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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Direct Observation of the Depth of Active Groundwater Circulation in an Alpine Watershed

Abstract The depth of active groundwater circulation is a fundamental control on stream flows and chemistry in mountain watersheds, yet it remains challenging to characterize and is rarely well constrained. We collected hydraulic conductivity, hydraulic head, temperature, chemical, noble gas, and 3 H/ 3 He groundwater age data from discrete levels in two boreholes 46 and 81 m deep in an alpine watershed, in combination with chemical and age data from shallow groundwater discharge, to discern groundwater flow rates at different depths and directly observe active and inactive groundwater. Vertical head gradients are steep (average of 0.4) and thermal profiles are consistent with typical linear conductive continental geotherms. Groundwater deeper than ∼20 m is distinct from shallow groundwater and creek water in its chemistry, noble gas signature, and age (dominantly >65 years compared to <9 years). Together these results suggest low vertical groundwater flow velocities and a relatively shallow active circulation depth of ∼20 m. This hypothesis is tested with a simple 2‐D numerical fluid flow and heat transport model representing a hillslope transect through the two boreholes. The modeling indicates that the subhorizontally bedded sedimentary rocks underlying the basin are highly anisotropic with low vertical hydraulic conductivity, and at most ∼10% of bedrock recharge (equivalent to <2% of stream baseflow) flows below a depth of 20 m. The study demonstrates the considerable value of discrete‐depth hydrogeologic, chemical, and age data for determining active circulation depth, and illustrates an approach for maximizing the utility of individual boreholes drilled for mountain bedrock aquifer characterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soil Temperature and Moisture, Council Road Mile Marker 71, Seward Peninsula, Alaska, beginning 2016

Daily averages of soil temperature and moisture measured once every hour at different heights, as well as daily averages of hourly measured snow depths located at Intensive Monitoring Stations at Council Road Mile Marker 71 site. Deployed at each site is an Onset HOBO U30 data logger with five smart temperature sensors and three smart soil moisture sensors (10HS). Three sites (CN_IS_4, CN_IS_6A, CN_IS_7) are equipped with a snow depth sensor. Data are retrieved annually since 2016. Contains 53 *.CSV files including a file inventory list by year. Data files have header rows, NaN fields indicate invalid or missing data, and negative vertical offsets are above ground. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Three-dimensional potential energy surfaces of ArNO ($\tilde{X}$ 2 Π)

Until now, the potential energy surfaces (PESs) of the ArNO complex found in the literature were two-dimensional, with the NO interatomic distance being fixed. In this work, we present the first accurate three-dimensional ground state X 2 Π PESs (both A ' and A ") of ArNO computed at the CCSD(T)/CBS level of theory. The equilibrium geometries and the well depths ( D e ) are compared to several other electronic structure methods. We found that using the multireference method, MRCI-F12 makes the surfaces much shallower (by 25%) and the depth of the surfaces does not agree with experimental data. The explicitly correlated coupled-cluster method underestimates the well depth as well. Analytic representations for both A ' and A " surfaces were fit to 4380 ab initio points to within 2.71 cm -1 .A three-dimensional Numerov propagator method in Delves coordinates is used to compute the bound state spectrum up to J tot = 6.5. The recommended dissociation energies are D 0 = 97.2 cm -1 for the adiabatic ground state and D e = 133.7 (128.1) cm -1 for A ' ( A ")

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Review of the Influence of Processing Parameters on ODS Steels Produced via Additive Manufacturing Techniques

Abstract This paper reviews current observations regarding processing conditions for oxide dispersion-strengthened steels consolidated through additive manufacturing techniques. Variations in ODS steels observed across process parameters include changes in grain size, grain texture, oxide size, density of oxides, porosity, melt pool characteristics, and mechanical properties. These properties were then compared across techniques to understand which techniques and processing conditions lead to the highest strength, ductility, and oxide density. Current literature suggests that a mix of grain types, in the form of either morphology or phase, can significantly increase the strength of printed ODS steels. Meanwhile, the most ductile samples, regardless of consolidation technique or matrix material, were made from feedstock with oxide additions located on the powder surface. Reported grain and oxide sizes were plotted against the ratio of laser power to scan speed, volumetric energy density, and normalized enthalpy. No strong correlation between these values and microstructural features was observed. The plots that were made suggest that a larger data set, more in-depth representative equations, and more defined material properties as a function of specific feedstock used are necessary to determine a value that can be correlated to the printed ODS steel microstructure.

