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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Topsoil bulk geochemical compositions - An updated harmonized global dataset

Mineral weathering is a key biogeochemical process because of the capacity of minerals to stabilize organic matter. However, predicting soil weathering status across large spatial areas still isn’t possible due to a lack of global data and theoretical frameworks. To address this knowledge gap, multiple global datasets of bulk topsoil geochemical compositions have been harmonized using R. These datasets document topsoil bulk geochemical compositions across five continents (n = ~16,000 observations). Source data for these observations include the EuroGEOSurveys Geochemical Baseline Database (FOREGS), the US Geological Survey National Geochemical Database (NASGLP), the Geochemical Atlas of Australia (GAA), the US Geological Survey Alaska Geochemical Database (AGD84), the National Cooperative Soil Survey (NCSS), the European Geochemical Mapping of Agricultural Soil (GEMAS), Ecorespira-Amazon (ERA), the New Zealand Geochemical Baseline Survey (NZ_GBS), and the African Soil Information Service (AFSIS). Major elements observed include Aluminum (Al), Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Sodium (Na), Titanium (Ti), Manganese (Mn), Phosphorus (P), Carbon (C), and Sulfur (S). This data package includes the harmonized dataset itself, and the R scripts necessary to harmonize these datasets, in addition to metadata that describes all columns, files, and databases used in this project. Methods & Sampling Step 1 – Databases of geochemical data identified This study aimed to leverage existing measurements of topsoil geochemical data. Databases were first identified and deemed appropriate for inclusion if they were measuring soils and performed these measurements on the <2mm soil fraction. Databases such as NCSS and AGD84 needed more post processing to include in the database and this was done using the NCSS_datamerge_031626 R file and Alaska_USGSmerge_031626 R file, respectively. Step 2 – Database harmonization Once appropriate databases were identified, they were harmonized for ease of analysis using the R script Database_Harmonization_031826. This included removing columns from original datasets that would not be used in analysis (removed columns are noted in the code). Then, data cleaning procedures specific to each dataset were undertaken. This includes standardizing columns to include units and adding metadata columns regarding procedures for analyzing specific elements. Functions for standardizing measurements and units are outline in R files: calculate element_mg_kg_031626, calculate_oxide_wt_perc_031626, change_oxide_caps_031626, and conv_2_numeric_031626. This also included adding a unique identifier for each sample to identify it with its respective database (see CD_ID in data dictionary). Geographic information: Data reflect a compilation of datasets collected globally. Geographic areas covered by each of the datasets include: - EuroGEOSurveys Geochemical Baseline Database (FOREGS) - European continent - North American Soil Geochemical Landscapes (NASGLP) - continental United States and limited parts of Canada (see database key for more details) - National Geochemical Survey of Australia (GAA) - Australia - Alaska geochemical database (AGDB4) - Alaska - National Cooperative Soil Survey (NCSS) - Global measurements, but concentrated in the continental United States - Geochemical data for arable land and land under permanent grass cover in continental Europe (GEMAS) - continental Europe - Ecorespira-Amazon (ERA) - Geochemical data from the Amazon basin - Geochemical baseline data for New Zealand (NZGBS) - New Zealand - Geochemical data collected across continental Africa (AfSIS) - Measurements across Africa

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2024

Growth, mortality, wood density, biomass data from BIONTE inventories in Manaus, Brazil

BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). Led by the National Institute for Amazon Research (INPA) in Brazil, the project aimed at assessing the effects of logging intensity on forest dynamics and enabling the creation of a model of forest management for the Central Amazon. The experiment included three levels of increasing selective logging intensity and controls, with 1 hectare sample plots (12 total) located at the center of 4 hectare treatment plots. The site’s Köppen classification is tropical rainforest (Af), characterized by high temperatures and humidity, with mean annual temperatures around 27 ℃ and mean annual precipitation around 2200 mm of rain. The vegetation has a high floristic diversity, the soils of the region are poor in nutrients, and the topography is characterized by plateaus (where BIONTE is located), and also valley bottoms and slopes. The inventory (growth and mortality) and biomass data included here covers the 1990 to 2019 period, with wood density being averaged from existing datasets. This dataset includes a data file in .csv file format and a .txt file, BIONTE_mortality-rates_headers.txt, that provides descriptions for the data file headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1983

