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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Soil erosion and lateral carbon fluxes from corn stover-derived biofuel

Abstract Crop residues hold promise to alleviate food vs. fuel competition and contribute to biofuel production. However, the impacts of lateral sediment and carbon fluxes caused by residue removal are not fully understood. Here we employ agroecosystem modeling to conservatively estimate lateral sediment and carbon fluxes resulting from partial corn stover removal in the U.S. Midwest. Results show substantial increases in soil erosion resulting from corn stover removal. For example, the area of continuous corn and corn soybean cropping systems exceeding soil erosion tolerance threshold could increase from 1.1 to 13.3% because of 66% corn stover removal. Depending on removal intensity, conservation, and crop rotation, the stover removal-induced increases in eroded soil organic carbon is equivalent to 3.9–12.5 gCO 2 e MJ −1 , which is comparable to other components of the life cycle impacts of corn stover-derived biofuel. Our findings highlight the need to consider the soil erosion and lateral carbon fluxes impacts of corn stover removal in designing supply chains for cellulosic biofuel production.

Science & Technology - Other Topics↗
DOE OSTI2020

Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R 2 >0.94 at 1° spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr -1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R 2 ~0.75) than with GPP from DGVMs (mean global pixel-wise R 2 ~0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R 2 >0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R 2 =0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO 2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Isolating the Impacts of Soil Moisture Initialization on Land Carbon Fluxes at the Sub-Seasonal to Seasonal Scale

Soil moisture, through its impact on transpiration, plays a significant role in controlling the productivity of terrestrial vegetation. Initial soil moisture conditions in a seasonal forecast may therefore affect the forecast of land carbon uptake. Here we investigate the relative impacts of soil moisture initialization and carbon reservoir initialization on forecasts of land carbon fluxes at the sub-seasonal to seasonal (S2S) scale. The bias-corrected, retrospective meteorology of NASA Global Modeling and Assimilation Office (GMAO)’s S2S ensemble forecast was used to force the stand-alone Catchment-CN model and thereby estimate terrestrial carbon responses out to nine lead months. Our results show that soil moisture initialization is a major contributor (approximately 44%) to the high global carbon uptake forecast skill seen during the first three lead months. The carbon reservoir initialization explains roughly another half of the monthly carbon forecast skill during this period and becomes relatively more important at longer leads (while the overall forecast skill decreases after the 3 lead month), suggesting a slower but longer-lasting influence of carbon reservoir initialization on carbon fluxes. Our results highlight the significance of a good soil moisture initialization for improved forecasts of carbon fluxes at leads of several months, further support for the usefulness of assimilating the satellite based soil moisture information into terrestrial biosphere models and of a short term carbon forecast to understand current events with often a lag in the availability of flux estimates.

Land carbon↗
NASA NTRS2018

Toward Integrated Seasonal Predictions of Land and Ocean Carbon Flux: Lessons from the 2015-16 El Nino

Seasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.

Ott, L.↗
NASA NTRS2015

Sensitivity of Simulated Global Ocean Carbon Flux Estimates to Forcing by Reanalysis Products

Reanalysis products from MERRA, NCEP2, NCEP1, and ECMWF were used to force an established ocean biogeochemical model to estimate air-sea carbon fluxes (FCO2) and partial pressure of carbon dioxide (pCO2) in the global oceans. Global air-sea carbon fluxes and pCO2 were relatively insensitive to the choice of forcing reanalysis. All global FCO2 estimates from the model forced by the four different reanalyses were within 20% of in situ estimates (MERRA and NCEP1 were within 7%), and all models exhibited statistically significant positive correlations with in situ estimates across the 12 major oceanographic basins. Global pCO2 estimates were within 1% of in situ estimates with ECMWF being the outlier at 0.6%. Basin correlations were similar to FCO2. There were, however, substantial departures among basin estimates from the different reanalysis forcings. The high latitudes and tropics had the largest ranges in estimated fluxes among the reanalyses. Regional pCO2 differences among the reanalysis forcings were muted relative to the FCO2 results. No individual reanalysis was uniformly better or worse in the major oceanographic basins. The results provide information on the characterization of uncertainty in ocean carbon models due to choice of reanalysis forcing.

