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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed

The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling

This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset.The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Thermal dissipation sensors enter a new age: Navigating frontiers in transpiration and hydrologic function

Thermal dissipation (TD) sensors have been used extensively for a wide array of sap flow applications, and yet recently several limitations of this approach have been identified, including a tendency to underestimate absolute flows, especially in small stems with very high flows. However, when used properly, the TD method remains the most cost effective and reliable approach for determining relative flows in comparison studies. Major advantages of this method include the ability to replicate maximally within trees, across trees of different sizes, and across multiple species to capture the full range of natural variation in the study system. From early studies, such variation has been deemed a major challenge for representing stand-level dynamics. In a series of recent comparison studies, we determined mean sap flux density dynamics within and among groups of trees. These studies were conducted within temperate coniferous forest (n=46), temperate oak savanna (n=36), temperate pine forest (n=49), temperate bottomland hardwood forest (n=23), tropical premontane rainforest (n=43), semiarid subtropical shrubland (n=25), and tropical dry forest (n=15), with a total of 237 sensors. Two study sites were also equipped with eddy covariance systems for determination of stand-level evapotranspiration. Sites subject to high background thermal gradients used the transient thermal dissipation method. Comparisons varied between studies but tended to focus on relative flow differences between species, between wetter and drier microsites, between understory and overstory components, or between wetter and drier periods of the growing season. Except for the shrubland sites, study trees tended to be at least 15-cm diameter and frequently exceeded 50-cm diameter. Results of this cross-site comparison highlight the inherent variation in natural stands and the importance of using large sample sizes. The TD approach remains a valuable and preferred method for the future determination of water use in trees. However, sensor replication can fundamentally impact a study's outcomes and should be more carefully considered in study design. Customized error mitigation strategies are best to address the sources of variation most problematic for a particular study. In conclusion, the development of “smart” calibration approaches that correct for fundamental effects of radial variation inherent in thermal dissipation studies is discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

2018 Long-Term Hydrologic Monitoring Program Report for Rio Blanco, Colorado, Site

This report presents the monitoring data collected by the U.S. Department of Energy (DOE) Office of Legacy Management (LM) at the Rio Blanco, Colorado, Site (Figure 1). The Rio Blanco site was the location of an underground nuclear test during which three nuclear devices were detonated nearly simultaneously in a single borehole in 1973. The test resulted in residual radionuclide contamination at the detonation depths of 5840, 6230, and 6690 feet (ft) (DOE 2015). Monitoring includes the collection of samples from groundwater wells and surface water locations near the site to assess for any potential impacts that may be attributed to the Rio Blanco nuclear test. This report summarizes the laboratory analytical results obtained from the sampling event conducted in 2018. This annual report and previous reports are available on the LM public website at https://www.lm.doe.gov/rio_blanco/Sites.aspx. Data collected during this and previous monitoring events are available on the Geospatial Environmental Mapping System (GEMS) website at https://gems.lm.doe.gov/#site=RBL.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Hydrologic Evaluation of Smallmouth Bass Spawning Sites in the Upper Green River Using LiDAR

The smallmouth bass has been considered one of the most problematic invasive species in the upper Colorado River basin. As a predator of larval fish and juveniles, this nonnative species is an impediment to conservation efforts for endangered fish species of the Colorado River basin including Colorado pikeminnow (Ptychocheilus lucius), razorback sucker (Xyrauchen texanus), humpback chub (Gila cypha), and bonytail chub (Gila elegans). Several measures have been recommended to control smallmouth bass populations to conserve the endangered fish species. Among these measures are efforts to control flows during the spawning season to disrupt smallmouth bass reproduction. Previous work has suggested that smallmouth bass spawning in areas of the Green River could be inhibited during higher flows from the Flaming Gorge Dam due to lack of suitable habitat. Little research has been conducted to evaluate an optimal range of flow increases (“flow spikes”) needed to disrupt smallmouth bass reproduction in the Green River. Current research proposals to evaluate the effectiveness of flow spikes on controlling smallmouth bass reproduction target post-spawning flows of 4,500 cfs, maintained for three days, to inundate side-channel spawning habitats. In this report, we present results of a topographic study of smallmouth bass spawning sites in the upper Green River to characterize flow levels needed to inundate spawning habitats. This study used remotely sensed Light Detection and Ranging (LiDAR) data collected along the entire Green River, including channel margins and floodplain areas, to model water surface elevations and flow spikes needed to connect and inundate smallmouth bass spawning sites.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computationally Tractable High-Fidelity Representation of Global Hydrology in ESMs via Machine Learning Approaches to Scale-Bridging

Focal Areas: This paper responds primarily to Focal Area 2, focusing on AI techniques to improve model fidelity. Science Challenge: “Hyperresolution” [1, 2] land surface models (LSMs) running at far higher resolution than typically employed in global Earth system models (ESMs) can help answer critical questions about the water cycle and associated ecosystem and biogeochemical feedbacks. Even with all foreseeable advances in computing power and efficient solver algorithms, however, employing hyperresolution LSMs inside ESMs for studies of long-term global climate is not computationally feasible. Instead, we argue for incorporating the fidelity of hyperresolution LSMs only where and when it is needed by using machine learning approaches to scale-bridging.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Model Hierarchy for Mountainous Hydrological Observatories (MH 2 O)

Focal area: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection)

58 GEOSCIENCES↗
DOE OSTI2021

Reliable modeling and prediction of precipitation & radiation for mountainous hydrology

This White Paper focuses on data-driven atmospheric process model emulation and atmospheric process surrogate model development. It proposes leveraging recent AI advances in these approaches to fill in unavoidable observational gaps and enable high-fidelity modeling/predictability of the atmosphere and land-surface interactions in mountainous watersheds. This approach will support studies and predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Representing the Unrepresented Impact of River Ice on Hydrology, Biogeochemistry, Vegetation, and Geomorphology: A Hybrid Physics-Machine Learning Approach

Focal areas include: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI.

54 ENVIRONMENTAL SCIENCES↗