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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

AmeriFlux FLUXNET-1F US-Ho2 Howland Forest (west tower)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ho2 Howland Forest (west tower). This is the FLUXNET version of the carbon flux data for the site US-Ho2 Howland Forest (west tower) produced by applying the standard ONEFlux (1F) software. Site Description - Closed conifer forest, minimal disturbance. References: Fernandez et al. (1993), Canadian Journal of Soil Science 73 317-328. Hollinger et al. (1999), Global Change Biology 5: 891-902. Savage KE, Davidson EA (2001), Global Biogeochemical Cycles 15 337-350. Scott et al. (2004), Environmental Management, Vol. 33, Supplement 1, pp. S9-S22. Hollinger et al. (2004), Global Change Biology 10: 1689-1706.

Ouimette, Andrew↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-Ho1 Howland Forest (main tower)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ho1 Howland Forest (main tower). This is the FLUXNET version of the carbon flux data for the site US-Ho1 Howland Forest (main tower) produced by applying the standard ONEFlux (1F) software. Site Description - Closed conifer forest, minimal disturbance. References: Fernandez et al. (1993), Canadian Journal of Soil Science 73 317-328. Hollinger et al. (1999), Global Change Biology 5: 891-902. Savage KE, Davidson EA (2001), Global Biogeochemical Cycles 15 337-350. Scott et al. (2004), Environmental Management, Vol. 33, Supplement 1, pp. S9-S22. Hollinger et al. (2004), Global Change Biology 10: 1689-1706.

Ouimette, Andrew↗
NASA NTRSDate not supplied

Can We Model Forest Demography Globally? Benchmarking of State-of-the-Art Demographic DGVMs

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. However, these models have so far not been benchmarked alongside each other. We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

Dynamic Global Vegetation Models↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Understanding the Shift of Drivers of Soil Erosion and Sedimentation Based on Regional Process-Based Modeling in the Mississippi River Basin During the Past Century

Soil erosion and sedimentation problems remain a major water quality concern for making watershed management policies in the Mississippi River Basin (MRB). It is unclear whether the observed decreasing trend of stream suspended sediment loading to the mouth of the MRB over the last eight decades truly reflects a decline in upland soil erosion in this large basin. Here, for this work, we improved a distributed regional land surface model, the Dynamic Land Ecosystem Model, to evaluate how climate and land use changes have impacted soil erosion and sediment yield over the entire MRB during the past century. Model results indicate that total sediment yield significantly increased during 1980–2018, despite no significant increase in annual precipitation and runoff. The increased soil erosion and sediment yield are mainly driven by intensified extreme precipitation (EP). Spatially, we found notable intensified EP events in the cropland-dominated Midwest region, resulting in a substantial increase in soil erosion and sediment yield. Land use change played a critical role in determining sediment yield from the 1910s to the 1930s, thereafter, climate variability increasingly became the dominant driver of soil erosion, which peaked in the 2010s. This study highlights the increasing influences of extreme climate in affecting soil erosion and sedimentation, thus, water quality. Therefore, existing forest and cropland Best Management Practices should be revisited to confront the impacts of climate change on water quality in the MRB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-Dk1 Duke Forest-open field

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Dk1 Duke Forest-open field. This is the FLUXNET version of the carbon flux data for the site US-Dk1 Duke Forest-open field produced by applying the standard ONEFlux (1F) software. Site Description - The Duke Forest grass field is approximately 480×305 m, dominated by the C3 grass Festuca arundinacea Shreb. (tall fescue) includes minor components of C3 herbs and the C4 grass Schizachyrium scoparium (Michx.) Nash, not considered here. The site was burned in 1979 and is mowed annually during the summer for hay according to local practices. Lai, C.T. and G.G. Katul, 2000, "The dynamic role of root-water uptake in coupling potential to actual transpiration" , Advances in Water Resources, 23, 427-439; Novick , K.A., P. C. Stoy, G. G. Katul, D. S. Ellsworth, M. B. S. Siqueira, J. Juang, R. Oren, 2004, Carbon dioxide and water vapor exchange in a warm temperate grassland, Oecologia, 138, 259-274; Stoy PC, Katul GG, Siqueira MBS, Juang J-Y, McCarthy HR, Oishi AC, Uebelherr JM, Kim H-S, Oren R (2006). Separating the effects of climate and vegetation on evapotranspiration along a successional chronosequence in the southeastern U.S. Global Change Biology 12:2115-2135

