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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulation of supersonic molecular beam injection fueling into H-mode plasmas on EAST using BOUT++

Transport dynamic 2D simulations for supersonic molecular beam injection (SMBI) fueling into H-mode deuterium plasmas on the Experimental Advanced Superconducting Tokamak (EAST) is first simulated using a seven-field two-fluid model in the BOUT++ framework. The SMB is assumed to be injected into plasma from the midplane at the low field side with a fixed width and constant molecular flux. The different densities and injection velocities of SMBI are investigated within the upper single-null geometry of EAST. The simulations indicate that the SMBI has a self-shielding effect on molecules' inward transport into the plasma, and the deposition of SMBI leads to a large increase in plasma density and decrease in plasma temperature. There is a velocity threshold for SMB penetrating the pedestal and depositing at the top of density pedestal. However, the deposition point would be back toward the plasma boundary after SMB arriving at the deepest penetration position. Comparing the different molecular injection velocities and densities, the outcomes demonstrate that the depth of deposition is closely related to the injection velocity rather than the molecular density. The simulated results show good agreement with the EAST experiments by comparing the electron density profiles obtained by simulation and experiment, respectively. These results will be helpful for guiding future experiments, as well as the design of the SMBI system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Characterization of beam ion loss in high poloidal beta regime on EAST

A critical issue for achieving the integrated operation of steady-state long-pulse high-confinement (H-mode) plasmas on experimental advanced superconducting tokamak (EAST) is to improve beam ion population confinement during neutral beam injection (NBI). To study the characterization of beam ion loss and improve beam ion confinement, the steady-state long pulse scenario discharges were conducted on EAST (β p $\geqslant$ 2.0, β N $\geqslant$ 1.7, q 95 $\geqslant$ 6.7 and H 98y2 $\geqslant$ 1.1) with NBI heating. Based on neutron yield, the beam voltage and line-averaged electron density were adjusted from 50 kV to 60 kV and 4.4 × 10 19 m –3 to 5.0 × 10 19 m –3 , respectively. The results show that the dominant mechanisms of beam ion loss are shine-through loss, prompt loss, and stochastic ripple loss. The shine-through loss fraction is determined by initial velocity, flight time and entire beam path. The change in prompt loss fraction is caused by the change in the deposition of beam ions. The change in stochastic ripple loss fraction is caused by the change in the initial fraction of trapped-confined ions. Detailed physics shows that the prompt loss fraction during counter-Ip injections (~45%) is far larger than during co-Ip injections (~5%) due to the finite orbit width. The lost ions are mainly deposited on the lower divertor or below the midplane since the direction of magnetic drift is vertical down. The orbit types of prompt loss during counter-Ip injections are mainly trapped-lost and ctr-passing lost. To minimize the prompt loss fraction during counter-Ip injections, a reversed Ip configuration (rev-Ip) discharge #94758 was conducted. The result suggests that the beam ion wall load fraction during counter-Ip tangential injection (~3%) is far lower than that in normal Ip configuration (nor-Ip) discharge #94820. It is also found that the confinement of beam ion population in the counter-Ip injection #94758 was greatly improved when compared to #94820. Finally, this study can provide unique support for the improvement of beam ion population confinement and for the performance evaluation of the NBI system on EAST and future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Comparison of divertor behavior and plasma confinement between argon and neon seeding in EAST

The exhaust of excessively high heat and particle fluxes on the divertor target is crucial for EAST long-pulse operation. In the recent EAST experiments, stable partial energy detachment around the upper outer strike point with H 98,y2 ~ 1 was achieved with either Ne or Ar seeding from the upper outer divetor target in the upper single null configuration with ITER-like tungsten divertor. With either Ar or Ne seeding, the electron temperature around the upper outer strike point (T et,UOSP ) was maintained at around 5 eV, the peak temperature of divertor target surface around the upper outer strike point (T div,UO ) decreased significantly, and material sputtering was well suppressed. Here, it was observed that there was less Ar seeding needed for partial energy detachment onset than Ne seeding, which shows that Ar is more efficient in the cooling of T et on the upper outer divertor than Ne. However, there was no detachment on the upper inner divertor with T et around strike point (T et,UISP ) remaining >10 eV with either Ar or Ne seeding from the upper outer divertor. Accompanied with the disappearance of double peak phenomenon of ion flux density on the upper inner divertor target (j s,UI ), the peak T div,UI around the strike point increased to around 300 °C. Although the heat flux on the upper inner divertor target (q t,UI ) is still in the acceptable level, either Ar or Ne seeding only from the upper outer divertor target is not enough to protect the upper inner divertor target from sputtering under current EAST conditions. On the other hand, Ar seeding always causes confinement degradation in the partial energy detachment state. It was observed that there is a slight confinement improvement (~10%) with Ne seeding, which may be due to density peaking, dilution effects and stabilization of the ion temperature gradient mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Type-I ELM mitigation by continuous lithium granule gravitational injection into the upper tungsten divertor in EAST