MERRA↗
NASA NTRS2018

Impacts of Land Carbon Flux Variability on Variations in Atmospheric CO2

To better understand the role of atmospheric CO2 in the global carbon cycle, it is important to understand the factors that control its variability in time and space. Here we investigate these factors using a coupled land-atmosphere modeling system (the NASA GEOS-5 model) fitted with land carbon physics and atmospheric CO2 transport. To separate the influences of land carbon flux variability and atmospheric transport variability on the variability of CO2 concentrations, we conducted two carbon-fitted GEOS-5 AGCM simulations run in replay mode (a technique that guides the model's weather to match that of the MERRA-2 reanalysis): (i) a control simulation of 15-year carbon cycle dynamics and climate, and (ii) a simulation in which the climatological seasonal cycles of net biosphere production (NBP), as determined from diagnostics produced in the control experiment, are applied at the surface to the atmosphere instead. The impact of land flux variability on atmospheric CO2 variability is then isolated by subtracting the variability inherent in the second simulation (as induced, e.g., by strong El Nino conditions) from that in the control. These results are also presented in the context of supplemental simulations that have examined the impact of imposed drought in a free running AGCM simulation on carbon fluxes and atmospheric CO2 variability.

Lee, Eunjee↗
NASA NTRS2001

Modeling Biogeochemical-Physical Interactions and Carbon Flux in the Sargasso Sea (Bermuda Atlantic Time-series Study site)

An ecosystem-carbon cycle model is used to analyze the biogeochemical-physical interactions and carbon fluxes in the Bermuda Atlantic Time-series Study (BATS) site for the period of 1992-1998. The model results compare well with observations (most variables are within 8% of observed values). The sea-air flux ranges from -0.32 to -0.50 mol C/sq m/yr, depending upon the gas transfer algorithm used. This estimate is within the range (-0.22 to -0.83 mol C/sq m/yr) of previously reported values which indicates that the BATS region is a weak sink of atmospheric CO2. The overall carbon balance consists of atmospheric CO2 uptake of 0.3 Mol C/sq m/yr, upward dissolved inorganic carbon (DIC) bottom flux of 1.1 Mol C/sq m/yr, and carbon export of 1.4 mol C/sq m/yr via sedimentation. Upper ocean DIC levels increased between 1992 and 1996 at a rate of approximately 1.2 (micro)mol/kg/yr, consistent with observations. However, this trend was reversed during 1997-1998 to -2.7 (micro)mol/kg/yr in response to hydrographic changes imposed by the El Nino-La Nina transition, which were manifested in the Sargasso Sea by the warmest SST and lowest surface salinity of the period (1992-1998).

Signorini, Sergio R.↗
DOE OSTI2021

Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community

Nutrient amendment diminished bacterial functional diversity, consolidating carbon flow through fewer bacterial taxa. Here, we show strong differences in the bacterial taxa responsible for respiration from four ecosystems, indicating the potential for taxon-specific control over soil carbon cycling. Trends in functional diversity, defined as the richness of bacteria contributing to carbon flux and their equitability of carbon use, paralleled trends in taxonomic diversity although functional diversity was lower overall. Among genera common to all ecosystems, Bradyrhizobium, the Acidobacteria genus RB41, and Streptomyces together composed 45–57% of carbon flow through bacterial productivity and respiration. Bacteria that utilized the most carbon amendment (glucose) were also those that utilized the most native soil carbon, suggesting that the behavior of key soil taxa may influence carbon balance. Mapping carbon flow through different microbial taxa as demonstrated here is crucial in developing taxon-sensitive soil carbon models that may reduce the uncertainty in climate change projections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Harmonizing direct and indirect anthropogenic land carbon fluxes indicates a substantial missing sink in the global carbon budget since the early 20th century