Oishi, Chris [USDA Forest Service]↗
DOE OSTI2020

AmeriFlux US-HB2 Hobcaw Barony Mature Longleaf Pine

This is the AmeriFlux version of the carbon flux data for the site US-HB2 Hobcaw Barony Mature Longleaf Pine. Site Description - A mature longleaf pine forest that is periodically managed with fire

Forsythe, Jeremy D.↗
DOE OSTI2020

AmeriFlux US-xCL NEON LBJ National Grassland (CLBJ)

This is the AmeriFlux version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ). Site Description - The LBJ Grasslands is 16,800 acres of land managed by the US Forest Service under the US Department of Agriculture. There is a rich legacy of land use, ranging back to the mid-19th century. Currently, LBJ Grasslands are used for recreation and hunting, livestock grazing, and fossil fuel extraction. Ongoing ecological monitoring is performed at the site, along with prescribed burning.

Network), NEON (National Ecological Observatory↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-xCL NEON LBJ National Grassland (CLBJ)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ). This is the FLUXNET version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ) produced by applying the standard ONEFlux (1F) software. Site Description - The LBJ Grasslands is 16,800 acres of land managed by the US Forest Service under the US Department of Agriculture. There is a rich legacy of land use, ranging back to the mid-19th century. Currently, LBJ Grasslands are used for recreation and hunting, livestock grazing, and fossil fuel extraction. Ongoing ecological monitoring is performed at the site, along with prescribed burning.

Network), NEON (National Ecological Observatory↗
NASA NTRS1972

A study of Minnesota forests and lakes using data from earth resources technology satellites

This project is to foster and develop new applications of remote sensing under an interdisciplinary effort. Seven reports make up the specific projects presently being conducted throughout the State of Minnesota in cooperation with several agencies and municipalities. These are included under the general headings of: (1) applications of aerial photography and ERTS-1 data to agricultural, forest, and water resources management; (2) classification and dynamics of water and wetland resources of Minnesota; (3) studies of Lake Superior Bay; and (4) feasibility of detecting major air pollutants by earth-oriented satellite-borne sensors.

Source record↗
NASA NTRS1974

A study of Minnesota forests and lakes using data from Earth Resources Technology Satellites

Highlights of research and practical benefits are discussed for the following projects which utilized ERTS 1 data to provide municipal, state, federal, and industrial users with environmental resource information for the state of Minnesota: (1) forest disease detection and control; (2) evaluation of water quality by remote sensing techniques; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) snowmelt flood prediction; (6) remote sensing applications to hydrology; (7) Rice Creek watershed project; (8) water quality in Lake Superior and the Duluth Superior Harbor; and (9) determination of Lake Superior currents from turbidity patterns.

Source record↗
NASA NTRS1981

Remote sensing aided procedure for conifer growth modeling in the northeastern Sierra Nevada

The objective of this study was to use remotely-sensed data with ground-acquired data for preparing inputs to a mathematical model for generation of a potential conifer growth map of a wildland area. The study area, jointly selected by the resource managers of the U.S. Forest Service at the Plumas National Forest and researchers, covers approximately 500 sq km in the northeastern scrapment of Sierra Nevada. The approach involved a computerized databank based on both remotely-sensed and ground-acquired data. The remotely-sensed data included Landsat Multispectral Scanner (MSS) data, NOAA-5 Very High Resolution Radiometer (VHRR) data and U-2 Color infrared photography. The ground data included U.S. Geological Survey (USGS) topographic maps, Defense Mapping Agency (DMA)/USGS digital terrain data, soil maps, and vegetation data. The results included a series of maps for the final product as well as the intermediate products. The intermediate products were potential evapotranspiration, aspect, and soil plant available water.