Large edge-localized modes (ELMs) were mitigated by gravitational injection of lithium granules into the upper X-point region of the experimental advanced superconducting tokamak (EAST) device with tungsten plasma-facing components. The maximum ELM size was reduced by ~70% in high β N H-mode plasmas. Large ELM stabilization was sustained for up to about 40 energy confinement times, with constant core radiated power and no evidence of high-Z or low-Z impurity accumulation. The lithium granules injection reduced the edge plasma pedestal density and temperature and their gradients, due to increased edge radiation and reduced recycling from the plasma-facing components. Ideal stability calculations using the ELITE code indicate that the stabilization of large ELMs correlates with improved stability of intermediate-n peeling-ballooning modes, due to reduced edge current resulting from the profile changes. The pedestal pressure reduction was partially offset by a core density increase, which resulted in a modest ~7% drop in core stored energy and normalized energy confinement time. We surmise that the remnant small ELMs are triggered by the penetration of multiple Li granules just past the separatrix, similar to small ELMs triggered by deuterium pellet Futatani et al (2014 Nucl. Fusion 54 073008). This study extends previous ELM elimination with Li powder injection Maingi et al (2018 Nucl. Fusion 58 024003) in EAST because (1) use of small, dust-like powder and the related potential health hazards were eliminated, and (2) use of macroscopic granules should be more applicable to future devices, due to deeper penetration than dust particles, e.g. inside the separatrix with velocities ~10 m s -1 in EAST.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Offshore lightning more active and intense in U.S. East Coast wind areas than in North Sea

Fast winds over open areas off the U.S. East Coast resulted in many offshore areas being opened for wind power deployment. Yet lightning is active in this same general area. Lightning damage has been noted as the leading cause of unplanned downtime of wind farms. Here we use lightning observations from 2020–2022 to quantify lightning activity—U.S. East Coast Wind Lease and Wind Planning areas received about 14-times the lightning strokes and about 18-times the energy transfer compared to the operational wind farms in the North Sea. A strong north-south U.S. gradient of lightning exists : low off the coast of Maine, growing higher off Chesapeake Bay, and high south of Virginia. Very high lightning activity also occurs about 250 km from the U.S. coastline, which is 50–100 km outside currently designated Wind Areas but likely relevant to planning floating wind farms further from shore. International lightning protection standards, such as IEC 614200-24, use lightning stroke density and turbine top-height to estimate damage to wind turbines, but it remains unclear if such standards could protect U.S. offshore wind turbines in active lightning areas where the turbine top-height can be 100 m taller than the next tallest object for tens to hundreds of kilometers. This analysis suggests U.S. East Coast offshore wind farms will be subjected to much higher lightning stroke and lightning energy densities than the North Sea counterparts. Preparing the turbine designs in anticipation of these more active U.S. lightning risks will help foster wind’s growing contribution to a reliable and resilient low-carbon energy future.

