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DOE OSTI2016

Seamless Digital Environment – Plan for Data Analytics Use Case Study

The U.S Department of Energy Light Water Reactor Sustainability (LWRS) Program initiated research in to what is needed in order to provide a roadmap or model for Nuclear Power Plants to reference when building an architecture that can support the growing data supply and demand flowing through their networks. The Digital Architecture project published report Digital Architecture Planning Model (Oxstrand et. al, 2016) discusses things to consider when building an architecture to support the increasing needs and demands of data throughout the plant. Once the plant is able to support the data demands it still needs to be able to provide the data in an easy, quick and reliable method. A common method is to create a “one stop shop” application that a user can go to get all the data they need. The creation of this leads to the need of creating a Seamless Digital Environment (SDE) to integrate all the “siloed” data. An SDE is the desired perception that should be presented to users by gathering the data from any data source (e.g., legacy applications and work management systems) without effort by the user. The goal for FY16 was to complete a feasibility study for data mining and analytics for employing information from computer-based procedures enabled technologies for use in developing improved business analytics. The research team collaborated with multiple organizations to identify use cases or scenarios, which could be beneficial to investigate in a feasibility study. Many interesting potential use cases were identified throughout the FY16 activity. Unfortunately, due to factors out of the research team’s control, none of the studies were initiated this year. However, the insights gained and the relationships built with both PVNGS and NextAxiom will be valuable when moving forward with future research. During the 2016 annual Nuclear Information Technology Strategic Leadership (NITSL) group meeting it was identified would be very beneficial to the industry to support a research effort focused on data analytics. It was suggested that the effort would develop and evaluate use cases for data mining and analytics for employing information from plant sensors and database for use in developing improved business analytics.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Efficient Modeling of Floating Wind Arrays Including Current Loads and Seabed Bathymetry: Preprint

Capabilities for modeling the effects of seabed bathymetry and current drag loads on a floating wind farm are now available in an open-source model for quasi-static analysis. In this model, mooring lines and dynamic cables are represented by a quasistatic solver that can quickly represent complex mooring/cabling arrangements and arrays of floating bodies. To account for seabed bathymetry, we expand the model to include a surface mesh that captures changes in water depth over a rectangular grid. We formulate modifications to the catenary equations that capture a mooring line's profile and tensions when contacting a slope seabed. To account for current drag loads on mooring lines and dynamic cables, we formulate a novel technique that rotates the reference frame so that the vector sum of the the weight and the current force are used in the catenary equations, while accounting for the seabed orientation. To complete the system, current drag loads on floating substructures are handled by inclusion of strip-theory drag calculations. These new capabilities are verified by comparing with results from the established offshore dynamics models MoorDyn and OrcaFlex in equivalent steady-state scenarios. The results show very good agreement for both sloped seabeds and current loads. With computation times of the quasi-static model typically under one second, the model additions are a useful capability toward rapidly evaluating a floating wind array's response to environmental loads under realistic site conditions.

bathymetry↗

DOE OSTI2020

X-ray and gamma-ray tomographic imaging of fuel relocation inside sodium fast reactor test assemblies during severe accidents

The present work reports on x-ray and gamma-ray high-spatial resolution computerized tomography measurements of the Pin Bundle Metallic Fuel Relocation (PBR) assemblies tested in the Metallic Uranium Safety Experiment (MUSE) facility at Argonne National Lab (ANL). The aim of the study was to characterize fuel relocation structures that develop during severe core accidents pertaining to SFR assemblies; these include but are not limited to advanced core disruption recreated in the PBR-1 assembly, and cladding breach recreated in the PBR-2 assembly. We report the x-ray tomography measurements were able to resolve small quantities of relocation fuel; with increased presence of relocation fuel, the x-ray 1measurements spatially mapped the material but could not resolve the inner regions of these. The gamma-tomography measurements showed improved results, resolving the relocation structures in great detail. The upper plenum of the PBR-1 assembly where the molten uranium was initially inserted presented high structural damage, reflected by the partial and complete disintegration of the central rods. Relocation fuel filled the subchannels, adhering to surviving cladding walls and the assembly casing. In the lower portion of the measured section, the tomogram degrades due to photon starvation effects hinting at the increased amount of relocation fuel potentially plugging the assembly; flow blockage in this section was difficult to determine due to the tomogram’s degradation from photon starvation. Small fragments were observed further down the assembly, dislodged from the initial insertion of the molten material. This section was used as an unperturbed assembly reference, with a calculated blockage of less than 1% from the present fragments. The PBR-2 assembly was characterized by columnar relocation structures propagating through the subchannels. Three of the relocation structures were captured in the measured section, with evidence of cross migration on to adjacent subchannels. The measured section in this assembly captures the leading edge of two structures. The calculated flow blockage was 16% in the planes where the three relocation structures are present, but this quickly decreases to approximately 5% past the leading edge of two of the structures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Ultrafast carrier dynamics in terahertz photoconductors and photomixers: beyond short-carrier-lifetime semiconductors

