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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment. Dataset.

To simulate the effects of mycorrhizal fungi on soil organic matter cycling, we incorporated mycorrhizal processes into the Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment (CORPSE) model to develop a new soil model Myco-CORPSE. The new model was calibrated and evaluated against soil measurements taken at temperate forests in New Hampshire (NH) and Georgia (GA). A series of scenario analysis were also conducted to explore the conditions under which ectomycorrhizal (ECM) N acquisition processes can induce different soil C accumulation in ECM systems compared to arbuscular (AM) systems.In this data package, we included:-The Python codes of the standard Myco-CORPSE model we developed: "Standard Myco_CORPSE python codes.zip". The main program is the "gradient_sim.py" which calculates the bulk soil microbes and CN content along a user defined gradient of clay, soil temperature, soil moisture and mycorrhizal dominance, and relies on two subprograms "CORPSE_deriv.py" and "CORPSE_integrate.py". "CORPSE_deriv.py" calculated the changes in all simulated soil stock within every time step and "CORPSE_integrate.py" integrate the changes in all simulated soil stock within simulated time period. The program "Plot.py" is used to plot the major outputs produced by the main program "gradient_sim.py".-The modified Python codes of Myco-CORPSE models with site-level environmental inputs (in NH and GA) used to conduct simulations in NH and GA sites: "NH_GA model simulations.zip". -The Python codes used to evaluate the Myco-CORPSE simulation outputs in NH and GA sites against site-level measurements: "Plot NH_GA simulation against measurements.zip". It includes both the evaluation Python code, the model outputs on NH and GA sites, and the measured soil properties in both sites.-The modified Python codes of Myco-CORPSE models "Scenario analysis_model simulations.zip" that is used to conduct scenario analysis of how different litter properties, mycorrhizal fungal traits, climate, and seasonal variation in temperature and vegetation phenology impact the mycorrhizal effects on soil CN properties. The sub file folder "Scenario analysis_litter traits" contains the codes for scenario analysis of different litter properties; The sub file folder "Scenario analysis_ECM types" contains the codes for scenario analysis of different ECM fungal traits; The sub file folder "Scenario analysis_climate&seasonality" contains the codes for scenario analysis of different climate and seasonalities;-"Scenario analysis_model results and plotting codes.zip" contains all the output files from the the scenario analysis of Myco-CORPSE model as described above and the plotting codes used to the generate the heatmaps and scatterplots shown in the manuscript "Ectomycorrhizal effects on decomposition are highly dependent on fungal traits, climate, and litter properties: A model-based assessment"The majority of the model outputs did not have specific geographic information or temporal coverage because the analysis we conducted are mainly hypothetical model simulations. We only provided geographic description, coordinates and temporal coverage for those soil measurements which we used for model evaluations (included in the "Plot NH_GA simulation against measurements.zip").

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon dioxide (CO2) flux, amplicon sequencing and liquid chromatography–mass spectrometry (LC-MS) data for pyrogenic organic matter (PYOM) amended soil incubations in lab, Madison, WI, 2021-22

This dataset comprises soil-pyrogenic organic matter (PyOM) incubation experiments conducted in a laboratory setting. The focus was on measuring carbon dioxide (CO2) flux emitted in the headspace of incubation jars over a one-month period. The aim was to estimate carbon mineralization from two different carbon fractions in PyOM, as well as carbon mineralization from soil organic carbon (SOC). Bacterial and fungal community profiles were tracked at various time points during the incubation to observe changes in the overall community and specific responders to PyOM. LC-MS analysis of soil samples was also performed to monitor changes in the soil's chemical composition.The provided dataset includes processed CO2 flux measurements obtained from the Picarro-multiplexer measurement setup for each sample incubation jar. This data is available in the "CO2Flux.csv" file. Additionally, the dataset includes CO2 flux data partitioned to estimate mean carbon mineralization from the two PyOM carbon fractions, bulk PyOM, and SOC. The cumulative carbon mineralization data is provided in "cml_respired.csv," while the rate of carbon mineralization data is in "rate_respired.csv." The file "cml_respired_lastcyc.csv" contains cumulative carbon mineralization data for the PyOM carbon fractions, bulk PyOM, and SOC throughout the entire incubation period.For the LC-MS analysis, the raw data can be found in the "20220721_NZ_PeakHeights_FinalAnalysis.csv" file, while sample metadata is provided in "20220721_NZpilot_Metadata.csv". Processed LC-MS data is available in the file "20220721_NZ_FINAL_msdat_sub3.csv". Lastly, the dataset includes the relative abundance data for bacterial genera that exhibited a significant positive response to the addition of PyOM produced at 350 degrees Celsius, which can be found in "resp_genera_350.csv".