lightning↗
DOE OSTI2020

Origins of East Asian Summer Monsoon Seasonality

The East Asian summer monsoon is unique among summer monsoon systems in its complex seasonality, exhibiting distinct intraseasonal stages. Previous studies have alluded to the downstream influence of the westerlies flowing around the Tibetan Plateau as key to its existence. We explore this hypothesis using an atmospheric general circulation model that simulates the intraseasonal stages with fidelity. Without a Tibetan Plateau, East Asia exhibits only one primary convective stage typical of other monsoons. As the plateau is introduced, the distinct rainfall stages—spring, pre-mei-yu, mei-yu, and midsummer—emerge, and rainfall becomes more intense overall. This emergence coincides with a pronounced modulation of the westerlies around the plateau and extratropical northerlies penetrating northeastern China. The northerlies meridionally constrain the moist southerly flow originating from the tropics, leading to a band of lower-tropospheric convergence and humidity front that produces the rainband. The northward migration of the westerlies away from the northern edge of the plateau leads to a weakening of the extratropical northerlies, which, coupled with stronger monsoonal southerlies, leads to the northward migration of the rainband. When the peak westerlies migrate north of the plateau during the midsummer stage, the extratropical northerlies disappear, leaving only the monsoon low-level circulation that penetrates northeastern China; the rainband disappears, leaving isolated convective rainfall over northeastern China. In short, East Asian rainfall seasonality results from the interaction of two seasonally evolving circulations—the monsoonal southerlies that strengthen and extend northward, and the midlatitude northerlies that weaken and eventually disappear—as summer progresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Extraordinary March 2022 East Antarctica “Heat” Wave. Part II: Impacts on the Antarctic Ice Sheet

Abstract Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) that caused these record-shattering temperature anomalies. Here, we continue our large collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall. These impacts included widespread rain and surface melt that was recorded along coastal areas, but this was outweighed by widespread high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloud-liquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Last, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea ice extent. Significance Statement Using our diverse collective expertise, we explored the impacts from the March 2022 heat wave and atmospheric river across East Antarctica. One key takeaway is that the Antarctic cryosphere is highly sensitive to meteorological extremes originating from the midlatitudes and subtropics. Despite the large positive temperature anomalies driven from strong downward longwave radiation, this event led to huge amounts of snowfall across the Antarctic interior desert. The isotopes in this snow of warm airmass origin will likely be detectable in future ice cores and potentially distort past climate reconstructions. Even measurements of space activity were affected. Also, the swells generated from this storm helped to trigger the final collapse of an already critically unstable Conger Ice Shelf while further degrading sea ice coverage.

Wille, Jonathan D.↗
DOE OSTI2026

Cation Data for the East River Watershed, Colorado (2014-2025)

This data package contains mean values for cation concentration for water samples taken from the East River Watershed in Colorado. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of elements of interest simultaneously for the East River Watershed, Colorado groundwater and surface water samples to inform insights on the biogeochemistry processes within the watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. For samples collected prior to 06-16-2021, the instrumentation, Elan DRC II, PerkinElmer SCIEX, automatically switches among the three models necessary to analyze all 37 elements. These 37 elements include: (1) Lithium (Li), Beryllium (Be), Boron (B), Sodium (Na), Magnesium (Mg), Aluminium (Al), Silicon (Si), Phosphorus (P), Titanium (Ti), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Caesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th), Uranium (U) using standard model, argon Ar as reaction gas, (2) Potassium (K), Calcium (Ca), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe) using dynamic reaction cell (DRC) model, ammonia NH3 as reaction gas, and (3) Phosphorus (P) and Selenium (Se) using DRC model, oxygen O2 as reaction gas. Note for the samples with higher concentrations of chloride (Cl-), asenic (As) concentrations were analysed with DRC model (oxygen O2 as reaction gas) to avoid the interference of chloride. For samples collected on and after 06-16-2021, an advanced Agilent 8900 triple quadrupole inductively coupled plasma mass spectrometry system (Agilent 8900 QQQ ICP-MS, Agilent Technologies) has been used to measure the concentrations of interested 36 elements simultaneously for environmental samples, including (1) Lithium (Li), Beryllium (Be) and Boron (B) using standard no gas mode, (2) Sodium (Na), Magnesium (Mg), Aluminium (Al) Phosphorus (P), Potassium (K), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Cesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th) and Uranium (U) using standard helium (He) collision mode, (3) Titanium (Ti) and Vanadium (V) using high Energy (HEHe) helium (He) collision mode, and (4) Silicon (Si), Calcium (Ca) and Selenium (Se) using standard H2 reaction mode. All samples were prepared/diluted with 2% (v/v) ultrapure nitric acid in Milli-Q water (18.2 mega ohm-cm), and analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (cation_data_2014_2025.zip) containing a total of 5,849 files: 5.848 data files of cation data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v6_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v6_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (4) PDF and docx files for the detemination of Method Detection Limits (MDLs) for ICP-MS PerkinElmer DRC II instrumentation (Detemination_of_Method_Detection_Limits__MDLs__for_ICP_MS__PerkinElmer_Elan_DRC_II__LBL_Bldg74_Lab214D) for samples before November 2021; (5) PDF and docx files for the determination of MDLs for ICP-MS Agilent 8900 QQQ instrumentation (ICP_MS_Analysis_detection_limits_and_QA_QC_WenmingDong_updated_2026-08-06) for samples November 2021 and onward. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 113 locations containing cation data. Update on 2021-04-11: Added Detemination of Method Detection Limits (MDLs) for ICP-MS document, which can be accessed as a PDF or with Microsoft Word. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated cation data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) removed suffix and prefix on two variables (“aqberylliumion_asberyllium” and “aqlithiumion_aslithium”), (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-10-16. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Updated versions of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-06, of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-June 2024. (DE-SC0021139)

