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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Organic colloid composition in variable-redox porewaters within a mountainous floodplain

Redox gradients, often driven by changes in sediment moisture levels in porous, heterogeneous groundwater systems, create dynamic conditions that may promote the production and transport of colloids within natural waters. While much research has focused on the inorganic composition of colloids, the organic composition remains less well understood. Organic matter (OM) in colloids may associate with minerals, complex metal ions, and serve as an electron donor for microbial respiration; therefore, its composition is of high interest. We examined the composition of porewater OM along a redox gradient in a riparian soil located along the Slate River in Crested Butte, Colorado, USA as a function of depth (90, 130, 200, and 350 cm below ground surface). All depths were oxic to suboxic, except 200 cm, where the products of iron and sulfate reduction were observed concomitant with an increase in dissolved and/or colloidal OM, pH, alkalinity, and conductivity. We investigated the composition of porewater using correlated scanning transmission X-ray microscopy and transmission electron microscopy. We observed a change in the OM chemistry from carboxylate-rich at the 200 cm depth (where it was also enmeshed with non-crystalline iron) to phenol- and substituted-aromatic-rich at other depths. Radiocarbon dating revealed carbon in porewater at 200 cm was younger than depths above and below. Soil porewater can flow down into the underlaying gravel bed during baseflow conditions, thus we speculate whether riparian porewater could transport iron- and carboxylate-rich organic colloids into surrounding surface waters through the gravel bed conduit.

Alpine floodplain↗
DOE OSTI2026

Data from Stewart et al. 2026 "Organic Colloid Composition in Variable-Redox Porewaters within a Mountainous Floodplain"

Redox gradients, often driven by changes in sediment moisture levels in porous, heterogeneous groundwater systems, create dynamic conditions that may promote the production and transport of colloids within natural waters. While much research has focused on the inorganic composition of colloids, the organic composition remains less well understood. Organic matter (OM) in colloids may associate with minerals, complex metal ions, and serve as an electron donor for microbial respiration; therefore, its composition is of high interest. We examined the composition of porewater OM along a redox gradient in a riparian soil located along the Slate River in Crested Butte, Colorado, USA as a function of depth (90, 130, 200, and 350 cm below ground surface). All depths were oxic to suboxic, except 200 cm, where the products of iron and sulfate reduction were observed concomitant with an increase in dissolved and/or colloidal OM, pH, alkalinity, and conductivity. We investigated the composition of porewater using correlated scanning transmission X-ray microscopy and transmission electron microscopy. We observed a change in the OM chemistry from carboxylate-rich at the 200 cm depth (where it was also enmeshed with non-crystalline iron) to phenol- and substituted-aromatic-rich at other depths. Radiocarbon dating revealed carbon in porewater at 200 cm was younger than depths above and below. Soil porewater can flow down into the underlaying gravel bed during baseflow conditions, thus we speculate whether riparian porewater could transport iron- and carboxylate-rich organic colloids into surrounding surface waters through the gravel bed conduit. This data package includes geochemical data (.csv) as a function of depth from porewaters in OBJ1. Measurements included are pH, conductivity, TOC, Fe concentration, sulfate concentration, dissolved oxygen. Additionally radiocarbon dating age of carbon in samples is provided. Additionally, this dataset contains metadata files (.csv) describing the methods, file contents, and column headers.

alpine floodplain↗
DOE OSTI2023

Soil water content, matric potential, carbon dioxide and oxygen concentrations, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of soil sensor data (temperature, water content, bulk electrical conductivity, porewater dissolved oxygen and porewater dissolved carbon dioxide) in a vertical profile from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package includes: (1) soil temperature, volumetric water content and electrical conductivity at 40, 60 and 82.5 cm depth; (2) soil matric potential at 40, 60, 79 and 100 cm depth; (3) soil CO2 concentrations at 40, 60 and 82.5 cm depth; and (4) soil oxygen concentrations at 60, 82.5, 100, 135, 170 and 182 cm depth. Both the carbon dioxide and oxygen sensors are optical sensors that can measure the partial pressure of oxygen in both saturated and unsaturated conditions. Unfortunately, soil CO2 in the profile is unexpectedly high and above the sensor calibration range (0-25,000 ppm). In addition, soil CO2 sensors failed within a year of deployment, so we only report CO2 data from 2019-2020.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 4 "*data.csv" files, one for each calendar year in the dataset. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding for each measured parameter. Because sensors have been added over time, not every sensor has data dating back to Oct 2018. Note that there is a data gap over winter 2019-2020 due to a power outage. While this repository currently only contains data through December 2021, the dataset will be updated as additional years are collected and processed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Stable Water Isotope Data for the East River Watershed, Colorado (2014-2025)

