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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Reductions in Productivity Due to Land Degradation in the Drylands of the Southwestern United States

Dryland degradation has long been recognized at regional, national, and global scales, yet there are no objective assessments of its location and severity. An assessment of reductions in net primary production (NPP) due to dryland degradation in the southwestern United States is reported. The local NPP scaling (LNS) approach was applied to map the extent and magnitude of degradation. LNS seeks to identify reference sites in which there is no degradation that can be used as a standard for comparison with other sites that share the same environment, except for degradation. Twelve years were analyzed (2000–2011), using Normalized Difference Vegetation Index (NDVI) data (250 m) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite‐borne multispectral sensor. The results indicated that the total NPP reductions in the study area were about 35.9 ± 4.7 Tg C/yr, which equates to 0.31 ± 0.04 Mg C/ha·y. The NPP reductions in grassland‐savanna and livestock grazing areas were large and mostly consistent between years in spite of large variations in overall NPP caused by differences in land‐use, interannual variations in rainfall, and other aspects of weather. In comparison with other cover types, forested land generally had higher NPP reduction per unit area. The maps also enable attribution of degradation from the finest management units to entire agencies, such as the Bureau of Land Management, which had 50% less production per unit area than the U.S. Forest Service. The degradation within Native American land was low with total NPP reduction of about 2.41 ± 0.24 Tg C/yr and unit area reduction of productivity of just 0.21 ± 0.02 Mg C/ha·yr, yet the percent reduction from potential was in equivalence with other land management agencies.

Noojipady, Praveen↗
NASA NTRS2014

Global Assessment of OMI Aerosol Single-scattering Albedo Using Ground-based AERONET and SKYNET Inversions

We compare the aerosol single-scattering albedo (SSA) retrieved by the near-UV two-channel algorithm (OMAERUV) applied to the Aura-Ozone Monitoring Instrument (OMI) measurements with an equivalent inversion made by the ground-based Aerosol Robotic Network (AERONET). This work is the first comprehensive effort to globally compare the OMI-retrieved SSA with that of AERONET using all available sites spanning the regions of biomass burning, dust, and urban pollution. An analysis of the co-located retrievals over 269 sites reveals that about 46 percent (69 percent) of OMI-AERONET matchups agree within the absolute difference of plus or minus 0.03 (plus or minus 0.05) for all aerosol types. The comparison improves to 52 percent (77 percent) when only 'smoke' and 'dust' aerosol types were identified by the OMAERUV algorithm. Regionally, the agreement between the two inversions was robust over the biomass burning sites of South America, Sahel, Indian subcontinent, and oceanic-coastal sites followed by a reasonable agreement over north-east Asia. Over the desert regions, OMI tends to retrieve higher SSA, particularly over the Arabian Peninsula. Globally, the OMI-AERONET matchups agree mostly within plus or minus 0.03 for the aerosol optical depth (440 nanometers) and UV-aerosol index larger than 0.4 and 1.0, respectively. We also compare the OMAERUV SSA against the inversion made by an independent network of ground-based radiometer called SKYNET with its operating sites in Japan, China, South-East Asia, India, and Europe. The advantage of the SKYNET database over AERONET is that it performs retrieval at near-UV wavelengths which facilitate the direct comparison of OMI retrievals with the equivalent ground-based inversion. Comparison of OMI and SKYNET over currently available sites reveals a good agreement between the two where more than 70 percent of matchups agree within the absolute difference of 0.05.

Aerosol Single-scattering Albedo↗
DOE OSTI2021

On the use of air temperature and precipitation as surrogate predictors in soil respiration modelling

Soil respiration (R S ), the soil-to-atmosphere CO 2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T soil ) and water content (SWC). Regional to global-scale R S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T air ) and monthly precipitation (P m ) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. In this report we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T air as a surrogate for T soil , and data from 507 sites to examine the suitability of P m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T soil and T air are highly correlated and explain similar R S variability. In contrast, P m is not a good surrogate for SWC, even though P m explains a similar amount of R S variability to SWC. The wide variability in the site-specific relationships between R S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T air in continental-to-global scale R S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Contributions of vegetation heterogeneity within tower footprint to CO 2 flux estimations through graph neural network modeling

