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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A global dataset of terrestrial biological nitrogen fixation

Biological nitrogen fixation (BNF) is the main natural source of new nitrogen inputs in terrestrial ecosystems, supporting terrestrial productivity, carbon uptake, and other Earth system processes. We assembled a comprehensive global dataset of field measurements of BNF in all major N-fixing niches across natural terrestrial biomes derived from the analysis of 376 BNF studies. The dataset comprises 32 variables, including site location, biome type, N-fixing niche, sampling year, quantification method, BNF rate (kg N ha −1 y −1 ), the percentage of nitrogen derived from the atmosphere (%N dfa ), N fixer or N-fixing substrate abundance, BNF rate per unit of N fixer abundance, and species identity. Overall, the dataset combines 1,207 BNF rates for trees, shrubs, herbs, soil, leaf litter, woody litter, dead wood, mosses, lichens, and biocrusts, 152 herb %N dfa values, 1,005 measurements of N fixer or N-fixing substrate abundance, and 762 BNF rates per unit of N fixer abundance for a total of 424 species across 66 countries. This dataset facilitates synthesis, meta-analysis, upscaling, and model benchmarking of BNF fluxes at multiple spatial scales.

Reis Ely, Carla R. [Oregon State Univ., Corvallis,↗
DOE OSTI2025

Seeing the forest for all the trees: Mycorrhizal‐associated nutrient economies are modulated by stem density and the synchrony between overstorey and understorey communities

1. Identifying relationships between plant communities and soil characteristics is a critical step in understanding the consequences of species gains and losses in ecosystems. The mycorrhizal associated nutrient economy (MANE) hypothesis predicts that the degree to which tree species and their mycorrhizal associates affect soil properties is driven by the relative dominance of different mycorrhizal types (often measured by relative basal area). While this approach emphasizes the importance of canopy trees, it does not account for how other factors (e.g. the density and composition of understorey trees) may alter tree–soil relationships. 2. We analysed tree–soil data from an eastern deciduous forest in southern Indiana, USA that contains >29,000 georeferenced stems, including 21 species that associate with arbuscular mycorrhizal (AM) fungi and 14 that associate with ectomycorrhizal (ECM) fungi. We sampled soils (upper 5 cm) from across the plot and modelled soil characteristics to tree communities. Given differences in soil characteristics among AM-dominated and ECM-dominated neighbourhoods, we hypothesized that relationships between tree-mycorrhizal dominance and soils would be affected by both the density of stems in the plot (owing to ‘Zinke’ individual plant effects) and composition of the understorey trees (owing to ‘trait divergence effects’) 3. In support of our Zinke hypothesis, we found that the relationships between tree-mycorrhizal dominance and soil variables (soil pH, nitrification rates and carbon to nitrogen ratio) were strengthened as stand density increased. In support of the trait divergence hypothesis, we found that as the mismatch between overstorey and understorey composition increased (e.g. AM-dominated understories beneath ECM-dominated overstories and vice versa), the relationship between tree dominance and soil variables weakened. We were able to use these insights to create mycorrhizal metrics to predict soil variables that were sensitive to the structural composition of neighbourhoods. 4. Synthesis. Our results indicate that relationships between plants and soils in forests are not only shaped by dominant overstorey trees but also the density and composition of understorey trees. Thus, efforts to predict the ecosystem consequences of species gains and losses may benefit from considering these elements of forest structure and not only the basal area of the dominant trees.

MANE framework↗
DOE OSTI2025

The Journal of Open Source Software (JOSS): Bringing Open-Source Software Practices to the Scholarly Publishing Community for Authors, Reviewers, Editors, and Publishers

Open-source software (OSS) is a critical component of open science, but contributions to the OSS ecosystem are systematically undervalued in the current academic system. The Journal of Open Source Software (JOSS) contributes to addressing this by providing a venue (that is itself free, diamond open access, and all open-source, built in a layered structure using widely available elements/services of the scholarly publishing ecosystem) for publishing OSS, run in the style of OSS itself. A particularly distinctive element of JOSS is that it uses open peer review in a collaborative, iterative format, unlike most publishers. Additionally, all the components of the process—from the reviews to the papers to the software that is the subject of the papers to the software that the journal runs—are open. We describe JOSS’s history and its peer review process using an editorial bot, and we present statistics gathered from JOSS’s public review history on GitHub showing an increasing number of peer reviewed papers each year. We discuss the new JOSSCast and use it as a data source to understand reasons why interviewed authors decided to publish in JOSS. JOSS’s process differs significantly from traditional journals, which has impeded JOSS’s inclusion in indexing services such as Web of Science. In turn, this discourages researchers within certain academic systems, such as Italy’s, which emphasize the importance of Web of Science and/or Scopus indexing for grant applications and promotions. JOSS is a fully diamond open-access journal with a cost of around US$\$$5 per paper for the 401 papers published in 2023. The scalability of running JOSS with volunteers and financing JOSS with grants and donations is discussed.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

