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DOE OSTI2026

Quantifying market volume sensitivity to material property modifications in polyhydroxybutyrate: A parametric analysis approach

Polyhydroxybutyrate (PHB), a biodegradable biopolymer, represents a promising alternative to petroleum-based thermoplastics. However, despite consistent market growth, PHB faces persistent commercialization challenges that limit widespread adoption. Existing research has focused predominantly on optimizing PHB production processes, leaving a critical gap in understanding which material property modifications would most effectively enhance market competitiveness. This study addresses this gap by systematically analyzing the relationship between polymer material properties and market performance using U.S. market data from 2008 to 2021 for 21 thermoplastic polymers across 19 material properties. We employed principal component regression to identify property modifications that could maximize market volume while reducing CO 2 emissions. Our parametric analysis revealed that two specific material properties – Hardness Shore A and Sheet Extrusion Temperature – significantly influence PHB marketability across different price points. Market simulations demonstrated that a 10% increase in Hardness Shore A could increase PHB market volume by 431.5 million kg while reducing emissions by 188.7 kg CO 2 . A similar 10% increase to Sheet Extrusion Temperature could yield a 297.5 million kg volume increase and a 99.2 kg CO 2 reduction in emissions. Critically, this approach is agnostic to the specific methods required to achieve these property changes, instead providing material scientists with quantitative, data-driven targets for R&D prioritization. Here, this framework offers a novel methodology for evaluating biopolymer competitiveness and supporting strategic decisions to accelerate PHB market adoption and contribute to decarbonization of the plastics industry.

09 BIOMASS FUELS↗

NASA NTRS1990

A study of mass data storage technology for rocket engine data

The results of a nine month study program on mass data storage technology for rocket engine (especially the Space Shuttle Main Engine) health monitoring and control are summarized. The program had the objective of recommending a candidate mass data storage technology development for rocket engine health monitoring and control and of formulating a project plan and specification for that technology development. The work was divided into three major technical tasks: (1) development of requirements; (2) survey of mass data storage technologies; and (3) definition of a project plan and specification for technology development. The first of these tasks reviewed current data storage technology and developed a prioritized set of requirements for the health monitoring and control applications. The second task included a survey of state-of-the-art and newly developing technologies and a matrix-based ranking of the technologies. It culminated in a recommendation of optical disk technology as the best candidate for technology development. The final task defined a proof-of-concept demonstration, including tasks required to develop, test, analyze, and demonstrate the technology advancement, plus an estimate of the level of effort required. The recommended demonstration emphasizes development of an optical disk system which incorporates an order-of-magnitude increase in writing speed above the current state of the art.

Ready, John F.↗

NASA NTRS2001

Science Autonomy in Robotic Exploration

Historical mission operations have involved: (1) return of scientific data; (2) evaluation of these data by scientists; (3) recommendations for future mission activity by scientists; (4) commands for these transmitted to the craft; and (5) the activity being undertaken. This cycle is repeated throughout the mission with command opportunities once or twice per day. For a rover, this historical cycle is not amenable to rapid long range traverses or rapid response to any novel or unexpected situations. In addition to real-time response issues, imaging and/or spectroscopic devices can produce tremendous data volumes during a traverse. However, such data volumes can rapidly exceed on-board memory capabilities prior to the ability to transmit it to Earth. Additionally, the necessary communication band-widths are restrictive enough so that only a small portion of these data can actually be returned to Earth. Such scenarios suggest enabling some science decisions to be made on-board the robots. These decisions involve automating various aspects of scientific discovery instead of the electromechanical control, health, and navigation issues associated with robotic operations. The robot retains access to the full data fidelity obtained by its scientific sensors, and is in the best position to implement actions based upon these data. Such an approach would eventually enable the robot to alter observations and assure only the highest quality data is obtained for analysis. Additionally, the robot can begin to understand what is scientifically interesting and implement alternative observing sequences, because the observed data deviate from expectations based upon current theories/models of planetary processes. Such interesting data and/or conclusions can then be prioritized and selectively transmitted to Earth; reducing memory and communications demands. Results of Ames' current work in this area will be presented.