Net ecosystem exchange of CO 2 (Fc) measured directly by eddy covariance towers is based on various assumptions, including large, flat and homogenous land cover type. In reality, often a tower site is not large enough for flux measurements, and landscapes consist of patches of different land cover types within the flux footprint. In addition, some portions of fluxes are contributed by different cover types when a footprint exceeds the size of the target ecosystem. The contributions of non-dominant patches to Fc are often ignored. Here, in this study, we propose a novel integrated modeling framework that combines random forest (RF) and XGBoost with a residual correction module based on a deep graph convolutional network (DeeperGCN) to simulate Fc for seven flux measurement sites in southwest Michigan. High-resolution remote sensing vegetation indices, soil properties, meteorological variables, and footprint-weighted spatial features were used as model inputs at three spatial resolutions (10 m, 20 m, 30 m), and their importance in predicting Fc with DeeperGCN was assessed. We found that residual correction using DeeperGCN significantly improved prediction accuracy, with the R 2 increasing from 0.9098 to 0.9479 for RF and from 0.9235 to 0.9433 for XGBoost. At site level, the maximum improvement in R 2 reached 0.1617. Paired t-tests confirmed that these improvements were statistically significant (p < 0.05). Among all predictors, leaf area index and incoming shortwave radiation emerged as the dominant drivers of spatial residual variation, followed by precipitation, relative humidity, and selected vegetation indices. The 20 m resolution yielded the best balance between model performance and computational efficiency. In conclusion, our modeling framework effectively captures both spatial heterogeneity and nonlinear interactions, offering a robust solution for spatially explicit flux modeling in structurally diverse ecosystems beyond the study sites.

footprint model↗
NASA NTRS1988

Canopy reflectance modeling in a tropical wooded grassland

The Li-Strahler canopy reflectance model, driven by LANDSAT Thematic Mapper (TM) data, provided regional estimates of tree size and density in two bioclimatic zones in Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple patameters measured in the field and in the imagery. Reflectance properties of the trees were measured in the study sites using a pole-mounted radiometer. The measurements showed that the assumptions of the simple Li-Strahler model are reasonable for these woodlands. The field radiometer measurements were used to calculate the normalized difference vegetation index (NDVI), and the integrated NDVI over the canopy was related to crown volume. Predictions of tree size and density from the canopy model were used with allometric equations from the literature to estimate woody biomass and potential foliar biomass for the sites and for the regions. Estimates were compared with independent measurements made in the Sahelian sites, and to typical values from the literature for these regions and for similar woodlands. In order to apply the inversion procedure regionally, an area must first be stratified into woodland cover classes, and dry-season TM data were used to generate a stratum map of the study areas with reasonable accuracy. The method used was unsupervised classification of multi-data principal components images.

Simonett, David↗
DOE OSTI2020

Real-time structural motif searching in proteins using an inverted index strategy

Biochemical and biological functions of proteins are the product of both the overall fold of the polypeptide chain, and, typically, structural motifs made up of smaller numbers of amino acids constituting a catalytic center or a binding site that may be remote from one another in amino acid sequence. Detection of such structural motifs can provide valuable insights into the function(s) of previously uncharacterized proteins. Technically, this remains an extremely challenging problem because of the size of the Protein Data Bank (PDB) archive. Existing methods depend on a clustering by sequence similarity and can be computationally slow. We have developed a new approach that uses an inverted index strategy capable of analyzing >170,000 PDB structures with unmatched speed. The efficiency of the inverted index method depends critically on identifying the small number of structures containing the query motif and ignoring most of the structures that are irrelevant. Our approach (implemented at motif.rcsb.org ) enables real-time retrieval and superposition of structural motifs, either extracted from a reference structure or uploaded by the user. Herein, we describe the method and present five case studies that exemplify its efficacy and speed for analyzing 3D structures of both proteins and nucleic acids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Evaluation of Data Catalog Software for Hanford Site Environmental Datasets

Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Water use in a young Pinus taeda bioenergy plantation: Effect of intensive management on stand evapotranspiration

