Keyword

Amazon

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From 1 - 10 / 13
  • This dataset holds the high resolution (0.5 x 0.5 deg; 8 vertical levels) monthly means of 5 atmospheric variables (air temperature, pressure, water vapour pressure, vertical velocity and horizontal wind speed) over the Amazon Basin for the period 1972 to 2009 (version 1.0). This data is public and citable (DOI: 10.5285/2dfce039-cd71-43b3-bed4-98978e78f1bb). It was constructed using the predictive capabilities of Time-Delayed Neural Networks (TDNN) method. Thirty years of monthly averages of current climate data (1971-2000) of the NCEP/NCAR reanalysis dataset were used to train the TDNNs, which were then validated on the next 10 years (2001-2010). Once validated, the downscaling model was fed with the higher resolution CRU TS3.1 data and SRTM-1km elevation data (thereby obtaining the higher resolution dataset).

  • While the Amazon rainforest area has a known effect on precipitation and global water vapour circulation, it is still poorly understood. This is in part due to the lack and inconsistency in atmospheric observations in the area. This dataset holds the high resolution (0.5 x 0.5 deg; 8 vertical levels) monthly means of 5 atmospheric variables (air temperature, pressure, water vapour pressure, vertical velocity and horizontal wind speed) over the Amazon Basin for the period 1972 to 2009. This data is public and in particular, version 1.0 is citable (DOI: 10.5285/2dfce039-cd71-43b3-bed4-98978e78f1bb).

  • This dataset contains greenhouse gas profile measurements from the Amazon Integrated Carbon Analysis (AMAZONICA) project. AMAZONICA was an UK-Brasil Consortium funded by NERC (Natural Environmental Reasearch Council, UK) which aimed to quantify the carbon balance of the Amazon Basin and its associated contribution to global atmospheric change, to apportion and understand the processes contributing to the net Basin-wide flux observed and, to allow improved assessments of the likely role of the Amazon Basin in contributing and/or alleviating future planetary change. Data were collected and collated by the AMAZONICA team in the UK and Brazil and were deposited at BADC before the end of the project (expected end 2012 - mid 2013).

  • [This dataset is embargoed until January 31, 2022]. Data are presented showing seedling height, diameter at ground height (DGH), total number of leaves, number of leaves with herbivory damage and leaf mortality, from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected bimonthlyfrom February 2019 to January 2020, by the dataset first author. Height measurements were made with a tape measure and DRH measurements were made with digital calipers. Leaf numbers, damage and mortality were made from visual observations. The data were collected to investigate the possible effects of different fertiliser applications on seedling height, totalnumber of leaves, number of leaves with herbivory damage and leaf mortality.The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, by the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintanance). Full details about this dataset can be found at https://doi.org/10.5285/2da56eb1-ff01-48de-ba2a-d3afceefc85f

  • Spatial data files holding gridded parameter maps of surface soil hydraulic parameters derived from a selection of pedotransfer functions. Modern land surface model simulations capture soil profile water movement through the use of soil hydraulics sub-models, but good hydraulic parameterisations are often lacking - especially in the tropics - and it is this lack that we fill here in the context of South America. Optimal hydraulic parameter values are given for the Brooks and Corey, Campbell, van Genuchten-Mualem and van Genuchten-Burdine soil hydraulic models, which are widely-used hydraulic sub-models in many land surface models (e.g. Joint UK Land Environment Simulator JULES). Full details about this dataset can be found at https://doi.org/10.5285/4078678b-768f-43ff-abba-b87712f648e9

  • [This dataset is embargoed until January 31, 2022]. Data are presented showing for individual seedling, herbivory damage at the leaf level; galls, pathogens, trail herbivory presence/absence qualitative data; and leaf mortality. Data were collected in each leaf from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected bimonthly from February 2019 to January 2020, by the dataset first author. Leaf loss in percentage was made using the choice for direct visual estimate. We also followed the recommendations proposed by the authors, sectoring the leaves with a millimetre grid, improving measurement accuracy. The presence of Galls, pathogens and trail herbivory presence/absence qualitative data were also collected in each leaf. The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintanance). Full details about this dataset can be found at https://doi.org/10.5285/2b8029ff-ddf5-47b2-9231-5fa0cbb6cd41

  • This data set consist of a single file which contains a set of optimised global surface fluxes of methane (CH4), produced through variational inverse methods using the TOMCAT chemical transport model, and the INVICAT inverse transport model. These surface fluxes are produced as monthly mean values on the (approximately) 5.6-degree horizontal model grid. The associated uncertainty for the flux from each grid cell is also included. The fluxes and uncertainties are global and cover the period Jan 2010 - Dec 2018. The emissions from fossil fuels are labelled FF_FLUX, whilst the uncertainties are labelled FF_ERROR. The emissions from natural, agricultural and biomass burning sources are labelled NAT_FLUX, whilst the uncertainties are labelled NAT_ERROR. These two sectors (fossil fuel and non-fossil fuel) are solved for separately in the inversion. Flux and uncertainty units are kg(CH4)/m2/s, and time units are days since January 1st 2010. These emissions show improved performance relative to independent observations when included in the TOMCAT model. Further details about the data can be found in Wilson et al. (2020) in the documentation section.

  • The data are concentrations of different fluvial carbon species (dissolved inorganic carbon, dissolved organic carbon and particulate organic carbon) which form part of the lateral transport of carbon from the terrestrial to aquatic system. This influences the terrestrial carbon balance as well as being a key part of the freshwater carbon cycle. The submission also contains hydrological (stage height, discharge and water temperature) and water chemistry data (pH, conductivity and oxygen saturation). The data were collected from Peruvian rainforest streams within the NERC funded Amazonica project (NE/F005482/1). Full details about this dataset can be found at https://doi.org/10.5285/507a5e1f-e056-454c-8ff6-d185f3da8556

  • Data are presented showing litterfall ant species and abundance from a plot based fertilisation experiment. The experiment was carried out at the Biological Dynamics of Forest Fragments Project (BDFFP) approximately 100 km north of Manaus. Data were collected in October 2018 and September 2019 by Santos-Neto. Sampling was carried out using a Wrinkler extractor. The data were collected to investigate the possible effects of different fertiliser applications on litterfall ant species and abundance. The work was carried out as part of the Amazon Fertilization Experiment (AFEX), funded by the Natural Environment Research Council (NERC), Award reference NE/L007223/1, by the Brazilian government (Researcher scholarship) and the Biological Dynamics of Forest Fragments Project (BDFFP - logistical support and camps maintenance). Full details about this dataset can be found at https://doi.org/10.5285/60e77fd4-7a24-4545-8d90-08e9dfcbd16a

  • The Quantifying the Amazon Isoprene Budget: Reconciling Top-down versus Bottom-up Emission Estimates project ran a unique high resolution model for the Amazon basin, able to simulate isoprene emissions and atmospheric chemistry. Model outputs are available through CEDA. This was a NERC funded project (NE/G013810/1).