Inconsistencies in the calculation of the two anthropogenic land flux terms of the global carbon cycle are investigated. The two terms—the direct anthropogenic flux (caused by direct human disturbance in anthromes, currently a carbon source to the atmosphere) and the indirect anthropogenic flux (caused indirectly by human activities that lead to global change and affecting all biomes, currently an atmospheric carbon sink)—are typically calculated independently, resulting in inconsistent underlying assumptions. We harmonize the estimation of the two anthropogenic land flux terms by incorporating previous estimates of these inconsistencies. We recalculate the global carbon budget (GCB) and apply change-point analysis to the cumulative budget imbalance. Cumulative over 1850–2018 (1959–2018), harmonization results in a 13% lesser (4% greater) land use source from anthromes and a 20% (23%) lesser land sink. This recalculation yields a greater non-closure of the GCB, indicating a missing carbon sink averaging 0.65 Pg C year -1 since the early 20th century. The imbalance likely results from a combination of method discontinuity and structural errors in the assessment of the direct anthropogenic land use flux, greater ocean carbon uptake, structural errors in land models, and in how these land terms are quantified for the budget. We caution against overconfidence in considering the GCB a solved problem and recommend further study of methodological discontinuities in budget terms. We strongly recommend studies that quantify the direct and indirect anthropogenic land fluxes simultaneously to ensure consistency, with a deeper understanding of human disturbance and legacy effects in anthromes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating the Benefits of Bayesian Hierarchical Methods for Analyzing Heterogeneous Environmental Datasets: A Case Study of Marine Organic Carbon Fluxes

Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets. Statistical complexities arise when analyzing compiled data due to unbalanced spatial sampling, variable environmental context, mixed measurement techniques, and other reasons. Hierarchical Bayesian modeling is increasingly used in environmental science to describe these complexities, however few studies explicitly compare the utility of hierarchical Bayesian models to simpler and more commonly applied methods. Here we demonstrate the utility of the hierarchical Bayesian approach with application to a large compiled environmental dataset consisting of 5,741 marine vertical organic carbon flux observations from 407 sampling locations spanning eight biomes across the global ocean. We fit a global scale Bayesian hierarchical model that describes the vertical profile of organic carbon flux with depth. Profile parameters within a particular biome are assumed to share a common deviation from the global mean profile. Individual station-level parameters are then modeled as deviations from the common biome-level profile. The hierarchical approach is shown to have several benefits over simpler and more common data aggregation methods. First, the hierarchical approach avoids statistical complexities introduced due to unbalanced sampling and allows for flexible incorporation of spatial heterogeneitites in model parameters. Second, the hierarchical approach uses the whole dataset simultaneously to fit the model parameters which shares information across datasets and reduces the uncertainty up to 95% in individual profiles. Third, the Bayesian approach incorporates prior scientific information about model parameters; for example, the non-negativity of chemical concentrations or mass-balance, which we apply here. We explicitly quantify each of these properties in turn. We emphasize the generality of the hierarchical Bayesian approach for diverse environmental applications and its increasing feasibility for large datasets due to recent developments in Markov Chain Monte Carlo algorithms and easy-to-use high-level software implementations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

GEOS-Carb III: Delivering Mature Carbon Flux and Concentration Datasets in Support of NASA's Carbon Monitoring System

The GEOS-Carb project seeks to make high quality, data-driven products that support NASA's Carbon Monitoring System Program. These include bottom-up land and ocean flux estimates along with fossil fuel emissions, all informed by satellite data and the MERRA-2 reanalysis. These flux data support atmospheric carbon products that include both simulations and data assimilation products. We show examples of this work and discuss applications to future mission planning.

Ott, L.↗
DOE OSTI2024

Hydrological control of chemical weathering and rock-carbon fluxes: East River, Colorado

This data package is used in the manuscript entitled “Hydrological control of chemical weathering and rock-carbon fluxes”. The field study was conducted in a lower montane hillslope of the East River watershed, underlain by Mancos Shale, within the lower 140 m section of a transect that extends nearly 1 km to its local peak. The data in this package, in CSV file format, were collected from Fall 2016 to fall 2021, including depth-resolved dynamic water table depths, subsurface water fluxes, solid phase (soil to bedrock) chemical compositions, geochemical properties of porewater and pore-gas, including radio carbon concentrations. The detailed methods of field studies, laboratory chemical analyses, and calculations are described in the Methods section and in the dataset file: Wan_et_al_Methods.pdf. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhanced Carbon Flux Response to Atmospheric Aridity and Water Storage Deficit During the 2015–2016 El Niño Compromised Carbon Balance Recovery in Tropical South America

