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DOE OSTI2026

SPRUCE FT-ICR MS, Bulk Chemistry, and Mass Loss from Litter Decomposition Study in Experimental Plots, Marcell Experimental Forest, Minnesota, 2015-2017

This dataset contains molecular, bulk chemical, and mass loss measurements from a litter decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. This site is in a Sphagnum spp. ombrotrophic bog forest. Litterbags were deployed into the peat in September 2015 across three warming levels (+0, +4.5, and +9°C) under ambient and elevated carbon dioxide (CO₂ - +500 ppm) and retrieved after roughly 0.5, 1, and 2 years of field incubation (2015-09-23 to 2017-08-02). Litterbags containing six peatland litter types: black spruce needles (Picea mariana - SPL), spruce fine roots (SPR), Sphagnum angustifolium (ANG), Sphagnum magellanicum (MAG), Labrador tea leaves (Rhododendron groenlandicum - LTL), and Labrador tea roots (LTR). Molecular composition of water-soluble organic matter extracts was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) at 9.4 Tesla, operated in negative ion mode with electrospray ionization, providing molecular formula assignments and compound-class distributions across the decomposition time series. Bulk chemical characterization included elemental analysis (percent carbon, nitrogen, and phosphorus) and Fourier Transform Infrared Spectroscopy (FTIR) to quantify functional group composition. Litter mass loss was tracked gravimetrically at each retrieval interval, expressed as percent mass remaining relative to initial dry mass for each litter type and treatment combination. These data are valuable for understanding how vegetation shifts driven by increased atmospheric CO2 and temperature in peatlands alter litter inputs and organic matter stabilization trajectories, with implications for projecting and modeling peatland carbon cycling. This dataset contains two data files in comma-separated value (.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

decomposition↗

DOE OSTI2026

Electromagnetic Induction (EMI) Data, 2024, Trail Creek, Colorado

This dataset contains Electromagnetic Induction (EMI) data collected at Trail Creek, Colorado, in 2024. EMI surveys were conducted to investigate the spatial distribution of electrical conductivity in the subsurface, providing insights into soil moisture and subsurface geological features. The surveys were performed along multiple transects to capture variations in conductivity influenced by changes in soil composition, moisture content, and underlying geological structures. This dataset complements other geophysical data collected in the region, including Electrical Resistivity Tomography (ERT) and Terrestrial LiDAR Scanning (TLS), providing a detailed understanding of the subsurface and its impact on surface vegetation and hydrological processes. The data are valuable for environmental geophysics, ecological research, and hydrological modeling in mountainous ecosystems. The files include: - data.zip: the raw EMI data (.csv) - inversion.zip: the inverted resistivity model (.csv and .kml) - kriging.zip: the kriging resistivity model (.csv, .tif, .kmz) - flmd.csv: file level metadata file describing all files within this dataset - dd.csv: data dictionary file describing the column headers within CSV files 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.

CMD Mini-Explorer↗

DOE OSTI2026

Fe(III) reducing bacterial activities in Old Woman Creek wetland sediments, June 2023

To evaluate the Fe(III) reducing microbiological activities in Old Woman Creek Nature Preserve (OWC) wetland sediments, we incubated OWC sediments under anoxic and oxic conditions and with or without Fe(III) amendment [as hydrous ferric oxide (HFO)]. No Fe(III) reduction was observed in heat-deactivated incubations. In non-sterile anoxic incubations, measurement of 0.5 M HCl-extractable Fe(II) indicated that Fe(III) reduction occurred in both Fe(III)-amended and -unamended incubations, indicating that abundant Fe(III) is associated with the OWC sediments. Little Fe(II) accumulated in solution, indicating that the most biogenic Fe(II) adsorbs to the sediments. When air was added to the headspace of non-sterile incubations, Fe(III) reduction was halted and any biogenic Fe(II) that accumulated was oxidized. These experiments were used to guide preparation and analyses of incubations to determine if electrochemical measuements can be used to detect microbiological activities in contrasting terminal electron accepting regimes (i.e., aerobic and Fe(III) reducing conditions). Data package includes methods and data from experiments, including dissolved anion concentrations, dissolved Fe(II) concentrations, and 0.5 M HCl-extractable Fe(II) concentrations. All files are either .txt or .csv and can be opened by any plain text editor application.

