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

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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Soil C stock and CO2 production data for Soong et al. 2021: Effects of five years of soil warming at Blodgett Forest, CA.

Subsoils below 20 cm are an important reservoir in the global carbon cycle, but little is known about their vulnerability under climate change. We measured subsoil carbon in warmed vs control plots of a conifer forest after 4.5 years of whole-soil warming (4°C). We also measured soil CO2 efflux at the surface and soil CO2 concentrations along the soil profile. Over the 4.5 years we observed that warming resulted in a loss of subsoil carbon stocks (−33 ± 11%), primarily from unprotected particulate organic matter. Warming also stimulated a sustained 30 ± 4% increase in soil CO2 efflux due to increased CO2 production through the whole-soil profile. The observed in situ decline in subsoil carbon stocks with warming is now definitive evidence of a positive soil carbon-climate feedback, which could not be concluded based on increases in CO2 effluxes alone. This dataset comprises (1) 13C and C recovery in 2014, 2016, and 2018 1m soil cores sampled in 10cm increments. (2) Monthly surface CO2 fluxes from 2014 to 2018. (3) Monthly gas well sample [CO2] and production data. And (4) density fraction data for three depths of cores recovered in 2017.

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 OSTI2023

Aerobic respiration controls on shale weathering, Geochimica et Cosmochimica Acta, 2023: Dataset

This data package was generated in order to support the development of a deep-time weathering model and to assess the coupling between shale weathering and aerobic respiration in the paper “Aerobic respiration controls on shale weathering” by Stolze et al., Geochimica et Cosmochimica Acta (2023). The package contains two csv files providing the average CO2(g) concentration profiles [ppm] and mineral concentration profiles [wt%], respectively. The CO2(g) concentration profiles were measured in the vicinity of the monitoring well PLM2 between January 2018 and April 2019. The gas samples were collected in the unsaturated zone to a depth of 1.52 m. The mineral concentration profiles were determined by X-Ray diffraction (XRD). The XRD measurements were performed on sub-core samples collected in the monitoring well PLM3 down to a depth of 7.01 m. 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.Update on 2024-05-28: Revised versions of the CSV data files (CO2_data_GCA_Stolze_et_al_2023.csv and XRD_data_GCA_Stolze_et_al_2023.csv) were made to apply ESS-DIVE's CSV reporting format guidelines. Updated versions of the File Level Metadata (v2_20240528_flmd.csv) and Data Dictionary (v2_20240528_dd.csv) files were updated to reflect the changes made to the CSV files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed

The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. 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.The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado

The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss. A Readme_062424.txt file provides instructions on how to download all files. Input and output files are provided for the historical simulation representative of water years 1987 to 2022 (i.e. baseline) and the all-year +4C warming scenario. Instructions are provided to run the seasonal warming scenarios with warming applied only to the autumn, winter, spring or summer months. Modeled output used in figures for Carroll et al., 2024 are also provided with metadata describing where these data were obtained. 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.Updated on 06-24-2024: The dataset was updated to include model outputs (updated base.zip and 4C.zip files), figure source files (Figure_Source_Files.zip), an updated Readme file (Readme_062424.txt), climate input files (climate_input_files.zip), and the adoption of ESS-DIVE File Level Metadata and CSV reporting formats (inclusion of flmd.csv and dd.csv files).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and Scripts Associated with the Manuscript “Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids”

This data package is associated with the publication “Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids” published in EGU Biogeochemistry (Laan et al. 2025). In this research, water column respiration (ERwc) data, surface water chemistry data, organic matter (OM) chemistry data, and publicly available geospatial data were used in analysis to evaluate the variability in ERwc at 47 sites across the Yakima River basin in Washington, USA. In addition to this 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. The data package includes the data inputs, and outputs, and R scripts to reproduce all the analyses performed in the manuscript and create manuscript figures. The data package is comprised of three main folders (Code, Data, and Figures). The Code folder is comprised of four scripts and three analysis-specific subfolders that contain the R scripts to perform the analyses described in the publication and create publication figures. The Data folder is comprised of two “.csv” files and four subfolders that contain data input and output files. The Published_Data folder contains a readme that directs the user to download the appropriate files and add to this folder when using scripts. The Figures folder includes figures from the manuscript in “.pdf” and “.png” formats and a folder with intermediate figure files. This data package is associated with a GitHub repository which can be found at https://github.com/river-corridors-sfa/rcsfa-RC2-SPS-ERwc. 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

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. 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 OSTI2023

Radon Isotopes and Stable Water Isotopes from Coal Creek Watershed, Colorado (2021)

