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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nutrient and carbon concentrations in dated soil cores at US-OWC Ameriflux wetland site (OWC NERR)

Soil cores were sampled at the Old Woman Creek (OWC) National Estuarine Research Reserve at the south shore of Lake Erie, near Huron, Ohio, USA. OWC is a temperate mineral soil marsh. We dated the soil cores using lead isotope analysis and measured the concentrations of carbon and nutrients (nitrogen, phosphorus) throughout the core depths. We analyzed 36 cores, sampled along three transects at areas of the wetland with different hydrological regimes. Each transect included three coring sites at different water depth categories (shallow, intermediate deep) with four core samples per coring site. The data can be used to determine the carbon sequestration rates and nutrient accumulation rates, at multiple locations throughout OWC wetland.Dataset, in csv format, with data variables in columns, and different cores and core depths slices in rows, includes results from 36 sediment cores (0-30 cm depth) taken at 9 locations (4 replicates at each location) along 3 gradients of water depth (shallow, intermediate, deep), each at a different hydrologic location (outflow, backflow, middle). At each depth slice within a core, we provide depth, date (using 210Pb), bulk density, and the concentrations of carbon, nitrogen, phosphorus, d13C, and d15N.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Terrestrial case studies of ilmenite exploration and lunar implications

The Space Exploration Initiative (SEI) includes space resource utilization as one of the four architectures to achieve U.S. goals in space. Space resource utilization will make use of lunar resources to support long term activities on the lunar surface. Lunar ilmenite and regolith are two of the materials that can be mined and processed for lunar oxygen production. During this investigation, several sources were reviewed to assess terrestrial exploration methods used for locating ilmenite resources. These sources included published reports on terrestrial ilmenite exploration methods, analytical methods, case histories, chemical and physical properties, and associations with other minerals. Using a terrestrial analog and considering the differences between terrestrial and lunar environmental conditions, rocks, and minerals, exploration methods and analytical instruments can be recommended for a lunar orbiter and lander for assessing lunar resources. Twelve terrestrial case histories were reviewed to gain insight into ilmenite exploration on the Moon. All exploration case histories follow the same pattern. They begin with a model, use remote geophysical techniques, define regional sampling sites from the model and geophysics, narrow down the area of exploration based on the preceding work, collect more samples and cores, and perform laboratory analyses of samples. An important part of this process is the collection of samples to determine the correctness of the model. Surface and core samples are collected in areas expected to contain both high and low concentration of the commodity to test the model. After samples are analyzed and the area of mineralization is defined, reserves are calculated to determine the cost/benefit ratio, the necessary capacity of the processing plant, and the life of the mine. The exploration methods used for locating terrestrial ilmenite resources are reviewed with respect to the petrology, chemistry, and mineral associations of the terrestrial and lunar environments.

Feldman, S. C.↗
NASA NTRS1989

A core handling device for the Mars Sample Return Mission

A core handling device for use on Mars is being designed. To provide a context for the design study, it was assumed that a Mars Rover/Sample Return (MRSR) Mission would have the following characteristics: a year or more in length; visits by the rover to 50 or more sites; 100 or more meter-long cores being drilled by the rover; and the capability of returning about 5 kg of Mars regolith to Earth. These characteristics lead to the belief that in order to bring back a variegated set of samples that can address the range of scientific objetives for a MRSR mission to Mars there needs to be considerable analysis done on board the rover. Furthermore, the discrepancy between the amount of sample gathered and the amount to be returned suggests that there needs to be some method of choosing the optimal set of samples. This type of analysis will require pristine material-unaltered by the drilling process. Since the core drill thermally and mechanically alters the outer diameter (about 10 pct) of the core sample, this outer area cannot be used. The primary function of the core handling device is to extract subsamples from the core and to position these subsamples, and the core itself if needed, with respect to the various analytical instruments that can be used to perform these analyses.

Gwynne, Owen↗
NASA NTRS2002

Distinguishing Indigenous from Contaminating Microorganisms in Rock Samples from a Deep Au Mine in South Africa