Abstract The increasing demand for plant‐derived bioenergy is projected to expand tree plantations with intensive silviculture and improved tree genetics. These silvicultural practices result in faster stand development and canopy closure, which may also influence the systems' water dynamics. Here, we studied the evapotranspiration (ET) of a young (5 years old) intensively managed loblolly pine ( Pinus taeda ) stand and investigated the components of ET to determine its contribution to overall water use. We also compared ET with plantations that received less intensive management to determine whether our stand used more water. We used the eddy covariance method to estimate ecosystem‐level total ET (ET EC ), while plot‐level estimates of ET (ET P ) were obtained via soil lysimeters, sap flow sensors, and throughfall collectors, enabling measurement of the components of ET. Soil evaporation (Es) was the largest component of ET P (36%) over the course of the study, while transpiration and canopy interception accounted for 27% and 22%, respectively. Es decreased with stand development, while transpiration and canopy interception increased. Leaf area index (LAI) and precipitation were the most significant factors controlling ET and its components. Compared to previous studies in different sites that have similar age but lower LAI, our stand had higher water use. This high water use in the early stages of stand development was primarily due to high Es before the canopy was fully developed. While there are potential sources of uncertainty when comparing ET EC and the component fluxes in ET P , results from the two methods were not significantly different. This study had the advantage of using multiple methods to understand and verify the component processes that contribute to ET. Therefore, we recommend that multiple measurement techniques be used in the long‐term observation of ET, and in particular for the evaluation of the impact that intensively managed forests have on water resources in the southeastern United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Environmental impacts of dam reservoir filling in the East Amazon

Mitigating the environmental impacts caused by hydroelectric dams is a worldwide challenge. The aquatic ecosystem is the most impacted during the reservoir filling phase, yet biogeochemical dynamics at this stage are not well-studied. Here we evaluate water quality and hydraulic parameters in the Araguari River (Amapá/Brazil) during the filling of the Ferreira Gomes Hydroelectric Power Plant reservoir (UHEFG). Five field campaigns were performed from July 2014 to August 2015 across nine sample sites within the reservoir (P1, P2, …P9) and only one downstream (P10). The following key variables were monitored: Trophic State Index (TSI), Total Coliforms (TC), E. coli (EC) and Chlorophyll-a (Chl-a), along with physical and chemical variables (Temperature, Suspended Solids, Total Dissolved Solids, Electrical Conductivity, Turbidity, Color, pH, DO, Al3+, NH4+, Cl-, Mg1+, Ca2+, total Phosphorous, NH3, NO3-, and SO42). Besides that, hydraulic-operational variables were also investigated: inflows (Qa), outflows (Qd), and variation in reservoir volume (Vol%). Multiple Regression Analyses showed that the key parameters were both significantly influenced by physio-chemical and hydraulic variables (0.46=R2adj=0.99, p<0.05). The DO showed significant spatial variation, being influenced by the turbulence from the Coaracy Nunes dam (UHECN) upstream and the UHEFG dam downstream. The Vol% influenced the TSI, which ranged from oligotrophic to hypertrophic and eventually stabilized at mesotrophic. The levels of TSI, TC, and Chl-a decreased and the level of E. coli increased (p<0.05) as a function of Vol%. A Cluster Analysis showed the formation of three spatial groups - two inside the reservoir and one downstream (P10). This suggests that in the rainy season or transition season, the hydraulic residence time in the reservoir is very low (16 = thr = 36 h) when hydrodynamic processes are dominant. In the dry season (thr ˜ 1 month), biogeochemical and hydrodynamic processes occur at similar time scales to the nutrient and microbial abundance. These results confirm the hypothesis that the filling phase has significant impacts on the key parameters of the water quality (p<0.05). We conclude that the filling phase of the UHEFG reservoir generated significant environmental impacts, which have repercussions even retrospectively.

Amazon, biogeochemistry, ecosystem, extreme event,↗
NASA NTRS1977

Applied regional monitoring of the vernal advancement and retrogradation (Green wave effect) of natural vegetation in the Great Plains corridor

The author has identified the following significant results. LANDSAT 2 has shown that digital data products can be effectively employed on a regional basis to monitor changes in vegetation conditions. The TV16 was successfully applied to an extended test site and the Great Plains Corridor in tests of the ability to assess green forage biomass on rangelands as an index to vegetation condition. A strategy for using TV16 on a regional basis was developed and tested. These studies have shown that: (1) for rangelands with good vegetative cover, such as most of the Great Plains, and which are not heavily infested with brush or undesirable weed species, the LANDSAT digital data can provide a good estimate (within 250 kg/ha) of the quantity of green forage biomass, and (2) at least five levels of pasture and range feed conditions can be adequately mapped for extended regions.