deJong, Matthew↗
DOE OSTI2022

On the thermal oxidation of nuclear graphite relevant to high-temperature gas cooled reactors

Thermal oxidation of nuclear graphite components is highly undesirable because it can cause structural and property degradation that negatively affect a reactor's intended operation. In this work, the body of knowledge of nuclear graphite oxidation is highlighted, including when O 2 , H 2 O, and/or CO 2 are the oxidant. Oxidation conditions relevant to high-temperature gas-cooled reactors (i.e., when oxidation could occur either as an acute or chronic phenomenon) are emphasized. Here, the objective is to summarize graphite oxidation data in a practical and accessible way to inform future research and regulatory requirements. Although each grade of nuclear graphite is different, the oxidation mechanism has underlying commonalities. Although oxidation behavior is grade dependent, the general temperature dependence is well described by a sequence of elementary steps which become rate limiting. Because the regime transition temperature depends on sample microstructure, size, and oxidant supply rate, extrapolating results beyond the experimental range should be done cautiously. Gravimetric oxidation rate measurements generally replicate well. However, caution must be exercised when rates are estimated by other methods or for samples that deviate significantly in size. Air oxidation data for IG-110, NBG-18, and PCEA graphite is critically reviewed to emphasize this point. Despite the amount of experimental data, gaps remain. Sample size and shape effects are not fully explained. Data on the oxidant penetration depth are insufficient. Analytical assessments demonstrate that lower temperature oxidation does not necessarily imply that oxidation is uniform throughout the bulk. Oxidation occurs faster at higher temperature but is more localized to the exposed surface. Paradoxically, at equal mass loss percentage, low temperature oxidation leads to greater property degradation than at high temperature. The isolated effect of oxidation is important; however, a gap remains in the systematic understanding of any potential effect of neutron irradiation on graphite structure and reactivity.

36 MATERIALS SCIENCE↗
DOE OSTI2022

To grind or not to grind? The influence of mechanical and thermal treatments on the $\mathrm{Cu/Zn}$ disorder in $\mathrm{Cu}$ 2 $\mathrm{ZnSn}$($\mathrm{S}$ x $\mathrm{Se}$ 1-x ) 4 monograins

We report that Kesterite-type based thin film solar cell technologies are mainly based on polycrystalline absorber layers. A promising low cost alternative technology uses Cu 2 ZnSn(S x Se 1-x ) 4 (CZTSSe) monograins (single crystals of 20–100 μm size) which are fixed in a polymer matrix to form a flexible solar cell. The Cu/Zn disorder is discussed as a possible reason for band tailing causing voltage losses limiting the efficiency of CZTSSe-based devices. The experimental determination of the order parameter Q which is a quantitative measure of Cu/Zn disorder, requires a differentiation between the isoelectronic cations Cu + and Zn 2+ . An in-depth analysis of neutron diffraction data allows the determination of type and concentration of intrinsic point defects including a distinction between Cu and Zn. Neutron diffraction requires large sample volumes, thus monograins offer the unique possibility to correlate structural disorder in CZTSSe with device performance parameters. In this study we tackle the influence of grinding the monograins on stoichiometry deviations, the Cu/Zn disorder as well as intrinsic point defects and optoelectronic properties of CZTSSe monograins. Moreover, an easy methodology based on Raman scattering spectroscopy is proposed for the assessment of Cu/Zn disorder in the CZTSSe compounds.

