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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 OSTI2025

Metagenome-assembled genomes from soil samples in control and warming plots in Blodgett Forest, CA (2014-2021)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of LBNL (Lawrence Berkeley National Laboratory) TES (Terrestrial Ecosystem Science) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from soil depth profiles collected from 2014 to 2021 from three paired control and warming plots. We collected soil samples across a range of depth profiles (spanning surface to 90 cm deep) from three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. 101 soil metagenomes were sequenced at JGI (Joint Genome Institute) and UCSF (University of California San Francisco) Center for Advanced Technology and can be found under the JGI (Joint Genome Institute) GOLD (Genomes Online Database) Sequencing project Gs0151586 and NCBI (National Center for Biotechnology Information) Projects PRJNA1225762 and PRJEB39497. Metagenomes were assembled using JGI (Joint Genome Institute) Metagenome Workflow (10.1128/mSystems.00804-20). For each metagenome, the assembled contigs were binned into genomes using 3 binning algorithms (cocacola, metabat, and maxbin) and the resulting bins were consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>50%) and contamination (<25%), and dereplicated at 99% ANI (average nucleotide identity) using dRep (https://github.com/MrOlm/drep).The dataset includes a zip file of 2321 MAG (Metagenome Assembled Genome) fasta files, the accession numbers for the underlying metagenomes, and a csv file with MAG (Metagenome Assembled Genome) quality metrics and taxonomic classification (GTDB -Genome Taxonomy Database-RS220). This dataset also 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. A sample metadata file (samples.csv) that contains site information has also been included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon flux measurements from chambers collected between July to October 2022 at Old Woman Creek, Huron, Ohio.

This dataset contains carbon dioxide and methane flux measurements collected via chamber sampling at Old Woman Creek National Estuarine Research Reserve in Huron, OH. These data were generated to understand temporal and vegetation patterns associated with wetland carbon cycling. Specifically, this dataset intends to answer how carbon dioxide and methane fluxes change monthly and hourly across sites with vegetation and without vegetation. Data includes chamber measurements that were measured in both sites with vegetation and without vegetation and that were collected hourly, for 12 hours, and monthly, for four months. The file soilrespiration_data.csv contains these data, and the metadata file (soilrespiration_chammetadata.csv) and location metadata file (soilrespiration_locationmetadata.csv) have information on locations where the chambers were placed and sampled in the wetland. Data processing was done on raw methane fluxes (Flux_CH4) to remove the influence of ebullition (Flux_CH4_ebullition) to get a diffusive flux (Flux_CH4_diffusive).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from Yosemite National Park, Water Year 2024

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed at 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are intended to support hydrologic modeling efforts to better resolve the fate of mountain water, and are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. A time-lapse camera at one site captures valley-scale seasonal snow cover variability.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into a short video stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with: “Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization”

This data package is associated with the publication “Burn severity and vegetation type control phosphorus concentration, molecular composition, and mobilization” published in European Geophysical Union - Biogeosciences (Barnes et al. 2025). This study investigates how phosphorus (P) biogeochemistry is altered by burn severity in contrasting types of vegetation chars. This data package documents the workflow used to process and generate the main figures and statistics in the manuscript. The R scripts reference minimally processed P nuclear magnetic resonance (P-NMR) and X-ray absorption near edge structure (P-XANES) data, as well as fully processed data including total elemental composition of the solid chars, total elemental composition of the char leachates (particulate and aqueous phases), and leachate aqueous phase molybdate reactive P concentration. These source data and associated metadata can be found on ESS-DIVE at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1894135 (Grieger et al. 2022; v3). Files and scripts included in this data package finish the processing workflow for P-NMR and P-XANES data. These data can be used to gain a better understanding of bulk chemical changes in chars and their leachates, as well as detailed molecular changes to P. This data package is associated with the GitHub repository found at https://github.com/river-corridors-sfa/rcsfa-RC3-BSLE_P. This data package is comprised of a “data” folder and a series of data processing and analysis scripts. Details on how to recreate the workflow can be found in the Critical Details section of the readme and the “workflow_readme.md” file. The file-level metadata file (file ending in “flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (file ending in “dd.csv”) describes all tabular data columns and their respective definitions and units.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data from: "Reply to ‘The challenge of defining effectively-no-snow’"

