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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamically Downscaled (WRF) 1km, Hourly Meteorological Conditions 1987-2020. East/Taylor Watersheds

This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time.The files contain the following data variables:EAST_MASK: binary mask of the watershed regionTAYLOR_MASK: binary mask of the watershed regionGLW downwelling longwave radiation (w/m2)HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere]LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET]PSFC Surface Barometric Pressure (hPa)Q2 Two-meter specific humidity (kg/kg)SWDOWN Downwelling shortwave solar radiation (w/m2)SWNORM Terrain-normal downwelling shortwave radiation (w/m2)T2 Two-meter air temperature (deg K)U10 10-m U-component of wind velocity (m/s)V10 10m V-component of wind velocity (m/s)XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

MultiSector Dynamics: 2023 Inaugural Workshop Report

Preface The MultiSector Dynamics (MSD) Community of Practice (CoP) hosted an inaugural workshop on October 3-5, 2023 at the University of California, Davis, to bring together members of the MSD community of practice to advance understanding of the co-evolution of human and natural systems, and to build the next generation of tools that bridge sectors, scales, and systems to realize a more resilient and equitable future. The theme of the workshop was "Advancing Complex Adaptive Human-Earth Systems Science in a World of Interconnected Risks". This document outlines the motivation for the workshop, its goals and objectives, the application process, the agenda, overviews of the training sessions offered to the workshop participants and a summary of each breakout session. The MSD workshop report further discusses the feedback from workshop participants and presents some reflections and next steps. The MSD Workshop organizers thank the DOE Office of Science, Earth and Environmental System Modeling, MultiSector Dynamics program area for financial support of its activities through the Integrated Multisector Multiscale Modeling (IM3) project. For more information related to the broader DOE MultiSector Dynamics Program please see https://climatemodeling.science.energy.gov/program-area/multisector-dynamics. D.L.M. and C.M.B. acknowledge support from the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the US Department of Energy (DOE). Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither theUnited States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, complete- ness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial products, process, or service by trade name,trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Pacific Northwest National Laboratory operated by Battelle for the United States Department of Energy Available from:Office of Scientific and Technical Information http://www.OSTI.gov multisectordynamics.org This work is made available under the terms of the Creative Commons Attribution- NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4 Suggested citation: Monier, E., Reed, P.M., Vernon, C.R., Hadjimichael, A., Brelsford, C.M., Burleyson, C.B., Dyreson, A.R., Fletcher, S.M., Giang, A., Gupta, R.S., Jackson, N.D., Jones, A.D., Lamontagne, J.R., McCollum, D.L., Morris, J.F., Moss, R.H., Peng, W., Saari, R.K., Srikrishnan, V., Szinai, J.K., Yoon, J. (2024) MultiSector Dynamics: 2023 Inaugural Workshop Report. MSD-LIVE Data Repository. doi:10.57931/2371710.

Monier, Erwan↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2022

Role of copper in nitrous oxide accumulation in wetland soils from Illinois and South Carolina and stream sediments from Tennessee

Denitrification is microbially-mediated through enzymes containing metal cofactors. Laboratory studies of pure cultures have highlighted that the availability of copper (Cu), required for the multicopper enzyme nitrous oxide reductase, can limit nitrous oxide (N2O) reduction. However, in natural aquatic systems, such as wetlands and hyporheic zones in stream beds, the role of Cu in controlling denitrification remains incompletely understood. In this study, we collected soils and sediments from three natural environments -- riparian wetlands, marsh wetlands, and a stream -- to investigate their nitrogen species transformation activity at background Cu levels and different supplemented Cu loadings. All of the systems contained solid-phase associated Cu below or around geological levels (40–280 nmol g-1) and exhibited low dissolved Cu (3–50 nM), which made them appropriate sites for evaluating the effect of limited Cu availability on denitrification.The dataset contains the variation in the nutrient (nitrate (NO3-), nitrite (NO2-), and ammonium (NH4+)) and N2O concentrations during the incubation experiments to evaluate the effect of Cu. We have also reported the variation in metal concentrations (Cu, iron(Fe) and manganese (Mn)) and dissolved organic carbon (DOC) concentrations during the incubations. The parameters obtained using a kinetic model to quantitatively report the effect of Cu on nitrogen species conversion in natural aquatic systems are also added in the dataset. The dataset also contains the labile concentration of Cu estimated using a speciation model in MINTEQ. All data are provided in text-based CSV format with header sections indicating the data contained in each file and the corresponding units.Our study suggests that high concentrations of N2O accumulated in all microcosms lacking Cu amendment except for one stream sediment sample. With Cu added to provide dissolved concentrations at trace levels (10–300 nM), the reduction rate of N2O to N2 in the wetland soils and stream sediments was enhanced. A kinetic model could account for the trends in nitrogen species by combining the reactions for microbial reduction of NO3- to NO2-/N2O/N2 and abiotic reduction of NO2 to nitrogen (N2_. The model revealed that the rate of N2O to N2 conversion increased significantly in the presence of Cu. For riparian wetland soils and stream sediments, the kinetic model also suggested that overall denitrification is driven by abiotic reduction of NO2- in the presence of inorganic electron donors. This study demonstrated that natural aquatic systems containing Cu at concentrations less than or equal to crustal abundances may display incomplete reduction of N2O to N2 that would cause N2O accumulation and release to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data for Regier et al. (2025), "Short-term experimental flooding impacts soil biogeochemistry but not aboveground vegetation in a coastal forest"