A census of fish passage facilities at US hydropower developments across the conterminous United States

Hydropower provides reliable and secure electricity and contributes significantly to the flexibility and stability of the US electricity grid. Hydropower dams generally are aquatic barriers and hazards to migratory fish in rivers. Fish passage facilities mitigate risks from hydropower to migratory fish, but information on these facilities is incomplete for the conterminous United States (CONUS). Here, we present the first CONUS-scale dataset of fish passage facilities at US hydropower developments in over 30 years. The existence of fish passage facilities (presence or absence) was specified for 1909 hydropower features, 390 of which had at least one facility. Most features had a single passage facility that provided passage in only one direction, with downstream being more common than upstream passage. Bypasses and ladders accounted for 60 % of the fish passage facility types. While we documented 659 fish passage facilities across CONUS, facilities were most common in the New England, Pacific Northwest, Mid-Atlantic, and Great Lakes regions. In general, fish passage facilities were more common at features that were closer to the ocean, at lower elevations, and at shorter dams, but not related to installed electrical generation capacity. Hydrologic sub-basins containing salmonids also contained the largest number of hydropower features, but the proportion of features with passage was generally higher in sub-basins containing multiple migratory taxa. This census provides valuable information on existing fish passage mitigation and is a benchmark to gauge progress toward a modernized hydropower fleet that provides affordable, reliable energy while protecting fishery resources and river ecosystems.

13 HYDRO ENERGY↗
DOE OSTI2025

Workshop Report: Rural Electric Cooperative Distributed Energy Resource Business Model Development Workshops

The United States' energy future is being shaped by decades of technology innovation, rapidly changing consumer requirements, and governmental focus on securing reliable, low-cost energy sources. With expanded electrification and increased emphasis on grid reliability coupled with the heightened complexity and risks associated with transmission infrastructure, the use of distributed energy resources (DERs) is more commonly being considered a safe, efficient, and cost-effective way to maintain grid resilience and reliability. Amidst improvements in performance, decreasing costs, and unprecedented federal policy support for DERs, electric cooperatives are considering new opportunities to use these assets to ensure secure and reliable operations for millions of customers. In light of these developments, a series of workshops were held to collaborate on the development of business models for DERs in rural electric cooperative ecosystems. The first workshop was held July 11, 2024, at the Tri-State Generation and Transmission Association Inc. headquarters in Westminster, Colorado (hereafter referred to as the "Tri-State Workshop"). The second workshop was hosted by PNGC Power on Oct. 9, 2024, in Portland, Oregon (hereafter referred to as the "PNGC Power Workshop"). These workshops convened representatives from G&Ts, distribution cooperatives,2 the energy industry, the financial community, the U.S. government, national and regional organizations, and national laboratories to share perspectives on the unique needs, challenges, and opportunities that they face as DERs are further integrated into the grid. The workshops were designed to address the following objectives: (1) develop the enabling attributes of electric cooperative DER business models, (2) explore pathways for generating community benefits from these assets, and (3) identify technical challenges and market risks for DERs in rural electric cooperative service territories. This report outlines key findings and major themes identified by workshop participants that federal agencies, G&Ts, distribution cooperatives, and other entities can consider to advance electric cooperative DER business models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. Here, we focused our analysis on standard meteorological variables; fluxes of CO 2 , sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO 2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m -2 y -1 , mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m -2 y -1 ). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.

Carbon cycle↗
DOE OSTI2022

The Climatic Impact‐Driver Framework for Assessment of Risk‐Relevant Climate Information

Abstract The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the “climatic impact‐driver” (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as “physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions.” We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Electric Vehicle Lithium-Ion Battery Life Cycle Management

There is no question that electric vehicles (EVs), which are key for addressing climate change impacts from the transportation sector, are quickly gaining popularity and availability. With limits and other challenges related to the supply of critical battery minerals, maximizing the use of EV batteries and ensuring recovery of battery minerals is imperative. Therefore, proper end-of-life-cycle management (reuse and recycling) of these batteries must be part of the EV ecosystem from the perspective of both the supply chain and environmental footprint. Second use of batteries for energy storage systems extends the initial life of these resources and provides a buffer until economical material recovery facilities are in place. Although there are multiple pathways to recycling and recovery of materials, new recovery technologies are moving toward commercially available hydrometallurgy and promising direct recycling, which analysis has shown has the lowest overall carbon footprint. Strategically locating these plants close to battery collection further reduces transportation and thus recycling and recovering costs. Proper life cycle management could alleviate future lithium-ion battery materials supply chains for EVs. Governments and other stakeholders around the world have started initiatives and proposed regulations to address the challenges associated with life cycle management of EV lithium batteries. Finally, as manufacturers are increasingly faced with the likelihood of such extensive regulatory requirements, attention should be given to new designs, sales, and service models that can reduce life cycle management costs.