The radon isotope and stable water isotope data for Coal Creek Watershed, Colorado, consists of d2H, d18O, and 222Rn values from samples collected at 8 stream location along Coal Creek, samples from 7 groundwater springs within the watershed, and precipitation isotope samples collected by Next Generation Water Observing System (NGWOS) from a collector within the watershed. All stream and spring samples were collected between June and October, 2021, and precipitation isotope samples were collected between November 2020 and September 2021. These data were collected to evaluate how groundwater contributions to Coal Creek originating from a fractured hillslope and alluvial fan respond to summer monsoon rains and 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 radon samples; (2) a csv of all stream and spring isotope samples; (3) a csv of precipitation 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 OSTI2024

Data and scripts associated with a manuscript on a meta-analysis synthesizing stream biogeochemical response to wildfires across space and time (v2)

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Timeseries Unlabeled and Labeled Photos, Modeled Stream Elevation, and (Meta)Data of Variably Inundated Streams Across The Yakima River Basin, Washington, United States (v2)

This dataset is associated with the “River Monitoring Photos” (RMP) study and subsequent manuscript (Bao et al. 2025. Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence doi: 10.1016/j.envsoft.2025.106715). Game camera timeseries photos were collected to evaluate stream variable inundation via changes in width. A subset of photos was labeled for training the YOLOv8 and Mask2Former models and used to segment water surface fractions from all the game camera photos.This data package was originally published in March 2024. It was updated in October 2025 (v2) to add additional photos and files associated with the manuscript (i.e., processed data, labeled photos, and trained models). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.In addition to a readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; and (5) folders containing game camera photos and manuscript-associated files. Each Yakima River Basin site has a folder that contains subfolders for each month photos were collected. There is also a folder for files associated with the manuscript which has subfolders for labeled data, trained models, Yakima River Basin site water surface fractions, and USGS site water surface fractions. All files are .csv, .json, .txt, .yaml, .pth, .pt, or .pdf. 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

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Streamflow measurements from four sites on the Tuolumne River in Yosemite National Park from Water Years 2002 to 2021

Regions with remote and complex terrain experience spatially varying streamflow patterns, but are often poorly sampled due to difficult access. This data package includes streamflow measurements collected using low-visibility and low-impact installations at four sites on the Tuolumne River in Yosemite National Park, for water years 2002 to 2021. The resulting data set offers a unique opportunity to explore hydrologic processes in complex terrain.This data package contains half-hourly recordings of unvented pressure, vented pressure, and water temperature are measured and used to estimate discharge and stage height. Discharge flags provide insight into data anomalies. This dataset is formatted in accordance with ESS-Dive's Hydrologic Monitoring and File Level Metadata Formats. It contains the following files:1) Folder containing four csv files of time series streamflow measurements (unvented pressure, vented pressure, estimated discharge, water temperature, stage height, and discharge flag) from four locations on the Tuolumne River2) Data dictionary (dd.csv) containing units, definitions, human readable column names, and data type for all column headers throughout the dataset3) File-level metadata (FLMD.csv) containing metadata for files contained in the dataset4) Installation methods (InstallationMethods.csv) containing metadata on sensor installation

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data associated with the manuscript “Radiative impact of record-breaking wildfires from integrated ground-based data” collected in Richland, Washington in September 2020

This data package is associated with the publication “Radiative impact of record-breaking wildfires from integrated ground-based data” submitted to Nature Scientific Reports (Kassianov et al., 2024). Data from ground-based measurements of shortwave and spectrally resolved irradiance and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges were assessed to quantify the radiative impact of the September 2020 wildfires that occurred in the Western United States. Data were collected in September 2020 by several ground-based instruments at the Atmospheric Measurements Laboratory (AML) located in Richland, Washington (46.3451, -119.2792). These data include (1) Aerosol Optical Depth (AOD); (2) spectrally resolved and shortwave (SW) irradiances; (3) backscatter profiles; (4) total sky images; and (5) near-surface ambient air temperatures.The data package consists of five sub-directories: (1) “AML_Ceilometer_”; (2)” AML_CSPHOT_”; (3) “AML_MFRSR_irradiances_”; (4) “AML_SW_irradiances_and_Temp_”; (5) “AML_TSI_images_”; and 6 files stored at the directory level, including the readme, file-level metadata file, and data dictionary. The file-level metadata file (the file ending in “_flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (the file ending in “_dd.csv”) describes each tabular column header’s unit, definition, and structure. Below are descriptions of each sub-directory:“AML_Ceilometer_” includes ceilometer data collected at the AML. These files contain the corresponding narratives of data. Details related to the ceilometer data can be found in Morris (2016). “AML_CSPHOT_” includes ascii files with high-temporal resolution (about 10-15 min) AML CSPHOT data and their daily-averaged counterparts. These two files contain the corresponding narratives of data. Details related to the CSPHOT data can be found in Gregory (2011). “AML_MFRSR_irradiances_” includes ascii files with the AML MFRSR-measured diffuse, normal, and total spectrally resolved irradiance. Details related to the MFRSR data can be found in Hodges and Michalsky (2016) and Koontz et al. (2013). “AML_SW_irradiances_+_Temp_” includes near-surface ambient air temperature and SW irradiances, namely direct normal, diffuse hemispherical, and total hemispheric (global), measured at the AML. These files also incorporate the corresponding narratives of data. Details related to the SW irradiances can be found in Andreas et al. (2018). “AML_TSI_images_” includes Total Sky Images (TSIs) collected at the AML. Details related to the TSI data can be found in Morris (2005).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with a manuscript on residence time distribution simulation in two 10-kilometer long river sections