Rising sea levels and intensifying storms increase flooding pressure on coastal forests, but the mechanisms that drive coastal forest mortality remain unclear. This study used an ecosystem-scale manipulation (TEMPEST, Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments) to simulate hurricane-level flooding of a coastal forest and explore the individual and interactive impacts of inundation and salinity. This dataset comprises the results reported by Regier et al. (2025) in their paper "Short-term experimental flooding impacts soil biogeochemistry but not aboveground vegetation in a coastal forest." It consists of measurements of:- Belowground conductance- Soil dissolved oxygen and redox- Soil and tree greenhouse gas fluxes- Leaf photosynthesis, stomatal conductance, and intercellular carbon dioxide- Sap flux- Soil volumetric water content and electrical conductivityAll files are plain-text CSV (comma separated value) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Patch-level CO2 and CH4 fluxes and porewater concentrations in experimental wetlands, 2 PPT saltwater intrusion simulations, Aug-Oct 2022: Louisiana

This dataset contains carbon dioxide (CO2) and methane (CH4) flux measurements from patches of wetland vegetation dominated by Typha domingensis and Panicum hemitomon, which were conducted to assess flux responses to acute saltwater intrusion. The measurements occurred before, during, and after simulated acute saltwater intrusion events of low concentrations of ~ 2 PPT. The measurements comprise gas fluxes from the wetland surface (i.e., soil-water column and vegetation) and fluxes from the soil-water column exclusively. These two sets of fluxes are separated into two files and are complemented with four more files containing porewater concentrations of CO2 and CH4 collected at 0-5 cm, 10-15 cm, and 20-25 cm depth increments, spectral indices measurements, biomass, and sediment elevation table measurements. The files can be opened with regular text editors or spreadsheet programs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This 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 OSTI2025

Manuscript model outputs, model source code, and figure scripts: The role of geomorphology in mediating biomass allocation impacts on salt-marsh resilience and carbon accumulation

This data package provides model source code (C++), model outputs, and figure generation scripts (R) needed to reproduce the following manuscript: Bruns, Nicholas E., Genevieve L. Noyce, and Matthew L. Kirwan. "The Role of Geomorphology in Mediating Biomass Allocation Impacts on Salt-Marsh Resilience and Carbon Accumulation." Estuarine, Coastal and Shelf Science 327 (December 2025): 109549. https://doi.org/10.1016/j.ecss.2025.109549. This manuscript investigates how geomorphology mediates the impact of biomass allocation shifts on salt marsh persistence and carbon (C) sequestration under sea level rise. We use a 1-D soil-column model (Kirwan and Mudd 2012) to perform experiments across a range of static root:shoot ratios (RSR = 1-4) spanning observed values. The model explicitly simulates interactions between tidal inundation, productivity, inorganic sediment deposition, and organic matter accumulation. Experiments determine whether geomorphic feedbacks amplify, dampen, or leave unchanged the ecosystem response to biomass allocation shifts. A first experiment uses constant sea level rise (2.5 mm/yr) to examine equilibrium dynamics and their influence on carbon accumulation rates across different suspended sediment concentrations (SSC). A second set of experiments calculates threshold sea level rise rates for marsh drowning across RSR and SSC combinations. Final experiments apply accelerating sea level rise scenarios (2000-2200) derived from NOAA projections (Sweet et al. 2022) to generate an envelope of expected responses, quantifying the importance of biomass allocation shifts on marsh carbon accumulation and persistence. All experiments are repeated across SSC ranging from 5-50 mg/L to investigate how these interactions vary in micro-tidal marshes with different sediment supplies. Package contents: * README.txt with detailed description of package contents and instructions for reproducing manuscript figures and model outputs * R scripts for generating all manuscript figures * C++ baseline model code from Kirwan and Mudd (2012) * C++ source code for 4 experimental model variants used in the manuscript, extending above baseline code * Model input files (.csv, .txt) including sea level rise scenarios * Model output files (.txt) used in manuscript analyses Temporal coverage: Model simulations span years 1900-2200, with accelerating sea level rise scenarios for 2000-2200. Key variables: Root:shoot ratio, suspended sediment concentration, carbon accumulation rate, vertical accretion rate, marsh elevation, inundation depth, threshold sea level rise rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impact of fire on chemical properties and respiration of boreal forest soils