Rouse, J. W., Jr.↗
NASA NTRS1988

Geobotanical remote sensing for determination of aggregate source material

Aggregate source material suitable for facility and roadbed construction is often a very limited and highly valuable resource. The location of suitable source material is crucial to construction operations once facility requirements are established. The application of airborne and spaceborne remote sensing to terrain information requirements has proved attractive because of the rapid processing time and extensive spatial coverage associated with remotely gathered imagery. Source material identification may be improved by the remote sensing of vegetation associated with the material, particularly in areas of high vegetative cover. A research study employing remote sensing techniques was initiated to determine if vegetation could be used to discriminate parent materials for suitability as aggregate source material. Two test sites representing potential alluvial and residual source areas were selected in a semiarid region of Central California. Methods developed for the study included field observations of vegetation characteristics associated with the two parent material types along with the analysis of Thematic Mapper Simulator data flown over the test sites. The most useful images were those composites that included bands from two of the techniques (i.e., a Perpendicular Vegetation Index (PVI) band combined with principal components bands).

Minor, Timothy↗
DOE OSTI2021

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

09 BIOMASS FUELS↗
DOE OSTI2022

Requirements for Cataloging Hanford Geophysical Datasets

Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, cleanup, and more. Managing and archiving that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. Part of that effort is developing a Hanford Environmental Information and Data Index (HEIDI) to organize the data and maximize its value by making it findable and available for reuse. The objective is to catalog the disparate data sets collected to address the evolving needs of planning, executing, and documenting cleanup over several decades up to the present day, including links to active data sources when available. A properly implemented data catalog makes finding environmental datasets related to an area or theme a routine, reliable process, without requiring the searcher to have special knowledge that a data set exists and where it may be stored. In this project, a working group, including the U.S. Department of Energy, the Hanford Site contractors, and Pacific Northwest National Laboratory staff, identified needs and requirements for handling complex site data. Geophysical data was chosen as a test case because it can be large and complex and often involves multiple processing steps to extract the information incorporated into deliverables. The ability to document those steps was one of the requirements identified for the catalog. In addition to developing requirements, other activities included selecting a metadata schema and initial testing with the objective of determining whether the workflow and capabilities of selected data catalog software platforms were sufficient to implement and impose the identified requirements. This initial testing involved running the default catalog instance using the software platform of interest and altering the configuration to achieve each requirement, if possible. Where configuration alone was insufficient, the possibility of modifying the software by changing the code was examined, but not implemented. A follow-on task is planned to reprogram the code as necessary to implement requirements in a prototype catalog.

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

UAS remote sensing (3DR SOLO platform): multispectral reflectance, canopy height model, normalized difference vegetation index, Seward Peninsula, Alaska, 2021

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) - operated by the Terrestrial Ecosystem Science Technology group at Brookhaven National Laboratory. This package includes data from 10 flights flown over the NGEE-Arctic Council Mile Marker (MM) 71, Kougarok MM64, and Teller MM27 sites on the Seward Peninsula, Alaska, in August 2021. Derived image products include point cloud, ortho-mosaiced multispectral image, ortho-mosaiced RGB image, a digital surface model (DSM) using the structure from motion (SfM) technique, a canopy height model (CHM), and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats. 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 OSTI2026

