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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Linking Manganese Fractions and Organic Carbon in Soils of Contrasting Land Use Systems

Manganese (Mn) is an essential micronutrient that influences carbon (C) cycling by binding or oxidizing soil organic matter. Mn fractions in soil and their plant availability depend largely on soil pH, which is commonly altered by agricultural practices. Fractions of Mn in soils range from readily available (e.g., bioavailable Mn, exchangeable Mn) to stabilized (e.g., Mn-oxide minerals, Mn contained in silicates). Furthermore, the distribution of soil Mn with depth was assessed in contrasting land use systems (organic agriculture, conventional agriculture, and unmanaged forest) using a sequential extraction method that targets Mn fractions ranging in bioavailability. Both agricultural sites had similar amounts of total Mn but had available Mn (1–7% of total Mn) lower than that of the unmanaged forested site (15% of total Mn). Manganese and organic C were generally positively correlated, but this relationship depended on soil depth, Mn fractions, and concentrations, while land management change had little influence.

Crops↗
DOE OSTI2021

SSP‐Based Land‐Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections

Abstract To better understand the role projected land‐use changes (LUCs) may play in future regional climate projections, we assess the combined effects of greenhouse‐gas (GHG)‐forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North‐American Coordinated Regional Downscaling Experiment (NA‐CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting (WRF) model forced by one global climate model, the MPI‐ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5°C–1.0°C. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4°C–5°C greater and minimum and maximum temperature projections are increased by 2.5°C–6°C, amounts that are on par with the warming due to GHG‐forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.

Bukovsky, Melissa S.↗
DOE OSTI2021

Combined Land Use of Solar Infrastructure and Agriculture for Socioeconomic and Environmental Co-Benefits in the Tropics

Solar photovoltaics (PV) are on the rise even in areas of low solar insolation. However, in developing countries with limited capital, land scarcity, or with geographically isolated agrarian communities, large solar infrastructures are often impractical. In these cases, implementation of low-density PV over existing crops may be required to integrate renewable energy services into rural communities. Here, using Indonesia as a model system, we investigated the land use, energy, greenhouse gas emissions, economic feasibility, and the environmental co-benefits associated with off-grid solar PV when combined with high value crop cultivation. The life cycle analyses indicate that small-scale dual land-use systems are economically viable in certain configurations and have the potential to provide several co-benefits including rural electrification, retrofitting diesel electricity generation, and electricity for processing agricultural products locally. A hypothetical full-density off-grid solar PV for a model village in Indonesia shows that electricity output (1907.5 GJ yr-1) is much higher than the total residential consumption (678 GJ yr-1), highlighting the opportunity to downscale the PV infrastructure by half to lower capital cost, to co-locate crops, and to support secondary income generating activities. Economic analysis shows that the 30-year net present cost of electricity from the half-density co-located PV system (12,257 million IDR) is significantly lower than that of the flat cost of diesel required to generate equivalent electricity (14,702 million IDR). Our analysis provides insights for smarter energy planning by optimizing the efficiency of land use and limiting conversion of agricultural and forested areas for energy production.

agrivoltaics↗
DOE OSTI2023

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗
DOE OSTI2024

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗
DOE OSTI2026

Intercomparison of flood inundation models across land use types and hydrological flood stages

Flood Inundation Mapping (FIM) model selection is a key operational decision because accurate, rapid mapping underpins early warning and resource allocation. FIM performance is context-dependent and can vary with hydrograph phase, land-use/land-cover (LULC), and the evaluation benchmark. Intercomparison studies typically assess a single near-peak snapshot against one reference dataset. Here, we provide a context-stratified intercomparison across (i) multiple hydrograph phases, (ii) LULC classes, and (iii) benchmark types, for five FIM approaches spanning a wide range of physical complexity and operational cost (TRITON, LISFLOOD-FP, HEC-RAS 2D, ARC-Curve2Flood, and OWP HAND-FIM). We use the Hurricane Matthew flood (2016) in the Neuse River Basin, North Carolina, USA, as a case study. Using high-resolution remote sensing-derived flood inundation maps, hand-labeled points, and building footprints, we assess model skill across two rising and two falling hydrograph limbs and across major LULC types. Results show that model rankings shift systematically across contexts: LISFLOOD-FP ranks highest in three of four flood phases, while TRITON leads during one rising limb phase; LISFLOOD-FP performs best in vegetated areas, whereas HEC-RAS improves relative performance in agricultural and urban areas; and benchmark choice influences conclusions, with LISFLOOD-FP performing best for flooded-building detection in the late falling limb, while TRITON ranks highest against hand-labeled points. We also report representative wall-clock runtimes for each workflow to provide use-case context for operational feasibility. Together, these results offer transferable guidance for model selection and for designing large-scale, benchmark-aware FIM intercomparison studies.