This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data, a row of column descriptors, and units of measurements. some data processing and QA/QC was done to filter out data from sensors that went bad and extreme outliers. CO2 sensors that went bad were replaced with new sensors as soon as possible. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input. This is the second version of this data set and was modified on 10/01/2024. The primary change in the data was the addition of data from the fall of 2022 to June of 2024. In addition, minor QA/QC was done to filter out data from sensors that went bad and extreme outliers. THe filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. This is now the third version of the data set, and was modified 03/25/2026. The primary change in the data was the addition of data from the June of 2024 to December 2025. Further r QA/QC was done with the new data to filter out bad data from faulty sensors and extreme outliers. The filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. ##This additional data was funded under DE-SC0024218( Responses of Plant and Microbial Respiration Sources to Changing Cold Season Climate Drivers in the East River Watershed)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- 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. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗
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

Denudation, solute export, landscape evolution modeling, and geographic information system data for the East River watershed, Colorado, USA (2020-2024)

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of lithologic controls on denudation, solute export, carbon-scaling relationships, and transient landscape evolution in the East River watershed near Crested Butte, Colorado, USA. The package includes GIS layers used to produce the Figure 2 map, including drainage, hillshade, lithology, sample locations, and basin polygons, together with comma-separated value (CSV) tables and matching CSV data dictionaries. One group of tables reports sample-level and catchment-level information for river-sediment samples analyzed for in situ-produced cosmogenic beryllium-10 (10Be), including sample names, outlet elevations, geographic coordinates, upstream drainage area, rock-type classes, production-rate scaling scheme, analyzed nuclide, catchment-averaged denudation rates, and associated lower and upper analytical uncertainties. Sample and catchment attributes provide the basis for comparing denudation rates across intrusive, shale, sedimentary, and mixed-lithology settings. A second group of tables reports supporting information for landscape-evolution modeling and the mapped geologic framework of the study area. Included files list parameter values and definitions for the two-phase landscape-evolution simulations, summarize full-domain model erosion fluxes and topographic metrics for different simulation configurations, provide a fixed-area carbon-model scaling table, and summarize mapped geologic units within the East River study domain, including geologic code, formation name, lithologic description, mapped area, and lithologic class grouping. Model outputs and geologic summaries support interpretation of transient landscape behavior and its relation to the mapped distribution of shale, intrusive, sedimentary, and surficial units. A third group of tables reports hydrologic and hydrochemical information used to quantify dissolved export from the watershed. Included files provide site-level values for drainage area, mean annual solute export, standard error of annual export, area-normalized solute yield, and equivalent weathering rate for five East River monitoring sites, along with metadata describing the number, sampling cadence, and date range of discharge records and partial and full total dissolved solids observations used in the solute-yield analyses. The package also contains a supplementary daily ion-load time series with daily mean discharge, discharge observation counts, dissolved concentrations, and daily loads for calcium, magnesium, sodium, potassium, chloride, sulfate, nitrate, fluoride, dissolved silica, charge-balance bicarbonate, and total dissolved solids. The package contains GIS files, comma-separated value files (.csv), CSV data dictionaries, a file-level metadata table, a package-tree text file, and a readme text file.