The concentration and distribution of microbial biomass within deep subsurface rock strata is not well known To date, most analyses are from water samples and a few cores. Hand samples, block samples and cores from an actively mined Carbon Leader ore zone at 3.2 kilometers depth were collected for microbial analyses. The Carbon Leader was comprised of quartz, S-bearing aromatic hydrocarbons, Fe(III) oxyhydroxides, sulfides, uraninite, Au and minor amounts of sulfate. The porosity of the ore was 1% and the maximum pore throat diameter was less than 0.1 microns; whereas, the porosity of the adjacent quartzite was .02 to .9% with a maximum pore throat diameter of 0.9 microns. Rhodamine dye, fluorescent microspheres, microbial enrichments, autoradiography, phospholipid fatty acid (PLEA) and 16S rDNA analyses were performed on these rock samples and the mining water. The date indicate that the levels of solute contamination less than 0.01% for pared rock samples. Despite this low level of contamination, PLEA, microbial enrichment, DNA and tracer analyses and calculations indicate that most of the viable microorganisms in the Carbon Leader represent gram negative aerobic heterotrophs and ammonia oxidizers that are phylogenetically identical or closely related to service water microorganisms. These microbial contaminants probably infiltrated the low permeability rock through mining-induced microfractures. Geochemical data also detected drilling water in a fault zone approx. 1 meter behind the rock face encountered during coring. The mining induced macrofractures that are common at these great depths act as pathways for the drilling water borne microorganisms into the lower temperature zone that extends several meters into rock strata from the rock face. Combined PLEA and T- RFLP analyses of the service water and Carbon Leader samples indicate that the concentration of indigenous microorganisms was less than 10(exp 2) cells/gram. Such a low concentrations result from the submicron pore throat diameters. PLFA. SO4-35 autoradiography and tracer analyses indicate that the bounding quartzite contains thermophilic sulfate reducing bacteria at 10(exp 3) cells/gram that are not attributable to drilling water contamination. The microorganisms may be surviving on sulfate generated by oxidation of sulfide by radiolytic reactions resulting from the high U concentration in the ore zone. The presence of up to 8,000 ppm of Fe(III) oxyhydroxides in the host rock will also act to recycle sulfide generated by the sulfate reducing bacteria into sulfate. The activity of these sulfate-reducing bacteria may be enhanced by mining induced fracturing which can propagate up to 40 meters into virgin rock where the temperatures are ca. 50 C, and decrepitate of sulfate rich fluid inclusions. In ultra deep mines, judicious application of tracers and multiple microbial characterization techniques can distinguish microbial contamination caused by the near field fracturing and drilling water migration from the indigenous microbial communities in rock strata. The importance of far field fracturing on indigenous microbial communities, however, remains unknown.

Onstott, T. C.↗
NASA NTRS2008

Analysis of Lunar Highland Regolith Samples From Apollo 16 Drive Core 64001/2 and Lunar Regolith Simulants - an Expanding Comparative Database

This slide presentation reviews the work to analyze the lunar highland regolith samples that came from the Apollo 16 core sample 64001/2 and simulants of lunar regolith, and build a comparative database. The work is part of a larger effort to compile an internally consistent database on lunar regolith (Apollo Samples) and lunar regolith simulants. This is in support of a future lunar outpost. The work is to characterize existing lunar regolith and simulants in terms of particle type, particle size distribution, particle shape distribution, bulk density, and other compositional characteristics, and to evaluate the regolith simulants by the same properties in comparison to the Apollo sample lunar regolith.

Schrader, Christian M.↗
DOE OSTI2015

Use of XRF to characterize pre-Hanford orchards in the 100-OL-1 operable unit - 15717