14 SOLAR ENERGY↗
DOE OSTI2023

Catalytic reforming of methane with H 2 S via dynamically stabilized sulfur on transition metal oxides and sulfides

Reforming of methane with H 2 S is a promising path to directly utilize sour natural gas reserves, although some aspects of the mechanism and structure–function relations remain elusive. In this report we show that metal oxides of group 4–6 elements, which are inert for steam and dry methane reforming reactions, are active and stable (pre)catalysts for the H 2 S reforming of methane. The key active sites are sulfur species (S*) that are dynamically bound to metal cations during catalysis. Similar H/D isotope exchange patterns and universal rate inhibition by H 2 on representative catalysts indicate that H 2 S decomposition and recombination of surface hydrogen atoms are quasi-equilibrated, whereas CH 4 dissociation steps are reversible, yet not quasi-equilibrated. An in-depth analysis of the kinetic data and isotopic substitution effects identifies S*-mediated C–H bond cleavage as the most plausible rate-limiting step common for all catalysts, with subtle yet essential differences between 3d and 4d/5d catalysts in the thermodynamic stability of S*.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Development of a Fast-Spectrum Self-Powered Neutron Detector for Molten Salt Experiments in the Versatile Test Reactor

The self-powered neutron detector (SPND) is a widely used flux monitor in thermal nuclear reactors. Although this is a mature technology, the current state of the art is tuned for a thermal neutron spectrum, so many of the devices currently in use lack sensitivity to fast neutrons. Because current in SPNDs is produced through nuclear reactions with the neutron flux inside a reactor, sensitivity in SPNDs is determined by the neutron cross section of the neutron-sensitive portion of the detector, termed the emitter. This neutron cross section drops by orders of magnitude between thermal and fast neutron energies for many emitters in currently used SPNDs, with a corresponding drop in current from the detector. This paper discusses efforts to develop a fast-spectrum self-powered neutron detector (FS-SPND) that is sensitive to neutrons with energies ranging from 0.025 eV up to 1 MeV. An in-depth analysis of Evaluated Nuclear Data File (ENDF)/B-VII.1 neutron-capture cross sections was performed, and four new materials were identified that are suitable emitter candidates for use in measuring fast neutrons. All four materials are stable mid-shell nuclei in the region between doubly magic 132Sn and 208Pb. Each candidate was simulated with the Geant4 Monte Carlo simulation toolkit to optimize overall detector efficiency.

Goetz, Callie↗
DOE OSTI2025

Data from a throughfall exclusion experiment: Fine root dynamics, morphology, chemistry, and AMF colonization across four lowland Panamanian forests

Fine roots regulate forest nutrient, carbon, and water cycling, yet their variation within and among tropical forests remains under-characterized. We quantified root productivity, disappearance, and stocks to 1 m using minirhizotron imaging, and we measured morphology, elemental composition [root carbon (C), root nitrogen (N), root phosphorus (P)], and arbuscular mycorrhizal fungi (AMF) colonization to 20 cm using ingrowth cores and sequential coring. Sampling took place in four distinct lowland Panamanian forests (32 plots; 8 per forest) from 2018 through 2022 under control and throughfall-exclusion (drought) treatments in the Panama Rainforest Changes with Experimental Drying (PARCHED) experiment.The dataset is presented as an Excel workbook with six tabs. The first tab is the data dictionary. Tab S1 contains ingrowth-core production and mortality, morphology and soil moisture. Tab S2 contains sequential-coring standing stocks with associated morphology and soil moisture. Tab S3 contains minirhizotron row data records to 1 m depth, including per-frame root length and diameter, normalized length metrics, and session timing. Tab S4 contains AMF colonization. Tab S5 contains fine-root chemistry at 0–10 cm, reporting %P, %C, %N, and C:N for samples collected via ingrowth cores and sequential-coring standing stocks. CSV mirrors for each tab are provided, and a KML file supplies coordinates for all 32 plots.Key variables span live and dead fine-root biomass (and coarse fractions where applicable), specific root length (SRL) and area (SRA), diameter, root tissue density (RTD), soil moisture, AMF colonization, root %N, %C, %P, and C:N, along with minirhizotron root length and diameter. Depth, season, treatment, and plot/site identifiers are included to support cross-tab integration and analysis from 0–100 cm (minirhizotron) and 0–20 cm (cores).Units are reported in-column and missing values are coded as NA. No special software is required to open or use the files (Excel, CSV, and KML compatible).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Development of a Fast-Spectrum Self-Powered Neutron Detector for Molten Salt Experiments in the Versatile Test Reactor