EARTH SCIENCE↗

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 ↗

DOE OSTI2026

Soil physical and chemical measurements for topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes soil physical and chemical measurements from topsoils collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. The soil measurements include soil bulk density, soil volumetric water content, soil microbial biomass C (Carbon), N (Nitrogen) and C:N (C to N ratio), soil DNA yield, soil total extractable organic C, soil total extractable N, soil extractable nitrate, soil extractable ammonium, soil dissolved inorganic N, soil dissolved organic N, soil pH, soil TOC400 (total organic carbon at 400°C), soil ROC (residual oxidizable carbon), soil TIC (total inorganic carbon), soil TOC (total organic carbon), soil TC (total carbon), soil N, soil OM (organic matter) loss on ignition. Additional associated site metadata can be found in the ESS-DIVE package 10.15485/1618130. The dataset includes (1) 2018_NEON_soil_physical_chemical_measurements.csv: soil physical and chemical measurements indexed by soil sample IGSNs; (2) samples.csv: sample metadata file used to register International Generic Sample Numbers (IGSNs); (3) flmd.csv: file level metadata file; and (4) dd.csv: data dictionary 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.

2018 NEON and 2025 CHESS (Catchment Hydrology and ↗

DOE OSTI2026

Timeseries Photos of a Variably Inundated Stream: Umtanum Creek, Washington, United States

This dataset is associated with a broader study using game camera timeseries photos collected to evaluate stream variable inundation via changes in width (i.e. wet fraction). Four game cameras were deployed along Umtanum Creek (Washington, United States) to track changes in stream inundation over time. Drone imagery was collected at the same location on October 18, 2024 which was used to construct a digital elevation model (DEM) of the streambed topography. The associated paper and data can be found at https://doi.org/10.1016/j.envsoft.2025.106715 (Bao et al., 2025a)) and https://doi.org/10.15485/2589885 (Bao et al., 2025b), respectively. 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 this readme, this data package also includes a file-level metadata (FLMD) files that describes each file and a 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; (5) field protocol; and (5) folders containing game camera photos. Game camera photos are organized into folders for each camera (CDL, CUL, CDR, CUR; see readme for information on camera naming) by the month photos were collected. All files are .csv, .jpg, or .pdf.

AI image segmentation↗

DOE OSTI2026

Leafweb: Leaf Gas Exchange and Pulse-Amplitude Modulated Fluorometry for C4 Species, June 2026 Release

This dataset contains leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry for 98 C4 species. The C4 photosynthetic pathway employs specialized CO2 concentration mechanisms and Kranz anatomy to enrich CO2 concentration around Rubisco, the enzyme that catalyzes carbon fixation in the Calvin-Benson cycle to suppress photorespiration and increase the use efficiencies of light, nitrogen, and water as compared to the C3 photosynthetic pathways. Large-scale C4 photosynthetic datasets are relatively scarce, which has affected C4 photosynthesis research. To improve C4 photosynthetic data availability, Leafweb organized an effort to systematically collect, compile, standardize, and organize measurements of leaf gas exchange and/or Pulse-Amplitude Modulated (PAM) fluorometry of C4 species. This derived a C4 photosynthetic dataset containing measurements made by independent researchers in multiple countries in various environments (field, garden, or greenhouse). It covers three biochemical subtypes – the nicotinamide adenine dinucleotide phosphate-malic enzyme (NADP-ME), nicotinamide adenine dinucleotide-malic enzyme (NAD-ME), and phosphoenolpyruvate carboxykinase (PEP-CK) subtypes. This dataset is useful for using Artificial Intelligence / Machine Learning and mechanistic models to study C4 photosynthesis and compare across different biochemical subtypes. This dataset contains 3 compressed (*.zip) folders containing 1,892 data files in comma-separate values (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Zhou, Haoran [Tianjin University, China]↗

DOE OSTI2026

Dated soil C–N–P profiles, water quality, and chamber fluxes across Ohio and Michigan wetlands (2024–2025)

This dataset includes dated soil core chemistry (bulk density, phosphorus, nitrogen and carbon concentrations), water quality, and chamber flux measurements collected from wetlands in the Midwest United States—12 sites in Ohio, one site in Indiana, one site in Michigan—collected in the spring or summer of 2024 or 2025, all in (.csv) format. These data were generated to examine how wetland restoration, management activities, and time since restoration affect biogeochemical processes, carbon sequestration, nutrient accumulation, water quality, and greenhouse gas emissions. Specifically, these data aim to investigate how restored wetlands differ from natural wetlands in terms of carbon, nitrogen, phosphorus dynamics, as well as carbon dioxide and methane fluxes. Also included are surface and porewater quality parameters and chamber flux measurements across these different wetlands. Sampling was conducted at various sites representing a range of restoration stages, from about 4 years post-restoration up to 105 years post-restoration, and also includes a natural wetland used as a reference in Michigan. These data can be used to determine carbon sequestration rates, nutrient cycling, and to enhance our understanding of biogeochemical responses to wetland restoration in temperate ecosystems. This data package contains (1) a csv file (Water_Quality.csv) containing water quality data (dissolved organic carbon, total dissolved nitrogen, and temperature) organized by location; (2) a csv file (Soil_C_N_P_Seq.csv) containing carbon, nitrogen, and phosphorus concentrations at each soil level and time of each soil level, as well as their sequestration rates; (3) a csv file (CH4_CO2_Flux.csv) including methane and carbon dioxide fluxes that were measured with a chamber; (4) a file-level metadata (FLMD.csv) file that lists each file contained in the dataset with associated metadata; (5) a data dictionary (DD.csv) file that contains terms/column headers used throughout the files along with a definition, units, and data type; and (6) a locations metadata file (Location_metadata.csv).