Prior to 1943, Hanford site included several small towns with approximately 5,000 acres of orchards; at the time lead arsenate (PbHAsO{sub 4}) was the most commonly used pesticide. High concentration of lead and arsenic have been recorded where trees and stumps are still visible. Pre-Hanford orchards have been designated as 100-OL-1 Operable Unit. An Optimization study evaluated optimal counting time and position of the XRF from soils collected at the site. A Pilot Study included the evaluation of a field portable x-ray fluorescence (XRF) analyzer determining if performance results meet quality assurance criteria for cleanup. XRF has only previously been used as a screening tool. Four Decision Units (DUs) are to be analyzed: Ol-14. OL-32. OL-IU6-4 and OL-FR2-1. The DUs vary in size and have had distinct historic activities; such as 46.4 acres OL-14 having a military camp in the 1950's unlike OL-FR2-1 having been disturbed as it is near the F area reactor. Together the DUs provide an adequate representation of the Operable Unit. Screening criteria for lead (250 mg/kg) and arsenic (20 mg/kg). Six samples were collected ranging above, at, and below the screening criteria of 250 mg/kg for Pb and 20 mg/kg for As at the Decision Units OL-14 and OL-IU6-4, and labeled respectively. Samples were hand sieved, homogenized and placed in sample cups. Three replicates were prepared for each sample. Each sample cup was analyzed 3 times for 30 sec. A Standard Reference Material (SRM) was analyzed after every 20 readings. Three samples were selected for 15, 30, 45, 60, 90, 120, 150 and 180 second fixed and variable analysis. Two transects at OL-14, and one at OL-IU6-4 were created by selecting locations 90. around ground zero at distances of 0.5, 1, 2, 4, 8 and 12 feet (16 ft for OL-14). Locations were scanned in triplicate for 60 seconds each. 4 Decision Units spanning roughly 370 acres were chosen to represent 100-OL-1 Operable Unit. 40 locations within each DU were selected using Visual Sample Plan (VSP) with a random start and systematic grid pattern. Each location would be analyzed with 3 replicates and a 60 second count time. The Standard Reference Material (SRM) and blank are analyzed after every 20 counts to ensure accuracy. Data is extracted from XRF to be implemented in GIS for visual representation of site with 1943 and 2013 aerial imagery. Discussion/Conclusion: - XRF provided a good indication of precision and accuracy relative to ICP with a linear correlation R{sup 2} value higher than anticipated at .9735; - MDL proved the detection limit is significantly lower than the screening criteria; - When comparing results from the first 40 locations of OL-14 to the total 80 locations of OL-14, the same variability was seen; - We expect high concentrations of Pb and As to be where past orchards are present though this does not occur as all sites. Significance: - XRF is competent for decision making as values fall within ±20% ICP data; - The highest concentrations are not where expected, which may be an issue for sites outside of Hanford that have had more development; - The size of the DU does not affect the number of samples measured. Future Work: - Verification and validation of the XRF Data (QA/QC); - Revise conceptual site model; - Statistical evaluations to investigate: Estimation of variability in surface soils, appropriate size for DU, and sample size per DU; - Coordinating sample results through CORE; - Displaying sample results in PHOENIX (GIS tool)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2013

Acquisition and Retaining Granular Samples via a Rotating Coring Bit

This device takes advantage of the centrifugal forces that are generated when a coring bit is rotated, and a granular sample is entered into the bit while it is spinning, making it adhere to the internal wall of the bit, where it compacts itself into the wall of the bit. The bit can be specially designed to increase the effectiveness of regolith capturing while turning and penetrating the subsurface. The bit teeth can be oriented such that they direct the regolith toward the bit axis during the rotation of the bit. The bit can be designed with an internal flute that directs the regolith upward inside the bit. The use of both the teeth and flute can be implemented in the same bit. The bit can also be designed with an internal spiral into which the various particles wedge. In another implementation, the bit can be designed to collect regolith primarily from a specific depth. For that implementation, the bit can be designed such that when turning one way, the teeth guide the regolith outward of the bit and when turning in the opposite direction, the teeth will guide the regolith inward into the bit internal section. This mechanism can be implemented with or without an internal flute. The device is based on the use of a spinning coring bit (hollow interior) as a means of retaining granular sample, and the acquisition is done by inserting the bit into the subsurface of a regolith, soil, or powder. To demonstrate the concept, a commercial drill and a coring bit were used. The bit was turned and inserted into the soil that was contained in a bucket. While spinning the bit (at speeds of 600 to 700 RPM), the drill was lifted and the soil was retained inside the bit. To prove this point, the drill was turned horizontally, and the acquired soil was still inside the bit. The basic theory behind the process of retaining unconsolidated mass that can be acquired by the centrifugal forces of the bit is determined by noting that in order to stay inside the interior of the bit, the frictional force must be greater than the weight of the sample. The bit can be designed with an internal sleeve to serve as a container for granular samples. This tube-shaped component can be extracted upon completion of the sampling, and the bottom can be capped by placing the bit onto a corklike component. Then, upon removal of the internal tube, the top section can be sealed. The novel features of this device are: center dot A mechanism of acquiring and retaining granular samples using a coring bit without a closed door. center dot An acquisition bit that has internal structure such as a waffle pattern for compartmentalizing or helical internal flute to propel the sample inside the bit and help in acquiring and retaining granular samples. center dot A bit with an internal spiral into which the various particles wedge. center dot A design that provides a method of testing frictional properties of the granular samples and potentially segregating particles based on size and density. A controlled acceleration or deceleration may be used to drop the least-frictional particles or to eventually shear the unconsolidated material near the bit center.