This repository contains the data and code associated with the paper titled "Reply to ‘The challenge of defining effectively-no-snow’" published in Nature Reviews Earth and Environment, 2026. In this reply, we argue that the 10th percentile of peak SWE (Snow Water Equivalent), which we propose in the original article, can be used as intended given it's a standardized, impact-based benchmark for comparing snow conditions across regions, not as a literal measure of snow absence. We present new evidence with SNOwpack TELemetry (SNOTEL) data showing that years meeting the threshold are overwhelmingly associated with subsequent drought (given United States Drought Monitor conditions), supporting its hydrologic and societal relevance. We conclude that while the distinction between "effectively no snow" and "zero snow" should be clearly communicated, the original definition remains appropriate for assessing impacts on snow-dependent water systems. The file code_nree_ML_reply_2026.Rmd contains the main processing scripts which analyze the SNOTEL data. Data from the US Drought Monitor was downloaded at: https://usdmdataservices using the Get Drought Severity Statistics By Area Percent' option, saved to the *_HUC4_delineated.csv files (Hydrologic Unit Code), which are labeled accordingly. 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.

EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE↗
DOE OSTI2025

Groundwater elevation data for monitoring wells within the East and Taylor River basins, Colorado (USA)

This dataset is comprised of temporal variations in groundwater elevation data for the 24 monitoring wells located throughout the East River watershed. Seasonal to annual variations in groundwater elevations are a critical property of mountainous watersheds needed to understand both hydrological and below ground biogeochemical processes. Such data serve as a critical constraint for numerical models describing coupled groundwater-surface water behavior within the watershed. Additionally, the offset between the maximum and minimum groundwater elevations defines the extent of the bedrock weathering zone, with annual excursions in the groundwater hydrographic (i.e., the rising and falling hydrographic limbs) imposing primary controls on bedrock saturation state and redox conditions that govern biogeochemical reactions impacting nitrogen, carbon, and metals cycling. Manufacturer-specific software is used to download pressure data from each transducer, with broadly available spreadsheet software (e.g. Microsoft Excel) used to convert temporal variations in water pressure to elevations in units of meters above mean sea level. As additional monitoring wells are installed within the East River watershed and new groundwater monitoring wells are installed in the Taylor River watershed, temporal groundwater elevation data will be included as a part of this master dataset. Details regarding the metadata associated with each monitoring well location, including well depths, screened intervals, well location coordinates, and bedrock type, are included, as is a standard operating procedure for generating groundwater elevation data from water pressure values recorded by the pressure transducers. This dataset includes: (1) a zip file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.zip), containing (a) PNG of groundwater hydrographs, (b) a CSV file with groundwater elevation data, and (c) CSV file containing metadata organized by location; (2) an Excel file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.xlsx) with the groundwater elevation data, groundwater hydrographs, and metadata organized by location; (3) a Word file (Groundwater_elevation_data_protocols.docx) and a PDF file version (Groundwater_elevation_data_protocols.pdf) containing field protocols and methods; (4) a location metadata (locations.csv) file; (5) a file level metadata (flmd.csv); and (6) data dictionary (dd.csv) file.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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

COMPASS-FME Synoptic Sites Level 2 Sensor Data v2-1

This is the version 2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. Please see v2-1 L2 Sensor Package QStart.pdf for detailed information on data package structure, temporal coverage, and versioning.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2025

Porewater chemistry in Typha-dominated brackish tidal marsh, PIE LTER, Plum Island Sound, MA, July 2022–September 2024