10Be↗
DOE OSTI2021

AmeriFlux FLUXNET-1F US-CF1 CAF-LTAR Cook East

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CF1 CAF-LTAR Cook East. This is the FLUXNET version of the carbon flux data for the site US-CF1 CAF-LTAR Cook East produced by applying the standard ONEFlux (1F) software. Site Description - CF1 has operated since May 2017 at the R.J. Cook Agronomy Farm outside of Pullman, Washington, and monitors the no-till side of a paired-catchment study that is part of the Longterm Agroecosystem Research (LTAR) site common experiment. CF1 is located in “Cook East,” a field that has been in no-till management since 1998, and represents the Alternative treatment of the LTAR common experiment. It is paired with the CF2 tower location in “Cook West,” which has been in conventional tillage since prior to 1998 and represents the Prevalent treatments of the common experiment. Cook Agronomy Farm is in the high precipitation agroecological zone of the Columbia Plateau’s dryland cropping region. Crop rotations are wheat-based and include winter wheat, spring wheat, chickpea, spring canola, and winter peas. Wheat is the principal cash crop, with other crops grown in rotation for diversity, nutrient, and pest management. Soils are predominantly silt loam texture Mollisols in the Palouse, Thatuna, Naff soil series. The CF1 tower replaced US-RC1, which operated 2012-2016 in a neighboring field that was also in no-till management since 1998. CF1 and RC1 have distinct footprints, aspects, and soil series compositions.

Phillips, Claire L.↗
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Tw5 East Pond Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw5 East Pond Wetland. This is the FLUXNET version of the carbon flux data for the site US-Tw5 East Pond Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell Wetland site is a 6.5 acre restored wetland on Twitchell Island, that is managed by the California Department of Water Resources (DWR) and the U.S. Geological Survey (USGS). In the fall of 1997, the site was permanently flooded to a depth of approximately 55 cm. The wetland remained fairly unvegetated in patches increasing in size towards the east. The site underwent a major disturbance in 2013 when the vegetation was removed to seed a nearby restored wetland. A flux tower equipped to analyze energy, H2O, CO2, and CH4 fluxes was installed on April 17, 2018.

Valach, Alex↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-Tw4 Twitchell East End Wetland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Tw4 Twitchell East End Wetland. This is the FLUXNET version of the carbon flux data for the site US-Tw4 Twitchell East End Wetland produced by applying the standard ONEFlux (1F) software. Site Description - The Twitchell East End Wetland is a newly constructed restored wetland on Twitchell Island, CA. This site and the surrounding region are part of the San Joaquin - Sacramento River Delta drained beginning in the 1850's and subsequently used for agriculture. The site was previously a corn field. The wetland was designed to have a mix of vegetated and open water channels and ponds (due to surface elevation differences). Flooding of the wetland was done gradually beginning in January, 2014. Berms wind throughout the wetland to allow vehicle access. Tule and Cattail plant material from a nearby wetland were spread along the berms immediately prior to flooding to facilitate plant establishment and stabilization of the berms from wind/water erosion. The tower was installed on November 25, 2013.

Eichelmann, Elke↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-NSa NASA HAQ - SJV East Irrigated Vineyard

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-NSa NASA HAQ - SJV East Irrigated Vineyard. This is the FLUXNET version of the carbon flux data for the site US-NSa NASA HAQ - SJV East Irrigated Vineyard produced by applying the standard ONEFlux (1F) software. Site Description - Irrigated vineyard site located on the east side of the San Joaquin Valley, utilizing drip irrigation. The flux tower is situated in the southeastern corner of a 200 * 300 m vineyard containing about 20 rows of grapevines. Surrounding land is primarily cultivated with other crops, mostly grapevines as well.