The stable water isotope data for the East River Watershed, Colorado, consists of delta2H (hydrogen) and delta18O (oxygen) values from samples collected at multiple, long-term monitoring sites including streams, groundwater wells, springs, and a precipitation collector used to establish a local meteoric water line (LMWL) for the watershed. These locations represent important and/or unique end-member locations for which stable isotope values can be diagnostic of the connection between precipitation inputs as snow and rain and riverine export. Such locations include drainages underline entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of water isotope values from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on water export. Such data may be combined with stream gaging stations co-located at each surface water monitoring site to relate seasonal variations in water export to their stable isotopic signature. Data for liquid water delta2H and delta18O values are reported in units of parts per thousand (per-mil; ‰). This data package contains (1) a zip file (isotope_data_2014-2025.zip) containing a total of 95 files: 96 data files of isotope 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; and (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. 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 43 locations containing isotope data. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated isotope data for all locations up to 2021-12-31 and (2) 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 2023-03-13. 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 2024-02-19. 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. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available isotope 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 isotope data was added, up until the end of WY2025 (September 30, 2025).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil and groundwater environmental sensor data, Wax Lake Delta, Louisiana, March 2023 - March 2024

This study evaluates how environmental parameters that integrate biogeochemical processes vary with water table fluctuations in the freshwater Wax Lake Delta (WLD) in Louisiana, U.S.A. This data package contains seven *.csv files and one Excel file that compiles all the data from the individual .csv files. This dataset reports high frequency (15-min) observations of water level, soil redox potential, specific conductance, and pH made for one year along elevation transects located on the older, proximal (OT) and younger, distal (YT) ends of a deltaic island. Water depth relative to the ground surface (cm; HOBO U20L-04; error ± 0.4 cm), water pH and temperature (HOBO MX2501), and specific conductance and temperature (HOBO U24-001) sensors were installed in March 2023. Water depth was corrected for barometric pressure recorded by a separate logger secured to a platform above the highest water level. Soil redox probes (SWAP ORP-40-4-B) were also installed in March 2023. Each probe had four Pt sensors (2 mm width) placed at 10 cm, 20 cm, 30 cm, and 40 cm below the ground surface. Redox data were referenced to an external Ag0/AgCl (3M KCl) reference probe placed in saturated ground and recorded on CR1000X dataloggers (Campbell Scientific) powered by solar panels. A second reference probe was positioned near the primary reference probe for backup and data correction. The tops of the soil redox probes and soil moisture probes were flush with the soil surface so that sensors are reported at their indicated depths below ground surface. Here, we report data collected between 15 March 2023 to 15 March 2024 for all sensors, with some differences due to exact dates of sensor placement or data gaps associated with sensor malfunction. For example, water depth at OT4 was not recorded between March to November 2023. Data flags indicate whether a value is valid (1) or was excluded from data analysis in the associated manuscript (-1).

EARTH SCIENCE > LAND SURFACE > SOILS↗
NASA NTRS2018

Intercomparison of Evapotranspiration Measurement Methods for Vegetable Crops in California

Recent drought events in California and legislation passed with the goal of increasing the sustainability of groundwater supplies have led to increased interest in tools to optimize irrigation schedules and increase on-farm water used efficiency. With more than 400 different crops produced in California, evapotranspiration-based irrigation scheduling is a promising and well-established approach. However, there is a need for accurate methods to estimate crop evapotranspiration (ET(sub c)) across the diverse range of crops grown, coupled with cost-effective methods for quantifying the accuracy of these tools. In this study, we evaluated remotely sensed estimates of ET(sub c) and associated crop water requirements from NASA's Satellite Irrigation Support (SIMS) system for two vegetable crops and measured crop evapotranspiration ET(sub c) using multiple methods, including weighing lysimeters, eddy covariance towers (EC), and surface renewal stations. We compared ET(sub c) data from these measurements with remotely sensed basal crop evapotranspiration (ET(sub cb)) data from SIMS as well as ET(sub c) data from a standard FAO-56 crop coefficient approach. Studies were conducted for sugar beets in Five Points, CA from 2014 to 2015 and studies are ongoing for fresh market tomatoes in Firebaugh, CA. We present results from these intercomparison studies and describe implications for future studies to quantify the accuracy of remotely sensed measures of ET(sub c). Highlights from results to date include strong correlations between ET measured with both surface renewal instrumentation and eddy covariance calculations using a 3D sonic anemometer and ET(sub c) data measured with the weighing lysimeter, with respective R2 values of 0.7964 (surface renewal) and 0.8034 (eddy covariance). This study provides insights into agreement between different approaches for monitoring evapotranspiration and provides another reference point for the community working to develop accurate and cost-effective tools that support growers in optimizing irrigation management.