Abstract—The self-powered neutron detector (SPND) is a widely used flux monitor in thermal nuclear reactors. Although this is a mature technology, the current state of the art is tuned for a thermal neutron spectrum, so many of the devices currently in use lack sensitivity to fast neutrons. Because current in SPNDs is produced through nuclear reactions with the neutron flux inside a reactor, sensitivity in SPNDs is determined by the neutron cross section of the neutron-sensitive portion of the detector, termed the emitter. This neutron cross section drops by orders of magnitude between thermal and fast neutron energies for many emitters in currently used SPNDs, with a corresponding drop in current from the detector. This paper discusses efforts to develop a fast-spectrum self-powered neutron detector (FS-SPND) that is sensitive to neutrons with energies ranging from 0.025 eV up to 1 MeV. An in-depth analysis of Evaluated Nuclear Data File (ENDF)/B-VII.1 neutron-capture cross sections was performed, and four new materials were identified that are suitable emitter candidates for use in measuring fast neutrons. All four materials are stable mid-shell nuclei in the region between doubly magic 132Sn and 208Pb. Each candidate was simulated with the Geant4 Monte Carlo simulation toolkit to optimize overall detector efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
DOE OSTI2020

Policy-Driven Sustainable Saline Drainage Disposal and Forage Production in the Western San Joaquin Valley of California

Environmental policies to address water quality impairments in the San Joaquin River of California have focused on the reduction of salinity and selenium-contaminated subsurface agricultural drainage loads from westside sources. On 31 December 2019, all of the agricultural drainage from a 44,000 ha subarea on the western side of the San Joaquin River basin was curtailed. This policy requires the on-site disposal of all of the agricultural drainage water in perpetuity, except during flooding events, when emergency drainage to the River is sanctioned. The reuse of this saline agricultural drainage water to irrigate forage crops, such as ‘Jose’ tall wheatgrass and alfalfa, in a 2428 ha reuse facility provides an economic return on this pollutant disposal option. Irrigation with brackish water requires careful management to prevent salt accumulation in the crop root zone, which can impact forage yields. The objective of this study was to optimize the sustainability of this reuse facility by maximizing the evaporation potential while achieving cost recovery. This was achieved by assessing the spatial and temporal distribution of the root zone salinity in selected fields of ‘Jose’ tall wheatgrass and alfalfa in the drainage reuse facility, some of which have been irrigated with brackish subsurface drainage water for over fifteen years. Electromagnetic soil surveys using an EM-38 instrument were used to measure the spatial variability of the salinity in the soil profile. The tall wheatgrass fields were irrigated with higher salinity water (1.2–9.3 dS m -1 ) compared to the fields of alfalfa (0.5–6.5 dS m -1 ). Correspondingly, the soil salinity in the tall wheatgrass fields was higher (12.5 dS m -1 –19.3 dS m -1 ) compared to the alfalfa fields (8.97 dS mm -1 –14.4 dS mm -1 ) for the years 2016 and 2017. Better leaching of salts was observed in the fields with a subsurface drainage system installed (13–1 and 13–2). The depth-averaged root zone salinity data sets are being used for the calibration of the transient hydro-salinity computer model CSUID-ID (a one-dimensional version of the Colorado State University Irrigation Drainage Model). This user-friendly decision support tool currently provides a useful framework for the data collection needed to make credible, field-scale salinity budgets. In time, it will provide guidance for appropriate leaching requirements and potential blending decisions for sustainable forage production. This paper shows the tie between environmental drainage policy and the role of local governance in the development of sustainable irrigation practices, and how well-directed collaborative field research can guide future resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Raw Data published in PeerJ, 2020 for Lower Subalpine needle decomposition field study 2017-2018

