Entity

Time filter

Source Type


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 Itajuba
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. Source


Anderson L.O.,National Center for Monitoring and Early Warning of Natural Disasters | Anderson L.O.,University of Oxford | Anderson L.O.,National Institute for Space Research | Aragao L.E.O.C.,National Institute for Space Research | And 11 more authors.
Global Biogeochemical Cycles | Year: 2015

In less than 15 years, the Amazon region experienced three major droughts. Links between droughts and fires have been demonstrated for the 1997/1998, 2005, and 2010 droughts. In 2010, emissions of 510 ± 120 Tg C were associated to fire alone in Amazonia. Existing approaches have, however, not yet disentangled the proportional contribution of multiple land cover sources to this total. We develop a novel integration of multisensor and multitemporal satellite-derived data on land cover, active fires, and burned area and an empirical model of fire-induced biomass loss to quantify the extent of burned areas and resulting biomass loss for multiple land covers in Mato Grosso (MT) state, southern Amazonia - the 2010 drought most impacted region. We show that 10.77% (96,855 km2) of MT burned. We estimated a gross carbon emission of 56.21 ± 22.5 Tg C from direct combustion of biomass, with an additional 29.4 ± 10 Tg C committed to be emitted in the following years due to dead wood decay. It is estimated that old-growth forest fires in the whole Brazilian Legal Amazon (BLA) have contributed to 14.81 Tg of C (11.75 Tg C to 17.87 Tg C) emissions to the atmosphere during the 2010 fire season, with an affected area of 27,555 km2. Total C loss from the 2010 fires in MT state and old-growth forest fires in the BLA represent, respectively, 77% (47% to 107%) and 86% (68.2% to 103%) of Brazil's National Plan on Climate Change annual target for Amazonia C emission reductions from deforestation. ©2015. The Authors. Source


News Article | June 30, 2016
Site: http://www.techtimes.com/rss/sections/environment.xml

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.


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. Source


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. Source

Discover hidden collaborations