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News Article | June 30, 2016
Site: www.techtimes.com

Leading scientists in the United States warn that the long-term effects of the El Niño phenomenon could cause the Amazon to experience more intense forest fires this year. The El Niño of 2015 and the early part of 2016 have impacted rainfall patterns in the different parts of the world. One of those significantly affected by the extreme weather condition is the Amazon, which saw a considerable decrease in the amount of rainfall during its wet season. This left the region to experience its driest point since 2002 by the time it entered its dry season this year, according to satellite data from NASA. Doug Morton, an expert on Earth science from NASA, said that this year's El Niño phenomenon has also made the Amazon more susceptible to wildfires than in 2005 and 2010, when the region suffered from widespread forest fires brought on by drought. Morton explained that the southern part of the Amazon is now at a high risk for wildfires due to the severe drought conditions the area has been undergoing since the beginning of the dry season. To find out the risks of forest fires in the Amazon, scientists made use of a system developed by NASA and the University of California, Irvine (UCI). This technology examines the relationship between climate and active fire detection data from NASA satellites in order to determine the severity of the region's fire season. The forecast model centers on the connection between fire activity and sea surface temperatures. The Amazon becomes more susceptible to wildfires whenever higher sea surface temperatures in the Atlantic and Pacific oceans alter weather patterns in the region, causing it to experience significantly less rainfall. The team also studied terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) mission in order to identify changes in the groundwater in the Amazon during its dry season. These measurements were used as a substitute for the relative dryness of forests and soils. NASA and UCI researchers have coordinated with scientists and officials in South America to raise their awareness on wildfire forecasts over the years. Liana Anderson, a scientist from Brazil's National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), said the wildfire forecasts are crucial since they allow them to know which particular areas are more likely to suffer forest fires. This gives them an opportunity to coordinate their plans in support of local efforts. According to recent estimates, El Niño-related conditions in the Amazon have become much drier this year than during the drought years of 2005 and 2010. NASA and UCI scientists have created a web tool to help them monitor the progress of the region's fire season almost in real time. Fire emission readings from each one of the forecast regions are updated every day using active fire detection data gathered through the Terra satellite's Moderate resolution Imaging Spectroradiometer (MODIS) instrument, as well as fire emissions data from previous years recorded in the Global Fire Emissions Database (GFED). Using these data, the researchers discovered that the Amazon has experienced more wildfires in recent times than in any other point in history, which is in accordance with the forecast on the region's fire severity. Jim Randerson, a scientist from UCI and one of the developers of the forecast model, said trees become more susceptible to fires and evaporate lower amounts of water into Earth's atmosphere when they don't have enough moisture to draw upon at the start of the dry season. During such scenarios, Randerson said millions of trees are exposed to higher levels of stress, lowering the available humidity across their region. This in turn causes forest fires to become larger than what they would typically be under normal conditions. © 2016 Tech Times, All rights reserved. Do not reproduce without permission.


Gimeno L.,University of Vigo | Dominguez F.,Urbana University | Nieto R.,University of Vigo | Nieto R.,University of Sao Paulo | And 7 more authors.
Annual Review of Environment and Resources | Year: 2016

We review the major conceptual models of atmospheric moisture transport, which describe the link between evaporation from the ocean and precipitation over the continents. We begin by summarizing some of the basic aspects of the structure and geographical distribution of the two major mechanisms of atmospheric moisture transport, namely low-level jets (LLJs) and atmospheric rivers (ARs). We then focus on a regional analysis of the role of these mechanisms in extreme precipitation events with particular attention to the intensification (or reduction) of moisture transport and the outcome, in terms of precipitation anomalies and subsequent flooding (drought), and consider changes in the position and occurrence of LLJs and ARs with respect to any associated flooding or drought. We then conclude with a graphical summary of the impacts of precipitation extremes, highlighting the usefulness of this information to hydrologists and policymakers, and describe some future research challenges including the effects of possible changes to ARs and LLJs within the context of future warmer climates. Copyright ©2016 by Annual Reviews. All rights reserved.