This data package contains text files that describe geochemical measurements collected from 2017-2018 during isolated conifer needle decomposition field studies at the Lower Subalpine region of Washington Gulch in Crested Butte, CO. The files within include total carbon and nitrogen (CN) content and fourier-transform infrared spectroscopy (FTIR) results for the initial needles collected in 2016 and after decomposition in October 2018. DI water extractions were conducted with the initial harvested needles in 2016 to determine extractable dissolved organic carbon (DOC), total nitrogen (TN), UV absorbance at 254 nm (UV254), and specific UV absorbance (SUVA). This dataset also includes soil extractable DOC, TN, nitrate, nitrite, ammonium, total organic nitrogen (TON), and pH results from soil collections in 2017. Soil porewater results for DOC, TN, UV254, and SUVA analyses at 15 cm soil depth are also provided from 2017-2018. Gas flux raw data provides CO2, CH4, N2O, and NH3 measurements above needle decomposition over the two study years. Finally, soil samples for microbial DNA extractions were collected from the upper soil depth. This raw data is available in the NCBI SRA database under SRA accession number PRJNA605259. These datasets were generated to investigate the isolated decomposition of spruce and lodgepole conifer needles. The goal of this work was to understand how needle litter impacts terrestrial biogeochemical carbon and nitrogen cycling. Results from this work is detailed in the reference paper "A comparison of lodgepole and spruce needle chemistry impacts on terrestrial biogeochemical processes during isolated needle decomposition. DOI: 10.7717/peerj.9538."

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MODAQ-BB (Modular Offshore Data Acquisition System - Blackbox) [SWR-24-72]

MODAQ-BB is a compact, rapid deployment data acquisition system that can withstand water depths up to 400m. Codenamed "BlackBox" (or simply BB), since its initial purpose was to track marine assets and record vital data streams that could later be recovered in the event of a mishap - much like a traditional black box used in aviation and shipping, the name has stuck. MODAQ BlackBox is a battery-operable microcontroller platform with internal inertial sensing, GPS, satellite communications, and additional I/O (input/output) support in a depth-rated pressure enclosure. Since BB is self-contained and relatively compact, it can be quickly deployed with minimal effort. The enclosure can be clamped to a tube (such as part of a railing) or mast in a location with unobstructed view of the sky using the available clamp accessory or a common hose clamp. While BB is designed to operate unattended, users can configure what data are uploaded and the frequency of satellite transmissions. Once data are uploaded, they can be relayed to an email distribution list, the MODAQ:Web operational dashboard, or a custom destination. BB has found utility in the National Laboratory of the Rockies' (NLR's) Waterpower projects beyond its original vision and has been configured and successfully deployed in more traditional data-gathering applications where a simple, battery-operated solution was indicated. As part of the MODAQ family, BB fills a space in the spectrum of missions that can be supported that were previously impractical using the traditional MODAQ hardware architecture due to factors such as weight, size, and cost.

Raye, Robert↗
DOE OSTI2023

Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84°N, 24.29°E), North Slope of Alaska (NSA; 71.32°N, 156.61°W), Ny-Ålesund (Ny-Å; 78.92°N, 11.93°E), and Summit (72.59°N, 38.42°W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Type–Curve Approach for Evaluating Aquifer Properties by Interpreting Shallow Strain Measured During Well Tests

Strains occur at shallow depths in response to pressure changes during well tests in an underlying aquifer, and recent developments in instrumentation have made it feasible to measure essentially the full strain tensor. Simulations using poroelastic analyses indicate that shallow normal strains are approximately proportional to the logarithm of time when a well is injecting into or pumping from a deep aquifer or reservoir. The drawdown is also a linear function of log time, as shown by the classic Cooper-Jacob type-curve analysis. The time when the semilog straight line intercepts the zero-strain axis is similar to the time determined from the Cooper-Jacob pressure analysis, and it can be used to estimate hydraulic diffusivity, suggesting that horizontal strain data can be used directly to estimate aquifer properties. This approach was validated using measurements from shallow (30-m deep) borehole strainmeters during an injection test at a 530-m-deep sandstone aquifer/reservoir in Oklahoma. The results show intercept times for the shallow normal strain data are essentially the same as for deep pressure data from an equivalent radial distance. The slopes of the semilog plots of the pressure and the strain increase at the same time, suggesting that they both respond to a lateral aquifer boundary. Significantly, though, strain was measured at shallow depths while the pressure data were measured at 530-m depth. This suggests that strain data from shallow depths could be an effective way to improve the characterization of an underlying aquifer.

54 ENVIRONMENTAL SCIENCES↗