An investigation of MAGSAT and complementary data emphasizing precambrian shields and adjacent areas of West Africa and South America

There is lack of acceptance of quantitative crustal models by specialists on regional geology because of the problem of east-west banding on MAGSAT anomaly maps. The possibility of some of the Earth's magnetic field being created in the mantle below the Curie isotherm is discussed as well as the need to remove spherical harmonics of degrees less than 15 from the anomaly map. Two dimensional models were produced for northern South America that might be considered as working models. These models suggest that the presumed Amazon River depression is better developed near Belem and in the upper Amazon basin than in the central Amazon basin. An attempt is proposed for separating the core field from the crustal field using a flat-Earth model.

Hastings, D. A.↗
NASA NTRS1988

Biomass-burning emissions and associated haze layers over Amazonia

The characteristics of haze layers, which were visually observed over the central Amazon Basin during many of the Amazon Boundary Layer Experiment 2A flights in July/August 1985, were investigated by remote and in situ measurements, using the broad range of instrumentation and sampling equipment on board the Electra aircraft. It was found that these layers strongly influenced the chemical and optical characteristics of the atmosphere over the eastern Amazon Basin. Relative to the regional background, the concentrations of CO, CO2, O3, and NO were significantly elevated in the plumes and haze layers, with the NO/CO ratio in fresh plumes much higher than in the aged haze layers. The haze aerosol was composed predominantly of organic material, NH4, K(+), NO3(-), SO4(2-), and organic anions (formate, acetate, and oxalate).

Andreae, M. O.↗
NASA NTRS1994

Biogenic nonmethane hydrocarbon emissions estimated from tethered balloon observations

A new technique for estimating surface fluxes of trace gases, the mixed-layer gradient technique, is used to calculate isoprene and terpene emissions from forests. The technique is applied to tethered balloon measurements made over the Amazon forest and a pine-oak forest in Alabama at altitudes up to 300 m. The observations were made during the dry season Amazon Boundary Layer Experiment (ABLE 2A) and the Rural Oxidants in the Southern Environment 1990 experiment (ROSE I). Results from large eddy simulations of scalar transport in the clear convective boundary layer are used to infer fluxes from the balloon profiles. Profiles from the Amazon give a mean daytime emission of 3630 +/- 1400 micrograms isoprene sq m/h, where the uncertainty represents the standard deviation of the mean of eight flux estimates. Twenty profiles from Alabama give emissions of 4470 +/- 3300 micrograms isoprene sq m/h, 1740 +/- 1060 micrograms alpha-pinene sq m/h, and 790 +/- 560 micrograms beta-pinene sq m/h, respectively. These results are in agreement with emissions derived from chemical budgets. The emissions may be overestimated because of uncertainty about how to incorporate the effects of the canopy on the mixed-layer gradients. The large variability in these emission estimates is probably due to the relatively short sampling times of the balloon profiles, though spatially heterogeneous emissions may also play a role. Fluxes derived using this technique are representative of an upwind footprint of several kilometers and are independent of hydrocarbon oxidation rate and mean advection.

Davis, K. J.↗
NASA NTRS2004

Dust Transport and Deposition Observed from the Terra-MODIS Space Observations

Meteorological observations, in situ data and satellite images of dust episodes were used already in the 1970s to estimate that 100 tg of dust are transported from Africa over the Atlantic Ocean every year between June and August and deposited in the Atlantic Ocean and the Americas. Desert dust is a main source of nutrients to oceanic biota and the Amazon forest, but deteriorates air quality and caries pathogens as shown for Florida. Dust affects the Earth radiation budget, thus participating in climate change and feedback mechanisms. There is an urgent need for new tools for quantitative evaluation of the dust distribution, transport and deposition. The Terra spacecraft launched at the dawn of the last millennium provides first systematic well calibrated multispectral measurements from the MODIS instrument, for daily global analysis of aerosol. MODIS data are used here to distinguish dust from smoke and maritime aerosols and evaluate the African dust column concentration, transport and deposition. We found that 230+/-80 tg of dust are transported annually from Africa to the Atlantic Ocean, 30 tg return to Africa and Europe, 70 tg reach the Caribbean, 45 tg fertilize the Amazon Basin, 4 times as previous estimates thus explaining a paradox regarding the source of nutrition to the Amazon forest, and 120+/-40 tg are deposited in the Atlantic Ocean. The results are compared favorably with dust transport models for particle radius less than or equal to 12 microns. This study is a first example of quantitative use of MODIS aerosol for a geophysical study.

