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

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Water level data collected in the Rifle Floodplain, Colorado, USA

Waterlevel data from 28 instrumented wells at the Old Rifle field site. This waterlevel data was calculated from vented pressure transducers which were deployed in these wells. For this calculation the TOC (Top of Casing), depth to transducer and water column above the transducer were used. The transducer data was obtained through the DOE LM Soars system. The raw data from this system are provided in a companion datapackage

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Sub-Basin Delineation for the Upper East River, Colorado, United States

Upper East sub-basins constructed 10/7/2016 using 10m digital elevation model obtained from the United States Geological Survey (USGS) National Elevation Data set (NED) and ArcGIS Hydrology tool box (ESRI v 10.3.1). Sub-watersheds are delineated as area contributing flow to SFA 2016 stream gage locations. Shapefile attributes include sub-basin ID, name, area (m2 and km2) and codes for spatial contributions to EBC and PH used by Carroll et al. (2018). Geospatial reference is UTM 1983 zone 13. JPEG image provided to show sub-basin delineation with respect to surface geology (Gothic Quadrangle: Gaskill et al., 1991; Maroon Bells Quadrangle: Bryant, 1969).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

58 GEOSCIENCES↗
DOE OSTI2023

Single-Well Push–Pull Tracer Test Analyses to Determine Aquifer Reactive Transport Parameters at a Former Uranium Mill Site (Grand Junction, Colorado)

At a former uranium mill site where tailings have been removed, prior work has determined several potential ongoing secondary uranium sources. These include locations with uranium sorbed to organic carbon, uranium in the unsaturated zone, and uranium associated with the presence of gypsum. To better understand uranium mobility controls at the site, four single-well push–pull tests (with a drift phase) were completed with the goal of deriving aquifer flow and contaminant transport parameters for inclusion in a future sitewide reactive transport model. This goes beyond the traditional use of a constant sorption distribution coefficient (K d ) and allows for the evaluation of alternative remedial injection fluids, which can produce variable K d values. Dispersion was first removed from the resulting data to determine possible reactions before conducting reactive transport simulations. These initial analyses indicated the potential need to include cation exchange, uranium sorption, and gypsum dissolution. A reactive transport model using multiple layers to account for partially penetrating wells was completed using the PHT-USG reactive transport modeling code and calibrated using PEST. The model results quantify the hydraulic conductivity and dispersion parameters using the injected tracer concentrations. Uranium sorption, cation exchange, and gypsum dissolution parameters were quantified by comparing the simulated versus observed geochemistry. All simulations required some cation exchange and calcite equilibrium, and one simulation required gypsum dissolution to improve the model fit for calcium and sulfate. Uranium sorption parameters were not strongly influenced by the other parameter values but were highly influenced by uranium concentrations during the drift phase, with possible kinetic rate limitations. Thus, a future recommendation for such push–pull tests is to collect more geochemical data during the drift phase. The final uranium sorption parameters were within the range of values determined from prior column testing. The flow and transport parameters derived from these single-well push–pull tests will provide initial parameters for any future sitewide reactive transport model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The influence of warm-season precipitation on the diel cycle of the surface energy balance and carbon dioxide at a Colorado subalpine forest site

Precipitation changes the physical and biological characteristics of an ecosystem. Using a precipitation-based conditional sampling technique and a 14 year data set from a 25 m micrometeorological tower in a high-elevation subalpine forest, we examined how warm-season precipitation affected the above-canopy diel cycle of wind and turbulence, net radiation R net , ecosystem eddy covariance fluxes (sensible heat H , latent heat LE, and CO 2 net ecosystem exchange NEE) and vertical profiles of scalars (air temperature T a , specific humidity q , and CO 2 dry mole fraction χ c ). This analysis allowed us to examine how precipitation modified these variables from hourly (i.e., the diel cycle) to multi-day time-scales (i.e., typical of a weather-system frontal passage). During mid-day we found the following: (i) even though precipitation caused mean changes on the order of 50–70 % to R net , H , and LE, the surface energy balance (SEB) was relatively insensitive to precipitation with mid-day closure values ranging between 90 and 110 %, and (ii) compared to a typical dry day, a day following a rainy day was characterized by increased ecosystem uptake of CO 2 (NEE increased by ≈ 10 %), enhanced evaporative cooling (mid-day LE increased by ≈ 30 W m −2 ), and a smaller amount of sensible heat transfer (mid-day H decreased by ≈ 70 W m −2 ). Based on the mean diel cycle, the evaporative contribution to total evapotranspiration was, on average, around 6 % in dry conditions and between 15 and 25 % in partially wet conditions. Furthermore, increased LE lasted at least 18 h following a rain event. At night, even though precipitation (and accompanying clouds) reduced the magnitude of R net , LE increased from ≈ 10 to over 20 W m −2 due to increased evaporation. Any effect of precipitation on the nocturnal SEB closure and NEE was overshadowed by atmospheric phenomena such as horizontal advection and decoupling that create measurement difficulties. Above-canopy mean χ c during wet conditions was found to be about 2–3 μmol mol −1 larger than χ c on dry days. This difference was fairly constant over the full diel cycle suggesting that it was due to synoptic weather patterns (different air masses and/or effects of barometric pressure). Finally, the effect of clouds on the timing and magnitude of daytime ecosystem fluxes is described.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leipzig University W-Band Cloud Radar, Gothic (Colorado), SAIL Campaign Second Winter (15.11.2022 - 05.06.2023)