Roush, Ted L.↗

NASA NTRS2001

Robotic Exploration: The Role of Science Autonomy

Historical mission operations have involved: (1) return of scientific data; (2) evaluation of these data by scientists; (3) recommendations for future mission activity by scientists; (4) commands for these transmitted to the craft; and (5) the activity being, undertaken. This cycle is repeated throughout the mission with command opportunities once or twice per day. For a rover, this historical cycle is not amenable to rapid long range traverses or rapid response to any novel or unexpected situations. In addition to real-time response issues, imaging and/or spectroscopic devices can produce tremendous data volumes during a traverse. However, such data volumes can rapidly exceed on-board memory capabilities prior to the ability to transmit it to Earth. Additionally, the necessary communication band-widths are restrictive enough so that only a small portion of these data can actually be returned to Earth. Such scenarios suggest enabling some science decisions to be made on-board the robots. These decisions involve automating various aspects of scientific discovery instead of the electromechanical control, health, and navigation issues associated with robotic operations. The robot retains access to the full data fidelity obtained by its scientific sensors, and is in the best position to implement actions based upon these data. Such an approach would eventually enable the robot to alter observations and assure only the highest quality data is obtained for analysis. Additionally, the robot can begin to understand what is scientifically interesting and implement alternative observing sequences, because the observed data deviate from expectations based upon current theories/models of planetary processes. Such interesting data and/or conclusions can then be prioritized and selectively transmitted to Earth; reducing memory and communications demands. Results of Ames' current work in this area will be presented.

Roush, Ted L.↗

NASA NTRSDate not supplied

Supporting Space Weather with the Geospace Dynamics Constellation

The Geospace Dynamics Constellation (GDC) mission, planned to launch at the end of the decade, is a six-satellite constellation that will fly through the ionosphere and thermosphere at around 400 km. While GDC is a science mission, its comprehensive instrumentation will not only contribute to our understanding of space weather phenomena in the ionosphere-thermosphere system, but will also provide valuable, space weather-relevant data. Data from GDC will be made available at low latency via real-time space weather data stream. We are working with operational partners to identify space weather data products and coordinate reception of the space weather beacon data. Alongside preparations for real-time GDC data streams, we are working to identify current model capabilities and needs, to ensure that space weather models that can make use of GDC measurements are moving along the Research-toOperations pipeline. Before GDC launch, Observing System Simulation Experiments (OSSEs) carried out with synthetic GDC data can be used to demonstrate the capabilities of models and to predict the impact of GDC data. Following launch, Observing System Experiments (OSEs) will demonstrate the impact of GDC space weather data. In preparation for GDC, the ITM space weather community should establish baseline metrics for space weather parameters of scientific and operational interest. These metrics, tracked over time before and after the launch of GDC, will allow us to track advancements in forecasting, nowcasting, and hindcasting of the ITM system and to trace the impact of scientific progress from space weather research into operations. By demonstrating the impact of real-time GDC data, specific data needs can be identified and prioritized for long term investment on future observing systems.

Katherine Garcia-Sage↗

NASA NTRS2015

Comparative Habitability of Transiting Exoplanets

Exoplanet habitability is traditionally assessed by comparing a planet’s semimajor axis to the location of its host star’s “habitable zone,” the shell around a star for which Earth-like planets can possess liquid surface water. The Kepler space telescope has discovered numerous planet candidates near the habitable zone, and many more are expected from missions such as K2, TESS, and PLATO. These candidates often require significant follow-up observations for validation, so prioritizing planets for habitability from transit data has become an important aspect of the search for life in the universe. We propose a method to compare transiting planets for their potential to support life based on transit data, stellar properties and previously reported limits on planetary emitted flux. For a planet in radiative equilibrium, the emitted flux increases with eccentricity, but decreases with albedo. As these parameters are often unconstrained, there is an “eccentricity-albedo degeneracy” for the habitability of transiting exoplanets. Our method mitigates this degeneracy, includes a penalty for large-radius planets, uses terrestrial mass–radius relationships, and, when available, constraints on eccentricity to compute a number we call the “habitability index for transiting exoplanets” that represents the relative probability that an exoplanet could support liquid surface water. We calculate it for Kepler objects of interest and find that planets that receive between 60% and 90% of the Earth’s incident radiation, assuming circular orbits, are most likely to be habitable. Finally, we make predictions for the upcoming TESS and James Webb Space Telescope missions.