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie.

Uploaded raw datasets from electrical resistivity tomography (ERT), ground penetrating radar (GPR), electromagnetic induction (EMI) and insitu sensors were acquired across land-lake interfaces along Lake Erie. These datasets were useful to delineate the soil architecture and subsurface stratigraphic heterogeneities. We combined multiple geophysical methods with lithostratigraphic logs from borehole cores and data from soil and borehole sensors to investigate spatial heterogeneities across land-lake interfaces. The apparent electrical conductivity (Eca) maps matched closely with the soil maps from public database and also detected additional soil units that were missing in the traditional soil maps. This implies that electromagnetic induction (EMI) could be relied upon for non invasive characterization of soils in sampling restricted sites where only non-invasive measurements are feasible. Results from electrical resistivity tomography (ERT) and Ground penetrating radar (GPR) are consistent with the surficial geology of the study area, and revealed that the vertical variabilities at the sites are mainly driven by stratigraphy.The ERT data (.stg) were acquired from three different sites portage river (PTR), crane creek (CRC) and old woman creek (OWC). The ".stg" files can be opened with ERT inversion software such as EarthImager, ResIpy or Res2DInv. The GPR data (.GPZ) were also acquired from the three sites, and the ".GPZ" data files can be opened with EKKO_project 5 software from Sensors & Software Inc., or with any other standard GPR software. The EM data (.csv) were acquired from the three sites while the soil sensor data (.csv) here were acquired only from crane creek site. The ".csv) files can be opened with MS Excel or any other spread sheet software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Building heights and urban canopy parameters for urban modeling

GLObal Building heights for Urban Studies (UT-GLOBUS) is a random forest model based framework that provides a level-of-detail-1 (LoD-1) building height dataset. The primary objective of UT-GLOBUS is not to precisely predict the height and footprint of individual buildings, but rather to offer a functional framework for generating building level information using open-source datasets for modeling applications. Specifically, UT-GLOBUS is tailored to meet the requirements of deriving urban canopy parameters (UCPs) for the multi-layer model within the Weather Research and Forecasting (WRF) model and building heights for the SOLWEIG and SUEWS model. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). The vector files employ the Universal Transverse Mercator (UTM) projection. The vector files are compatible with GIS platforms like QGIS and ArcGIS, and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the multi-layer urban model in the urban WRF in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These binary files can be directly incorporated into the WRF pre-processing system (WPS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Duke Forest FACE (FACTS-I): Plant and Soil Response Data

This dataset describing the responses of plant and soil pools and fluxes to elevated atmospheric CO2 concentration and increased nitrogen supply was collected from Duke Forest Free Air CO2 Enrichment (FACE) – Forest-Atmosphere Carbon Transfer and Storage (FACTS-I) experiment from 1996 to 2012. The dataset includes data files for allometry (diameter at breast height, tree height, and height to live crown base), leaf area index, biomass (stem, branch, foliage, and root biomass, tree density, and basal area), net primary productivity (stem, branch, foliage, reproductive, and coarse root NPP), sap flux density, soil CO2 efflux, and stem temperature. Data files were formatted as .csv (Microsoft Excel or other spreadsheet programs can be used to read the format) and file descriptions, including variable name, unit, and data range, can be found in ‘FileDescription_[data_name].txt’ files. The Duke FACE experiment was in a loblolly pine (Pinus taeda L.) plantation established in 1983. Naturally regenerated broadleaved species including sweetgum (Liquidambar styraciflua L.) and tulip poplar (Liriodendron tulipifera L.), mostly in the overstory, and winged elm (Ulmus alata Michx.) and red maple (Acer rubrum L.) were common in the understory. The FACE experiment commenced with two plots (plots 7-8) in 1994 (Oren et al. 2001), with six additional plots (plots 1-6) coming online on 27 August 1996. CO2 enrichment was terminated on 31 October 2010 and post-enrichment data collection continued through 2012. Complete fertilization was applied annually to half of plots 7-8 from 1998 to 2004. The nutrient addition experiment expanded to half of plots 1-6 with a common protocol of N-fertilization in 2005 and continued until 2012. The levels of treatment in this dataset were expressed as ambient CO2 (AMB) or elevated CO2 (ELE) for CO2 treatment and control soil (CONT) or fertilized soil (FERT) for N treatment, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Snow Camera Photos at the Teller 27 Field Site from 2022-2023, Seward Peninsula, Alaska