PubMed | National Center for Monitoring and Early Warning of Natural Disasters, University of Brasilia, Planetary Skin Institute and National Institute for Space Research
Type: Journal Article | Journal: Proceedings of the National Academy of Sciences of the United States of America | Year: 2016

For half a century, the process of economic integration of the Amazon has been based on intensive use of renewable and nonrenewable natural resources, which has brought significant basin-wide environmental alterations. The rural development in the Amazonia pushed the agricultural frontier swiftly, resulting in widespread land-cover change, but agriculture in the Amazon has been of low productivity and unsustainable. The loss of biodiversity and continued deforestation will lead to high risks of irreversible change of its tropical forests. It has been established by modeling studies that the Amazon may have two tipping points, namely, temperature increase of 4 C or deforestation exceeding 40% of the forest area. If transgressed, large-scale savannization of mostly southern and eastern Amazon may take place. The region has warmed about 1 C over the last 60 y, and total deforestation is reaching 20% of the forested area. The recent significant reductions in deforestation-80% reduction in the Brazilian Amazon in the last decade-opens up opportunities for a novel sustainable development paradigm for the future of the Amazon. We argue for a new development paradigm-away from only attempting to reconcile maximizing conservation versus intensification of traditional agriculture and expansion of hydropower capacity-in which we research, develop, and scale a high-tech innovation approach that sees the Amazon as a global public good of biological assets that can enable the creation of innovative high-value products, services, and platforms through combining advanced digital, biological, and material technologies of the Fourth Industrial Revolution in progress.


Rodrigues D.B.B.,University of Sao Paulo | Gupta H.V.,University of Arizona | Mendiondo E.M.,University of Sao Paulo | Mendiondo E.M.,National Center for Monitoring and Early Warning of Natural Disasters | Oliveira P.T.S.,University of Sao Paulo
Water Resources Research | Year: 2015

Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision-making process. Key Points: Uncertainty analysis of scarcity and vulnerability indicators This multimodel/resampling-based framework includes several uncertainty sources Viable uncertainty analysis to be included into a robust decision-making process. © 2015. American Geophysical Union. All Rights Reserved.


Marengo J.A.,National Center for Monitoring and Early Warning of Natural Disasters | Alves L.M.,National Institute for Space Research | Torres R.R.,Federal University of Itajubá
Climate Research | Year: 2016

In the Brazilian Pantanal, hydrometeorological conditions exhibit a large interannual variability. This variability includes the seasonality of floods and droughts which can be related to land surface processes and to El Niño/La Niña. Based on regional climate change projections derived from the Eta-HadGEM2 ES models with 20 km latitude-longitude resolution for the RCP8.5 for 2071-2100, it is expected that there will be an annual mean warming of up to or above 5-7°C and a 30% reduction in rainfall by the end of the 21st century. As a consequence of higher temperatures and reduced rainfall, an increased water deficit would be expected, particularly in the central and eastern parts of the basin during spring and summer, which could affect the pulse of the Paraguay River. While the changes projected by the Eta-HadGEM2 ES are consistent with the changes produced by the CMIP5 models for the same scenario and time slice, we can affirm that changes in the hydrology of the Pantanal are uncertain, because in a comparison of CMIP5 and Eta-HadGEM2 ES model projections, some show increases in rainfall and in the discharges of the Paraguay Basin, while others show reductions. © Inter-Research 2016.