Nikrou, Parvaneh [University of Alabama]↗
DOE OSTI2025

The Freundlich isotherm equation best represents phosphate sorption across soil orders and land use types in tropical soils of Puerto Rico

Biomass production in the lowland wet tropical forest is greater than in any other biome, and it is typically limited by soil phosphorus (P) availability. However, the mechanisms involved in the P cycle remain poorly represented in Earth System Models (ESMs). Soil P sorption processes are key in the P cycle and for understanding the extent of P limitation for plant productivity. Currently, a few ESMs include isotherm equations to model these processes. Although the Langmuir equation is widely cited, other isotherm equations may better describe sorption in tropical soils. Here, we use a diverse range of soil samples from Puerto Rico to test the validity of the Langmuir, Freundlich, and Temkin equation. We found that across four soil orders (Inceptisols, Mollisols, Oxisols, Ultisols), and forested and cultivated land use types, the Freundlich equation best represented soil P sorption. Furthermore, the Langmuir and the Temkin equations poorly represent soil P adsorption, especially at low P concentrations. Specifically, the Langmuir equation underestimated soil P adsorption by 40% and the Temkin equation overestimated adsorption by 76%. We also found, as expected, that soil clay content and pH were the most important parameters explaining the variability of the Freundlich (K f ) constant. Greater clay content and lower pH, common in highly weathered Ultisols and Oxisols which are abundant in the tropics, led to greater K f values. Overall, our results suggest that a diversity of soils can prompt underestimation of P sorption when using the Langmuir isotherm, which leads to an overestimation of available P that can have repercussions on ESM predictions of the P cycle and tropical forest productivity.

Earth system models↗
DOE OSTI2026

Atmospheric Gases as Quantitative Indicators of Urbanization Across Land-Use Gradients

The urban heat island (UHI) effect is a significant phenomenon characterized by higher surface temperatures in cities compared to surrounding rural areas. This change in the Earth's energy balance presents considerable challenges for urban populations, increasing heat exposure, straining energy infrastructure, and exacerbating health vulnerabilities. Quantifying UHI intensity is challenging due to blurred distinctions between urban and rural areas as land-use patterns evolve. To address this challenge, the present study builds on the framework established by Salvador et al. (2025) and investigates the use of atmospheric constituents, specifically volatile organic compounds (VOCs) and inorganic gases, as tracers of urbanization levels. A key improvement in this approach is the inclusion of a designated true rural site for comparison. Field measurements were conducted at two locations in the Southeastern United States during the summer of 2025. Inorganic gases were monitored continuously, while VOC samples were collected biweekly and analyzed using a proton transfer reaction time-of-flight mass spectrometer. The urban site exhibited elevated concentrations of anthropogenic inorganic gases, with carbon monoxide and nitrogen dioxide averaging 878 ppb and 17.99 ppb, respectively. In contrast, the rural site showed lower levels of these pollutants, 796 ppb for carbon monoxide and 12.46 ppb for nitrogen dioxide, while displaying higher concentrations of biogenic VOCs, including isoprene and monoterpenes at 1.24 ppb and 0.08 ppb, respectively. Extended mass spectral analyses revealed distinct chemical differences between the two sites, supporting the observed variability of major gases. Overall, these findings are consistent with our previous research and highlight the value of atmospheric constituents as effective indicators of urbanization, thereby improving our understanding of UHI dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Soil Carbon Dynamics Following Land Use Changes and Conversion to Oil Palm Plantations in Tropical Lowlands Inferred From Radiocarbon