Measurement↗
NASA NTRS2019

New Perspectives of Ancient Mars: Mineral Diversity and Crystal Chemistry at Gale Crater, Mars from the CheMin X-Ray Diffractometer

The Mars Science Laboratory Curiosity rover arrived at Mars in August 2012 with a primary goal of characterizing the habitability of ancient and modern environments. Curiosity landed in Gale crater to study a sequence of ~3.5 Ga old sedimentary rocks that, based on orbital visible/near-infrared reflectance spectra, contain secondary minerals that suggest deposition and/or alteration in liquid water. The sedimentary sequence that comprises the lower slopes of Mount Sharp within Gale crater may preserve a dramatic shift on early Mars from a relatively warm and wet climate to a cold and dry climate based on a transition from smectite-bearing strata to sulfate-bearing strata. The rover is equipped with cameras and geochemical and mineralogical instruments to examine the sedimentology and identify compositional changes within the stratigraphy. These observations provide information about variations in depositional and diagenetic environments over time. The Chemistry and Mineralogy (CheMin) instrument is one of two internal laboratories on Curiosity and includes a transmission X-ray diffractometer (XRD) and X-ray fluorescence (XRF) spectrometer with a Co-K source. CheMin measures loose sediment samples scooped from the surface and drilled rock powders. The XRD provides quantitative mineralogy of scooped and drilled samples to a detection limit of ~1 wt.%. Curiosity has traversed >20 km since landing and has primarily been exploring the site of a predominantly ancient lake environment fed by groundwater and streams emanating from the crater rim. Results from CheMin demonstrate an incredible diversity in the mineralogy of fluvio-lacustrine rocks that signify variations in source rock composition, sediment transport mechanisms, and depositional and diagenetic fluid chemistry. Abundant trioctahedral smectite and magnetite at the base of the section may have formed from low-salinity pore waters with a circumneutral pH within lake sediments. A transition to dioctahedral smectite, hematite, and Ca-sulfate going up section suggests a change to more saline and oxidative aqueous conditions within the lake waters themselves and/or within diagenetic fluids. The primary minerals detected in fluvio-lacustrine samples by CheMin also suggest diversity in the igneous source regions for the sediments, where abundant pyroxene and plagioclase in most samples suggest a basaltic protolith, but sanidine and pyroxene in one sample may have been sourced from a potassic trachyte, and tridymite and sanidine in another sample may have been transported from a rhyolitic source. Crystal chemistry of major phases in each sample have been calculated from refined unit-cell parameters, providing further constraints on aqueous alteration processes and igneous protoliths for the sediments. Perhaps one of the biggest mysteries revealed by the CheMin instrument is the high abundance of X-ray amorphous materials (15 to 73 wt.%) in all samples measured to date. X-ray amorphous materials were detected by CheMin based on the observation of broad humps in XRD patterns. How these materials formed, their composition, and why they persist near the martian surface remain a topic of debate. The sedimentology and composition of the rocks analyzed by Curiosity demonstrate that habitable environments persisted intermittently on the surface or in the subsurface of Gale crater for perhaps more than a billion years.

Rampe, E. B.↗
DOE OSTI2026

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

The anion data for the East River Watershed, Colorado, consist of fluoride, chloride, sulfate, nitrate, and phosphate concentrations collected at multiple, long-term monitoring sites that include stream, groundwater, and spring sampling locations. These locations represent important and/or unique end-member locations for which solute concentrations can be diagnostic of the connection between terrestrial and aquatic systems. Such locations include drainages underlined entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of solute concentrations from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on the riverine export of multiple anionic species. Such data may be combined with stream gauging stations co-located at each monitoring site to directly quantify the seasonal and annual mass flux of these anionic species out of the watershed. This data package contains (1) a zip file (anion_data_2014_2025.zip) containing a total of 386 files: 387 data files of anion 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 (v7_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v7_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) a anion MDL fact sheet (anion_MDLs_202608 in PDF and docx formats). 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 47 locations containing anion data. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated anion 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) restructure of units to comply with CSV reporting format requirements, and (5) 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 2022-12-20: Updates were made to both the data files and reporting format specific files. Conversion issues affecting ER-PLM locations for anion data was resolved for the data files. Additionally, the flmd and dd files were updated to reflect the updated versions of these files. Available data was added up until 2022-03-14. 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 2023-05-19. 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 2023-09-11. 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. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available anion 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 anion data was added, up until the end of WY2025 (September 30, 2025). An anion MDL document was included in this update.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of Hydrology and Interim Measure Performance in the Chromium Plume at Los Alamos National Laboratory - 20498