Davis, Kenneth [The Pennsylvania State University]↗
DOE OSTI2020

Performance Assessment for the Disposal of Low Level Waste in the 200 East Area Burial Grounds (FY2019 Annual Status Report)

This annual review provides the projected dose estimates of radionuclide inventories disposed in the 200 East Area Low-Level Waste Burial Grounds (LLBGs) since September 26, 1988. These estimates are calculated using the original dose methodology developed in the performance assessment (PA) analysis (WHC-SD-WM-TI-730) and are compared with U.S. Department of Energy requirements (DOE O 435.1 and performance objectives defined in companion documents DOE M 435.1 and DOE-STD-5002-2017). All performance objectives are currently satisfied, and operational waste acceptance criteria (HNF-EP-0063) and waste acceptance practices continue to be sufficient to maintain compliance with performance objectives. Inventory estimates and associated dose estimates from future waste disposal actions are unchanged from previous years’ evaluations, indicating that potential impacts are well below performance objectives; therefore, future compliance with DOE O 435.1 is expected. A new PA study has been initiated in this fiscal year (FY) 2019 for evaluation of active disposal sites within the 200 West and East Areas (Trenches 31 and 34 in 200 West; Trench 94 in 200 East) because extended time has elapsed between the current annual status report and the original PA (WHC-SD-WM-TI-370) for the active disposal sites. The new PA for the active disposal sites is expected to be completed in FY 2021.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Conceptual Model Update of Mercury Sources and Flux at Y-12 and Upper East Fork Poplar Creek, Oak Ridge,Tennessee

The interactions between the key processes that control the fate and transport of mercury (Hg) in the sediments, groundwater, and stream water within the Y-12 National Security Complex (Y-12) in Oak Ridge, Tennessee are complex, and many aspects are not well understood. In contaminated environments such as Y-12, conceptual models are frequently developed to aid in visualizing and understanding the dynamic nature of the hydrologic, geochemical, and physical environment. These conceptual models integrate data in an internally consistent manner to understand processes that control the fate and transport of contaminants. Over the past few decades of environmental investigation at Y-12, a number of conceptual models have been developed to identify and define various technical processes at various scales. The main purpose of the activities described in this report is to update the 2011 conceptual model for Upper East Fork Poplar Creek using the most recent Hg concentration and flux data and informed scientific interpretation. Emphasis was placed on improving the previous model by implementing a more detailed spatial and temporal approach to visualize transport pathways in the watershed and trends in flux and concentration over time, and to compare baseflow and stormflow system dynamics. Detailed descriptions of historical and current Hg sources and transport pathways are also provided for the east and west ends of the facility. This conceptual model will allow the US Department of Energy to evaluate past and present remedial activities and provide a strong technical basis for prioritizing and optimizing remedial responses in a cost-effective and efficient manner. Twenty years ago, Hg flux from Outfall (OF) 200 at the headwaters of East Fork Poplar Creek represented approximately 20% of the overall flux leaving Y-12. By the time of the 2011 conceptual model report, that percentage had increased to 70%–80%. Flux estimates compiled for this report using data from 2009 to 2018 show that OF200 accounted for roughly 60% of the flux leaving Y-12. The relative role of the four storm drain conduits to OF200 appeared similar to 2011, with OF163 being the greatest contributor to downstream flux. An increase in Hg concentrations and flux occurred throughout and downstream of west end storm drains in 2011 in response to a storm drain cleanout. Leading up to the 2011 storm drain cleanout, annual baseflow flux was higher at OF200A6 than at Station 17. From 2011 to 2018, the trend reversed with concentrations and flux being higher at Station 17 than at OF200A6. A spike in concentrations occurred in July 2018, potentially due to a one-time influx of Hg that occurred during COLEX decontamination and decommissioning activities at Y-12. This report includes recommendations for improving our conceptual understanding of Hg sources, transport pathways, and flux at Y-12. Important recommendations include further evaluation of transport pathways, concurrent measurement of Hg concentration and flux under baseflow and stormflow conditions at multiple sites, and research to understand the connections among periphyton and methylmercury concentration, bioavailability, and bioaccumulation. Ongoing facility decontamination and decommissioning efforts, site characterization, remedial actions, and research are facilitating the collection of new Hg data at Y-12. The conceptual model update provided herein takes advantage of the extensive recent Hg sampling to provide an updated assessment of Hg mass balance and support a refined understanding of Hg behavior at or near Y-12. This assessment should assist in future environmental management decisions and in mitigating the impacts of Hg on the surrounding environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