We collected both organic-rich and sandy soil cores from 12 sites (site_location_and_texture.csv) within Wood Buffalo National Park, Alberta, Canada. Laboratory burns were conducted by exposing intact soil cores to 60 kW m^-2 in a mass loss calorimeter to simulate boreal forest crown fires in order to measure the effects of burning (temp_data.csv) on soil properties including pH, total C, and total nitrogen (N) (metadata.csv). We used 70-day soil incubations and two-pool exponential decay models to characterize the impacts of burning and burn-induced changes in soil properties on soil respiration (soil_moisture_at_end_of_incubation.csv; soil_mass_at_end_of_incubation.csv; respiration_data.csv). Laboratory burns successfully captured a range of soil temperatures that were realistic for natural wildfire events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Tree Inventory

This is the tree inventory (diameter, species, and live/dead status) data from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field Measurements and Experiments; see https://compass.pnnl.gov/FME/COMPASSFME) project. It addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in eastern Maryland, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.This dataset includes:- An overall dataset README file.- The tree inventory data in both "wide" and "long" forms. These contain the same information but are structured differently, with the former more useful for human viewers and the latter more amenable for programmatic analyses.- A key to the species/genus codes used, which follow the U.S. Department of Agriculture's PLANTS schema (https://plants.usda.gov/).- A copy of the R code used to generate the wide- and long-form data files.All files are comma-separated value (CSV) and no special software is required to read them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Terrestrial laser scanning data (Levels 0 and 1) from Urban Biogeochemistry Pilot Project sites, Knoxville, Tennessee, Jul 2024 - Jul 2025

This data package contains data from terrestrial laser scanning (TLS) at five urban park sites in Knoxville, Tennessee, USA. All parks include open-grown and/or closed-canopy trees and mixed nearby land use. These study sites were established as part of the Urban Biogeochemistry Pilot Project, which has an overall goal of better understanding how hydrobiogeochemical cycling is altered within the human environment. These five sites represent a gradient of urbanization, and were instrumented to understand hydrological and biogeochemical cycling (e.g., soil moisture, soil physical properties and biogeochemistry, tree transpiration, species type). The TLS data archived here were collected to provide detailed, three-dimensional information about forest structure. Specifically, data were collected to allow tree- and stand-level characterization of woody structure and leaf area. TLS scans were placed to capture the area around trees with sap flow sensors, and as much of a 50 m radius area around the meteorological station as possible given site property limits. Derived products will allow upscaling of water content and transpiration data. This data package contains the following data: - High-level files document further details of the campaign and data package: 1_CampaignSummary.csv provides details about the campaign and study site, 2_ScanAreasDetail.csv provides details about each separate scan area (groups of scans post-processed into a single point cloud), 3_TerrestrialLidarSensor.csv provides further technical details about the Riegl VZ-400i TLS sensor, TLS_CSV_dd.csv is a CSV Data Dictionary providing information about the fields in CSV files following the ESS-DIVE CSV File Formatting Guidelines Reporting Format, TLS_flmd.csv is a File Level Metadata file providing information about each file in the data package following the ESS-DIVE File Level Metadata Reporting Format, and README.txt is a text file describing the overall project and file structure. - Level 0 data are the raw data (.PROJ folders) as recorded by the Riegl VZ-400i TLS instrument before scan co-registration and post-processing with the Riegl's proprietary RiSCAN PRO software, which requires a license. - Level 1 data contain post-processed, co-registered data from each scan area. The "PointClouds" folder for each scan area contains a .las file with 1 cm resolution point cloud data exported from RiSCAN PRO. These are the main files likely to be of interest to most users and can be further processed with any software capable of manipulating .las files (e.g. Python, R CloudCompare). The "Project Information" folder contains log files from post-processing in RiSCAN PRO that may be of interest to users who want to see detailed records of post-processing, including all PDF reports generated by RiSCAN PRO. The "ScanPositions" folder contains information about the final position of all TLS scans, after post-processing, in multiple formats. The file ScanPositions_*.csv provides final geo-referenced scan positions, and the file SOP_backup_*.csv can be used in RiSCAN PRO to restore the co-registered scan positions if users wish to re-process raw data (Level 0 .PROJ folders) with RiSCAN PRO software (e.g., subsample to a different resolution, exclude a certain scan position, or apply different filters on reflectance or deviation values) without redoing time-consuming co-registration steps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Carbon dioxide, water vapor and methane soil efflux (soil respiration) in a Pinus palustris root exclusion in Georgetown, SC

