Publicações

A política de reforma agrária no final dos anos 80 e início dos anos 90 estimulou a criação de incentivos crescentes para o surgimento ou aumento de conflitos entre invasores e entre invasores e proprietários de terras, pois a invasão de terras era (e ainda é) uma técnica comum para obter controle e, eventualmente, um título de posse da terra. Com o objetivo de resolver os conflitos entre os diversos grupos de interesse presentes na Amazônia, o Estado procurou ordenar a regulamentação da terra como parte de sua nova política agrícola e ambiental no Brasil. Os assentamentos rurais se tornaram um dos principais instrumentos de ordenamento territorial e regularização fundiária na Amazônia brasileira, representando quase um terço das terras usadas e quase 74% dos estabelecimentos rurais. O Estado do Pará concentra o maior número de famílias assentadas do país, com mais de 30% de sua área coberta por assentamentos. Assim, este trabalho tem por objetivo realizar um levantamento dos projetos de assentamentos criados no estado do Pará, considerando as diferentes categorias e sua abrangência.

Este relatório da bolsa iniciada em agosto de 2019, teve como objetivo mostrar todos os itens aprendidos e aplicados durante 1 (um) ano de bolsa de Iniciação em Desenvolvimento Tecnológico e Inovação (PIBITI), que foram implementados no desenvolvimento do site da Divisão de Impactos, Adaptação e Vulnerabilidade (DIIAV). Foram utilizados conhecimentos de PHP, Bootstrap e WordPress para desenvolvimento do portal, e para maior eficiência nas implementações, foram utilizados dados coletados provenientes do Google Analytics. Os dados coletados foram estudados e convertidos em melhorias para a plataforma, com a intenção de aprimorar o acesso à publicações realizadas pelos pesquisadores do DIIAV e pelo público em geral do portal. Para que isso fosse realizado, foram realizados treinamentos para compreender de maneira mais efetiva a utilizar o WordPress, além das linguagens PHP e Bootstrap. Após realização dos treinamentos, iniciou-se a etapa de desenvolvimento do sistema propriamente dito. Durante a etapa de desenvolvimento, tivemos várias reuniões com os solicitantes do sistema para decidir o que seria mudado e acrescentado no sistema, para que o sistema ficasse o mais completo possível. Também fizemos reuniões internas, para decidir como seria a melhor maneira de implementar as atualizações do portal, tanto evoluções estéticas como do próprio sistema. Na etapa do desenvolvimento de código, preferimos dar maior importância para as implementações de funções do sistema, por se tratar de uma parte mais sensível e trabalhosa do trabalho. Por se tratar de um portal em crescente evolução e diária disponibilidade de conteúdo, o projeto necessita de constante atualização, manutenção e possíveis adaptações futuras para que se possa oferecer um portal com excelência em conteúdo e informação para o público do DIIAV e a comunidade científica.

Artificial Neural Networks (ANN) are being used on several fields mostly as a mapper from input domain variables into output application area results. Several methods are being used on the automatic assessment of clouds from surface to predict solar power generation, assisted by a camera, side sensors, etc. The present Systematic Literature Review (SLR) is intended to search the related scientific articles, to find the state of the art in the area. We were able to find gaps in researches in regards to validation metrics for prediction of solar power generation as well as a small number of works in this domain area using computational intelligence (machine learning) methods, with the majority of works relying on classical statistics approaches. Results show that most works rely on images captured by Total Sky-imagers (TSI) and most works using computational intelligence rely on classical approaches like Artificial Neural Networks, Convolutional Neural Networks (CNN) and Multilayer Perceptrons (MLP) and that there still a relevant amount of works published from the last three years using classical statistics.