Planet and satellite surfaces↗

DOE OSTI2022

National Community Solar Partnership Multifamily Affordable Housing Portfolio Screening Approach

Community solar offers multiple benefits to providers and residents of multifamily affordable housing (MFAH), including access to affordable renewable energy for low- and moderate-income households, tax credits and other financial benefits for property owners, and increased community resilience when paired with energy storage. Even so, MFAH providers interested in realizing these benefits may need support to identify where to start or how to expand and accelerate solar deployment across their property portfolios once they have completed their first few installations. This fact sheet describes a portfolio screening approach for MFAH providers to evaluate their multi-state building portfolios for community solar project viability. The approach was developed by the National Renewable Energy Laboratory as part of its support for the National Community Solar Partnership (NCSP) MFAH Collaborative. By utilizing NREL's companion Policy Screening Workbook in conjunction with the web-based tools PVWatts® Calculator and REopt™ (Renewable Energy Integration and Optimization), MFAH providers can take a data-informed approach to identifying and prioritizing buildings in their portfolios that are most amenable to community solar development.

community solar↗

NASA NTRS1973

Mission requirements for a manned earth observatory. Task 2: Reference mission definition and analyiss, volume 2

The mission requirements and conceptual design of manned earth observatory payloads for the 1980 time period are discussed. Projections of 1980 sensor technology and user data requirements were used to formulate typical basic criteria pertaining to experiments, sensor complements, and reference missions. The subjects discussed are: (1) mission selection and prioritization, (2) baseline mission analysis, (3) earth observation data handling and contingency plans, and (4) analysis of low cost mission definition and rationale.

Source record↗

NASA NTRS1982

Development of preliminary design concept for a multifunction display and control system for the Orbiter crew station. Task 4: Design concept recommendation

Application of multifunction display and control systems to the NASA Orbiter spacecraft offers the potential for reducing crew workload and improving the presentation of system status and operational data to the crew. A design concept is presented for the application of a multifunction display and control system (MFDCS) to the Orbital Maneuvering System and Electrical Power Distribution and Control System on the Orbiter spacecraft. The MFDCS would provide the capability for automation of procedures, fault prioritization and software reconfiguration of the MFDCS data base. The MFDCS would operate as a stand-alone processor to minimize the impact on the current Orbiter software. Supervisory crew command of all current functions would be retained through the use of several operating modes in the system. Both the design concept and the processes followed in defining the concept are described.

Spiger, R. J.↗

DOE OSTI2024

Integration of equitable resilience metrics into climate-informed electric utility planning processes: phase one

Working together, Sandia National Laboratories, Southern California Edison (SCE) - an Investor-Owned Utility (IOU) - and the California Public Utilities Commission (CPUC) are studying how electric utilities can use equity and resilience metrics to help inform the prioritization and sequencing of resilience-driven infrastructure investments. To this end, this project evaluated “Social Burden,” an equitable resilience metric which measures the potential impact of disruptions in access to non-electric critical services on people and estimates community resilience to these disruptions. The Social Burden was expanded to incorporate SCE’s existing equity metric and applied to evaluate the potential impacts from a range of climate-informed hypothetical outage scenarios developed under SCE’s 2022 Climate Adaptation Vulnerability Assessment. One baseline (“blue-sky”) state and eight different outage scenarios were evaluated to measure the potential impacts of the outages on non-electric infrastructure, critical services, and people. Key findings include: 1) the Social Burden framework is flexible enough to adapt to and build upon existing utility equity and/or resilience metrics, 2) Social Burden results highlight the high degree of non-electric service redundancy within the SCE service area with most (6/8) hypothetical outage scenarios predicted to increase people’s Social Burden by less than 10%; however, 3) access to critical services and people’s ability to obtain them is unequal and spatially clustered, meaning that there are some hypothetical outage scenarios (2/8) that will exert a higher toll on communities directly experiencing the outage as well as some nearby communities with pre-existing vulnerabilities. The report concludes with recommendations for potential use cases of the expanded Social Burden metric and identifies priority follow-on work. Potential use cases may include incorporating equity into IOU’s prioritization of climate resilience investments. Additionally, Social Burden analysis may provide additional data and insights to augment grid planning, potentially by identifying additional needs and/or prioritizing previously identified needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A Comparative Multi-System Approach to Characterizing Bioactivity of Commonly Occurring Chemicals