Abstract Efficient terahertz generation and detection are a key prerequisite for high performance terahertz systems. Major advancements in realizing efficient terahertz emitters and detectors were enabled through photonics-driven semiconductor devices, thanks to the extremely wide bandwidth available at optical frequencies. Through the efficient generation and ultrafast transport of charge carriers within a photo-absorbing semiconductor material, terahertz frequency components are created from the mixing products of the optical frequency components that drive the terahertz device – a process usually referred to as photomixing. The created terahertz frequency components, which are in the physical form of oscillating carrier concentrations, can feed a terahertz antenna and get radiated in case of a terahertz emitter, or mix with an incoming terahertz wave to down-convert to DC or to a low frequency photocurrent in case of a terahertz detector. Realizing terahertz photoconductors typically relies on short-carrier-lifetime semiconductors as the photo-absorbing material, where photocarriers are quickly trapped within one picosecond or less after generation, leading to ultrafast carrier dynamics that facilitates high-frequency device operation. However, while enabling broadband operation, a sub-picosecond lifetime of the photocarriers results in a substantial loss of photoconductive gain and optical responsivity. In addition, growth of short-carrier-lifetime semiconductors in many cases relies on the use of rare elements and non-standard processes with limited accessibility. Therefore, there is a strong motivation to explore and develop alternative techniques for realizing terahertz photomixers that do not rely on these defect-introduced short-carrier-lifetime semiconductors. This review will provide an overview of several promising approaches to realize terahertz emitters and detectors without short-carrier-lifetime semiconductors. These novel approaches utilize p-i-n diode junctions, plasmonic nanostructures, ultrafast spintronics, and low-dimensional materials to offer ultrafast carrier response. These innovative directions have great potentials for extending the applicability and accessibility of the terahertz spectrum for a wide range of applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Protorheology

We all instinctively poke, bounce, scoop, and observe materials to understand rheological properties quickly. Yet, these observations are rarely analyzed quantitatively. To address this, here we introduce the paradigm of protorheology: approximate quantitative inference from simple observations. Several case studies demonstrate how protorheology is an inclusive entry to rheology for a broad range of practitioners and strengthens the confidence and interpretation of accurate laboratory measurements. We survey a range of creative tests according to which rheological phenomenon is revealed. Some new working equations are derived, and all working equations are summarized for convenient reference and comparison across different methods. Finally, this establishes a framework to enable increased use of photos, videos, and quantitative inference and to support the increasing interest in digital image analysis, inverse methods, and high-throughput characterization being applied to rheological properties.

42 ENGINEERING↗

DOE OSTI2022

The synergistic effects of rosehip oil and matrine against Icerya aegyptiaca (Douglas) (Hemiptera: Coccoidea) and the underlying mechanisms

Abstract BACKGROUND Icerya aegyptiaca (Douglas) is an important agricultural pest that has a protective wax shell. Insecticides generally fail to achieve the desired control of I. aegyptiaca because of difficulties penetrating this wax shell. Plant essential oils are rich in terpenoids and have strong penetrability and expansibility, which can enable the rapid penetration and absorption of insecticides, thereby improving the control effect. Matrine is a botanical insecticide with contact and stomach toxicity, low toxicity toward non‐target organisms and is environmentally friendly. In this study, we tested the insecticidal activity of rosehip oil (Ro)/matrine combinations and revealed the synergistic mechanism of Ro and its components with matrine in terms of physiology and biochemistry. RESULTS Ro/matrine combinations have strong penetrating power, enabling matrine to quickly penetrate the wax shell of I. aegyptiaca and enter the insect body. This improves the insecticidal activity and enhances inhibition of acetylcholinesterase activity. Ro and its main chemical constituents, cineole and (+)‐camphor, showed synergistic effects on matrine with synergic ratios of 4.79, 3.49 and 4.21, respectively. CONCLUSION Combinations of Ro, cineole and (+)‐camphor with matrine have good insecticidal effects on I. aegyptiaca while remaining safe to the environment. These combinations of biological insecticides have excellent development prospects and provide a new reference for the pest management of scale insects. © 2022 Society of Chemical Industry.