Bar-Cohen, Yoseph↗
NASA NTRS1994

X-ray spectra of a complete sample of extragalactic core-dominated radio sources

We present ROSAT soft X-ray spectra for the members of a complete sample of 13 core-dominated, flat radio spectrum sources. The sample comprises all radio sources from a flux-limited radio catalog (S(sub 5GHz) greater than 1 Jy; Kuehr et al. 1981) which are north of delta = 70 deg, at galactic latitudes b greater than 10 deg, and have a flat radio spectrum between 1.4 and 5 GHz (alpha(sub r) less than 0.5; f approximately nu(sup -alpha)). The sources have already undergone much study at radio and optical wavelengths and are classified in broad terms as quasars (8 sources) and BL Lac objects (5 sources). We find mean X-ray power-law energy indices of alpha(sub x) = 0.59 +/- 0.19 for the quasars and 1.36 +/- 0.27 for the BL Lac objects (68% confidence range for two parameters of interest as determined by a maximum likelihood method), supporting earlier Einstein Observatory results for heterogeneous samples of sources (Worrall & Wilkes 1990). A non-zero dispersion on alpha(sub x) is found for both the quasars and the BL Lac objects. When we incorporate published radio, mm, and optical measurements and compare the X-ray and broad-band spectral indices alpha(sub x), alpha(sub rx), alpha(sub mm,x), and alpha(sub ox), the most obvious difference between the quasar and BL Lac subsamples lies within the X-ray band. We have fitted the multi-wavelength data to inhomogeneous synchotron-self-Compton models and find that, for the BL Lac objects with steep X-ray spectra, synchotron emission can account for the radio to soft X-ray measurements, whereas the BL Lac objects with hard X-ray spectra and the quasars require significant Compton emission to model the spectral flattening indicated by alpha(sub x) less than alpha(sub ox).

Brunner, H.↗
DOE OSTI2015

The SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Quasar target selection

As part of the Sloan Digital Sky Survey (SDSS) IV the extended Baryon Oscillation Spectroscopic Survey (eBOSS) will improve measurements of the cosmological distance scale by applying the Baryon Acoustic Oscillation (BAO) method to quasar samples. eBOSS will adopt two approaches to target quasars over 7500 deg{sup 2}. First, a “CORE” quasar sample will combine the optical selection in ugriz using a likelihood-based routine called XDQSOz, with a mid-IR-optical color cut. eBOSS CORE selection (to g < 22 or r < 22) should return ∼70 deg{sup −2} quasars at redshifts 0.9 < z < 2.2 and ∼7 deg{sup −2}z > 2.1 quasars. Second, a selection based on variability in multi-epoch imaging from the Palomar Transient Factory should recover an additional ∼3–4 deg{sup −2}z > 2.1 quasars to g < 22.5. A linear model of how imaging systematics affect target density recovers the angular distribution of eBOSS CORE quasars over 96.7% (76.7%) of the SDSS north (south) Galactic Cap area. The eBOSS CORE quasar sample should thus be sufficiently dense and homogeneous over 0.9 < z < 2.2 to yield the first few-percent-level BAO constraint near z-bar ∼1.5.eBOSS quasars at z > 2.1 will be used to improve BAO measurements in the Lyα Forest. Beyond its key cosmological goals, eBOSS should be the next-generation quasar survey, comprising >500,000 new quasars and >500,000 uniformly selected spectroscopically confirmed 0.9 < z < 2.2 quasars. At the conclusion of eBOSS, the SDSS will have provided unique spectra for more than 800,000 quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Analysis of Saltstone Cores Taken from Saltstone Disposal Unit Cell 2A - 17350

At the Savannah River Site, low-level waste is immobilized in cementitious waste form known as saltstone. The saltstone Performance Assessment models the performance of the waste form and other aspects of the Saltstone Disposal Facility over ten thousand years after closure to better understand the transport history of radionuclides and other hazardous constituents in low-level waste. As part of ongoing Performance Assessment Maintenance, Savannah River Remediation (SRR), with Savannah River National Laboratory (SRNL), designed a sampling and analyses plan to correlate the properties of field-emplaced samples and samples processed and cured in the laboratory. The primary objectives of the Sampling and Analyses Plan are; (1) to demonstrate a correlation between the measured properties of laboratory-prepared, simulant samples (termed Sample Set 3), and the field-emplaced saltstone samples (termed Sample Set 9), and (2) to validate property values assumed for the Saltstone Disposal Facility (SDF) Performance Assessment modeling. The analysis and property data for Sample Set 9 (i.e. core samples extracted from Saltstone Disposal Unit Cell 2A (SDU2A)) are documented, and where applicable, the results are compared to the results for Sample Set 3. SRNL received, stored and analyzed the samples cored from SDU2A. This paper discusses the results of the SRNL analysis of the SDU2A samples as well as the simulant saltstone samples. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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 NTRS1988