A 2019 retrospective study analyzed wristband personal samplers from fourteen different communities across three different continents for over 1530 organic chemicals. Investigators identified fourteen chemicals (G14) detected in over 50% of personal samplers. The G14 represent a group of chemicals that individuals are commonly exposed to, and are mainly associated with consumer products including plasticizers, fragrances, flame retardants, and pesticides. The high frequency of exposure to these chemicals raises questions of their potential adverse human health effects. Additionally, the possibility of exposure to mixtures of these chemicals is likely due to their co-occurrence; thus, the potential for mixtures to induce differential bioactivity warrants further investigation. This study describes a novel approach to broadly evaluate the hazards of personal chemical exposures by coupling data from personal sampling devices with high-throughput bioactivity screenings using in vitro and non-mammalian in vivo models. To account for species and sensitivity differences, screening was conducted using primary normal human bronchial epithelial (NHBE) cells and early life-stage zebrafish. Mixtures of the G14 and most potent G14 chemicals were created to assess potential mixture effects. Chemical bioactivity was dependent on the model system, with five and eleven chemicals deemed bioactive in NHBE and zebrafish, respectively, supporting the use of a multi-system approach for bioactivity testing and highlighting sensitivity differences between the models. In both NHBE and zebrafish, mixture effects were observed when screening mixtures of the most potent chemicals. Observations of BMC-based mixtures in NHBE (NHBE BMC Mix) and zebrafish (ZF BMC Mix) suggested antagonistic effects. In this study, consumer product-related chemicals were prioritized for bioactivity screening using personal exposure data. High-throughput high-content screening was utilized to assess the chemical bioactivity and mixture effects of the most potent chemicals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Quality Assurance of the Irradiated EBR-II Metallic Fuels Data

The U.S. DOE-NE’s Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) and the NE-4 Advanced Fuels Campaign (AFC) have jointly undertaken efforts to qualify post-irradiation examination (PIE) data for Experimental Breeder Reactor II (EBR-II) and Fast Flux Test Facility (FFTF) metallic fuels. Among the thousands of PIE datasets in the Metallic Fuels Irradiation & Physics Database (FIPD), data from selected EBR-II experiments have been prioritized as the most valuable to the U.S. nuclear industry for licensing activities related to metallic fuel-based advanced fast reactors. This report summarizes the general data qualification progress of the EBR-II and FFTF experiments, with focus on the selected EBR-II experiments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2018

More Efficient Precipitation Rate Data Acquisition with the STORM Swath-Based Analysis Tool

A multitude of locations exist for acquiring satellite-derived precipitation rate data. Because the volume of data exceeds what can reasonably be analyzed manually, online tools have been developed to help researchers make more informed data acquisition decisions. Some interfaces provide sample images generated from prioritized variables to enable efficient visual inspection, while others include the ability to subset geographically to reduce the volume of the data to the area of interest. Building upon such value-added services is the STORM (the NASA Precipitation Processing System (PPS) data product ordering system) Swath-Based Analysis Tool (https://storm.pps.eosdis.nasa.gov/storm/Analysis.jsp). With it, users can compare overflights of multiple precipitation satellites simultaneously and from this make determinations about which swaths will best serve their research. They can preview these overflights with two distinct visualization tools. Finally, they can then download geographically subset swaths that have precipitation rate characteristics matching their research needs. A user starts by making up to six selections. They then select their date range of interest going back to 1997. Finally, they select a geographical region either by inputting latitude-longitude corners of a rectangle or drawing that rectangle on an interactive map. The system processes each file, determining aggregate statistics about the precipitation rates within the selected region as observed by each instrument. These statistical values are added to a time series graph as a "time remaining" counter is continuously updated. The chart is fully interactive, with detailed values displayed upon mouseover, and numerous options available upon clicking each point. Since the user is likely to need the relevant files for their own research, they can generate an order from their request. All files are geographically subset to the region of interest, and the user can use the statistical values to limit which files are added to the order. This flexibility helps to ensure researchers are only downloading the files they need for their work, making for a more efficient process both from a time-consumption and a storage perspective. This paper will introduce the Swath-Based Analysis Tool and explore its numerous features. It will discuss several use cases for precipitation scientists.