Hexavalent chromium, Cr(VI), is present in the regional aquifer beneath Los Alamos National Laboratory (LANL) at concentrations greater than the New Mexico groundwater standard (50 ppb). The source of the Cr is blowdown from a power plant where potassium dichromate was used as a corrosion inhibitor from 1956 to 1972. Blowdown comingled with effluent from another source and migrated approximately 3 miles down a canyon, primarily as surface flow, until reaching an alluvial infiltration zone. The infiltrating water moved vertically through a thick (approximately 900 ft), complex stratigraphic sequence of unsaturated tuffs, basalt, and unconsolidated alluvial deposits. Horizontal transport also took place within spatially limited perched groundwater horizons in the vadose zone (VZ). Breakthrough at the regional aquifer water table is thought to have occurred in multiple hydraulic windows or 'drip points' located approximately 3 miles from the initial release site at the power plant. The regional aquifer is highly heterogeneous and anisotropic, and displays unconfined behavior in an upper zone and confined behavior at depth. The Cr(VI) contamination has so far remained confined to the upper approximately 25 m of the aquifer. The ambient hydraulic gradient in the regional aquifer is toward the east/southeast, but the water table is extremely flat in the Cr plume area compared to surrounding areas. There is some local variability in flow direction, potentially related to effects of seasonal pumping from nearby Los Alamos county water-supply wells, which draw from the regional aquifer several hundred feet below the water table. An interim measure (IM) is currently operating at the site to achieve hydraulic control of plume migration using a series of extraction wells, ion-exchange treatment, and injection along the downgradient portion of the plume. The migration of Cr contamination in the aquifer has been previously evaluated using a calibrated probabilistic numerical model, which has also been used to design the spatial configuration of extraction and injection wells for the IM, and to identify locations for monitoring wells. The model is built using the Finite Element Heat and Mass (FEHM) transfer code, and the Model Analysis and Decision Support (MADS) software is used for analyses. To date, the portion of the IM system that has operated in a continuous manner has been very effective at hydraulic control. Modifications to the flow field associated with the influence of IM pumping and injection are evaluated here, with modeling constraints to be provided by recent observations of tracers injected into both monitoring wells and IM injection wells, as well as opportunistic observations of geochemical signatures of treated ion-exchange effluent injected into IM injection wells. The tracer and geochemical data provide insights into modified flow directions within the Cr plume area that cannot be otherwise obtained. Results of the modeling work are being used to inform adaptive management of IM operations and will be a valuable tool for development and refinement of long-term remediation strategies. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Building an EPA Class VI Permit Application

Summary To accelerate the commercialization of carbon capture and storage (CCS), the US Department of Energy (US DOE) is building on decades of characterization efforts and pilot-scale projects through their CarbonSAFE program. Administered through their National Energy Technology Laboratory, this program seeks to bring fully integrated projects to the sector that can store more than 50 million tonnes of CO2 over a 30-year period. The program, which was enacted before the enhancement of Internal Revenue Code Section 45Q, is in the capture assessment, characterization, and permitting phase. The objectives of this paper are to discuss (a) the injection permitting requirements of the CarbonSAFE projects; (b) information gathering in support of the permit; (c) the timelines of field development and permit-related activities; (d) the major technical components of the field development plan; and (e) early feedback from the regulators toward acceptance of the permit. In Mississippi, more than 30,000 acres have been characterized by six deep characterization wells, a deep groundwater well, and 92 line miles of 2D seismic as part of the CarbonSAFE Project ECO2S. During the acquisition of seismic data, all receiver lines were live, which resulted in the generation of a pseudo-3D seismic design. The incorporation of a 3D seismic survey was not included as part of this project due to logistical difficulties presented by the undulating, wooded surface terrain. A suite of openhole geophysical logs was taken from each well, allowing for a detailed interpretation of prospective storage reservoirs and confining intervals to complement the analysis carried out on the 290 ft of a whole core that was cut through the prospective confining zone and storage reservoir. The detailed geologic and reservoir data were assembled and entered into a 3D model to assess the injection capacity and the area of review (AoR). This information fed into the detailed corrective action, monitoring, testing, and postinjection site care (PISC) modeling. The results have been exceptional. The geologic assessment has revealed three primary storage targets, ranging in depth from 3,500 ft to 6,000 ft. These storage reservoirs net 1,300 ft of sandstone, with mean porosity and permeability of 29% and 3.6 darcies, respectively. Together, these reservoirs have storage capacities that may exceed 20 million tonnes per square mile, making this a gigatonne prospect. Forward modeling of the project resulted in an AoR of 16 sq miles, injecting about 8000 t/d, for 30 years, via two deep injection wells. The excellent confining characteristics of the caprock, relatively simple geologic structure, and lack of historical well drilling activity in this area provide excellent containment of the injected CO2. Based on this work, the project has proposed 20 years of PISC. To date, only two US CO2 injection permits have been granted. These projects relied on a singular capture point feeding a singular sequestration point (source to sink), and considerations have not been made to garner CO2 emissions from other industrial sources. The Kemper County Storage Complex is a first-of-its-kind storage hub concept that looks to develop an area capable of storing significant quantities of CO2 from the region. Also, this work will show how characterization efforts, geological and numerical modeling efforts, and plan development were constructed in support of permit and incentives acceptance.