This dataset contains profile measurements of porewater constituents taken on 3-4 days across the growing seasons in 2022, 2023, and 2024 in a tidal brackish marsh within the Plum Island Ecosystems Long Term Ecological Research site (PIE LTER), located in the Plum Island Sound, Massachusetts (MA). Measurements were taken to monitor changes in porewater chemistry induced by seasonal saltwater intrusion at the site. Samples were taken in two locations: one was close to the creek bank and the other in the marsh interior. Water was sampled from 2-5 depths between the surface to 50cm using a sipper consisting of a hollow stainless steel rod with an opening at the end similar to that described in (Berg & McGlathery, 2001). The rod was pushed into the sediment to the desired depth, typically every 10cm, and water samples were taken by syringe. Water was not obtained at all depths. Samples were preserved and analyzed in the lab. Metadata files Typha_porewater_sipper_dd.csv and Typha_porewater_sipper_flmd.csv contain detailed information on data variables, sampling and QA/QC methods, and site location.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with a manuscript investigating dissolved organic matter and microbial community linkages across seven globally distributed rivers

This data package is associated with the publication “Meta-metabolome ecology reveals that geochemistry and microbial functional potential are linked to organic matter development across seven rivers” submitted to Science of the Total Environment. This data package includes the data necessary to replicate the analyses presented within the manuscript to investigate dissolved organic matter (DOM) development across broad spatial distances and within divergent biomes. Specifically, we included the Fourier transform ion cyclotron mass spectrometry (FTICR-MS) data, geochemistry data, annotated metagenomic data, and results from ecological null modeling analyses in this data package. Additionally, we included the scripts necessary to generate the figures from the manuscript. Complete metagenomic data associated with this data package can be found at the National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. This dataset consists of (1) four folders; (2) a file-level metadata (flmd) file; (3) a data dictionary (dd) file; (4) a factor sheet describing samples; and (5) a readme. The FTICR Data folder contains (1) the processed Fourier transform ion cyclotron mass spectrometry (FTICR-MS) data; (2) a transformation-weighted characteristics dendrogram generated from the FTICR-MS data; and (3) the script used to generate all FTICR-MS related figures. The Geochemical Data folder contains (1) the single geochemistry data file and (2) the R script responsible for generating associated figures. The Metagenomic Data folder contains (1) annotation information across different levels; (2) carbohydrate active enzyme (CAZyme) information from the dbCAN database (Yin et al., 2012); (3) phylogenetic tree data (FASTAs, alignments, and tree file); and (4) the scripts necessary to analyze all of these data and generate figures. The Null Modeling Data folder contains (1) data generated during null modeling for each river and all rivers combined and (2) the R scripts necessary to process the data. All files are .csv, .pdf, .tsv, .tre, .faa, .afa, .tree, or .R.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

WHONDRS Surface Water Dissolved Organic Carbon and FTICR-MS across Stream Orders in Four United States Watersheds in 2019 and 2020 (v3)

This dataset supports the broader Watershed Rules of Life (WROL) study examining spatial and temporal biogeochemical and microbial relationships across the Connecticut River, Deschutes River, Gunnison River, and Willamette River watersheds. The dataset provides geochemistry and organic matter characterization data generated from surface water collected from August 2019 to September 2020 in Colorado, Connecticut, Oregon, and Vermont. Related data were collected and will be published separately in collaboration with P. A. Raymond and B. C. Crump as part of WROL. This dataset is comprised of one folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (6) surface water sampling protocol; (7) methods codes; (8) international generic sample number (IGSN) mapping file; and (9) a folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. This data package was originally published in October 2022. It was updated in July 2023 (v2) and again in November 2023 (v3). See the change history section in the readme for more details.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Geochemistry and Strontium Isotopes for Coal Creek Watershed, Colorado, 2021-2022

The geochemistry and strontium isotope data for Coal Creek Watershed, Colorado, consists of cation, anion, and 87Sr/87Sr isotope values from samples collected at 8 stream location along Coal Creek, samples from two groundwater springs within the watershed, and a shallow subsurface piezometer. All stream and spring samples were collected between June and October, 2021, and the shallow, near stream piezometer sample was collected in July of 2022. These data were collected to evaluate how groundwater contributions to Coal Creek originating from shallow vs deep flow paths respond seasonal drying. Understanding of groundwater-surface water interactions in montane systems in critical for the future of water availability in the Western US as groundwater contributions are expected to become more important for sustaining summer stream flows. This data package contains: (1) a csv of all cation samples; (2) a csv of all anion samples; (3) a csv of all 87Sr/87Sr isotope samples; and (4) a csv of locations for each sampling site. 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 OSTI2026

Processed sap flow and fine-root trait data associated with summer drought responses in temperate trees in Lisle, Illinois, USA (2019–2021)

These data support the manuscript “Acquisitive root exploration strategies help maintain higher peak sap flux rates during summer drought, but more root biomass does not”. The dataset includes processed sap flow measurements and fine-root trait data collected between 2019 and 2021 from temperate monodominant tree plots established in the 1920s to 1930s ranging in size from 0.05 to 0.8 ha at The Morton Arboretum in Lisle, IL. Sap flow was measured with ICT sap flow sensors using the heat ratio method. Fine-root traits were measured from soil cores which includes specific root length (SRL), specific root area (SRA), diameter, biomass, and length for diameter classes ≤1 mm and ≤2 mm. The package contains comma separated value (CSV) data files and associated metadata that can be viewed and analyzed using common software such as spreadsheet programs, R, and Python. These data are used to investigate how variation in fine-root traits relate to tree water use and drought response during summer drought linking belowground root traits and aboveground physiological responses.

drought↗
DOE OSTI2024

Pinus ponderosa physiology data from the 2022 shade manipulation field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains physiology data on Pinus ponderosa saplings, including: leaf water potential (MPa, megapascals), leaf osmotic potential (MPa, megapascals), leaf pressure potential (MPa, megapascals), leaf relative water content (% saturated mass), as well as soluble sugars (% dry mass),starch (% dry mass), and total nonstructural carbohydrates (% dry mass) found in branch phloem and xylem. Data files and data dictionary(ies) are uploaded as .csv files and .xlsx files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Synoptic NO3 in Slate River Watershed, Colorado (2022)

The synoptic nitrate (NO3) dataset in the Slate River Watershed, Colorado consists of NO3 data collected at 19 locations three times during the summer of 2022. Stream samples were collected in early summer (early July), mid summer (late August), and late summer (late September). The samples include mainstem, tributary, and point source input water samples. These data were collected to evaluate spatiotemporal variability in stream NO3 during the summer, and evaluate anthropogenic controls on stream NO3 dynamics. This data package contains: (1) a csv of all NO3 samples and (2) a csv of locations for each sampling site. 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. 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.

EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > FRESHWATE↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Model Inputs, Outputs, and Scripts associated with: “Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin”

This data package is associated with the publication “Combined effects of stream hydrology and land use on basin‐scale hyporheic zone denitrification in the Columbia River Basin”, published in Water Resource Research (Son et al.2022) available at https://doi.org/10.1029/2021WR031131. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) denitrification at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions, including two-step denitrification and aerobic respiration, are considered within the HZ. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values. This study uses the RCM to explore the spatial patterns of HZ denitrification across reaches with different sizes and land use in the CRB. Our main objective is to use the RCM as a virtual reality model, and the machine-learning models as surrogates that encapsulate the complexities of the physics-based model while identifying the importance of different variables that are not evident in the model conceptualization. We do not include a direct comparison of the modeled HZ denitrification and measurements; however, the RCM can capture the overall spatial patterns of the HZ denitrification because the model inputs and its reaction networks are based on well-established theory and a physical-based model. The combination of the model-based predictions and a machine-learning approach (e.g., random forest) is used to improve our understanding of what variables of the model are associated with spatial patterns of the modeled denitrification across reaches with different sizes and land uses, and to develop a proxy model using measurable variables to reproduce the simulated patterns.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

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↗