon-nitrogen models. Our results suggest that the resilience of the region to climate change may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models is considerable, ranging from 5 to 140 g C m(-)2 yr(-1), owing to the contrasting plant phosphorus use and acquisition strategies considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under elevated CO2.

Purpose: This study aims to exploit the abundance of solar energy resources for socioeconomic development in the semi -arid Northeastern Brazil as a potent adaptation tool to global climate change. It points out a set of conjuncture factors that allow us to foresee a new paradigm of sustainable development for the region by transforming the suns radiant energy into electricity through distributed photovoltaic generation. The new paradigm, as presented in this essay, has the transformative potential to free the region from past regional development dogma, which was dependent on the scarce water resource, and the marginal and predatory use of its Caatinga Biome. Design/methodology/approach: The research uses a pre ante design, following the procedures of scenario building, as an adaptation mechanism to climate change in the sector of energy generation and socioeconomic inclusion. Findings: The scenarios of socioeconomic resilience to climate change based on the abundance of solar radiation, rather than the scarcity of water, demonstrates its potential as a global adaptation paradigm to climate change. Research limitations/implications: The developments proposed are dependent on federal legislation changes, allowing the small producer to be remunerated by the energy produced. Practical implications: The proposed smart grid photovoltaic generation program increases the country’s resiliency to the effect of droughts and climate change. Social implications: As proposed, the program allows for the reversion of a pattern of long term poverty in semi-arid Northeast Brazil. Originality/value: The exploitation of the characteristics of abundance of the semiarid climate, i.e. its very condition of semi-aridity with abundant solar radiation, is itself an advantage factor toward adaption to unforeseen drought events. Extensive previous research has focused on weighting and monitoring drought i.e. the paradigm of scarcity. The interplay between exploiting Northeast Brazils abundant factors and climate change adaptation, especially at the small farmer levels constitutes a discovery never before contemplated.

Accurate estimates of aboveground biomass (AGB) in tropical forests are critical for supporting strategies of ecosystem functioning conservation and climate change mitigation. However, such estimates at regional and local scales are still highly uncertain. Airborne Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) can characterize the structural and functional diversity of forests with high accuracy at a sub-meter resolution, and potentially improve the AGB estimations. In this study, we compared the ability of different data sources (airborne LiDAR and HSI, and their combination) and regression methods (linear model – LM, linear model with ridge regularization – LMR, Support Vector Regression – SVR, Random Forest – RF, Stochastic Gradient Boosting – SGB, and Cubist – CB) to improve AGB predictions in the Brazilian Amazon. We used georeferenced inventory data from 132 sample plots to obtain a reference field AGB and calculated 333 metrics (45 from LiDAR and 288 from HSI) that could be used as predictors for statistical AGB models. We submitted the metrics to a correlation filtering followed by a feature selection procedure (recursive feature elimination) to optimize the performance of the models and to reduce their complexity. Results showed that both LiDAR and HSI data used alone provided relatively high accurate models if adequate metrics and algorithms are chosen (RMSE = 67.6 Mg.ha−1 , RMSE% = 36%, R2 = 0.58, for the best LiDAR model; RMSE = 68.1 Mg.ha−1 , RMSE % = 36%, R2 = 0.58, for the best HSI model). However, HSI-only models required more metrics (512) than LiDAR-only models (25). Models combining metrics from both datasets resulted in more accurate AGB estimates, regardless of the regression method (RMSE = 57.7 Mg.ha−1 , RMSE% = 31%, R2 = 0.70, for the best model). The most important LiDAR metrics for estimating AGB were related to the upper canopy cover and tree height percentiles, while the most important HSI metrics were associated with the near infrared and shortwave infrared spectral regions, particularly the leaf/canopy water and lignin-cellulose absorption bands. Finally, an analysis of variance (ANOVA) showed that the remote sensing data source (LiDAR, HSI, or their combination) had a greater effect size than the regression algorithms. Thus, no single algorithm outperformed the others, although the LM method was less suitable when applied to the HSI and hybrid datasets. Results show that the synergistic use of LiDAR and hyperspectral data has great potential for improving the accuracy of the biomass estimates in the Brazilian Amazon.