Amazon regions climate is particularly sensitive to surface processes and properties such as heat fluxes and vegetation coverage. Rainfall is a key expression of such land surface-atmosphere interactions in the region due to its strong dependence on forest transpiration. While a large number of past studies have shown the impacts of large-scale deforestation on annual rainfall, studies on the isolated effects of elevated atmospheric CO2 concentration (eCO2) on plant physiology (i.e. the β effect), for example on canopy transpiration and rainfall, are scarcer. Here we make a systematic comparison of the plant physiological effects of eCO2 and deforestation on Amazon rainfall. We use the CPTEC-Brazilian Atmospheric Model (BAM) with dynamic vegetation under a 1.5xCO2 and a 100% substitution of the forest by pasture grassland, with all other conditions held similar between the two scenarios. We find that both scenarios result in equivalent average annual rainfall reductions (Physiology: -252 mm,-12%; Deforestation: -292 mm, -13%) that are well above observed Amazon rainfall interannual variability of 5.1%. Rainfall decrease in the two scenarios are caused by a reduction of approximately 20% of canopy transpiration, but for different reasons: eCO2-driven reduction of stomatal conductance in the Physiology run; decreased leaf area index of pasture (-66%) and its dry-season lower surface vegetation coverage in the Deforestation run. Walker circulation is strengthened in the two scenarios (with enhanced convection over the Andes and a weak subsidence branch over east Amazon) but, again, through different mechanisms: enhanced west winds from the Pacific and reduced easterlies entering the basin in Physiology, and strongly increased easterlies in Deforestation. Although our results for the Deforestation scenario are in agreement with previous observational and modelling studies, the lack of direct field-based ecosystem-level experimental evidence on the effect of eCO2 in moisture fluxes of tropical forests confers a substantial level of uncertainty to this and any other projections on the physiological effect of eCO2 on Amazon rainfall. Furthermore, our results denote the incurred responsibilities of both Amazonian and non- Amazonian countries to mitigate potential future climatic change and its impacts in the region driven either by local deforestation (to be tackled by Amazonian countries) or global CO2 emissions (to be handled by all countries).

The consequences of climate change in South and Central America are already widespread and take on many forms. Albeit there is an increasing number of studies focusing on specific climate change-related risks in the region, a synthesis of risks for the 21st century, together with current and future adaptation options is lacking. This study synthesizes major climate related risks in South and Central America, while also looking at implications for adaptation measures and (un)avoidable loss and damage. A review of over 100 peer-reviewed articles published since 2013 was completed to examine the current and projected state of the risks. We identify eight key risks in South and Central America that have the potential to become severe with climate change during the 21st century. The criteria for a severe risk relate to the number of people potentially affected, the severity of the negative effects of the risk, the importance of the affected systems, and the irreversibility versus potential to reduce the risk. The risks are analysed in relation to different climate scenarios, and changes in associated hazards, exposure, and/or vulnerability. The risks include 1: risk of food insecurity due to repeated and/or extreme drought conditions; 2: risk to life and infrastructure due to floods and landslides; 3: risk of water insecurity in Central America and the Andes region; 4: systemic risks of surpassing infrastructure and public service system capacities due to cascading impacts of storms, floods and epidemics; 5: risk of severe health effects due to increasing epidemics (in particular vector-borne diseases); 6: risk of large-scale ecological transformation of the Amazon forest; 7: risk to coral reef ecosystems due to coral bleaching in Central America; 8: risk to coastal socio-ecological systems due to sea level rise, intensification of upwelling and ocean acidification. In addition, we focus on already implemented and possible adaptation measures for each of the risks. Subsequently, we draw conclusions of the potential losses and damages caused by each risk. Our assessment of risks in the Central and South America region show that several risks have the potential to become severe already in the near future. The extent of the severity is driven by the specific regions exposure, vulnerability and adaptation capacity. Adaptation capacity is in turn dependent on physical as well as socio-economic systems. Inequalities, corruption, and poor communication between decision makers, stakeholders and the scientific community together with a lack of available data can critically limit adaptation options. Still, many adaptation options are available, and efforts to thoroughly research further adaptation measures should be of highest priority. This will undoubtedly save both lives and severe economic damage as South and Central America face the consequences of climate change.