Energy & Fuels↗
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↗
NASA NTRS2022

KSC Headquarters Building Area SWMU 104 Soil Interim Measure Report for LOC 2D and LOC 2E Kennedy Space Center, Florida

This Interim Measure (IM) Report for Location of Concern (LOC) 2D and LOC 2E summarizes the activities associated with implementation of an IM to address soil and concrete contamination at the Kennedy Space Center (KSC) Headquarters Building Area (KHQA) located at KSC, Florida. KHQA has been designated Solid Waste Management Unit (SWMU) 104 under KSC’s Resource Conservation and Recovery Act Corrective Action Program. The IM consisted of excavating/removing residual polychlorinated biphenyl (PCB) contamination in soil and concrete from two separate areas, LOC 2D and LOC 2E, which were associated with electrical transformer locations. IM activities were conducted in accordance with the Interim Measure Work Plan (IMWP), Revision 1, for KHQA dated June 2018, which was approved by the Florida Department of Environmental Protection in a correspondence letter dated April 2016 and the United States Environmental Protection Agency in a correspondence letter dated August 2018. The objective of the IM was to remove all remaining contaminated media with total PCB concentrations greater than the State of Florida Residential Soil Cleanup Target Level (RSCTL) (0.5 milligrams per kilogram [mg/kg]). This cleanup goal meets unrestrictive re-use conditions in Florida, and is below federal regulations for PCB Bulk Remediation Waste Cleanup of 1 mg/kg for high occupancy, unrestricted use areas, as outlined in 40 Code of Federal Regulations (CFR) 761.61(a). The timeline of the IM was coordinated with demolition of the former KSC Headquarters Building (demolished in 2021) in order to safely remove contaminated media after electrical equipment was de-energized and removed from the site. Excavation activities were conducted between January 10, 2022, and February 8, 2022, and included two waste streams for off-site disposal: (1) PCB-remediation waste greater than or equal to 50 mg/kg, and (2) PCB-remediation waste less than 50 mg/kg. A total of approximately 347 cubic yards (478 tons) of PCB-contaminated soil and concrete was initially removed from the LOC 2D and LOC 2E areas, as defined in the IMWP. An additional 4.35 tons of soil and concrete were removed based on verification sampling, which was conducted until the cleanup goal was achieved. The total area of excavation from both LOC 2D and LOC 2E was approximately 5,186 square feet (0.12 acres), and excavation depths ranged from 0.5 to 4.0 feet below land surface Excavated soils and concrete were stockpiled based on waste stream and subsequently transported and disposed of at Waste Connections of Osceola County, LLC – JED Landfill in St. Cloud, Florida (PCBs less than 50 mg/kg) or the Chemical Waste Management Landfill in Emelle, Alabama (PCBs greater than or equal to 50 mg/kg). Following excavation, clean backfill was brought to the site and provided to the Demolition Contractor to complete backfill and grading in conjunction with their final site restoration activities from building demolition. Post-IM groundwater sampling was conducted in May 2022 at three monitoring wells installed in April 2022 to confirm that PCBs did not leach into groundwater. These wells were installed where the highest PCB concentrations were detected at LOC 2D, LOC 2E, and from the Building Perimeter IM (previously completed in 2020). All PCB results in groundwater were non-detect. Volatile organic compounds were also analyzed based on potential past use of solvents in transformers with PCB-dielectric fluids and the potential release of dielectric fluids containing volatile organic compounds, and all results were non-detect. Based on completion of a successful IM at LOC 2D and LOC 2E, as well as previous remedial actions that have addressed other contamination at KHQA (SWMU 104), No Further Action without controls will be recommended for SWMU 104. NASA will submit separate Site Rehabilitation Completion Order request documentation for SWMU 104 to support an NFA without controls recommendation.