Kaufman, Y. J.↗
NASA NTRS2004

Dust Transport and Deposition Observed from the Terra-Moderate Image Spectrometer (MODIS) Space Observations

Meteorological observations, in situ data and satellite images of dust episodes were used already in the 1970s to estimate that 100 tg of dust are transported from Africa over the Atlantic Ocean every year between June and August and deposited in the Atlantic Ocean and the Americas. Desert dust is a main source of nutrients to oceanic biota and the Amazon forest, but deteriorates air quality and caries pathogens as shown for Florida. Dust affects the Earth radiation budget, thus participating in climate change and feedback mechanisms. There is an urgent need for new tools for quantitative evaluation of the dust distribution, transport and deposition. The Terra spacecraft launched at the dawn of the last millennium provides first systematic well calibrated multispectral measurements from the MODIS instrument, for daily global analysis of aerosol. MODIS data are used here to distinguish dust from smoke and maritime aerosols and evaluate the African dust column concentration, transport and deposition. We found that 230 plus or minus 80 tg of dust are transported annually from Africa to the Atlantic Ocean, 30 tg return to Africa and Europe, 70 tg reach the Caribbean, 45 tg fertilize the Amazon Basin, 4 times as previous estimates thus explaining a paradox regarding the source of nutrition to the Amazon forest, and 120 plus or minus 40 tg are deposited in the Atlantic Ocean. The results are compared favorably with dust transport models for particle radius less than or equal to 12 microns. This study is a first example of quantitative use of MODIS aerosol for a geophysical study.

Kaufman, Y.↗
NASA NTRS2004

Eo-1 Hyperion Measures Canopy Drought Stress In Amazonia

The central, south and southeast portions of the Amazon Basin experience a period of decreased cloud cover and precipitation from June through November. There are likely important effects of seasonal and interannual rainfall variation on forest leaf area index, canopy water stress, productivity and regional carbon cycling in the Amazon. While both ground and spaceborne studies of precipitation continue to improve, there has been almost no progress made in observing forest canopy responses to rainfall variability in the humid tropics. This shortfall stems from the large stature of the vegetation and great spatial extent of tropical forests, both of which strongly impede field studies of forest responses to water availability. Those few studies employing satellite measures of canopy responses to seasonal and interannual drought (e.g., Bohlman et al. 1998, Asner et al. 2000) have been limited by the spectral resolution and sampling available from Landsat and AVHRR sensors. We report on a study combining the first landscape-level, managed drought experiment in Amazon tropical forest with the first spaceborne imaging spectrometer observations of this experimental area. Using extensive field data on rainfall inputs, soil water content, and both leaf and canopy responses, we test the hypothesis that spectroscopic signatures unique to hyperspectral observations can be used to quantify relative differences in canopy stress resulting from water availability.

Asner, Gregory P.↗
NASA NTRS2002

Land Use and Land Cover Change in Forest Frontiers: The Role of Household Life Cycles

Tropical deforestation remains a critical issue given its present rate and a widespread consensus regarding its implications for the global carbon cycle and biodiversity. Nowhere is the problem more pronounced than in the Amazon basin, home to the world's largest intact, tropical forest. This article addresses land cover change processes at household level in the Amazon basin, and to this end adapts a concept of domestic life cycle to the current institutional environment of tropical frontiers. In particular, it poses a risk minimization model that integrates demography with market-based factors such as transportation costs and accessibility. In essence, the article merges the theory of Chayanov with the household economy framework, in which markets exist for inputs (including labor), outputs, and capital. The risk model is specified and estimated, using survey data for 261 small producers along the Transamazon Highway in the eastern sector of the Brazilian Amazon.