25 ENERGY STORAGE↗
DOE OSTI2025

Managing ecosystem damage from extreme events

Large disturbances to ecosystems can severely impact the stability of a region's natural resources, habitats, and outdoor recreation. Because extreme events can be large and relatively infrequent, they test institutional capacity to support recovery and restoration. Here, when hurricanes and other large-scale disturbances like wildfires occur, much of the impacted landscape receives little to no active management. Ecosystems are often allowed to either recover or transition without much direct intervention, and successional dynamics are sometimes altered by novel invasive species, management history, or other environmental changes. Recovery and restoration are especially challenging for landscapes with highly fragmented private ownership, such as the forests of the eastern US. Acting alone, non-industrial private forest landowners have little capacity to effectively respond to unexpected forest loss and to oversee forest recovery, as the scale of actions needed after extreme events may require cooperation across ownerships or jurisdictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Intrinsic and environmental drivers of pairwise cohesion in wild Canis social groups

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Overestimated natural biological nitrogen fixation translates to an exaggerated CO 2 fertilization effect in Earth system models

CO 2 fertilization of the terrestrial biosphere is limited by nitrogen. Biological nitrogen fixation (BNF) is the dominant natural nitrogen source to the terrestrial biosphere and can alleviate nitrogen limitation but is poorly constrained in Earth system models (ESMs). Here, in this study, we compare terrestrial BNF from an ensemble of ESMs of the 6th Coupled Model Intercomparison Project to a new global synthesis of observations across natural and agricultural biomes. We find that compared to observations, ESMs underestimate agricultural BNF but overestimate natural BNF in the present day by over 50%. Natural BNF is overestimated in the most productive ecosystems that contribute most to the terrestrial carbon sink (forests and grasslands). ESMs with different BNF representations yield a range of BNF responses to CO 2 enrichment. Some ESMs with phenomenological representations of BNF predict a natural BNF increase in response to a doubling of CO 2 that aligns with a meta-analysis of CO 2 enrichment experiments (31% increase) but fail to account for the substantial carbon cost of BNF. In contrast, ESMs with mechanistic representations of BNF account for its carbon cost as well as its regulation by nitrogen limitation but overestimate the BNF response to a doubling of CO 2 (135% increase). Overall, all current BNF representations in ESMs fall short of fully capturing its response to rising atmospheric CO 2 . Finally, we find a positive correlation between modeled present-day natural BNF and the CO 2 fertilization effect across ESMs, suggesting that overestimated natural BNF translates to an exaggerated CO 2 fertilization effect of approximately 11% in ESMs.

Biological nitrogen fixation↗
DOE OSTI2026

Linking Community‐Climate Disequilibrium to Ecosystem Function

Turnover in species composition often lags behind the pace of climate change, resulting in mismatches between climate and communities. However, the impact of these community‐climate disequilibria on ecosystem functions is rarely considered, and current methods for measuring disequilibria assume that species ranges were, until recently, in equilibrium with climate. Here, in this work, we develop a simple theoretical model to address both of these problems by linking community‐climate disequilibrium with ecosystem functioning. We show how disequilibrium can impair functioning in the near‐term even when climate change is expected to enhance functioning in the long‐term. Responses are most likely to change over time in communities where turnover is slow, the impact of disequilibrium counteracts the direct effects of climate on ecosystem function, and pre‐existing disequilibrium is large. These findings emphasise the importance of precise and unbiased estimates of community‐climate disequilibria for improving ecological forecasts. By fitting our model to time series of both climate and ecosystem function from a metacommunity simulation, we show the potential for community‐climate disequilibrium to be inferred without direct knowledge about species' distributions or climatic tolerances. We end by outlining a research agenda to apply dynamic disequilibrium concepts and test novel hypotheses across diverse ecosystems.

climate change↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output 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).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank 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. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

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 “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this 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 data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank 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. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ChargeX OCPI Recommendations