Tropical rainforests are recognized as one of the terrestrial tipping elements which could have profound impacts on the global climate, once their vegetation has transitioned into savanna or grassland states. While several studies investigated the savannization of, e.g., the Amazon rainforest, few studies considered the influence of fire. Fire is expected to potentially shift the savanna-forest boundary and hence impact the dynamical equilibrium between these two possible vegetation states under changing climate. To investigate the climate-induced hysteresis in pan-tropical forests and the impact of fire under future climate conditions, we coupled the well established and comprehensively validated Dynamic Global Vegetation Model LPJmL5.0-FMS to the coupled climate model CM2Mc, which is based on the atmosphere model AM2 and the ocean model MOM5 (CM2Mc-LPJmL v1.0). In CM2Mc, we replaced the simple land surface model LaD with LPJmL and fully coupled the water and energy cycles. Exchanging LaD by LPJmL, and therefore switching from a static and prescribed vegetation to a dynamic vegetation, allows us to model important biosphere processes, including wildfire, tree mortality, permafrost, hydrological cycling, and the impacts of managed land (crop growth and irrigation). With CM2Mc-LPJmL we conducted simulation experiments where atmospheric CO2 concentrations increased from a pre-industrial level up to 1280 ppm (impact phase) followed by a recovery phase where CO2 concentrations reach pre-industrial levels again. This experiment is performed with and without allowing for wildfires. We find a hysteresis of the biomass and vegetation cover in tropical forest systems, with a strong regional heterogeneity. After biomass loss along increasing atmospheric CO2 concentrations and accompanied mean surface temperature increase of about 4°C (impact phase), the system does not recover completely into its original state on its return path, even though atmospheric CO2 concentrations return to their original state. While not detecting large-scale tipping points, our results show a climate-induced hysteresis in tropical forest and lagged responses in forest recovery after the climate has returned to its original state. Wildfires slightly widen the climate-induced hysteresis in tropical forests and lead to a lagged response in forest recovery by ca. 30 years.

Unprecedented fire events in recent years are leading to a demand for improved understanding of how climate change is already affecting fires, and how this could change in the future. Increased fire activity in South America is one of the most concerning of all the recent events, given the potential impacts on local health and the global climate from loss of large carbon stores under future environmental change. However, due to the complexity of interactions and feedbacks, and lack of complete representation of fire biogeochemistry in many climate models, there is currently low agreement on whether climate change will cause fires to become more or less frequent in the future, and what impact this will have on ecosystems. Here we use the latest climate simulations from the UK Earth System Model UKESM1 to understand feedbacks in fire, dynamic vegetation, and terrestrial carbon stores using the fire-enabled land surface model JULES-INFERNO, taking into account future scenarios of change in emissions and land use. Based on evaluation of the modelling framework performance for the present day, we address the specific policy-relevant question: how much fire-induced carbon loss will there be over South America at different global warming levels in the future? We find that burned area and fire emissions are projected to increase in the future due to hotter and drier conditions, which leads to large reductions in carbon storage especially when combined with increasing land-use conversion. The model simulates a 38% loss of carbon at 4°C under the highest emission scenario, which could be reduced to 8% if temperature rise is limited to 1.5°C. Our results provide a critical assessment of ecosystem resilience under future climate change, and could inform the way fire and land-use is managed in the future to reduce the most deleterious impacts of climate change.

When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season, even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban and rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes (“SMUrF”), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance (EC) flux data, and ancillary remote-sensing-based products, and we developed algorithms to gap-fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) fluxes are extracted from SMUrF and evaluated against (1) non-gap-filled measurements at 67 EC sites from FLUXNET during 2010-2014 (r>0.7 for most data-rich biomes), (2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from August 2017 to December 2018 (rCombining double low line0.75), and (3) an urban biospheric model based on fine-grained land cover classification in Los Angeles (rCombining double low line0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban-rural contrast in both the magnitude and timing of CO2 fluxes. To illustrate the application of SMUrF, we used it to interpret a few summertime satellite tracks over four cities and compared the urban-rural gradient in column CO2 (XCO2) anomalies due to NEE against XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful to inform the biogenic CO2 fluxes over highly vegetated regions during the growing season.