Publicações

This paper introduces a new terrain model named HAND, and reports on the calibration and validation of landscape classes representing soil environments in Amazonia, which were derived using it. The HAND model normalizes topography according to the local relative heights found along the drainage network, and in this way, presents the topology of the relative soil gravitational potentials, or local draining potentials. The HAND model has been demonstrated to show a high correlation with the depth of the water table, providing an accurate spatial representation of soil water environments. Normalized draining potentials can be classified according to the relative vertical flowpath-distances to the nearest drainages, defining classes of soil water environments. These classes have been shown to be comparable and have verifiable and reproducible hydrological significance across the studied catchment and for surrounding ungauged catchments. The robust validation of this model over an area of 18,000 km(2) in the lower Rio Negro catchment has demonstrated its capacity to map expansive environments using only remotely acquired topography data as inputs. The classified HAND model has also preliminarily demonstrated robustness when applied to ungauged catchments elsewhere with contrasting geologies, geomorphologies and soil types. The HAND model and the derived soil water maps can help to advance physically based hydrological models and be applied to a host of disciplines that focus on soil moisture and ground water dynamics. As an original assessment of soil water in the landscape, the HAND model explores the synergy between digital topography data and terrain modeling, presenting an opportunity for solving many difficult problems in hydrology.

New specimens of gavialoids collected from the Pirabas Formation, Brazil, provide additional information about the evolutionary evolution of Gavialoidea during the late Oligocene-early Miocene. We describe a specimen that has a more gracile symphyseal mandible than any other South American gavialoid. This fossil represents an unusually diverse radiation of gavialoids that were probably ecologically differentiated from each other by size and dietary specialization.

O aproveitamento da energia solar vem se tornando viável economicamente e, atualmente, é a que vem apresentando maior crescimento mundial. O conhecimento da disponibilidade de energia solar na superfície é um fator essencial para incentivar novos investimentos em sistemas de geração o de energia solar. Em locais onde não existem sistemas de medida, estimativas da incidência de radiação solar podem ser realizadas por interpolação/extrapolação de medidas realizadas em estações vizinhas ou através de modelos numéricos de transferência radiativa. Ambas são formas aproximadas de estimativa, e seus empregos dependem da quantificação de suas incertezas. Neste trabalho, dados de radiação solar coletados em 18 plataformas de coleta de dados (PCDs), situadas no sudeste do Brasil, foram empregados na geração de extrapolações e interpolações considerando diferentes distâncias das PCDs. Os desvios apresentados por estas estimativas crescem com a distância, e são comparáveis com os desvios do modelo satelital de transferência radiativa BRASIL-SR apenas para distâncias inferiores a 60 km. Os métodos de interpolação/extrapolação apresentaram desvios dentro do intervalo limitado pelos desvios RMSE (root mean square errors ) máximo e mínimo observados com o uso do modelo. Dessa forma, pode-se concluir que o uso de modelos físicos de transferência radiativa alimentados por dados de satélites representam a melhor solução para a estimativa da disponibilidade de energia solar em locais distantes das estações radiométricas.

Brazils primary energy matrix is based on more than 47% of renewables, and more than 85% of its electricity is generated by hydro power sources. Despite this large fraction of renewable energy resources, less than 0.3% of the national energy supply comes from solar or wind sources. This paper presents a diagnostic review on the penetration of the solar and wind energy technologies in Brazil. It also includes a survey of the latest government policies and incentives for renewable energies deployment by entrepreneurs, industry and commercial and residential consumers. In addition, the paper analyses how to best meet the requirements for policy support and information technology to boost the deployment of solar technology and wind energy in Brazil. This study was mostly based on results of a widely distributed survey covering key issues, and also by personal interviews carried out with key stakeholders in order to better understand the issues highlighted in the survey responses. The study pointed out some of the main obstacles to effectively promote and improve government policies and actions for investment in solar and wind energy market in Brazil.