Walker, Robert↗
NASA NTRS2011

Predicting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. Here we investigated the relationship between year-to-year changes in satellite-derived estimates of fire activity in South America and sea surface temperature (SST) anomalies. We found that the Oceanic Ni o Index (ONI) was correlated with interannual fire activity in the eastern Amazon whereas the Atlantic Multidecadal Oscillation (AMO) index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model that predicted regional annual fire season severity (FSS) with 3-5 month lead times. Our approach provides the foundation for an early warning system for forecasting the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for mitigation of greenhouse gas and air pollutant emissions.

Chen, Yang↗
NASA NTRS2011

Forecasting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. We investigated the relationship between year-to-year changes in fire activity in South America and sea surface temperatures. We found that the Oceanic Ni o Index was correlated with interannual fire activity in the eastern Amazon, whereas the Atlantic Multidecadal Oscillation index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model to forecast regional fire season severity with lead times of 3 to 5 months. Our approach may contribute to the development of an early warning system for anticipating the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for climate and air quality.

Chen, Yang↗
NASA NTRS2023

Future Increases in Amazonia Water 1 Stress from CO2 Physiology and Deforestation

Several different drivers are contributing to climate change within the Amazon basin, including forcing from greenhouse gases and aerosols, plant physiology responses to rising CO 2 , and deforestation. Attribution among these drivers has not been quantified for Shared Socioeconomic Pathway (SSP) climate simulations. Here we identify the contribution of CO 2 physiology and deforestation to future hydroclimate change in the Amazon basin by combining information from four experiments and eight different Earth system models in Coupled Model Intercomparison Project Phase 6. Together, forcing from CO 2 physiology and deforestation account for about 44% of the projected annual precipitation decline, 48% of surface relative humidity decline and 11% of warming over the Amazon basin by 2100 for SSP3-7.0. Other Coupled Model Intercomparison Project Phase 6 SSP simulations have similar contributions from the two drivers. Insight from our attribution analysis can aid in identifying research priorities aimed at reducing uncertainty in future projections of water availability, carbon dynamics and wildfire risk.

Yue Li↗
NASA NTRS2023

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.

modeling↗
DOE OSTI2024

Final report

The bulk of the research results supported by this grant had to do with the diurnal cycle of convection and/or convective organization over the Amazon with some analysis also done over the Southern Great Plains, and large-scale drivers of changes in these convective properties like the Amazonian low-level jet. A comprehensive suite of DOE observational and modeling tools was utilized. Overall, the research results collectively emphasize the significance of accurately observing and modeling mesoscale convective systems and the diurnal cycle of convection, particularly in the Amazon, to enhance understanding and forecasting of regional precipitation patterns and climate dynamics.

58 GEOSCIENCES↗
DOE OSTI2021

Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Cloud Computing↗
NASA NTRS2015

Thermodynamics and Cloud Radiative Effect from the First Year of GoAmazon

Deforestation is an ongoing concern for the Amazon Rainforest of Brazil and associated changes to the land surface have been hypothesized to alter the climate in the region. A comprehensive set of meteorological observations at the surface and within the lower troposphere above Manacapuru, Brazil and data from the Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) are used to evaluate the seasonal cycle of cloudiness, thermodynamics, and the radiation budget. While ample moisture is present in the Amazon Rainforest year round, the northward progression of the Hadley circulation during the dry season contributes to a drying of the middle troposphere and inhibits the formation of deep convection. This results in a reduction in cloudiness and precipitation as well as an increase in the height of the lifting condensation level, which is shown to have a negative correlation to the fraction of low clouds. Frequent cloudiness prevents solar radiation from reaching the surface and clouds are often reflective with high values of shortwave cloud radiative effect at the surface and top of the atmosphere. Cloud radiative effect is reduced during the dry season however the dry season surface shortwave cloud radiative effect is still double what is observed during the wet season in other tropical locations. Within the column, the impact of clouds on the radiation budget is more prevalent in the longwave part of the spectrum, with a net warming in the wet season.

Amazon Rainforest↗