Earth Science > Atmosphere > Atmospheric Chemistry↗

DOE OSTI2026

WHONDRS Surface Water Geochemistry and Organic Matter Characterization Data from Streams Distributed across Latin America

This dataset supports a broader study examining global transferability of stream biogeochemistry and was generated in collaboration with the MicroSudAqua (µSudAqua) network (https://microsudaqua.netlify.app/en/). The dataset provides surface water geochemistry (dissolved organic carbon, total dissolved nitrogen, cations) and organic matter characterization (FTICR-MS) from streams in Argentina, Brazil, Chile, and Colombia. Samples were collected across stream orders (1st to 6th order) within five basins. Related data were collected and will be published separately in collaboration with the µSudAqua network. 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 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. This dataset is comprised of (1) a folder of field photos; (2) a folder of surface water sample data, (3) a folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data; (4) file-level metadata; (5) data dictionary; (6) field metadata; (7) readme; (8) international generic sample number (IGSN) mapping file; and (9) field protocol. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) total dissolved nitrogen data and averages; (3) anions and averages; (4) methods codes; (5) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4.

Anions↗

DOE OSTI2026

SPRUCE Soil Volumetric Water Content in Experimental Plots, Marcell Experimental Forest, Minnesota, 2017-2025

This dataset contains soil volumetric water content (VWC) measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) Experiment. Measurements were made inside SPRUCE experimental plots in the S1 Bog at the US Forest Service Marcell Experimental Forest in northern Minnesota, USA from 2018-2025 (2018-06-14 to 2025-12-31). Observations were made with METER 10HS soil moisture sensors. To reduce bulk density related variability, 10HS sensors were placed inside mesh tubes filled with peat at a standard bulk density. The observations under standard bulk density represent relative differences in water content between hummock and hollow positions, and in response to experimental treatments. Productivity of peatlands, and their keystone species sphagnum, are highly dependent on water availability, which is affected by lateral inputs, precipitation, ground water depth and evapotranspiration. Knowledge of near surface and sphagnum water content is useful to understand peatland function. This dataset contains 8 data files in comma-separate values (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Warren, Jeffrey [ORNL] (ORCID:0000000206804697)↗

DOE OSTI2026

WHONDRS 2016 Sediment Organic Matter Characterization Data from Streams across HJ Andrews Experimental Forest, Oregon

This dataset supports a broader synoptic effort to map morphological, hydrological, chemical, and biological conditions across a fifth-order mountain stream network. Samples were generated through a collaborative synoptic sampling effort in 2016. The dataset provides sediment Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) from 60 sites across the HJ Andrews Experimental Forest, Oregon (https://andrewsforest.oregonstate.edu). Related data were collected as part of the event and were published separately in collaboration with other team members. The data are available at http://www.hydroshare.org/resource/ea6c0832885a46c3939e7bb22e48e754 and are described within https://doi.org/10.5194/essd-11-1567-2019 (Ward et al., 2019). The hydroshare data package contains processed FTICR-MS data from the samples included in this data package. The data were processed via Formultitude (previously called Formularity; https://github.com/PNNL-Comp-Mass-Spec/Formultitude). However, we have re-processed the data using Core-MS and included it in this data package. Additional related data collected in 2025 from a similar effort can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3023310 and http://www.hydroshare.org/resource/b274c4a234bf4b12b7cb8a54a696c629. 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 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 (1) a folder of sample data; (2) data dictionary; (3) file-level metadata; (4); (5) coordinates; and (6) readme. The sample data subfolder contains 12 Tesla (12T) FTICR-MS data. This folder contains the processed data and three subfolders, one containing the .xml files, one containing the CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .xml, .Rmd, .py, .cal, or .json.