The Big Mac (R), McDonald’s (R) signature burger, is a global food served in over 100 countries. We measured carbon (delta(13)C) and nitrogen (delta(15)N) stable isotope ratios of Big Macs (R) from twenty-six countries. The delta(13)C values varied from -25.4%. to -11.1%, representing cattle-rearing systems based exclusively on C(3) plants, exclusively on C(4) plants, or both C(3) and C(4) plants. Median (delta(15)N value was 6.6 parts per thousand; the 25th and 75th percentile were 5.9 parts per thousand and 7.3 parts per thousand, respectively. Calculated percentages of imported beef were consistent with the delta(13)C values of purchased Big Macs (R). Japanese patties had higher delta(13)C values than expected based on that country’s C(3) agriculture, however Japan imports beef from Australia, where C(4) plants are prevalent. Lower latitude countries generally had higher delta(13)C values than higher latitude countries, reflecting the larger distribution of C(4) plants in warm regions. Although Big Macs (R) are a global food, we conclude they also contain local components.

The year 2010 featured a widespread drought in the Amazon rain forest, which was more severe than the “once-in-a-century” drought of 2005. Water levels of major Amazon tributaries fell drastically to unprecedented low values, and isolated the floodplain population whose transportation depends upon on local streams which completely dried up. The drought of 2010 in Amazonia started in early austral summer during El Nino and then was intensified as a consequence of the warming of the tropical North Atlantic. An observed tendency for an increase in dry and very dry events, particularly in southern Amazonia during the dry season, is concomitant with an increase in the length of the dry season. Our results suggest that it is by means of a longer dry season that warming in the tropical North Atlantic affects the hydrology of the Amazon Rivers at the end of the recession period (austral spring). This process is, sometimes, further aggravated by deficient rainfall in the previous wet season.

Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions differentiate land uses by classification algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps (SOM) algorithm, which exploits the spatial correlation among near pixels. The neurons in the SOM will cluster the objects found in the image, and such objects will define the image segments.

The climatic effects of concurrent land-cover changes in Amazonia and Northeast Brazil (NEB) were evaluated by simulations using the Center for Weather Forecasting and Climate Studies Atmospheric General Circulation Model (CPTEC-AGCM). Three experiments were performed: Amazon savannization, NEB desertification and both land-cover changes occurring concurrently. We found that land-cover change from adjacent areas do affect both Amazon and NEB regions and that the negative precipitation anomaly in NEB due to concurrent land-cover changes in Amazon and NEB is weaker than the linear addition of the anomalies considering the land-cover changes separately (synergistic behaviour). A simple mechanism was proposed to explain this behaviour.

This study presents monthly estimates of groundwater anomalies in a large river basin dominated by extensive floodplains, the Negro River Basin, based on the synergistic analysis using multisatellite observations and hydrological models. For the period 2003-2004, changes in water stored in the aquifer is isolated from the total water storage measured by GRACE by removing contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by WGHM and LaD hydrological models. The groundwater anomalies show a realistic spatial pattern compared with the hydrogeological map of the basin, and similar temporal variations to local in situ groundwater observations and altimetry-derived level height measurements. Results highlight the potential of combining multiple satellite techniques with hydrological modeling to estimate the evolution of groundwater storag.

In this study, a zonally-averaged statistical climate model (SDM) is used to investigate the impact of global warming on the distribution of the geobotanic zones over the globe. The model includes a parameterization of the biogeophysical feedback mechanism that links the state of surface to the atmosphere (a bidirectional interaction between vegetation and climate). In the control experiment (simulation of the present-day climate) the geobotanic state is well simulated by the model, so that the distribution of the geobotanic zones over the globe shows a very good agreement with the observed ones. The impact of global warming on the distribution of the geobotanic zones is investigated considering the increase of CO2 concentration for the B1, A2 and A1FI scenarios. The results showed that the geobotanic zones over the entire earth can be modified in future due to global warming. Expansion of subtropical desert and semi-desert zones in the Northern and Southern Hemispheres, retreat of glaciers and sea-ice, with the Arctic region being particularly affected and a reduction of the tropical rainforest and boreal forest can occur due to the increase of the greenhouse gases concentration. The effects were more pronounced in the A1FI and A2 scenarios compared with the B1 scenario. The SDM results confirm IPCC AR4 projections of future climate and are consistent with simulations of more complex GCMs, reinforcing the necessity of the mitigation of climate change associated to global warming.

This work aims at discussing the artificial neural networks (ANN) and some applications in renewable energy assessment. First, the paper describes the statistical relevance of this tool in different areas of knowledge and the main ANN concepts and configurations. Finally, the paper presents and discusses the use of ANN for the solar energy assessment in Brazil by using data collected in SONDA sites operated by the Center for Earth System Science of the Brazilian Institute for Space Research. The results show that ANN can provide reliable estimates with better performance than other statistical tools.