The instrument is a polarimetric W-Band simultaneous transmission simultaneous reception (STSR) Doppler Cloud Radar manufactured by Radiometer Physics GmbH (RPG, instrument type RPG-FMCW-94-DP). It was deployed about 500m away from the ARM M1 facility in Gothic (CO) near the Ore House between Nov 15, 2022 and Jun 5, 2023 at an altitude of 2913 m. The Doppler cloud radar was mounted on a prototype of a cold temperature scanner manufactured by RPG. The dataset contains common radar variables like equivalent radar reflectivity factor, mean Doppler velocity, Doppler spectrum width as well as common polarimetric radar variables like differential phase shift (PhiDP), specific differential phase shift (KDP), correlation coefficient (RHV) and differential reflectivity (ZDR). Between Nov 15, 2022, and Feb 7, 2023 the radar was operated at 40° constant elevation and 151° azimuth (towards the KAZR at the AMF1). Between Feb 7 and Mar 15, 2023 the following scan cycle was repeated in 15min intervals: zenith pointing measurements, RHI scans from 0° to 90° and 90° to 0° elevation at 151° azimuth, constant elevation measurements at 40° elevation and 151° azimuth and PPI scans at 85° elevation for wind retrievals (one PPI per hour). Between Mar 15 18:18 UTC and Apr 8, 2023 11:04 UTC, the scanner was stuck at 72° elevation, which resulted in corrupted files for this period. For that period, the files containing data are uploaded but should be treated with care. Between Apr 8 and Apr 14, 2023 the scanning pattern described above was performed again. Afterwards, except for May 17 and May 18, 2023 where scanning patterns as described above were performed, measurements continued at 40° elevation and 151° azimuth until May 22, 2023 09:00 UTC. The scanning pattern was then started again, but without hourly PPI measurements until May 31, 2023. Between May 31 and Jun 5, 2023 solely zenith-pointing observations were performed. The data provided here are the RPG Level 1 (L1) files, Doppler spectra Level 0 (L0) files are available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗
DOE OSTI2024

Bifacial Vertical Testbed and Ground Irradiance Data in Golden, Colorado

This data was collected for Tonita et al., “Vertical bifacial photovoltaic system model validation: study with field data, various orientations, and latitudes,” for validation of optical models for vertically-oriented photovoltaics under high albedo. Ground irradiance data for vertical PV arrays modeling in agrivoltaics is also provided. The dataset is provided for further use or study as open source. For any questions on the dataset, email silvana.ovaitt@nlr.gov.

14 SOLAR ENERGY↗
NASA NTRS1972

Pattern recognition of native plant communities: Manitou Colorado test site

Optimum channel selection among 12 channels of multispectral scanner imagery identified six as providing the best information about 11 vegetation classes and two nonvegetation classes at the Manitou Experimental Forest. Intensive preprocessing of the scanner signals was required to eliminate a serious scan angle effect. Final processing of the normalized data provided acceptable recognition results of generalized plant community types. Serious errors occurred with attempts to classify specific community types within upland grassland areas. The consideration of the convex mixtures concept (effects of amounts of live plant cover, exposed soil, and plant litter cover on apparent scene radiances) significantly improved the classification of some of the grassland classes.

Driscoll, R. S.↗