PCB↗
DOE OSTI2024

Data for Grogan et al. "Bringing Hydrologic Realism to Water Markets"

This data set provides model output and post-processing files required to reproduce the results, tables, and figures in the paper "Bringing Hydrologic Realism to Water Markets" by Grogan et al. (in review). Other input data used in this study includes: Lisk, M., Grogan, D., Zuidema, S., Caccese, R., Peklak, D., Zheng, J., Fisher-Vanden, K., Lammers, R., Olmstead, S., & Fowler, L. (2023). Harmonized Database of Western U.S. Water Rights (HarDWR) (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2205619 Two models were used in this study: (1) The University of New Hampshire Water Balance Model WBM, and (2) a Water Market Model. Market model code and model output post-processing code that make use of these data can be found here Model output files are: 1. WBM output files: scenario[x]_wbm_output.zip Where [x] is one of 1, 2, 2a, 3, and 3a Each zipped directory contains 7 gridded NetCDF files, each reporting the 10-year annual average value of a given variable, in units of average mm/day: File Name: wbm_indUseGross_yc.nc; Description: Water withdrawals by industry (part of the urban sector) File Name: wbm_domUseGross_yc.nc; Description: Water withdrawals by the domestic sector (part of the urban sector) File Name: wbm_irrigationGross_yc.nc; Description: Water withdrawals for agriculture File Name: wbm_irrigationExtra_yc.nc; Description: Water withdrawals from unsustainable groundwater for agriculture File Name: wbm_indUseEvap_yc.nc; Description: Consumptive water use by industry File Name: wbm_domUseEvap_yc.nc; Description: Consumptive water use by the domestic sector File Name: wbm_irrigationNet_yc.nc; Description: Consumptive water use by agriculture The file full_cell_area.nc gives the area of each grid cell in km2, which is used for converting water depth to water volume. 2. Water market model output & post processing output Folder: marketTrdSummaries/ Description: Files in this folder are used as input to code 1_WelfareCalculation_actual_trades.R. They summarize historical water right trade transactions in each state. File Name: welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: welfare_data_actual.rdata; Description: welfare gains by WMA from actual historical trades, as shown in Figure 3A. This data is the output of code 1_WelfareCalculation_actual_trades.R File Name: welfare_summary_simulated.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 1. Produced by code 2_DemandCurves_simulated_trades.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_summary_cutoffs_SGMS.xlsx; Description: Welfare gains by state as simulated by the market model in Scenario 2a. Produced by code 3_DemandCurves_simulated_trades_cutoffs.R, and used in code 4_WelfareCalculation.R. File Name: welfare_data_actual.rdata; Description: Spatial data, actual historical welfare gains by WMA as shown in Figure 3A. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated.rdata; Description: Spatial data, simulated Scenario 1 welfare gains by WMA as shown in Figure 3B. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs.rdata; Description: Spatial data, simulated Scenario 2 welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. File Name: welfare_data_simulated_cutoffs_SGMA.rdata; Description: Spatial data, simulated Scenario 2a welfare gains by WMA. Produced by code 4_WelfareCalculation.R and used by code Figure3.R. Additional files are provided for efficient reproduction of tables and figures. These include: File Name: wma_thresold_dates_Scenario2(a).csv; Description: Wet vs. paper right threshold dates for each WMA. Shown in Figure 2A,B. Produced and used by code calculate_thresolds_Figure2.R File Name: WWRTradeBounds (directory); Description: Trade boundary shapefile required to reproduce Figure 3A-D. Used in code Figure3.R File Name: welfare_region_totals.csv; Description: Welfare gains for the entire study region, as shown in Figure 3E. Used in code Figure3.R File Name: Welfare_gain_by_state_sector.csv; Description: Welfare gains by state and sector, as shown in Figure 3F. Used in code Figure3.R and produced (as a .xlsx file) by code 2_DemandCurves_simulated_trades.R File Name: WECC_MERIT_5min_v3b_mask.nc; Description: Gridded file that identified which land grid cells are in the WBM model domain, used for processing in code Figure4.py File Name: Table_1.csv; Description: All data in Table 1, reproducible from WBM output files using code table_1.R

Economics↗
DOE OSTI2025

Response of Subsurface Nitrogen-Cycling Microbial Communities to Environmental Fluctuations (Final Technical Report)

