Estimativas climatológicas de evaporação em lagos

The physical background and empirical evidence used in currently available models that estimate evaporation in lakes from climatological data are presented, followed by a description of some of these models. A critical review of each model is done, in terms of its data requirements, the aproximations it makes, etc. Climatological data collected at the region of the Sobradinho reservoir is used to estimate potential and lake evaporation. Sensitivity studies evaluate the relative importance of each input data to a climatological model, and the spatial variability of such models, compared with evaporimeter-data based estimates.Os fundamentos físicos e as evidências empíricas usados nos modelos atualmente disponíveis para a estimação da evaporação em lagos a partir de dados climatológicos são apresentados, seguindo-se uma descrição de alguns destes modelos. É feita uma revisão crítica de cada modelo em termos de requerimento de dados, aproximações feitas, etc. Dados climatológicos coletados na região do reservatório de Sobradinho são usados para estimar a evaporação potencial e a evaporação em lago. Estudos de sensibilidade permitem constatar a importância de cada dado de entrada de um modelo climatológico de evaporação, e a variabilidade espacial dos modelos climatológicos em comparação com estimativas baseadas em dados evaporimétricos

Análise comparativa da velocidade do vento e da temperatura do ar, entre dados gerados por reanálises meteorológicas e dados observacionais na região de Minas Gerais

The use of alternative sources of meteorological data has become increasingly common, making it possible to evaluate areas with no long or continuous series of meteorological data. In this context, the main objective of this study is to evaluate the performance of data series from the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) for the state of Minas Gerais and verify the possible use of them in the absence of data observations of air temperature and wind speed. The analyzes were performed by comparing observation data from 17 meteorological stations and reanalysis data of the CFSR and CFSV2 models. From the results of the statistical analysis, it is observed that the air temperature reanalysis data presented a good performance in the region of study. However, wind speed data show a weak correlation. These results show that the air temperature data from these reanalyses have the potential to be used as an alternative source of data. Further studies are suggested regarding the use of wind speed data from these reanalyses.A utilização de fontes alternativas de dados meteorológicos tem se tornado cada vez mais usual, possibilitando uma avaliação de áreas com ausência de séries longas ou continuas de dados meteorológicos. Neste contexto, o principal objetivo deste estudo é avaliar o desempenho de séries de dados dos National Centers for Environmental Prediction / Nacional Center for Atmospheric Research (NCEP/NCAR) para o estado de Minas Gerais e verificar a possível utilização das mesmas na ausência de dados observados de temperatura do ar e velocidade do vento. As análises foram realizadas a partir da comparação entre dados observacionais de 17 estações meteorológicas e dados de reanálise dos modelos CFSR e CFSV2. A partir dos resultados das análises estatísticas, observa-se que os dados de reanálise de temperatura do ar apresentaram um bom desempenho na região de estudo. Porém os dados de velocidade do vento apontam uma correlação fraca. Esses resultados mostram que os dados de temperatura do ar dessas reanálises têm potencial para serem utilizados como fonte alternativa de dados. Sugere-se mais estudos em relação à utilização dos dados de velocidade do vento dessas reanálises

Fluxos Turbulentos de Dióxido de Carbono Sobre o Reservatório da Usina Hidrelétrica de Itaipu – PR

Fluxos turbulentos de dióxido de carbono (CO2) foram medidos sobre o reservatório da usina hidrelétrica de Itaipu durante 13 dias de dezembro de 2012. Nessa campanha, notou-se que no período diurno os fluxos de CO2 eram controlados pela radiação solar incidente, sendo que se constatou fixação de  CO2 pelo reservatório durante o dia

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

The Amazon Tall Tower Observatory (ATTO): Overview of pilot measurements on ecosystem ecology, meteorology, trace gases, and aerosols

The Amazon Basin plays key roles in the carbon and water cycles, climate change, atmospheric chemistry, and biodiversity. It has already been changed significantly by human activities, and more pervasive change is expected to occur in the coming decades. It is therefore essential to establish long-term measurement sites that provide a baseline record of present-day climatic, biogeochemical, and atmospheric conditions and that will be operated over coming decades to monitor change in the Amazon region, as human perturbations increase in the future. The Amazon Tall Tower Observatory (ATTO) has been set up in a pristine rain forest region in the central Amazon Basin, about 150 km northeast of the city of Manaus. Two 80 m towers have been operated at the site since 2012, and a 325 m tower is nearing completion in mid-2015. An ecological survey including a biodiversity assessment has been conducted in the forest region surrounding the site. Measurements of micrometeorological and atmospheric chemical variables were initiated in 2012, and their range has continued to broaden over the last few years. The meteorological and micrometeorological measurements include temperature and wind profiles, precipitation, water and energy fluxes, turbulence components, soil temperature profiles and soil heat fluxes, radiation fluxes, and visibility. A tree has been instrumented to measure stem profiles of temperature, light intensity, and water content in cryptogamic covers. The trace gas measurements comprise continuous monitoring of carbon dioxide, carbon monoxide, methane, and ozone at five to eight different heights, complemented by a variety of additional species measured during intensive campaigns (e.g., VOC, NO, NO2, and OH reactivity). Aerosol optical, microphysical, and chemical measurements are being made above the canopy as well as in the canopy space. They include aerosol light scattering and absorption, fluorescence, number and volume size distributions, chemical composition, cloud condensation nuclei (CCN) concentrations, and hygroscopicity. In this paper, we discuss the scientific context of the ATTO observatory and present an overview of results from ecological, meteorological, and chemical pilot studies at the ATTO site. © Author(s) 2015

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost