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DOE OSTI2022

ESS-DIVE Reporting Format for Sample-based Water and Soil Chemistry Measurements

The ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) reporting format for sample-based water and soil chemistry measurements are written guidelines and spreadsheet templates, that facilitate archiving water/soil/sediment chemistry data. The templates and terminology lists provided as part of the reporting format can help organize data and potentially enable data reuse.The reporting format consists of a general instructions file (instructions.md) as well as more detailed instructions files for each template (files beginning “Detailed_Instructions_*.md). The “examples” folder includes an example of each data template with just the limited set of required fields filled out as well as other examples with both required and optional fields complete.The 'templates' folder contains CSV templates for data, methods, and terminology file templates. Similar to the examples folder, the blank templates are provided with versions for required fields only as well as required and optional fields. Lastly, the term list folder provides common terminology used in each of the templates as well as a definition and any constraints for the fields.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Advanced Terrestrial Simulator (ATS) evaluation dataset at 7 catchments across the continental United States

This dataset comprises of the input files and other files required for Advanced Terrestrial Simulator (ATS) simulations at 7 catchments across the continental United States. ATS is an integrated surface-subsurface hydrology model. We include Jupyter notebooks (within scripts folder) for individual catchments showing information (including data sources, river network, soil, geology, landuse types etc.) on preparing the machine readable input files. ATS observation output files are provided in the output folder. Figures and analyses (.xlsx sheets) are also provided. The catchments include, Taylor River Upstream (Colorado); (b) Cossatot River (Arkansas); (c) Panther Creek (Alabama); (d) Little Tennessee River (North Carolina and Georgia); (e) Mayo River (Virginia); (f) Flat Brook (New Jersey); (g) Neversink River headwaters (New York). Readme files are provided inside the directories providing more details. Files types include: .xml, .h5, .xlsx, .png, .ipynb, .py, .nc, .txt. All of the files types can be accessed by open source software, details on software requirements are following: .xml (any text editors including notepad and textedit), .h5 (in python using hdf libraries), .xlsx (WPS Office Spreadsheets, OpenOffice Calc, LibreOffice Calc, Microsoft Office etc.), .png (any image viewer), .ipynb (Jupyter notebook), .py (any text editors including notepad and textedit), .nc (using python or other open source software).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

CO2 and CH4 leaf-level fluxes and soil porewater concentrations from common vegetation patches in Louisiana’s coastal wetlands

This dataset contains leaf-level flux and soil porewater concentration measurements of carbon dioxide (CO2) and methane (CH4 ) in plots in the footprint of Ameriflux sites US-LA2 and US-LA3. Leaf fluxes in US-LA2 were measured on patches dominated by Sagittaria lancifolia and co-dominated by Sagittaria lancifolia and Typha latifolia. In US-LA3, fluxes were measured from distinct Juncus roemerianus and Spartina alterniflora patches. The porewater concentrations were collected across a vertical profile (~50 cm depth) at centric locations within 25 m2 plots where we measured the leaf fluxes. US-LA3 included an additional set of measurements in open water spots. We aimed to evaluate differences in leaf fluxes and porewater pools of CO2 and CH4 of representative ecohydrological patches across a salinity gradient. We also used this dataset to help develop ELM-Wet, a more realistic representation of wetland carbon biogeochemical processes within the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELM v.1). The files can be opened with regular text editors or spreadsheet programs. Version 2.0 (8/26/2025): This is the latest version of this dataset. The update includes additional samples of soil porewater CH4/CO2 concentrations from June-2021 to November-2022, as well as minor adjustments made to V1 samples via changing Henry's solubility to account for porewater salinity. Additionally leaf-level measurments of spectral indices, PSRI, NDVI, and PRI have been added to complement Leaf-level flux measurements. All V1 data sets have been integrated into V2 sheets, ensuring data from the previous version is contained with the additional samples and consistent with V2 metadata.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Riverbed and Near-Surface Water Quality Data, Hanford Reach, Columbia River, February 2021 - April 2022

This dataset contains longitudinal profiles of natural groundwater tracers (temperature, electrical conductivity (EC), and Radon-222 (Rn)) collected to identify locations of hydrologic exchange flows along a 75-km reach of the Columbia River near Richland, Washington.A jetboat was used to tow a 30 foot long, weighted tether that had one set of temperature and EC sensors just below the water surface and one set of temperature and EC sensors on the end (just above the river bed). The dataset contains profiles of temperature/EC data (resolution: 1 second) collected along the left and right banks during three different sampling events. During the final sampling event, we revisited some "deep holes" where the river was more than 30 feet deep with a weighted tether that was 75 feet long. The dataset has been processed to remove data collected when the sensors were not in the water, or when the water was too deep to keep the tether on the riverbed. We collected grab samples of water for analysis of dissolved Rn in places where the temperature/EC was different from the background river water. The Rn data was joined to the temperature/EC profiles based on the sample times.This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) installation methods; (4) February 2021 data; (5) July 2021 data; (6) April 2022 data; and (7) April 2022 deep hole data. All files are .csv format and can be opened with a spreadsheet program (such as Microsoft Excel or OpenOffice).

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

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 OSTI2025

Observed and Imputed Volumetric Soil Water Content Timeseries for the New Mexico Elevation Gradient

Reliable soil water content (SWC) data are essential for understanding dryland ecosystem dynamics, but high-frequency SWC sensors often fail, creating gaps in critical datasets. To address this, we developed a Bayesian mixture model that imputes missing SWC using both linear interpolation and an ecosystem water balance model (SOILWAT2), tested across six AmeriFlux eddy covariance tower sites in the New Mexico Elevation Gradient, demonstrating its effectiveness in reconstructing SWC patterns while providing insights into the factors driving SWC variability. Daily volumetric soil water content (SWC) data are provided as csv-formatted spreadsheets for the six AmeriFlux sites (US-Seg, US-Ses, US-Wjs, US-Mpi, US-Vcp, and US-Vcs). For each site there is an observed SWC file (site_SWC_gapfill.csv) and a file that contains imputed SWC (imputed_SWC_site.csv). The observed SWC files contain temperature corrected sensor values, tower precipitation data, as well as outputs from SOILWAT2 simulations that were used to impute SWC. The imputed files contain the original observed SWC values and the imputed missing SWC values. When SWC was missing from the original data, the missing value was imputed based on the Bayesian imputation mixture model. The posterior mean of all imputed values is reported as "mean_X". When the observed SWC was NOT missing, mean_X = observed SWC value (original data). The standard deviation, 2.5th percentile and the 97.5th percentile for the imputed values are also reported in the imputed files. There are readme text files for each file type explaining the contents of each column.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Processed Soil Respiration at the TRACE experimental Warming project, Aug 2015 - Sep 2017, Sabana, Luquillo, Puerto Rico

This data package contains processed measurements of soil carbon dioxide (CO₂) efflux collected using LI-COR LI-8100 soil respiration chambers at the Tropical Responses to Altered Climate Experiment (TRACE) located at the Sabana Field Research Station near Luquillo, Puerto Rico. The TRACE site is a mature, closed-canopy tropical wet forest within the Luquillo Experimental Forest. These data quantify soil surface CO₂ fluxes from both ambient (control) and experimentally warmed plots to evaluate how long-term soil warming affects belowground carbon cycling in tropical ecosystems. The data files include time-series tables of CO₂ flux (µmol CO₂ m⁻² s⁻¹), soil temperature (°C), and ancillary environmental variables, stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement intervals, and processing steps. These data were generated to address the research question: How does sustained soil warming influence soil respiration and carbon flux dynamics in tropical wet forests?

54 ENVIRONMENTAL SCIENCES↗

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

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater"

This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

atmospheric warming↗

DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

DOE OSTI2026

Fungal mat growth and leaf colonization at the TRACE warming experiment, Mar - Aug 2024, Luquillo, Puerto Rico

This data package contains processed measurements on the growth of litter mat-forming fungi and the time to leaf colonization at the Tropical Responses to Altered Climate Experiment (TRACE). Located near the Sabana Field Research Station in Luquillo, Puerto Rico, the TRACE site is located in a mature, closed-canopy tropical rainforest within the Luquillo Experimental Forest (LEF). These data quantify fungal mat growth and the time to leaf colonization of fungi species Gymnopus johnstonii and Marasmius crinis-equi. The experiment was conducted in ambient (control) and experimentally warmed plots (4°C above ambient) during spring and summer periods to assess how litter mat-forming fungi respond to a range of environmental conditions of tropical wet forests. The data files include tables of relative fungal mat growth rates, time to leaf colonization, averages of soil temperature (°C), and number of dry days before leaf attachment. The data are stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement descriptions, and processing steps.

Agaric fungi↗

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

Gross and Net Soil Methane Flux and Ancillary Data, Edgewater, MD, USA, summer 2022

This data package contains measurements used to quantify methane cycling and environmental conditions in coastal forest soils during the 2022 growing season. It includes time‑series data of soil methane flux, soil respiration, soil temperature, and volumetric water content collected from soil monoliths transplanted along an inundation and salinity gradient. The package also provides one‑time measurements from a stable‑isotope pool‑dilution incubation, including gravimetric water content, methane headspace concentrations, and ¹³CH₄ enrichment over time. Data files are provided in comma‑separated values (CSV) format, with accompanying metadata and readme documentation in PDF and plain‑text formats. All files can be opened with standard software such as R, Python, or spreadsheet programs capable of handling CSV files. The metadata file describes variable definitions, units, processing steps, and the structure of each data table to support reuse and integration with other datasets.

13-C↗

DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗

DOE OSTI2026

Soil Temperature Sensor Data, May - December 2024, Five sites in Knoxville, Tennessee

This dataset contains surface soil temperature measurements from five urban parks in Knoxville: Cumberland Estates Park (CE), Socially Equal Energy Efficient Development (SD), West View Park (WV), Victor Ashe Park (VA), and West Hills Park (WH). The dataset comprises 29 Excel (.xlsx) and Comma-Separated Values (.csv) files documenting soil temperature recorded by six HOBO Pendant MX Water Temperature Data Loggers at each site, except for CE, which has five sensors. Each logger was installed at a depth of 10 inches and positioned approximately 3 to 6 feet from the weather station at each site. To open both csv and xlsx files, users can utilize spreadsheet applications such as Microsoft Excel, Google Sheets, LibreOffice Calc, and Apple Numbers. This dataset is part of a broader study examining the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville, Tennessee.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2026

Data from: "Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics"

This data package was generated to support the manuscript “Towards CONUS-Wide Machine Learning-Augmented Conceptually Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics.” It provides input files, model outputs, plotting data, scripts, notebooks, and documentation used to develop, evaluate, and reproduce Mass-Conserving Perceptron (MCP)-based hydrologic modeling experiments across 513 selected Catchment Attributes and Meteorology for Large-sample Studies in the United States (CAMELS-US) basins. The files are organized by modeling component and analysis purpose, including rainfall–runoff experiments, snow module experiments, coupled hydrologic-snow experiments, Long Short-Term Memory (LSTM) benchmark results, model skill metrics, initialization and epoch records, cell-state normalization files, Akaike Information Criterion (AIC)-based model comparison files, and data used to generate manuscript figures. Tabular files can be opened using standard spreadsheet software or Python/R data-analysis tools. Python scripts, Jupyter notebooks, and selected MATLAB scripts are included for model execution, postprocessing, plotting, and statistical analysis. Quality assurance and quality control were conducted through the source-data selection and modeling workflow. Meteorological forcing, streamflow, and static catchment attributes were derived from the CAMELS-US dataset, and snow water equivalent data were derived from the University of Arizona (UA) Snow Water Equivalent dataset. Selected basins and time periods were screened during the associated research workflow to avoid missing observations or poor-quality cases. Static geospatial features were processed primarily using Quantum Geographic Information System (QGIS) and Geospatial Data Abstraction Library (GDAL) workflows. Additional details are provided in the associated manuscript and documentation.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