Snow distribution in the Arctic is highly variable and driven by high winds and microtopography, which result in deep snow drifts and shallow scoured areas. To better understand snow drifting and scouring, 4 Reconyx HyperFire 2 game cameras were installed at the Teller 27 Watershed Field Site on the Seward Peninsula, Alaska from September 2022 to September 2023. Game cameras were installed at 4 locations across the watershed to capture pictures of snow in drift and scour areas. Three-meter tall, red PVC poles were used as snow-stakes to measure snow depth from these pictures. One photo was taken at each camera once per hour during daylight. Distances between cameras and snow stakes were measured so that snow depth can later be calculated from the pictures. This dataset contains four folders of *.jpg files from each camera and one *.kml file of camera locations.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Model output from simulations of manganese-carbon interactions in temperate forest soil profiles

This archive contains model output, code, and scripts for simulations of coupled manganese-carbon cycling in temperate forest soil profiles. These model results were generated as part of a study investigating how manganese availability influences soil organic carbon stocks and demonstrating a new model framework for coupling carbon and manganese cycling. The simulations were in support of a manuscript: "Modeling interactive effects of manganese bioavailability, nitrogen deposition, and warming on soil carbon storage." The study addresses the research questions: How does Mn bioavailability, as driven by subsurface mineral properties, pH, and redox status, affect temperate forest soil organic carbon and litter carbon stocks?How is the relationship between Mn bioavailability and carbon cycling affected by changes in temperature and nitrogen deposition?"Model simulations were conducted in a reactive transport modeling framework using PFLOTRAN coupled to python. Multiple model simulations testing different Mn-bearing mineral solubilities, hydrological patterns, nitrogen deposition rates, and temperatures are included. Soil properties, including total and exchangeable Mn concentrations, are based on values reported for the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), a temperate forested watershed in central Pennsylvania, U.S.A where Mn cycling through vegetation has been documented.File formats include netCDF (.nc), python script (.py), shell script (.sh), plain text PFLOTRAN input file (.in), and plain text PFLOTRAN database file (.dat), and gzipped tar archive (tar.gz).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra: Supporting Data

High-resolution classification maps derived from occupied aerial systems (UASs). The UAS data were collected in August 2021 using a Skydio 2+ drone equipped with a 4K resolution red-green-blue (RGB) camera (2024 Skydio Inc) and a 3DR SOLO Quadcopter carried a Parrot Sequoia+ Multispectral Sensor (2023 Parrot Drone SAS). This package includes vegetation classification maps at four locations around Next Generation Ecosystem Experiment Arctic (NGEE Arctic) Council watershed study site on the Seward Peninsula, Alaska. The classification maps were generated using a combination of RGB and canopy height information. The map data and metadata are provided as ENVI image (.dat) and text (.txt, *hdr) formats. Additional map quicklooks are provided as GIS *.kml files. These datasets are provided in support of Yang et al., (In review), “Topography and Functional Traits Control the Distribution of Key Shrub Plant Functional Types in Low-Arctic Tundra”.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Comparing the Recovery of Arbuscular and Ectomycorrhizal Stands from Long-Term Nitrogen Fertilization at the Fernow Experimental Forest, WV (2021 and 2022)

This data was generated to answer the research question: After the end of a 30-year nitrogen (N) fertilization experiment in an Eastern temperate forest, to what extent do plant microbial interactions and carbon cycling in arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) dominated stands follow different recovery trajectories?The soil and the roots used in this dataset were sampled from two watersheds (N-fertilized watershed 3 and Reference watershed 7) with 6 AM and 6 ECM dominated stands each, over the course of three months (June, July, August) in 2021 and 2022. The "All_Roots.csv" data includes fine root biomass (in g) scaled to sampled soil (m2) (Scaled_Rt_Bm_gm2), and AM and ECM root colonization (%) in two soil fractions: the organic horizon (O) and the mineral horizon (B). The "All_Enzymes.csv" data includes extracellular soil enzyme activity (N-acetyl-glucosaminidase (NAG), acid phosphatase (AP), β-glucosidase (BG), phenol oxidase and peroxidase), and the ratios of BG to AP (BG:AP) and BG to NAG (BG:NAG) in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).The "All_Nmin" data includes inorganic N (NO3- and NH4+) pools (unit: μg of N per g of dry soil) measured pre- (I_Nitrate_ugNgsoil and I_Ammonia_ugNgsoil) and post-incubation (F_Nitrate_ugNgsoil and F_Ammonia_ugNgsoil), as well as calculated nitrification (Nitrification_day) and nitrogen mineralization (Mineralization_day) rates per day, in three soil fractions: the organic horizon (O), the bulk soil (B), and the rhizosphere (R).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data