This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with the manuscript "Organic Molecules are Deterministically Assembled in River Sediments"

This data package is associated with the publication "Organic Molecules are Deterministically Assembled in River Sediments" submitted to Scientific Reports (Stegen et al., 2024). The study applies community ecology methods to dissolved organic matter (DOM) chemistry from variably inundated riverbed sediments to uncover principles governing DOM composition at a reach-scale. This data package documents the workflow used to process and generate the main findings in the manuscript. The R scripts reference the raw, unprocessed Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data from another data package, available on ESS-DIVE at https://data.ess-dive.lbl.gov/view/doi:10.15485/1834208. The scripts then process the raw FTICR-MS data and generate the findings and figures presented in the associated manuscript. In brief, this study demonstrates that DOM assemblages in variably inundated sediments are primarily governed by deterministic variable selection, including sediment moisture effecting the degree of deterministic assembly. See the manuscript for more details pertaining to interpretation and implications of the findings. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/ECA_2020_Sed.This data package is comprised of 6 scripts and 7 folders. 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. The FTICR_Processing_Scripts produce the outputs found in the "Processed_Data" folder. The remaining scripts (located in the parent directory) produce the outputs found in the following four folders: (1) "MCD_Dendrograms", "MCD_Randomizations", "MCD_bNTI_Outcomes", and "OM_Null_Modeling". The fifth script additionally takes the three comma-separated values (CSV) files found in the parent directory as input ("VGC_texture.csv", "merged_weights.csv", and "ECA2_FTICR_BetaDisp.csv"). The outputs of each of the five scripts serve as the input to the following script, with the final outputs stored in the folder "OM_Null_Modeling".

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Data and scripts associated with “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” (v2)

This data package is associated with the publication “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” submitted to Biogeochemistry by Ryan et al., 2024 (DOI: https://doi.org/10.1007/s10533-024-01169-5). This study aims to investigate fundamental and transferable drivers of dissolved organic matter (DOM) diversity across five nested watersheds within the contiguous United States. DOM diversity was explored using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The samples and the unprocessed FTICR-MS data used in this study are publicly available on the Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository (see DOIs below). The data for the Willamette, Gunnison, Connecticut, and Deschutes basins were collected as part of a collaboration between the Watershed Rules of Life (WROL) project and Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS). The data for the Yakima River basin (YRB) was collected by the PNNL River Corridor SFA. The raw, unprocessed FTICR-MS data with additional (meta)data can be found at doi:10.15485/1895159 for WROL samples and doi:10.15485/1898912 for YRB samples. This data package contains the processed data used in the associated manuscript. This package also contains ancillary geospatial, hydrological, and geochemical information that supports the interpretation of the FTICR-MS data within Ryan et al., 2024. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/rcsfa-RC4-WROL-YRB_DOM_Diversity. This data package was originally published August 2024. It was updated January 2025 (modified files). See the change history in the readme more details. At the directory level, the data package is comprised of three folders: (1) data, (2) output, and (3) src; and five additional files including the data dictionary (file ending in "_dd.csv”) and file-level metadata (file ending in “_flmd.csv”). The “src” folder contains the scripts used to process the FTICR data, conduct the analyses, and produce the manuscript figures. The inputs for these scripts are in the “data” folder and the returned outputs in the “output” folder. Inputs include temporal and spatial metadata associated with the sampling efforts, processed FTICR data, and total and normalized putative biochemical transformations per sample. Outputs include cleaned and combined data presented as tables, descriptive statistics, and plots. The file-level metadata file lists all files contained in this data package and descriptions for each. The data dictionary describes the units and definitions for each tabular data column or row header.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023

This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method.The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground.Update on 2024-10-18: Updates were made to the 2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv and dd.csv files to correct units (ppb instead of ppm).

54 ENVIRONMENTAL SCIENCES↗