Zhou, Yi↗

DOE OSTI2023

Effect of an oxide layer on high velocity impact of tantalum particles characterized using molecular dynamics

Micro-cold spray, also commonly referred to as the aerosol deposition method or vacuum kinetic spraying, is a process for depositing nanostructured films of metals and ceramics by impacting solid nanoparticles at high velocity onto a substrate at room temperature and at low pressures. Tantalum films deposited by micro-cold spray are desirable because tantalum is non-reactive to certain molten metals and can therefore serve as a non-reactive barrier coating. However, unlike most other metals deposited by micro-cold spray, tantalum quickly forms an oxide layer when exposed to air. In the deposition of films using traditional cold spray with much larger particles, native oxides must fracture for particle–substrate bonding to occur. Furthermore, the fraction of the particle that is oxidized in micro-cold spray and the strain rates experienced by the particles upon impact are both much larger than in cold spray, suggesting that the role of the oxide on film deposition may be distinct. The effect of this oxide layer on particle deformation and adherence to a substrate under high strain rates is studied using molecular dynamics simulations. The effect of oxide layer thickness, particle diameter, and impact velocity are examined.

42 ENGINEERING↗

DOE OSTI2014

Sci—Thur AM: YIS - 10: Use of the Microsoft Kinect for applications of patient surface data to radiotherapy

Current techniques to acquire patient surface data are often very expensive and lack flexibility. In this study, the use of the Microsoft Kinect to reliably acquire 3D scans of patient surface is investigated. A design is presented to make the system easily applicable to the clinic. Potential applications of the device to radiotherapy are also presented. Scan reproducibility was tested by repeatedly scanning an anthropomorphic phantom. Scan accuracy was tested by comparing Kinect scans to the surface extracted from a CT dataset of a Rando® anthropomorphic phantom, which was considered as the true reference surface. Average signed distances of 0.12 ± 2.34 mm and 0.13 ± 2.04 mm were obtained between the compared surfaces for reproducibility and accuracy respectively. This is conclusive, since it indicates that the variations observed come largely from noise distributed around an average distance close to 0 mm. Moreover, the range of the noise is small enough for the system to reliably capture a patient's surface. A system was also designed using two Kinects used together to acquire 3D surfaces in a quick and stable way that is applicable to the clinic. Finally, applications of the device to radiotherapy are demonstrated. Its use to detect local positioning errors is presented, where small local variations difficult to see with the naked eye are clearly visible. The system was also used to predict collisions using gantry and patient scans and thus ensure the safety of unconventional trajectories.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

DECENTRALIZED APPROACH TO MULTI-ZONE GREY-BOX MODELING FOR MODEL-BASED PREDICTIVE CONTROL

This study aims to improve the easiness of utilizing the grey-box model (i.e., Resistance-Capacitance circuit) for Model-based Predictive Control (MPC). The primary barrier of implementing the MPC is estimating the control-oriented building model that needs to be computationally inexpensive and quick but reasonably precise in predicting building load and indoor conditions. The estimating of the model parameters becomes more complicated when the building scale is larger; e.g., multi-zone building. In this study, a decentralized approach is introduced; each zone is split and individually estimated with measured boundary temperature from adjacent zones integrated into one single system model. The proposed decentralized method is demonstrated with experimental data from a full-scale multi-zone test cell compared with the centralized reference case.

Joe, Jaewan↗

DOE OSTI2014

The Porifera Ontology (PORO): enhancing sponge systematics with an anatomy ontology