Marginal Soils Index Analysis & Geospatial Data

This data package contains output files associated with the Mongird et al. paper entitled "Can US power grid expansion avoid prime agricultural lands?" and is organized into four dataset directories. Each dataset is described in more detail below. 1. Marginal Soils Index Analysis Description: This folder contains a csv file with land needs and availability by state, power generating technology type, and scenario in 2050 when suitable siting areas are additionally constrained to areas with increasing levels of soil marginality. Files: msi_constrained_siting_availability_2050.csv Variables: Scenario - Projected 2050 scenario name State - US state abbreviation Technology - Generating technology type solar = solar photovoltaic gas_cc_re = natural gas combined cycle (recirculating cooling) wind = onshore wind gas_cc_ccs_re = natural gas combined cycle with carbon capture sequestration (recirculating cooling) gas_cc_dry = natural gas combined cycle with (dry cooling) gas_cc_pond = natural gas combined cycle with (pond cooling) coal_conv_ccs_re = conventional coal with carbon capture sequestration (recirculating cooling) Req_Capacity_MW - The amount of rated capacity required in 2050 of the given technology type in the given state and under the given scenario from the capacity expansion plan Req_Capacity_Factor - The assumed capacity factor (fraction between 0 and 1) for the given technology type in the given state and under the given scenario by the capacity expansion plan Req_Land_km2 - The amount of land required (in km-squared) to host the required generating capacity that is capable of meeting the specified capacity factor for the given technology type in the given state and under the given scenario Req_Energy_TWh - Product of Req_Capacity_MW, Req_Capacity_Factor, and 8760/1e6 for the given technology type in the given state and under the given scenario MSI_Case - The level of MSI that siting the given technology is additionally constrained to, where >0 means siting is additionally constrained to suitable land areas that have an MSI value greater than 0 >=1 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 1 >=2 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 2 >=3 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 3 Soil Attribute Rasters Description: This folder contains geospatial raster files for individual soil parameters upscaled to the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of non-missing 30m resolution values. All raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. Files: avg_cond_raster_ .tif - Average conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm max_cond_raster_ .tif - Maximum conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm min_ph_raster_ .tif- Min pH values across all soil horizons within a depth of 40 inches. avg_ph_raster_ .tif - Average pH value across all soil horizons within a depth of 40 inches. max_ph_raster_ .tif- Max pH value across all soil horizons within a depth of 40 inches. erosion_factor_raster_ .tif - Product of k-factor and percent slope flood_freq_raster_ .tif - Number of months of the year during which the area is commonly, frequently, or very frequently flooded. max_sar_raster_ .tif - Maximum sodium adsorption ratio across all horizons within a depth of 40 inches rock_frac_raster_ .tif - Fraction of the upper 6 inches of soil composed of rock fragments larger than 3 inches. temp_regime_raster_ .tif - Soil temperature regime with the following key: 0 = pergelic 1 = gelic 2 = cryic 3 = frigid 4 = isofrigid 5 = mesic 6 = isomesic 7 = thermic 8 = isothermic 9 = hyperthermic 10 =isohyperthermic Marginal Soils Index Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index at the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of 30m resolution. Both raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. A value of 0 indicates that there were no soil attributes present that indicate marginal soil. NA values indicate that data was unavailable or bodies of water. Files: marginal_soils_index_30m_raster.tif marginal_soils_index_1km_raster.tif Marginal Soils Index Resource Potential Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index + Resource Potential (MSIxRP) score at 1km resolution for geothermal, solar, and wind technologies. Raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. NA values indicate that the location is not suitable for siting the given technology due to policy, environmental, socioeconomic, topological, and other constraints regardless of soil marginality level. Areas with values greater than or equal to zero represent the product of the normalized MSI value and the normalized resource potential value. Files: geothermal_msi_rp_score_raster.tif solar_msi_rp_score_raster.tif wind_msi_rp_score_raster.tif Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Agriculture↗
DOE OSTI2026

Marginal Soils Index Analysis & Geospatial Data

This data package contains output files associated with Mongird et al. (in prep) organized into four dataset directories. Each dataset is described in more detail below. 1. Marginal Soils Index Analysis Description: This folder contains a csv file with land needs and availability by state, power generating technology type, and scenario in 2050 when suitable siting areas are additionally constrained to areas with increasing levels of soil marginality. Files: msi_constrained_siting_availability_2050.csv Variables: Scenario - Projected 2050 scenario name State - US state abbreviation Technology - Generating technology type solar = solar photovoltaic gas_cc_re = natural gas combined cycle (recirculating cooling) wind = onshore wind gas_cc_ccs_re = natural gas combined cycle with carbon capture sequestration (recirculating cooling) gas_cc_dry = natural gas combined cycle with (dry cooling) gas_cc_pond = natural gas combined cycle with (pond cooling) coal_conv_ccs_re = conventional coal with carbon capture sequestration (recirculating cooling) Req_Capacity_MW - The amount of rated capacity required in 2050 of the given technology type in the given state and under the given scenario from the capacity expansion plan Req_Capacity_Factor - The assumed capacity factor (fraction between 0 and 1) for the given technology type in the given state and under the given scenario by the capacity expansion plan Req_Land_km2 - The amount of land required (in km-squared) to host the required generating capacity that is capable of meeting the specified capacity factor for the given technology type in the given state and under the given scenario Req_Energy_TWh - Product of Req_Capacity_MW, Req_Capacity_Factor, and 8760/1e6 for the given technology type in the given state and under the given scenario MSI_Case - The level of MSI that siting the given technology is additionally constrained to, where >0 means siting is additionally constrained to suitable land areas that have an MSI value greater than 0 >=1 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 1 >=2 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 2 >=3 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 3 Soil Attribute Rasters Description: This folder contains geospatial raster files for individual soil parameters upscaled to the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of non-missing 30m resolution values. All raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. Files: avg_cond_raster_ .tif - Average conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm max_cond_raster_ .tif - Maximum conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm min_ph_raster_ .tif- Min pH values across all soil horizons within a depth of 40 inches. avg_ph_raster_ .tif - Average pH value across all soil horizons within a depth of 40 inches. max_ph_raster_ .tif- Max pH value across all soil horizons within a depth of 40 inches. erosion_factor_raster_ .tif - Product of k-factor and percent slope flood_freq_raster_ .tif - Number of months of the year during which the area is commonly, frequently, or very frequently flooded. max_sar_raster_ .tif - Maximum sodium adsorption ratio across all horizons within a depth of 40 inches rock_frac_raster_ .tif - Fraction of the upper 6 inches of soil composed of rock fragments larger than 3 inches. temp_regime_raster_ .tif - Soil temperature regime with the following key: 0 = pergelic 1 = gelic 2 = cryic 3 = frigid 4 = isofrigid 5 = mesic 6 = isomesic 7 = thermic 8 = isothermic 9 = hyperthermic 10 =isohyperthermic Marginal Soils Index Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index at the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of 30m resolution. Both raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. A value of 0 indicates that there were no soil attributes present that indicate marginal soil. NA values indicate that data was unavailable or bodies of water. Files: marginal_soils_index_30m_raster.tif marginal_soils_index_1km_raster.tif Marginal Soils Index Resource Potential Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index + Resource Potential (MSI+RP) score at 1km resolution for geothermal, solar, and wind technologies. Raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. NA values indicate that the location is not suitable for siting the given technology due to policy, environmental, socioeconomic, topological, and other constraints regardless of soil marginality level. Areas with values greater than or equal to zero represent the product of the normalized MSI value and the normalized resource potential value. Files: geothermal_msi_ep_score_raster.tif solar_msi_ep_score_raster.tif wind_msi_ep_score_raster.tif Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Agriculture↗
NASA NTRS2020