The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland

Data from the characterisation of the Point Reyes Field Site, published in AGU Earth's Future under the title: The importance of accounting for landscape position when investigating grasslands: A multidisciplinary characterisation of a Californian coastal grassland. This paper explored the effect of landscape position on the response of a Californian grassland to seasonal changes. All files are csv files. The EMI data contains 8 csv files with a metadata csv explaining the columns. The dataset also includes soil variables including total concentrations calculated from fused samples, then dissolved and measured on ICP-AES for whole-rock elements and ICP-MS for trace elements. Mineral composition was attained using X-ray diffraction at BL 11-3 at SSRL . Data was then run through the High Score database to characterise different mineral phases. total It also includes a table with bulk soil characteristics such as soil pH, cation exchange capacity, and soil textural data. Data from Teros 12 Meter soil moisture, electrical conductivity and temperature sensors are presented in SMS Csv file. While the WL bottom and top files contain data from Piezometers measuring the ground water table. We have included a csv file that contains soil CO2 efflux data from Feb 2021-Oct 2021 in the Point Reyes Grassland Experiment We have included the spatially orientated (easting northing) remotely sensed datasets that were used in the K-means clustering analysis conducted on our site with electrical conductivity, normalised difference vegetation index, elevation, slope, solar radiation, topographic position and wetness index, and a clustering score. Finally there is a list of all the identified grassland species at the site.For more information on flux data, please contact the corresponding author.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Soil carbon, nitrogen, and ice content data (2014-2018) at Happy Valley and Sagwon Hills in Northern Alaska, United States

This dataset contains three data files from soil data collected by sampling soil cores and pits across a toposequence on two hillslopes in permafrost affected soils in Northern Alaska, USA. Soil cores and profiles were excavated to 3m or until auger refusal. Samples were collected by genetic soil horizons for laboratory analysis and organized into complete soil profiles ordered depth. Soil carbon, nitrogen, and ice stocks (stocks_by_core.csv) were calculated from the core dataset . Data for mean values by depth increments (20,50,100,150,200 cm) of total soc, total nitrogen, bulk density, and ice content were determined and made available in this dataset (soc-tn-bd-ice-slab_tables.csv). Stocks by site are included in a separate file (stocks_by_site.csv). Each of these three files also has an associated data dictionary file (stocks_by_core_dd.csv, soc-tn-bd-ice-slab_tables_dd.csv, stocks_by_site_dd.csv). Finally, an FLMD file is also included (FLMD_Happy_Valley_Sagwon.csv).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Root biomass and traits across four lowland Panamanian forests from 0 - 1.2 m soil depths

Fine roots are key to ecosystem-scale nutrient, carbon (C), and water cycling, yet our understanding of fine root traits variation within and among tropical forests, one of Earth’s most C-rich ecosystems, is limited. We characterized root biomass, morphology, nutrient content, and arbuscular mycorrhizal fungal (AMF) colonization in 10 cm increments to 1.2 m depth across four distinct lowland Panamanian forests. The datasets provided include a .xlsx file for fine root characteristics across 10 cm increment depths to 1.2 m collected from late 2017 to 2018 across four different forests. Root characteristics include live fine root biomass, dead fine root biomass, coarse root biomass, specific root length, root diameter, root tissue density, specific root area, arbuscular mycorrhizal fungi colonization, root chemistry (e.g., organic chemistry), root %N, root %C, root C/N ratio, and root radiocarbon content. This .xlsx file contain four tabs with 1) Dataset; 2) Metadata with information about each column in the dataset; 3) The sampling methods summarized; 4) Sites information. We also provided csv files for each of these tabs. Additionally, a .kml file is provided with coordinates for all 32 plots included in the study across four forests (n = 8 plots per site/forest). This dataset serves as baseline data before a throughfall exclusion experiment, Panama Rainforest Changes with Experimental Drying (PARCHED), was implemented. No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Parcel scale land cover and land use for Arizona’s urbanized areas, Flagstaff, Phoenix-Mesa, Tucson, Dec 2023