Riparian floodplains are dynamic ecosystems linking terrestrial and riverine systems. These floodplains experience hydrological shifts such as changes in water table height, flooding, and drought and can be ‘hotspots’ of biogeochemical cycling due to shifting sediment moisture (and saturation) and subsurface exchanges of water, nutrients, and other compounds across different sediment layers. Subsurface microbial communities are the primary drivers of biogeochemical processes in floodplains, and thus their structure and function can directly influence both surface and groundwater quality. The microbial nitrogen (N) cycle is particularly important in floodplains as it affects nutrient availability and removal. Two functional guilds of chemoautotrophic (i.e. CO2-fixing) microorganisms are responsible for the first oxidative step of the N cycle, nitrification: ammonia-oxidizing archaea (AOA) and bacteria (AOB) catalyze the oxidation of ammonia to nitrite, while nitrite-oxidizing bacteria (NOB) oxidize nitrite to nitrate. Despite the critical role nitrification plays in N-cycling in both terrestrial and aquatic ecosystems, our understanding of the diversity, ecophysiology, and activity of nitrifying organisms in subsurface floodplain soils/sediments is extremely limited. To help address this critical knowledge gap, the overarching goal of this project was to determine how shifts in key environmental parameters and gradients impact microbial N-cycling communities/processes, with particular emphasis on nitrification, within hydrologically-variable floodplain sediments in the Wind River Basin near Riverton, Wyoming. The three specific objectives of this project were to: (1) to associate in situ environmental drivers of N cycling with distinct functional guilds; (2) determine the guild response to variation in key ecosystem drivers; and (3) develop a dynamic ecosystem model of the microbial N cycle with the Riverton subsurface using community genomic and biogeochemical data collected in the first two objectives. Over the course of this project, we employed both 16S rRNA gene amplicon sequencing and genome-resolved metagenomics to examine the phylogenetic diversity and metabolic potential of subsurface nitrifier communities within 68 samples collected across multiple sites, depths, and time points within the Riverton floodplain, allowing for both spatial and temporal investigations at different scales. This project benefitted tremendously from recent advances in high-throughput sequencing technologies coupled with dramatic improvements in the computational tools and algorithms available for analyzing such large, complex genomic datasets. By pairing these cutting-edge genomic approaches with depth-resolved sampling and detailed geochemical analyses of the Riverton floodplain, we have gained novel insights into the structure and function of subsurface nitrifier communities in relation to both hydrology and biogeochemistry. This project resulted in the most detailed and comprehensive characterization of N-cycling floodplain microbial communities to date and will hopefully inspire and pave the way for future studies using similar approaches in other floodplains. Indeed, such information is critical for understanding subsurface biogeochemical cycling and how elemental stores are altered from perturbations initiated by the water cycle within floodplains. Finally, because of the terrestrial-aquatic nature of the Riverton floodplain, results from this project are also of relevance to disciplines such as soil science, estuarine science, limnology & oceanography, biogeochemistry, geobiology, environmental engineering, as well as genomics and data science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗
DOE OSTI2020

A Comparison of the Environmental Effects of Open-Loop and Closed-Loop Pumped Storage Hydropower

Pumped storage hydropower (PSH) capabilities are generally characterized as either open-loop or closed-loop. The U.S. Department of Energy (DOE) defines open-loop PSH as “continuously connected to a naturally flowing water feature,” and closed-loop PSH as “not continuously connected to a naturally flowing water feature.” All the PSH projects constructed in the United States to date are open-loop, so the potential environmental effects of closed-loop systems are not as well documented as the effects of open-loop systems. To address this knowledge gap, the DOE Water Power Technologies Office, under its HydroWIRES Initiative, has prepared this report to: (1) compare the potential environmental effects of open-loop PSH projects with those of closed-loop PSH projects; (2) describe how these effects are being avoided, minimized, or mitigated at existing projects in other countries and proposed projects in the United States; and (3) discuss the relative significance of the environmental issues. The report begins with a brief introduction on the current status of PSH development in the United States. Next, the report describes the current Federal Energy Regulatory Commission (FERC) hydropower licensing process, emphasizing some recent regulatory changes that affect closed-loop PSH development. The report then provides a summary comparison of the environmental effects of constructing and operating open-loop and closed-loop PSH systems and discusses the relative significance of those issues. This comparison and discussion are based on the results of a literature review and a review of FERC licensing records. The literature review includes journal articles, technical reports, and presentations on the environmental effects of PSH systems. It includes literature from the United States as well as countries where closed-loop PSH projects have been constructed. The FERC records review examines the FERC licensing record (e.g., National Environmental Policy Act documents and license orders) to identify the environmental effects anticipated and mitigation measures proposed for six of the closed-loop PSH projects currently licensed or permitted in the United States. For comparison, the FERC records review also discusses the environmental effects and mitigation measures for four open-loop PSH projects proposed or currently operating in the United States. The comparison of effects between open-loop and closed-loop projects is relative; that is, it characterizes the impacts of each project type as generally lower or higher than another project type. The comparison reflects general trends among project types because there are sometimes exceptions to the examples cited. The comparison is based on both spatial (location) and temporal (duration) factors and reflects both the severity and likelihood of effects. The report concludes that the environmental effects of closed-loop PSH projects are generally lower (i.e., more localized and of shorter duration) than those of open-loop PSH projects because they are located “off-stream,” minimizing aquatic and terrestrial impacts, and often have greater siting flexibility than open-loop PSH projects. For certain closed-loop project designs, however, impacts to groundwater may be higher relative to open-loop projects and should be considered in any environmental review.