Smith, H. G.↗
NASA NTRS2005

Pre-Launch GOES-R Risk Reduction Activities for the Geostationary Lightning Mapper

The GOES-R Geostationary Lightning Mapper (GLM) is a new instrument planned for GOES-R that will greatly improve storm hazard nowcasting and increase warning lead time day and night. Daytime detection of lightning is a particularly significant technological advance given the fact that the solar illuminated cloud-top signal can exceed the intensity of the lightning signal by a factor of one hundred. Our approach is detailed across three broad themes which include: Data Processing Algorithm Readiness, Forecast Applications, and Radiance Data Mining. These themes address how the data will be processed and distributed, and the algorithms and models for developing, producing, and using the data products. These pre-launch risk reduction activities will accelerate the operational and research use of the GLM data once GOES-R begins on-orbit operations. The GLM will provide unprecedented capabilities for tracking thunderstorms and earlier warning of impending severe and hazardous weather threats. By providing direct information on lightning initiation, propagation, extent, and rate, the GLM will also capture the updraft dynamics and life cycle of convective storms, as well as internal ice precipitation processes. The GLM provides information directly from the heart of the thunderstorm as opposed to cloud-top only. Nowcasting applications enabled by the GLM data will expedite the warning and response time of emergency management systems, improve the dispatch of electric power utility repair crews, and improve airline routing around thunderstorms thereby improving safety and efficiency, saving fuel and reducing delays. The use of GLM data will assist the Bureau of Land Management (BLM) and the Forest Service in quickly detecting lightning ground strikes that have a high probability of causing fires. Finally, GLM data will help assess the role of thunderstorms and deep convection in global climate, and will improve regional air quality and global chemistry/climate modeling. The GLM has a robust design that benefits and improves upon its strong heritage of NASA-developed LEO predecessors, the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). GLM will have a substantially larger number of pixels within the focal plane, two lens systems, and multiple Real-Time Event Processors REPS for on-board event detection and data compression to provide continuous observations of the Americas and adjacent oceans.

Goodman, S. J.↗
NASA NTRS2014

Satellite Data Aid Monitoring of Nation's Forests

The USDA Forest Service’s Asheville, North Carolina-based Eastern Forest Environmental Threat Assessment Center and Prineville, Oregon-based Western Wildlands Environmental Threat Assessment Center partnered with Stennis Space Center and other agencies to create an early warning system to identify, characterize, and track disturbances from potential forest threats. The result was ForWarn, which is now being used by federal and state forest and natural resource managers.

Source record↗
NASA NTRS2018

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.

Shanti Shrestha↗
NASA NTRS2020

Global Declines in Human-Driven Mangrove Loss

Global mangrove loss has been attributed primarily to human activity. Anthropogenic loss hotspots across Southeast Asia and around the world have characterized the ecosystem as highly threatened, though natural processes such as erosion can also play a significant role in forest vulnerability. However, the extent of human and natural threats has not been fully quantified at the global scale. Here, using a Random Forest-based analysis of over one million Landsat images, we present the first 30-meter resolution global maps of the drivers of mangrove loss from 2000-2016, capturing both human-driven and natural stressors. We estimate that 62% of global losses between 2000-2016 resulted from land-use change, primarily through conversion to aquaculture and agriculture. Up to 80% of these human-driven losses occurred within six Southeast Asian nations, reflecting the regional emphasis on enhancing aquaculture for export to support economic development. Both anthropogenic and natural losses declined between 2000-2016, though slower declines in natural loss caused an increase in their relative contribution to total global loss area. We attribute the decline in anthropogenic losses to the regionally-dependent combination of increased emphasis on conservation efforts and a lack of remaining mangroves viable for conversion. While efforts to restore and protect mangroves appear to be effective over decadal time scales, the emergence of natural drivers of loss presents an immediate challenge for coastal adaptation. We anticipate that our results will inform decision making within conservation and restoration initiatives by providing a locally-relevant understanding of the causes of mangrove loss.

Coastal wetlands↗