remote sensing↗

DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Strawman Philosophical Guide for Developing International Network of GPM GV Sites

The creation of an international network of ground validation (GV) sites that will support the Global Precipitation Measurement (GPM) Mission's international science programme will require detailed planning of mechanisms for exchanging technical information, GV data products, and scientific results. An important component of the planning will be the philosophical guide under which the network will grow and emerge as a successful element of the GPM Mission. This philosophical guide should be able to serve the mission in developing scientific pathways for ground validation research which will ensure the highest possible quality measurement record of global precipitation products. The philosophical issues, in this regard, partly stem from the financial architecture under which the GV network will be developed, i.e., each participating country will provide its own financial support through committed institutions -- regardless of whether a national or international space agency is involved.At the 1st International GPM Ground Validation Workshop held in Abingdon, UK in November-2003, most of the basic tenants behind the development of the international GV network were identified and discussed. Therefore, with this progress in mind, this presentation is intended to put forth a strawman philosophical guide supporting the development of the international network of GPM GV sites, noting that the initial progress has been reported in the Proceedings of the 1st International GPM GV Workshop -- available online. The central philosophical issues themselves, all flow from the fact that each participating institution can only bring to the table, GV facilities and scientific personnel that are affordable to the sanctioning (funding) national agency (be that a research, research-support, or operational agency). This situation imposes on the network, heterogeneity in the measuring sensors, data collection periods, data collection procedures, data latencies, and data reporting capabilities. Therefore, in order for the network to be effective in supporting the central scientific goals of the GPM mission, there must be a basic agreed upon doctrine under which the network participants function vis-a-vis: (1) an overriding set of general scientific requirements, (2) a minimal set of policies governing the free flow of GV data between the scientific participants, (3) a few basic definitions concerning the prioritization of measurements and their respective value to the mission, (4) a few basic procedures concerning data formats, data reporting procedures, data access, and data archiving, and (5) a simple means to differentiate GV sites according to their level of effort and ability to perform near real-time data acquisition - data reporting tasks. Most important, in case they choose to operate as a near real-time data collection-data distribution site, they would be expected to operate under a fairly narrowly defined protocol needed to ensure smooth GV support operations. This presentation will suggest measures responsive to items (1) - (5) from which to proceed,. In addition, this presentation will seek to stimulate discussion and debate concerning how much heterogeneity is tolerable within the eventual GV site network, given that the any individual GV site can only be considered scientifically useful if it supports the achievement of the central GPM Mission goals. Only ground validation research that has a direct connection to the space mission should be considered justifiable given the overarching scientific goals of the mission. Therefore each site will have to seek some level of accommodation to what the GPM Mission requires in the way of retrieval error characterization, retrieval error detection and reporting, and generation of GV data products that support assessment and improvement of the mission's standard precipitation retrieval algorithms. These are all important scientific issues that will be best resolved in open scientific debate.

Smith, Eric A.↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all 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.

EARTH SCIENCE > LAND SURFACE > SOILS↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all 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.

EARTH SCIENCE > LAND SURFACE > SOILS↗

NASA NTRS2015

Prioritized Contact Transport Stream

A detection process, contact recognition process, classification process, and identification process are applied to raw sensor data to produce an identified contact record set containing one or more identified contact records. A prioritization process is applied to the identified contact record set to assign a contact priority to each contact record in the identified contact record set. Data are removed from the contact records in the identified contact record set based on the contact priorities assigned to those contact records. A first contact stream is produced from the resulting contact records. The first contact stream is streamed in a contact transport stream. The contact transport stream may include and stream additional contact streams. The contact transport stream may be varied dynamically over time based on parameters such as available bandwidth, contact priority, presence/absence of contacts, system state, and configuration parameters.

Hunt, Walter Lee, Jr.↗