The health impacts of heat exposure are unequally experienced, with certain subpopulations particularly vulnerable to the effects of rising global temperatures. Manufactured Housing (MH) – transportable residential units constructed off-site before being moved to a parcel, organized either in Parks or Non-Parks tenure types – presents a diverse housing type that is hypothesized to be more vulnerable to extreme heat. This dataset contains land cover and housing type for 1.7 million parcels in three of Arizona’s urbanized areas. The data supports research confirming the hypothesis that residents of MH are more likely to experience heat vulnerability than those in single-family housing, multifamily housing, and condos/townhouses due to an average lack of proximate vegetation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Global Carbon Budget 1800-2018 Loss of Additional Sink Capacity and Present Transient Difference

A dataset of two global carbon fluxes: the loss of additional sink capacity (LASC) and present versus transient difference (PTD). The loss of additional sink capacity is the loss of indirect anthropogenic sink capacity in land ecosystems caused by the loss of ecosystems that can increase carbon in response to changing environments, primarily forests. The present transient difference refers to the direct anthropogenic flux in land ecosystems (from land cover and land use change) and the difference in that flux when calculated assuming "present day" (actually more like 1980) ecosystem carbon stocks or when calculated assuming transient carbon stocks (i.e. caused by environmental change). In this dataset, the LASC and PTD fluxes were calculated using the TRENDY ensemble of Dynamic Global Vegetation Models (DGVMs; Sitch et al., 2024) following the methods described therein and in Obermeier et al. (2021). For further details see Obermeier et al. (2021), Friedlingstein et al. (2019), Sitch et al. (2024), and Walker et al. (2025). The dataset includes ensemble means and standard deviations for annual fluxes, fluxes cumulated annually over the whole time period, and cumulated since 1959. These data were originally collected to compare different approaches (DGVMs versus bookkeeping models) for estimating land use and land cover change (LULCC) emissions (Obermeier et al. 2021). More recently, and similarly, these data were used to harmonize the estimates of the direct and indirect anthropogenic fluxes in the global carbon cycle and to recalculate the global carbon budget (Walker et al., 2025).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Rhizosphere Soil Biogeochemical Data and Photosynthetic Data of Vicia Faba in a Rhizobox

Here we share the data in column format via csv files for pH, redox, and dissolved oxygen collected at hourly resolution from microelectrodes. Dissolved organic carbon concentrations collected from TOC are also provided in a similar format but are composited samples from hourly microdialysis collection. This provided resolution of diel rhizosphere dynamics belowground. Plant physiological data was also collected at every 5 min for 24 hr cycles in order to capture diel dynamics aboveground. This data was used to parameterize the reaction transport model eSTOMP-ROOTS, which examines the rhizosphere biogeochemistry of a growing Vicia faba plant. The aim was to investigate plant activity and belowground biogeochemical processes, particularly their impact on mineral-organic associations in the rhizosphere. We combined in-situ rhizosphere microsensor and plant physiological measurements with a 3-D plant-soil reactive transport model to explore the behavior of dissolved organic carbon (DOC) in the rhizosphere. Over several days, microdialysis probes placed at the root-soil interface in live soil showed distinct daily patterns of DOC concentration in the pore water. Spikes in DOC concentrations during the day aligned with peaks in leaf-level photosynthesis, accompanied by decreasing redox potential and dissolved oxygen levels, and increasing pH in the rhizosphere. This new mechanistic modeling framework, which integrates aboveground plant physiological data with non-destructive, high-resolution monitoring of rhizosphere processes, offers significant potential for studying the factors that control carbon storage in soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data for “Tree root nutrient uptake kinetics vary with nutrient availability, environmental conditions, and root traits: A global analysis”

This data package contains data and code used in the paper “Tree root nutrient uptake kinetics vary with nutrient availability, environmental conditions, and root traits: A global analysis”. The central product is a global dataset of root inorganic nutrient uptake rates and kinetics parameters covering temperate, boreal, and sub/tropical tree species, representing a collection of nutrient uptake data from published studies. This dataset enables tree investigation of root nutrient uptake rates across species, space, and experimental conditions. The data can also be combined with supplementary data on root and soil traits or with external datasets (e.g. R scripts contained within use data from FRED 3.0; (Iversen et al., 2021)). Contained within is the main nutrient data “uptake_data.csv” as well as 4 additional .csv files that link uptake data to supplementary measurements, source references, taxonomic information, and additional nutrient uptake measurements across nutrient gradients, and 1 .csv file that records meta-analysis results for plotting with the R scripts. There are seven R scripts that support data analysis and creation of the figures in the related publication.

54 ENVIRONMENTAL SCIENCES↗