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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SPRUCE Methane Transport in Plants at S1 Bog, Marcell Experimental Forest, Minnesota, 2017-2019

This data set contains measurements of methane (CH4) transport by plants (both ground-layer and trees) and diffusion, as well as whole-plot emissions, taken in September 2018 and June 2019 in S1 Bog outside of the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experimental enclosures. Additionally, CH4 and carbon dioxide (CO2) stable isotope data in porewater and atmospheric emissions were taken in July 2017 in the SPRUCE enclosures to explore the relative magnitude of CH4 oxidation. Episodic ebullition rates for S1 Bog are taken from Gill et al. (2017). Methane transport is an important component of many ecosystem models of peatlands. The results were compared to two methane models that have been developed for the SPRUCE project, ELM-SPRUCE (Earth Land Model) and TECO_SPRUCE (Terrestrial ECOsystem model). This dataset contains six data files in comma separate (.csv) format. Additional metadata are provided: six data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2024

A Biochar Classification System and Associated Test Methods

In this chapter, a biochar classification system related to its use as soil amendment is proposed. This document builds upon previous work: “Standardized product definition and product testing guidelines for biochar that is used in soil” (IBI, 2015) (aka IBI Biochar Standards) and “Guidelines for biochar production for a sustainable production of biochar: European Biochar Certificate” (EBC, 2012-2022) (aka EBC Biochar Standards). It should be noted that the EBC guidelines constitute the basis for biochar certification through the independent government accredited inspection agency, Bio.Inspecta AG. The present document constrains its scope to materials with properties that satisfy the criteria for biochar as defined by either the IBI Biochar Standards or the EBC Standards (Annex I), as required by the current methodologies developed for accounting carbon credits associated with the biochar use in the voluntary market (e.g., Verra, Puro.Earth). The classification system envisions enabling stakeholders and commercial entities to (i) identify the most suitable biochar to fulfil the requirements for a particular soil and/or land-use, (ii) distinguish the application of biochar for specific niches (e.g., soilless agriculture), and (iii) assess the climate-impact value based on biochar properties, production efficiency, and alternative feedstock uses. It is based on the best current knowledge and will need to be improved as new data and knowledge become available in the scientific literature.

biochar, climate change impacts, soil amendment, c↗
DOE OSTI2023

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A critical review of operational strategies for the management of harmful algal blooms (HABs) in inland reservoirs

Occurrences of freshwater harmful algal blooms (HABs) are increasing on a global scale, largely in part due to increased nutrient input and changing climate patterns. While reservoir management strategies that can influence phytoplankton are known, there is no published guideline or protocol for the management of harmful algal blooms. There is a need to establish what factors are the predominant drivers of blooms, and how common reservoir management strategies specifically influence each factor. The following literature review seeks to establish the benefits and drawbacks of operational management strategies that currently exist. The main focus is altering hydrodynamic conditions (hypolimnetic withdrawals, surface flushing, pulsed inflow, artificial mixing), in order to induce environmental changes within the reservoir itself. Since excess nutrients are one of the biggest contributors to worsening bloom conditions, internal nutrient dynamics and reduction are also discussed. Additionally, we review the predominant seasonal factors (stratification, light, temperature, and wind) that affect likelihood of bloom occurrence and duration. In conclusion, the ultimate objective of this review is to increase understanding of the relationships between HAB drivers and reservoir operations in order to inform the development of data, modeling, and management strategies for the prevention and mitigation of blooms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗
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

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

This repository contains the data and code associated with the paper titled "Reply to ‘The challenge of defining effectively-no-snow’" published in Nature Reviews Earth and Environment, 2026. In this reply, we argue that the 10th percentile of peak SWE (Snow Water Equivalent), which we propose in the original article, can be used as intended given it's a standardized, impact-based benchmark for comparing snow conditions across regions, not as a literal measure of snow absence. We present new evidence with SNOwpack TELemetry (SNOTEL) data showing that years meeting the threshold are overwhelmingly associated with subsequent drought (given United States Drought Monitor conditions), supporting its hydrologic and societal relevance. We conclude that while the distinction between "effectively no snow" and "zero snow" should be clearly communicated, the original definition remains appropriate for assessing impacts on snow-dependent water systems. The file code_nree_ML_reply_2026.Rmd contains the main processing scripts which analyze the SNOTEL data. Data from the US Drought Monitor was downloaded at: https://usdmdataservices using the Get Drought Severity Statistics By Area Percent' option, saved to the *_HUC4_delineated.csv files (Hydrologic Unit Code), which are labeled accordingly. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

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

SPRUCE Photosynthesis and Respiration of Picea mariana and Larix laricina in SPRUCE Experimental Plots, 2019