Devisscher T.,University of Oxford | Devisscher T.,Stockholm Environment Institute | Anderson L.O.,University of Oxford | Anderson L.O.,National Center for Monitoring and Early Warning of Natural Disasters | And 5 more authors.
PLoS ONE | Year: 2016

Wildfires are becoming increasingly dominant in tropical landscapes due to reinforcing feedbacks between land cover change and more severe dry conditions. This study focused on the Bolivian Chiquitania, a region located at the southern edge of Amazonia. The extensive, unique and well-conserved tropical dry forest in this region is susceptible to wildfires due to a marked seasonality. We used a novel approach to assess fire risk at the regional level driven by different development trajectories interacting with changing climatic conditions. Possible future risk scenarioswere simulated using maximum entropy modelling with presence-only data, combining land cover, anthropogenic and climatic variables. We found that important determinants of fire risk in the region are distance to roads, recent deforestation and density of human settlements. Severely dry conditions alone increased the area of high fire risk by 69%, affecting all categories of land use and land cover. Interactions between extreme dry conditions and rapid frontier expansion further increased fire risk, resulting in potential biomass loss of 2.44±0.8 Tg in high risk area, about 1.8 times higher than the estimates for the 2010 drought. These interactions showed particularlyhigh fire risk in land used for 'extensive cattle ranching', 'agro-silvopastoral use' and 'intensive cattle ranching and agriculture'. These findings have serious implications for subsistence activities and the economy in the Chiquitania, which greatly depend on the forestry, agriculture and livestock sectors. Results are particularly concerning if considering the current development policies promoting frontier expansion. Departmental protected areas inhibited wildfires when strategically established in areas of high risk, even under drought conditions. However, further research is needed to assess their effectiveness accounting for more specific contextual factors. This novel and simple modelling approach can informfire and land management decisions in the Chiquitania and other tropical forest landscapes to better anticipate and manage large wildfires in the future. © 2016 Devisscher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Brito T.T.,Federal University of Fluminense | Oliveira-Junior J.F.,Federal Rural University of Rio de Janeiro | Lyra G.B.,Federal Rural University of Rio de Janeiro | Gois G.,Federal Rural University of Rio de Janeiro | Zeri M.,National Center for Monitoring and Early Warning of Natural Disasters
Meteorology and Atmospheric Physics | Year: 2016

Spatial and temporal patterns of rainfall were identified over the state of Rio de Janeiro, southeast Brazil. The proximity to the coast and the complex topography create great diversity of rainfall over space and time. The dataset consisted of time series (1967–2013) of monthly rainfall over 100 meteorological stations. Clustering analysis made it possible to divide the stations into six groups (G1, G2, G3, G4, G5 and G6) with similar rainfall spatio-temporal patterns. A linear regression model was applied to a time series and a reference. The reference series was calculated from the average rainfall within a group, using nearby stations with higher correlation (Pearson). Based on t-test (p < 0.05) all stations had a linear spatiotemporal trend. According to the clustering analysis, the first group (G1) contains stations located over the coastal lowlands and also over the ocean facing area of Serra do Mar (Sea ridge), a 1500 km long mountain range over the coastal Southeastern Brazil. The second group (G2) contains stations over all the state, from Serra da Mantiqueira (Mantiqueira Mountains) and Costa Verde (Green coast), to the south, up to stations in the Northern parts of the state. Group 3 (G3) contains stations in the highlands over the state (Serrana region), while group 4 (G4) has stations over the northern areas and the continent-facing side of Serra do Mar. The last two groups were formed with stations around Paraíba River (G5) and the metropolitan area of the city of Rio de Janeiro (G6). The driest months in all regions were June, July and August, while November, December and January were the rainiest months. Sharp transitions occurred when considering monthly accumulated rainfall: from January to February, and from February to March, likely associated with episodes of “veranicos”, i.e., periods of 4–15 days of duration with no rainfall. © 2016 Springer-Verlag Wien


de Moura Y.M.,National Institute for Space Research | Hilker T.,Oregon State University | Lyapustin A.I.,NASA | Galvao L.S.,National Institute for Space Research | And 5 more authors.
Remote Sensing of Environment | Year: 2015