Global Distribution and 14-Year Changes in Erythemal Irradiance, UV Atmospheric Transmission, and Total Column Ozone 2005 – 2018 Estimated from OMI and EPIC Observations

Satellite data from the Ozone Measuring Instrument (OMI) and Earth Polychromatic Imaging Camera (EPIC) are used to study changes and global distribution of UV erythemal irradiance in mW/m(exp 2) E(ζ,φ,z,t) and the dimensionless UV index (E/25 mWm(exp 2)) over the Earth’s surface as a function of latitude ζ, longitude φ, altitude z, and time t. OMI time series data starting in January 2005 to December 2018 are used to estimate 14-year changes in erythemal irradiance, total column ozone TCO3, scene reflectivity LER, and aerosol absorption τA at specific land sites in the Northern and Southern Hemispheres using publicly available OMI data. Estimates of changes in atmospheric transmission ΔCτ(ζ,φ,z,t) derived from OMI measured cloud and haze reflectivity show an increase between 60°S to 45°S almost no average 14-year change from 55°S to 30°N but show an increase from 35°N to 55°N. This implies increased solar insolation at higher latitudes that suggests positive feedback for global warming. Changes in aerosol absorption of UV irradiance ΔCA(ζ,φ,z,t) derived from changes in τA are mostly in the equatorial region and at higher southern latitudes resulting in correlated changes in E. TCO3 derived from OMI data has increased at a rate of 2% per decade for the latitudes between 60°S to 10°N changing to a decrease of 1% per decade between 40°N to 60°N. The result is an average decrease in E(ζ,φ,z,t) at a rate of 2% per decade in the Southern Hemisphere and an increase between 40°N to 60°N. For some specific sites (latitudes from 55°S to 45°N) there has been little or no change in E(ζ,φ,z,t) for the period 2005 – 2018. Nearly half the sites show strong correlation effects of both short- and long-term cloud and absorbing aerosol change as well as anticorrelation with total column ozone change ΔTCO3. Synoptic EPIC data from the sunlit Earth are used to derive ozone and reflectivity needed for global images of the distribution of E(ζ,φ,z,t) from sunrise to sunset centered on the Americas, Europe-Africa, and Asia. EPIC data are used to show the latitudinal distribution of E(ζ,φ,z,t) from the equator to 75° for specific longitudes. Dangerously high amounts of erythemal irradiance (12 < UV index < 18) are found for many low latitude and high-altitude sites (e.g., San Pedro, Chile (2.45 km), La Paz, Bolivia (3.78 km). Lower UV indices at some equatorial or high-altitude sites (e.g., Quito, Ecuador) are moderated by the presence of persistent cloud effects. High UVI levels (UVI > 6) are also found at most mid-latitude sites during the summer months.

Ozone Measuring Instrument (OMI) and Earth Polychr↗