This dataset contains physiological, morphological, and chemical measurements of the two dominant coniferous species, Picea mariana and Larix laricina, in August 2019 (2019-08-20 to 2019-08-22) at the SPRUCE (Spruce and Peatland Responses under Changing Environments) experiment site in the Marcell Experimental Forest in northern Minnesota, USA. These observations help to assess the effects of whole ecosystem scale warming and elevated carbon dioxide (CO2) concentrations on peatland ecosystems. Measurements include light-saturated photosynthesis and foliar dark respiration measurements under standard conditions and growth conditions involving varying temperatures and atmospheric CO2 concentrations, as well as leaf morphology measurements (leaf mass per unit leaf area) and nitrogen content based on mass and leaf area. Net photosynthesis and dark respiration measurements were taken using portable photosynthesis systems (LI6400XT, LI6800, LI-COR Biosciences, USA). 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.

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 OSTI2024

First-principles and cluster expansion study of the effect of magnetism on short-range order in Fe–Ni–Cr austenitic stainless steels

Short-range order (SRO), the regular and predictable arrangement of atoms over short distances, alters the mechanical properties of technologically relevant structural materials such as medium/high entropy alloys and austenitic stainless steels. In this study, we present a generalized spin cluster expansion (CE) model and show that magnetism is a primary factor influencing the level of SRO present in austenitic Fe-Ni-Cr alloys. The spin CE consists of a chemical cluster expansion combined with an Ising model for Fe-Ni-Cr austenitic alloys. It explicitly accounts for local magnetic exchange interactions, thereby capturing the effects of finite temperature magnetism on SRO. Model parameters are obtained by fitting to a first-principles data set comprising both chemically and magnetically diverse FCC configurations. The magnitude of the magnetic exchange interactions are found to be comparable to the chemical interactions. Compared to a conventional implicit magnetism CE built from only magnetic ground state configurations, the spin CE shows improved performance on several experimental benchmarks over a broad spectrum of compositions, particularly at higher temperatures due to the explicit treatment of magnetic disorder. We find that SRO is strongly influenced by alloy Cr content, since Cr atoms prefer to align antiferromagnetically with nearest neighbors but become magnetically frustrated with increasing Cr concentration. Using the spin CE, we predict that increasing the Cr concentration in typical austenitic stainless steels promotes the formation of SRO and increases order-disorder transition temperatures. Furthermore, this study underscores the significance of considering magnetic interactions explicitly when exploring the thermodynamic properties of complex transition metal alloys. It also highlights guidelines for customizing SRO through adjustments of alloy composition.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PV Performance Modeling and Stakeholder Engagement (Final Technical Report)

This core capability project’s objective is to increase the value of photovoltaic (PV) performance models by improving their functionality, demonstrating, and quantifying their validity, and offering a wide range of stakeholder engagement opportunities. In FY22-24, we developed new and improved modeling algorithms and functions to represent PV performance more accurately in a variety of environments and conditions. The “Model parameter toolkit” was developed and includes functions to translate between different module temperature models, incidence angle modifier models, and single-diode models. A new modeling capability named “PV Atlas” was also developed leveraging Sandia’s High Performance Computing resources. This capability allows us to investigate several questions and provide climate-specific best practices and geographic data files; all these are hosted on an interactive website on Sandia’s GitHub and can be used for training, system optimization, or to provide best practices for uncertainty reduction. For model validation, we published high-quality PV performance, and weather data; these data are well documented, filtered, and processed for quality and include examples on how to run PV simulations. We also developed well documented, standardized methods for validating PV models and ran independent model validation and 2 blind modeling intercomparisons engaging with 49 organizations from 17 countries. We co-led and contributed to a growing, well documented and maintained suite of open-source functions for PV modeling (i.e., the pvlib-python) and we outreached to the PV modeling stakeholders via the PVPMC workshops and web resources. In addition, this project supported US representation and leadership for the International Energy Agency (IEA) PVPS Task 13; specifically, members of our team led and supported 3 subtasks on: 1) Best practices for the optimization of bifacial photovoltaic tracking, 2) Extreme weather events and their multiple impact on PV power plants: Risks, failure mechanisms and mitigation strategies, and 3) Best practice guidelines for the use of economic and technical Key Performance Indicators (KPIs). This project resulted in the publications of 14 peer reviewed journal papers, 37 conference presentations, 6 SAND reports, 5 public datasets and 6 new webpages on the PVPMC website. It supported the release of 13 pvlib-python versions where 28 enhancements were from this PV Performance Modeling project. We co-organized 5 PVPMC workshops in FY22-24 with the participation of 214 unique institutions and around 700 participants. The PVPMC website was redesigned, and its reliability was improved; it receives over 50,000 visitors/year from 202 unique countries.

14 SOLAR ENERGY↗
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 OSTI2025

Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)

This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland. The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

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

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

54 ENVIRONMENTAL SCIENCES↗
DOE 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]↗