Seasonality and drought in Amazon rainforests have been controversially discussed in the literature, partially due to a limited ability of current remote sensing techniques to detect its impacts on tropical vegetation. We use a multi-angle remote sensing approach to determine changes in vegetation structure from differences in directional scattering (anisotropy) observed by the Moderate Resolution Imaging Spectroradiometer (MODIS) with data atmospherically corrected by the Multi-Angle Implementation Atmospheric Correction Algorithm (MAIAC). Our results show a strong linear relationship between anisotropy and field (r2=0.70) and LiDAR (r2=0.88) based estimates of LAI even in dense canopies (LAI≤7m2m-2). This allowed us to obtain improved estimates of vegetation structure from optical remote sensing. We used anisotropy to analyze Amazon seasonality based on spatially explicit estimates of onset and length of dry season obtained from the Tropical Rainfall Measurement Mission (TRMM). An increase in vegetation greening was observed during the beginning of dry season (across ~7% of the basin), which was followed by a decline (browning) later during the dry season (across ~5% of the basin). Anomalies in vegetation browning were particularly strong during the 2005 and 2010 drought years (~10% of the basin). We show that the magnitude of seasonal changes can be significantly affected by regional differences in onset and duration of the dry season. Seasonal changes were much less pronounced when assuming a fixed dry season from June through September across the Amazon Basin. Our findings reconcile remote sensing studies with field based observations and model results as they provide a sounder basis for the argument that tropical vegetation growth increases during the beginning of the dry season, but declines after extended drought periods. The multi-angle approach used in this work may help quantify drought tolerance and seasonality in the Amazonian forests. © 2015 Elsevier Inc.


Carvalho T.,National Center for Monitoring and Early Warning of Natural Disasters
Electronic Letters on Computer Vision and Image Analysis | Year: 2015

Think about how capture device's technology is improved day after day. Add to this condition that digital image manipulation tools are increasingly powerful and simple to use. Finally, when a malicious user is added at this equation, the result is an astonishing number of digital images forgeries spread out in the internet as fast as possible. Cases as the fake dead of Ozama Bin Laden or the fake criminal record of Brazilian current president Dilma Rousseff [1] are just a few examples of how digital images forgeries can be malicious and dangerous. In special, image splicing* is a very usual kind of image forgery. It consists in use parts of two or more images to compose a new one depicting a moment that never happen. Figure 1 depicts an overview of image splicing creation process. For helping forensics community to fight, specifically, against this kind of digital image forgeries, this work† present a collection of four different methods for detecting forgeries created by image composition. Given that human are not quiet reliable to detect illumination inconsistencies in images [2], which makes a perfect illumination matching almost impossible to achieve when creating images compositions, our methods look for this kind of inconsistencies to detect if some image is, or not, an splicing.


Ribeiro B.Z.,National Institute for Space Research | Seluchi M.E.,National Center for Monitoring and Early Warning of Natural Disasters | Chou S.C.,National Institute for Space Research
International Journal of Climatology | Year: 2016

This study shows a synoptic climatology of warm fronts in Southeastern South America (SESA). Data from Climate Forecast System Reanalysis (CFSR) was used to identify warm fronts from 1979 to 2010. The identification method was based on the magnitude of meridional gradient of 850-hPa equivalent potential temperature (θe) and 850-hPa wind fields. Composites of the most important atmospheric variables were constructed from 1 day before until 1 day after the formation of the warm front. An average frequency of two warm fronts per month is observed, with higher frequencies in austral winter. Most warm fronts precede the formation of extratropical cyclones over Uruguay and form because of the southward movement of previous cold/stationary fronts. Warm fronts form on average around southern Paraguay, northeastern Argentina and western part of southern Brazil and Uruguay, coupled to the eastern edge of the Chaco Low (CL) and the Northwestern Argentinean Low (NAL) where north/northwesterly flow predominates. An upper-level wave of wavenumber eight supports warm frontogenesis. Location and intensity of synoptic systems associated with a warm front event differ from winter to summer. Elevated instability is commonly present near warm fronts, and the average warm-front slope is 1:110, agreeing with other studies. Instability indices increase after the warm-front passage, leading to greater rainfall 1 day after the warm front forms. © 2016 Royal Meteorological Society.

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