Background: Porifera (sponges) are ancient basal metazoans that lack organs. They provide insight into key evolutionary transitions, such as the emergence of multicellularity and the nervous system. In addition, their ability to synthesize unusual compounds offers potential biotechnical applications. However, much of the knowledge of these organisms has not previously been codified in a machine-readable way using modern web standards. Results: The Porifera Ontology is intended as a standardized coding system for sponge anatomical features currently used in systematics. The ontology is available from http://purl.obolibrary.org/obo/poro.owl, or from the project homepage http://porifera-ontology.googlecode.com/. The version referred to in this manuscript is permanently available from http://purl.obolibrary.org/obo/poro/releases/2014-03-06/. Conclusions: By standardizing character representations, we hope to facilitate more rapid description and identification of sponge taxa, to allow integration with other evolutionary database systems, and to perform character mapping across the major clades of sponges to better understand the evolution of morphological features. Future applications of the ontology will focus on creating (1) ontology-based species descriptions; (2) taxonomic keys that use the nested terms of the ontology to more quickly facilitate species identifications; and (3) methods to map anatomical characters onto molecular phylogenies of sponges. In addition to modern taxa, the ontology is being extended to include features of fossil taxa.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Laser Incident Lessons Learned and Action List

On Thursday November 19, 2015, LANL postdoc received an eye injury from a reflected, nonvisible laser beam (Class 4, pulsed, wavelength 800 nanometer). The setup is configured to split the laser output into two work areas in which qualified operators conduct research experiments. During this incident, the laser output beam was being projected to both experimental work areas, although only one experimental area was actively being used. The second laser beam directed to the second work area was blocked by an inappropriate device (Plexiglas, reflective, non-normal incidence) that reflected substantial portion of the beam toward the first setup. In preparation for the measurements, worker stepped on the stepstool and decided to remove the laser goggles to better see the micrometer readings which were difficult to see due to insufficient lighting. Immediately, he noticed a flash of light in his eye. The operator quickly replaced the laser eye-wear and then, using an infrared viewer, located a stray laser beam being reflected from the plexiglas beam block. The operator did not think he had sustained any injury and continued working. Later that day, however, he noticed a blurry spot in the vision of his left eye. He notified his supervisor on Friday morning, November 20, 2015, and was taken by CINT management to Sandia National Laboratories (SNL) medical facility for evaluation. SNL Medical did not find any abnormalities, but referred the operator to a local ophthalmologist for further evaluation. Further evaluations by the ophthalmologist on November 21 and November 23 identified a small spot of inflammation near the fovea on the retina in his left eye. The ophthalmologist stated that this spot would most likely heal on its own and that the blurry spot on the operator's vision would go away. A follow-up visit was scheduled. The employee was released back to work without restrictions.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

On the reliability of nanoindentation hardness of Al{sub 2}O{sub 3} films grown on Si-wafer by atomic layer deposition

The interest in applying thin films on Si-wafer substrate for microelectromechanical systems devices by using atomic layer deposition (ALD) has raised the demand on reliable mechanical property data of the films. This study aims to find a quick method for obtaining nanoindentation hardness of thin films on silicon with improved reliability. This is achieved by ensuring that the film hardness is determined under the condition that no plastic deformation occurs in the substrate. In the study, ALD Al{sub 2}O{sub 3} films having thickness varying from 10 to 600 nm were deposited on a single-side polished silicon wafer at 300 °C. A sharp cube-corner indenter was used for the nanoindentation measurements. A thorough study on the Si-wafer reference revealed that at a specific contact depth of about 8 nm the wafer deformation in loading transferred from elastic to elastic–plastic state. Furthermore, the occurrence of this transition was associated with a sharp increase of the power-law exponent, m, when the unloading data were fitted to a power-law relation. Since m is only slightly material dependent and should fall between 1.2 and 1.6 for different indenter geometry having elastic contact to common materials, it is proposed that the high m values are the results from the inelastic events during unloading. This inelasticity is linked to phase transformations during pressure releasing, a unique phenomenon widely observed in single crystal silicon. Therefore, it is concluded that m could be used to monitor the mechanical state of the Si substrate when the whole coating system is loaded. A suggested indentation depth range can then be assigned to each film thickness to provide guidelines for obtaining reliable property data. The results show good consistence for films thicker than 20 nm and the nanoindentation hardness is about 11 GPa independent of film thickness.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Earth Filter Mathematica Toolbox V4 User’s Guide: Simplifying, Visualizing and Quick Modeling of All Fission Products from 60 Nuclear Sources