13 HYDRO ENERGY↗
DOE OSTI2026

Dissolved Inorganic Carbon and Dissolved Organic Carbon Data for the East River Watershed, Colorado (2015-2025)

This data package contains mean values for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. DOC and DIC concentrations in water samples were determined using a TOC-VCPH analyzer (Shimadzu Corporation, Japan). DOC was analyzed as non-purgeable organic carbon (NPOC) by purging HCl-acidified samples with carbon-free air to remove DIC prior to measurement. After the acidified sample has been sparged, it is injected into a combustion tube filled with oxidation catalyst heated to 680 oC. The DOC in samples is combusted to CO2 and measured by a non-dispersive infrared (NDIR) detector. The peak area of the analog signal produced by the NDIR detector is proportional to the DOC concentration of the sample. DIC was determined by acidifying the samples with HCl first, and then purging with carbon-free air to release CO2 for analysis by NDIR detector. Total dissolved nitrogen (TDN) was analyzed using a Shimadzu Total Nitrogen Module (TNM-L) combined with the TOC-L analyzer (Shimadzu Corporation, Japan). TNM-L is a non-specific measurement of total nitrogen (TN). All nitrogen species in samples are combusted to nitrogen monoxide and nitrogen dioxide, then reacted with ozone to form an excited state of nitrogen dioxide. Upon returning to ground state, light energy is emitted. Then, TDN is measured using a chemiluminescence detector. All data reported are the mean values upon minimum of three replicate measurements, with a relative standard deviation < 3%. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (dic_npoc_data_2014-2025.zip) containing a total of 337 files: 336 data files of DIC and NPOC 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_20250901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v6_20250901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) PDF and docx files for the determiniation of Method Detection Limits (MDLs) for DIC and NPOC data, which has been updated in 2026-08. 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 DIC/NPOC data. Update on 2020-10-07: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update on 2021-04-11: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses document, which can be accessed as a PDF or with Microsoft Word.Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated dissolved inorganic carbon and dissolved organic carbon 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) restructure of units to comply with CSV reporting format requirements, (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 2023-11-21. 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. Revised versions of the PDF and docx files for determination of MDLs for DIC and NPOC were added to replace previous versions. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available DIC and NPOC 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 DIC and NPOC data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-10, of the PDF and docx files for determination of MDLs for DIC and NPOC data were added to this dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting future well performance for environmental remediation design using deep learning

Here in this study, we developed a deep learning (DL) framework with a multi-channel three-dimensional convolutional neural network (MC3D-CNN) to predict well performance and thereby assist future environmental remediation design. Such prediction of extraction well performance at designated locations is critical for configuring pump-and-treat (P&T) well network design and operation, setting reasonable target closure dates for overall remedying, and estimating remedy costs. The framework is developed with operational and monitoring data routinely collected during P&T remedy operations, including well extraction and injection rates as well as in situ contaminant concentrations. Traditionally, the collected data were rarely used for purposes other than assessing past well performance and the accuracy of the conceptual site model. However, recent advances in data-driven computational approaches enable better use of the large datasets to inform future well performance, enhance site characterization, and improve remediation planning. In this study, we established a DL framework to integrate transient three-dimensional contaminant plumes and multiple aquifer properties (e.g., hydraulic conductivity and hydrostratigraphic maps) to identify characteristic patterns controlling and representing extraction well mass recovery, aiming at providing future mass recovery estimates for existing wells and candidate wells at any proposed locations. We evaluated our framework by using a realistic synthetic dataset generated from a well-calibrated flow and transport model used in the 200 West Area of the U.S. Department of Energy’s Hanford Site in southeastern Washington state. The multi-channel feature in our framework allows integration of various types and temporal densities of training datasets for DL model development. Overall, we found that the trained DL model achieved an accuracy of over 90% in ranking extraction well performance in validation datasets, and over 80% in predicting high-performance-ranking well locations. This data-informed approach provides a flexible tool to support adaptive site management, streamline decision-making, and potentially reduce remediation time and costs. Our DL framework can be used as a filtering tool to improve the current P&T network optimization design by reducing the number of candidate well locations.

54 ENVIRONMENTAL SCIENCES↗