During the 2015–2016 El Niño, the Amazon basin released almost one gigaton of carbon (GtC) into the atmosphere due to extreme temperatures and drought. The link between the drought impact and recovery of the total carbon pools and its biogeochemical drivers is still unknown. With satellite-constrained net carbon exchange and its component fluxes including gross primary production and fire emissions, we show that the total carbon loss caused by the 2015–2016 El Niño had not recovered by the end of 2018. Forest ecosystems over the Northeastern (NE) Amazon suffered a cumulative total carbon loss of ~0.6 GtC through December 2018, driven primarily by a suppression of photosynthesis whereas southeastern savannah carbon loss was driven in part by fire. We attribute the slow recovery to the unexpected large carbon loss caused by the severe atmospheric aridity coupled with a water storage deficit during drought. We show the attenuation of carbon uptake is three times higher than expected from the pre-drought sensitivity to atmospheric aridity and ground water supply. Our study fills an important knowledge gap in our understanding of the unexpectedly enhanced response of carbon fluxes to atmospheric aridity and water storage deficit and its impact on regional post-drought recovery as a function of the vegetation types and climate perturbations. Our results suggest that the disproportionate impact of water supply and demand could compromise resiliency of the Amazonian carbon balance to future increases in extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Miscanthus carbon flux data for SABR and UIEF

This eddy covariance (EC) gap-filled and partitioned flux dataset for Miscanthus from the SABR and UIEF farms was collected to evaluate carbon fluxes during the establishment phase of Miscanthus. The goal of this research is to quantify net carbon exchange using the EC technique at two sites and assess how carbon uptake varies with different planting densities. The dataset is contains two .csv files.

Aslan Sungur, Guler (Rojda) [Department of Agronom↗
NASA NTRS2019

Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental U.S.

Accurate representation of vegetation states is required for the modeling of terrestrial water-energy-carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing-based Leaf Area Index (LAI) retrievals over the Continental U.S. in the Noah-MP land surface model, during a time period of 2000 to 2017. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms such as soil moisture, evapotranspiration, snow depth, terrestrial water storage and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the U.S., where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

anthropogenic vegetation↗
NASA NTRS2019

Potential Improvements in Global Carbon Flux Estimates from a Network of Laser Heterodyne Radiometer Measurements of Column Carbon Dioxide

We present observing system simulation experiments (OSSEs) to evaluate the impact of a proposed network of ground-based miniaturized laser heterodyne radiometer (mini-LHR) instruments that measure atmospheric column-averaged carbon dioxide (XCO2) with a 1 ppm precision. A particular strength of this passive measurement approach is its insensitivity to clouds and aerosols due to its direct sun pointing and narrow field of view (0.2°). Developed at the NASA Goddard Space Flight Center (GSFC), these portable, low-cost mini-LHR instruments were designed to operate in tandem with the sun photometers used by the AErosol RObotic NETwork (AERONET). This partnership allows us to leverage the existing framework of AERONET's global ground network of more than 500 sites as well as providing simultaneous measurements of aerosols that are known to be a major source of error in retrievals of XCO2 from passive nadir-viewing satellite observations. We show, using the global 3-D GEOS-Chem chemistry transport model, that a deployment of 50 mini-LHRs at strategic (but not optimized) AERONET sites significantly improves our knowledge of global and regional land-based CO2 fluxes. This improvement varies seasonally and ranges 58%–81% over southern lands, 47%–76% over tropical lands, 71%–92% over northern lands, and 64%–91% globally. We also show significant added value from combining mini-LHR instruments with the existing ground-based NOAA flask network. Collectively, these data result in improved a posteriori CO2 flux estimates on spatial scales of ∼10 km2, especially over North America and Europe, where the ground-based networks are densest. Our studies suggest that the mini-LHR network could also play a substantive role in reducing carbon flux uncertainty in Arctic and tropical systems by filling in geographical gaps in measurements left by ground-based networks and space-based observations. A realized network would also provide necessary data for the quinquennial global stock takes that form part of the Paris Agreement.

Palmer, Paul I.↗
DOE OSTI2021

Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