The Open Charge Point Interface (OCPI) is an open protocol that enables electric vehicle (EV) charging systems to work together across networks. It supports communication and data sharing between Charge Point Operators (CPOs), who manage charging stations, and e-Mobility Service Providers (eMSPs), who provide charging services to EV drivers. OCPI facilitates functions like user authorization, remote charge point control, charging session data exchange, and billing through Charge Detail Records (CDRs). This allows EV roaming, so drivers can charge at different networks without multiple accounts. As the EV market grows due to increased adoption and technological advancements, OCPI faces higher demands. This has revealed issues with CDR format consistency, timestamp standardization across regions, transmission of EV-side error codes for troubleshooting, and support for new use cases. These challenges can affect operations and user experience, particularly as the industry starts considering Vehicle-to-Grid (V2G) systems, where EVs supply energy to the grid, and Vehicle-to-Everything (V2X) technologies for broader energy interactions. Using feedback from the ChargeX Diagnostics taskforce discussions, industry 1-on-1 meetings, technical standards, and OCPI’s evolution through versions (e.g., OCPI 2.1.1, 2.2, and 2.2.1), this report identifies these issues and suggests practical recommendations. These aim to improve interoperability, streamline operations, and prepare OCPI for future trends in the EV charging ecosystem.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2023

SPRUCE Climate Warming and Elevated CO2 Rapidly Alter Peatland Soil Carbon Sources and Stability: Supporting Data

This data set reports a suite of complementary biogeochemical analyses of peat samples from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) experiment. Results were collected using quantitative molecular analysis of bulk soil carbon to assess the stability of soil organic carbon following whole-ecosystem warming and exposure to elevated carbon dioxide concentrations (eCO2). Targeted soil organic carbon components include solvent-extractable compounds (alkanoic acids, alkanols, alkanes, steroids, and terpenoids), ester-bound hydrolysable biopolymers (cutin and suberin markers), lignin phenols, and pyrogenic carbon. Bulk peat samples were analysed by Soxhlet extraction and solid phase separation for solvent-extractable compounds, alkaline hydrolysis to extract hydrolysable biopolymers, copper (II) oxide oxidation to extract lignin phenols and benzene polycarboxylic acids (BPCAs) as an approximation of pyrogenic carbon. Samples were analysed by gas chromatography (GC) equipped with a flame ionization detector (GC-FID) and compound identification was performed on GC coupled to mass selective detector (MS) for solvent-extractable compounds, ester-bound hydrolysable biopolymers and lignin phenols, and high-performance liquid chromatograph (HPLC) for pyrogenic carbon. Results are presented in Ofiti et al. (accepted). The experimental work was conducted on samples collected in August 2018 at the SPRUCE climate manipulation experiment in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). Samples were collected and later analysed in a 10 cm increments over 0 to 50 cm depth and 25 cm intervals from 50 to 75 cm. Samples were analyzed for lignin phenols over 0 to 30 cm depth. This data set contains one file in comma separate (*.csv) format. This dataset contains data used to produce: Ofiti, N.O.E., Schmidt, M.W.I., Abiven, S., Hanson, P.J., Iversen, C.M., Wilson, R.M., Kostka, J.E., Wiesenberg, G.L.B., Malhotra, A. 2023. Climate warming and elevated CO2 rapidly alter peatland soil carbon sources and stability. Nat Commun 14, 7533. https://doi.org/10.1038/s41467-023-43410-z.

SPRUCE experiment, Marcell Experimental Forest, so↗
DOE OSTI2020

SPRUCE Plot-Level Water Table Data Assessments for Absolute Elevations and Height with Respect to Mean Hollows Beginning in 2015

This data set provides a record of the half-hourly automated water table depth data collected for 12 SPRUCE plots (4, 6, 7, 8, 10, 11, 13, 14, 16, 17, 19, 20, and 21) beginning during deep peat heating (DPH) in 2015 and continuing throughout the whole ecosystem warming (WEW) manipulations for the SPRUCE Project (Hanson et al. 2017). The data currently cover 2015-05-12 through 2024-12-31 and will be updated periodically throughout the duration of the SPRUCE project. Also provided are daily plot normalized data for water table depths with respect to mean hollow locations within each plot. The hollow referenced data provide the basis for the intercomparison of peat saturation or aeration across treatment plots. A limited data set for manual water table observations made periodically from 2015-2022 are also provided as a reference to the plot center automated observations. In August 2015, WEW was initiated at 5 warming levels (+0, +2. +4.5, +6.75 and +9 °C) with 2 plots per warming level. DPH measurements were underway before the initiation of WEW heating treatments and both are expected to operate through 2025. Elevated CO2 treatments were added in June 2016 (Enclosures 4, 10, 11, 16 and 19). All measurements were made at the 8.1-ha S1 bog forest site in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). This dataset contains three data files in comma separated (*.csv) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