Biogeochemistry↗

DOE OSTI2026

Surface water nitrogen and sediment potential nitrate reduction rates, nutrient stocks, and stable isotopes from nine wetlands at the Tanglewood Biological Station, Alabama

This dataset supports a broader study investigating wetland hydrologic and biogeochemical responses to inundation disturbances. Bimonthly surface water and sediment sampling events were conducted at nine wetland sites situated within the Tanglewood Biological Station in Alabama from April 2023 to February 2024. The contents included in the data package include surface water nitrogen (nitrogen oxides and ammonium) and sediment potential nitrate reduction rates (measured as potential denitrification and dissimilatory nitrate reduction to ammonium processing), nutrient stocks (total carbon, total nitrogen, and organic matter), and stable isotopes (carbon and nitrogen). Water level data related to each wetland location can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253 (Kirker et al., 2024) and related water geochemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3001967 (Forbes et al., 2025). In addition to a 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 (1) file-level metadata; (2) data dictionary; (3) field metadata and international generic sample numbers (IGSNs); (4) readme; (5) the field protocol; and (6) a subfolder with sample data. The sample data subfolder contains (1) sediment potential denitrification rate, (2) sediment potential dissimilatory nitrate reduction to ammonium (DNRA) rate, (3) sediment total carbon and nitrogen content, (4) sediment stable isotopes (delta nitrogen-15 and delta carbon-13), (5) sediment percent organic matter, (6) surface water nitrous oxides, (7) surface water ammonium, and (8) methods codes. All files are .csv or .pdf.

Ammonium↗

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 OSTI2026

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗

DOE OSTI2026

Soil nitrogen mineralization rates, nutrient stocks, stable isotopes, and water volumetric measurements across terrestrial-aquatic interfaces from three wetlands at the Tanglewood Biological Station, Alabama

This dataset supports a broader study investigating wetland hydrologic and biogeochemical responses to inundation events. Soil samples were collected across four sampling events along terrestrial-aquatic gradients at three wetland sites located within the Tanglewood Biological Station in Alabama from April 2024 to June 2025. The contents in this data package include soil in-situ nitrogen mineralization rates (measurements of net nitrification, net ammonification, and net mineralization), nutrient stocks (total carbon, total nitrogen, and organic matter), stable isotopes (carbon and nitrogen), and water volumetric measurements (water-filled pore space). Water level data related to each wetland location can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/2530253 (Kirker et al., 2024), related water geochemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3001967 (Forbes et al., 2025), and related surface water sediment chemistry data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3377325 (Molina Serpas et al., 2026). 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. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata and international generic sample numbers (IGSNs); (4) readme; (5) the field protocol; and (6) a subfolder with sample data. The sample data subfolder contains (1) net nitrification rate, (2) net ammonification rate, (3) areal net mineralization rate, (4) percent organic matter, (5) water-filled pore space, (6) total carbon content, (7) total nitrogen content, (8) stable carbon isotope (delta carbon-13), and (9) stable nitrogen isotope (delta nitrogen-15), and (10) methods codes. All files are .csv or .pdf.

13-C↗

DOE OSTI2026

SPRUCE Bud Cold Hardiness of Trees and Shrubs in Experimental Plots, Marcell Experimental Forest, Minnesota, 2021-2025

This dataset contains bud cold hardiness measurements from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site (Hanson et al, 2017) within the Marcell Experimental Forest in northern Minnesota, USA. Cold hardiness was quantified as the temperature at which low temperature exotherms (LTEs) were identified (i.e., the temperature at which supercooled water within a bud freezes) measured by differential thermal analysis (DTA). Buds were sampled at semi-regular intervals from September 2021 through May 2025 (2021-09-25 to 2025-05-21) across four seasons (2021-2022 through 2024-2025) for four co-occurring boreal peatland tree and shrub species: Picea mariana (PIMA), Larix laricina (LALA), Rhododendron groenlandicum (RHGR), and Chamaedaphne calyculata (CHCA). Samples were collected across the experiment's warming gradient (ambient to +9 degrees Celsius (C)) and pooled by species within each enclosure. These data were used to assess the species- and season-specific effects of experimental warming on cold hardiness and cold damage risk in boreal peatland vegetation (Campos-Arguedas et al, accepted). This dataset contains one data file in comma-separate values (*.csv) format. Additional metadata are provided: a data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Chamaedaphne calyculata↗

DOE OSTI2026

Data and scripts associated with “Non-random processes impacting organic matter chemistry are maximized in mid-order streams”

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data, model inputs, and analysis scripts associated with a manuscript on stream intermittency controls across spatial scales in Pacific Northwest watersheds

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).

Advanced Terrestrial Simulator↗