The Earth Filter Mathematica toolbox is a set of user-friendly routines that accurately calculate the ingrowth and decay of any of the 908 fission products for an infinite combination of sources over a large dynamic range and large time scales. This toolbox was developed by Dr. W. P. Brug at Los Alamos National Laboratory and eliminates the need for the user to define and solve differential equations by automatically scanning the ENDF-349 dataset and creating a complete set of differential equations (including delayed neutron branches to both the ground and metastable states) for every fission product. Additionally, this code allows the user to choose the data source for half-lives from several key references (i.e., TORI, IAEA, ENDF or NUDAT.) The complete set of ENDF-349 independent and cumulative fission yields for all 60 isotopic targets and 4 different neutron spectrums are included. This allows nearly any nuclear fission source to be accurately modeled as a linear combination of endpoint sources. The Earth Filter Toolbox has no limit as to the length of split list, and split lists of 5 or more endpoint sources can be quite common.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network

Earthquake location algorithms typically require travel time calculation. Doing this calculation in 3D, despite advances in algorithm efficiency and computational power, can still be prohibitively expensive in terms of resources and storage. Implementation of high-resolution 3D models in routine earthquake location would be a significant step forward in most of the world. Machine learning algorithms have potential to act as substitutes for travel time calculation algorithms or stored travel time tables. We investigate EikoNet - a physics informed neural network machine learning model that estimates travel times very quickly and comes with negligible memory-overhead. Specifically, we apply EikoNet to the Wasatch Fault Community Velocity Model (WFCVM), a highly detailed and complex 3D velocity model of the Salt Lake City, UT region. While routine locations in the area and studies of the 2020 Magna, UT earthquake sequence used a 1D velocity model, a 3D model may help better our understanding the structure of the major fault in the region. Our primary goal was to test the speed, memory requirements, and accuracy of EikoNet compared to a reference eikonal solver. We find that while the EikoNet is exceedingly fast and requires little memory overhead, achieving acceptable accuracy in estimated travel times is difficult and requires extensive computational resources.

58 GEOSCIENCES↗

DOE OSTI2025

Post-composing ontology terms for efficient phenotyping in plant breeding

Abstract Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored to diverse use cases, post-composing user-defined terms reconciles the demands for standardization on the one hand and flexibility on the other. In many instances of Breedbase, a digital ecosystem for plant breeding designed for genomic selection, the goal is to capture phenotypic data using highly curated and rigorous crop ontologies, while adapting to the specific requirements of plant breeders to record data quickly and efficiently. For example, post-composing enables users to tailor ontology terms to suit specific and granular use cases such as repeated measurements on different plant parts and special sample preparation techniques. To achieve this, we have implemented a post-composing tool based on orthogonal ontologies providing users with the ability to introduce additional levels of phenotyping granularity tailored to unique experimental designs. Post-composed terms are designed to be reused by all breeding programs within a Breedbase instance but are not exported to the crop reference ontologies. Breedbase users can post-compose terms across various categories, such as plant anatomy, treatments, temporal events, and breeding cycles, and, as a result, generate highly specific terms for more accurate phenotyping.

Mathematical & Computational Biology↗

DOE OSTI2023

diffReplication - An Energy-Aware Fault Tolerance Model for Silent Error Detection and Mitigation in Heterogeneous Extreme-scale Computing Environment

At extreme scale, the frequency of silent errors – a class of errors that remain undetected by low-level error detection mechanisms – increases significantly with the computational complexity of the application and the scale of the computing infrastructure. As hardware and software advances are made to usher in the next scientific era of computing, developing new approaches to mitigate the impact of silent errors remains a challenging problem. In this work, we propose an energy-aware fault-tolerance model, referred to diffReplication to overcome silent errors. In the proposed model, the main process is associated with one replica that executes at the same rate as the main process, and one diffReplica that is executed at a fraction of the main process' execution rate. If the main and its replica reach consensus at the end of a computation phase, the state of the diffReplica is updated and computation is resumed. If the synchronization attempt results in a disagreement, however, the diffReplica increases its execution speed to complete the computation and quickly reach the synchronization barrier. Assuming a single error over any given synchronization interval, a majority voting is used to reach consensus and tolerate silent errors. To further enhance its performance, diffReplication is augmented with speculative execution, whereby the main or its fast replica is selected to continue execution without waiting for the diffReplica. The selection process is based on the previous behaviour of the main and its replica. A performance analysis study is carried out to assess the performance of diffReplication, in terms of the energy saving and time-to-completion reduction achieved by the diffReplication scheme. The experiment shows that speculative execution reduces the time to completion with additional energy, and dynamic decision-making balances the energy consumption and time to completion.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

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 near Trail Creek. 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 TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores 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 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 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 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.

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