Cosmic-ray-produced Kr in St. Severin core AIII

Kr isotopic abundances in 10 samples from core AIII of the St. Severin chondrite are reported, and the variation with depth of cosmic-ray produced Kr is discussed. It is shown that the ratio (Kr-78/Kr-83)c changes with depth and can be used as an irradiation hardness monitor. Cosmic-ray-produced Kr and Ne in the St. Severin core are compared with ratios observed in bulk chondrites. A linear correlation between (Ne-22/Ne-21)c and (Kr-78/Kr-83)c exists for bulk samples of chondrites of varying preatmospheric size, but does not exist in the St. Severin core. The calculated Kr-83 production rates, P83, are similar in H, L, and LL chondrites for samples of comparable shielding conditions. However, P83 rates increase by a factor of about 2 with shielding depth. The maximum observed P83 values correspond to (Kr-78/Kr-83)c factor of about 2 with shielding depth. The maximum observed P83 values correspond to (Kr-78/Kr-83)c less than about 0.14.

Lavielle, B.↗
NASA NTRS1993

Composition and Maturity of Apollo 16 Regolith Core 60013/14

Samples from every half-centimeter dissection interval of double drive tube 60013/14 (sections 60013 and 60014) were analyzed by magnetic techniques for Fe concentration and surface maturity parameter I(sub s)/ Fe(O), and by neutron activation for concentrations of 25 lithophile and siderophile elements. Core 60013/14 is one of three regolith cores taken in a triangular array 40-50 m apart on the Cayley plains during Apollo 16 mission to the Moon. The core can be divided into three zones based both on I(sub s)/FeO and composition. Unit A (0-44 cm depth) is compositionally similar to other soils from the surface of the central region of the site and is mature throughout, although maturity decreases with depth. Unit B (44-59 cm) is submature and compositionally more feldspathic than Unit A. Regions of lowest maturity in Unit B are characterized by lower Sm/Sc ratios than any soil obtained from the Cayley plains as a result of some unidentified lithologic component with low surface maturity. The component is probably some type of mafic anorthosite that does not occur in such high abundance in any of the other returned soils. Unit C (59-62 cm) is more mature than Unit B and compositionally equivalent to an 87: 13 mixture of soil such as that from Unit A and plagioclase such as found in ferroan anorthosite. Similar soils, but containing greater abundances of anorthosite (plagioclase), are found at depth in the other two cores of the array. These units of immature to submature soil enriched to varying degrees (compared to the mature surface soil) in ferroan anorthosite consisting of approx. 99% plagioclase are the only compositionally distinct subsurface similarities among the three cores. Each of the cores contains other units that are compositionally dissimilar to any soil unit in the other two cores. These compositionally distinct units probably derive from local subsurface blocks deposited by the event(s) that formed the Cayley plains. The ferroan anorthosite with approx. 99% plagioclase, however, must represent some subsurface lithology that is significant on the scale of tens of meters. The compositional uniformity of the surface soil (0-10 cm depth) over distances of kilometers reflects the large-scale uniformity of the plains deposits; the fine- structure reflects small-scale nonuniformity and the inefficiency of the impact-mixing process at depths as shallow as even one meter.

Korotev, Randy T.↗
DOE OSTI2015

Post-Test SEM Examination of Brady Core, Desert Peak Core, and Stripa Granite Samples

As part of the shear-induced permeability test of Brady well BCH-03, Desert Peak well DP 35-13, and Stripa granite samples, the three post-test (reacted) rock samples were examined using a Scanning Electron Microscope (SEM). The SEM images of the core and elemental analysis are included in this submission for each sample.

15 GEOTHERMAL ENERGY↗
NASA NTRS2009

Planetary Sample Caching System Design Options

Potential Mars Sample Return missions would aspire to collect small core and regolith samples using a rover with a sample acquisition tool and sample caching system. Samples would need to be stored in individual sealed tubes in a canister that could be transfered to a Mars ascent vehicle and returned to Earth. A sample handling, encapsulation and containerization system (SHEC) has been developed as part of an integrated system for acquiring and storing core samples for application to future potential MSR and other potential sample return missions. Requirements and design options for the SHEC system were studied and a recommended design concept developed. Two families of solutions were explored: 1)transfer of a raw sample from the tool to the SHEC subsystem and 2)transfer of a tube containing the sample to the SHEC subsystem. The recommended design utilizes sample tool bit change out as the mechanism for transferring tubes to and samples in tubes from the tool. The SHEC subsystem design, called the Bit Changeout Caching(BiCC) design, is intended for operations on a MER class rover.

MSR↗