This dataset contains processed data from a combination of survey flux chambers and long-term automated flux chambers. Soil flux measurements were conducted from June 2023 through December 2025 in a mature longleaf pine forest in Georgetown, SC. Soil respiration measurements were conducted approximately biweekly for two and a half years, before and after a root exclusion that took place on May 5, 2024. Processed, QAQC’d data for the treatment (root exclusion) and control (roots intact) before and after the root exclusion can be found in the file: 1_DATA_ESS_DOE_HR_RS_HB2_QAQC_Survey_Data_20260223.csv. Two multiday deployments were also conducted prior to the root exclusion using long-term automated chambers to continuously monitor greenhouse gas soil efflux. Processed, QAQC’d data for both long-term deployments can be found in the file: 2_DATA_ESS_DOE_HR_RS_HB2_QAQC_Longterm_Data_20260209.csv. Raw and working data files (.json, .81x, & .82z format) from LI-COR equipment are included for reference and can be accessed using SoilFluxPro software. CSV metadata files describe the raw data and modifications made using SoilFluxPro v5 and Matlab R2024b, as well as formatting and units for processed CSVs. Matlab code is included for reading in the processed CSVs, with sample figures comparing treatment and control. This research was performed as part of the project: “Improving models of stand and watershed carbon and water fluxes with more accurate representations of soil-plant-water dynamics in southern pine ecosystems”, which examines in part the effects hydraulic redistribution on soil efflux of carbon dioxide, water vapor and methane, as well as soil moisture and temperature in a southern pine ecosystem with sandy soils and high water table.

CARBON DIOXIDE FLUX↗
DOE OSTI2023

Riverbank temperatures on the Selawik River, Alaska 2010-2012, and Koyukuk River, Alaska June to July 2018.

The data package includes temperature measurements from riverbanks along the Selawik and Koyukuk Rivers in northwest Alaska. The Selawik River data also includes meteorological data collected locally and the Koyukuk River includes bulk density and ice content data from the riverbanks. The Selawik River data was collected between 2010 and 2013 with meteorological data collected for the entire time period and riverbank temperatures collected in shorter time intervals between 2010 to 2012. All of the Selawik River data were collected a single river bend. The Koyukuk River temperature data was collected from late June to early July 2018 at a total of five locations on three riverbanks. Ice content measurements on the Koyukuk were made at five riverbank locations. The data collection on both rivers was done in support of studies on the influence of permafrost on riverbank erosion. The temperature data was collected to monitor the thermal conditions of the river banks both seasonally and during periods of erosion. The ice content of the banks was measured to better understand how bank erosion rates are influenced by ice in the frozen riverbanks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CROCUS Low Cost All-in-One Weather Station AMB-001 Data Argonne National Laboratory Prairie Site

The Ambient Weather WS-2902D (AMB) is a low cost weather station that has become very useful for filling data gaps in harder to deploy locations. These low cost weather stations collect 13 second data, which is averaged to a five minute data output available to users through an API key. The data files contain measurements for precipitation, temperature, wind chill/heat index, relative humidity, dew point, UV index, solar radiation, wind speed, wind direction, wind gust, and with an external particulate matter 2.5 (PM 2.5) sensor. Having all of these measurements in one condense system allows for fast deploying and dense network capabilities. Three of the AMB weather stations were deployed at the Argonne Testbed for Multiscale Observational Science (ATMOS), a 20-acre prairie site at Argonne National Laboratory in Lemont, Illinois. The instruments are denoted by their three digit identifier (CMS-AMB-xxx) format. The data is presented as daily NetCDF (.nc) files, each containing approximately 24 hours of observations. Files follow the naming convention of: the project (CROCUS), location (atmos), instrument name (CMS-AMB-001), data level (raw, a1), and date (year, month, day). The NetCDF format can be accessed using common scientific software such as Python using xarray, netCDF4 or ACT-DOE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil Core Chemistry of Wetland, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2023-10-24 to 2024-05-20

This dataset contains the chemistry data of soil samples collected from a wetland, referred to as The Cove, at Old Woman Creek Estuarine Research Reserve in Huron, OH. Soil core extractions were performed to analyze what nutrient and/or metal constituents were present at different depths and what biogeochemical activity this could indicate. Three soil cores were collected at three locations within The Cove. The soil cores were removed from their core tubing and were cut into 4 segments down the length (or depth) of the core: top to 1-inch deep, from 1 inch to 5 inches, 5 inches to 7 inches, and 7 inches to the bottom of the core (approximately 10 inches). These soils segments were each homogenized and sub-sampled for various chemical analyses. Soil chemistry measurements are reported in the SoilChem_DataTable.csv file. Collection information about the samples can be found within the SoilChem_SampleMetdata.csv file. All files associated with this dataset are listed in the SoilChem_FLMD.csv file.

54 ENVIRONMENTAL SCIENCES↗