We measured the 14C and 13C isotopic values of soil organic carbon in mineral soil from lowland tropical forests to provide insight into how quickly carbon is turning over in the soil following conversion of primary forests to oil palm plantations. In addition to areas converted to oil palm plantations in Peru, Indonesia, and Cameroon, we examine pastures and secondary forests in Peru as a comparison to the carbon cycling processes operating in the oil palm plantations.This dataset includes radiocarbon (Δ14C) and stable carbon (δ13C) isotopes of soil organic carbon in mineral soils from natural lowland forests and oil palm plantations in Peru, Indonesia, and Cameroon. We additionally examine plots of secondary forests following agricultural use and pastures on cleared natural forest in Peru. In addition to isotopic data, this dataset includes soil carbon and nitrogen concentrations and stock, soil texture (percent sand, silt, and clay), pH, ECEC (effective cation exchange capacity), base saturation, and bulk density. Soils were sampled in 4 depth increments to 100 cm depth.This dataset supports the publication Finstad et al., 2020. Finstad, K., van Straaten, O., Veldkamp, E., & McFarlane, K. (2020). Soil carbon dynamics following land use changes and conversion to oil palm plantations in tropical lowlands inferred from radiocarbon. Global Biogeochemical Cycles, 34, e2019GB006461. https://doi.org/10.1029/2019GB006461

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cropland abandonment between 1986 and 2018 across the United States: spatiotemporal patterns and current land uses

Knowing where and when croplands have been abandoned or otherwise removed from cultivation is fundamental to evaluating future uses of these areas, e.g. as sites for ecological restoration, recultivation, bioenergy production, or other uses. However, large uncertainties remain about the location and time of cropland abandonment and how this process and the availability of associated lands vary spatially and temporally across the United States. Here, we present a nationwide, 30 m resolution map of croplands abandoned throughout the period of 1986–2018 for the conterminous United States (CONUS). We mapped the location and time of abandonment from annual cropland layers we created in Google Earth Engine from 30 m resolution Landsat imagery using an automated classification method and training data from the U.S. Department of Agriculture Cropland Data Layer. Our abandonment map has overall accuracies of 0.91 and 0.65 for the location and time of abandonment, respectively. From 1986 to 2018, 12.3 (±2.87) million hectares (Mha) of croplands were abandoned across CONUS, with areas of greatest change over the Ogallala Aquifer, the southern Mississippi Alluvial Plain, the Atlantic Coast, North Dakota, northern Montana, and eastern Washington state. The average annual nationwide abandoned area across our study period was 0.51 Mha per year. Annual abandonment peaked between 1997 and 1999 at a rate of 0.63 Mha year –1 , followed by a continuous decrease to 0.41 Mha year –1 in 2009–2011. Among the abandoned croplands, 53% (6.5 Mha) changed to grassland and pasture, 18.6% (2.28 Mha) to shrubland and forest, 8.4% (1.03 Mha) to wetlands, and 4.6% (0.56 Mha) to non-vegetated lands. Of the areas that we mapped as abandoned, 19.6% (2.41 Mha) were enrolled in the Conservation Reserve Program as of 2020. Our new map highlights the long-term dynamic nature of agricultural land use and its relation to various competitive pressures and land use policies in the United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Three Decades of Divergent Land Use and Plant Community Change Alters Soil C and N Content in Tallgrass Prairie

Frequent fire and grazing by megafauna are important determinants of tallgrass prairie plant community structure. However, fire suppression and removal of native grazers have altered these natural disturbance regimes and changed grassland plant communities with potential long-term consequences for soil carbon (C) and nitrogen (N) storage. We investigated multidecade changes in soil C and N pools in response to contrasting long-term burning and grazing treatments. Fire suppression with or without grazers and exclusion of grazers in annually burned prairie increased soil C content and shifted the δ 13 C signature of soil C over time, concomitant with changes in plant community composition. Soil δ 13 C values indicated that increased soil C content was associated with an increased contribution from plants using a C 3 photosynthetic pathway (i.e., woody shrubs) under fire suppression. Soil N content also increased when fire was suppressed, relative to frequently burned grassland, but the rate of increase was slower when grazers were present. Additionally, changes in δ 15 N values suggested that grazing increased the openness of the N cycle, presumably due to greater N losses. By coupling long-term fire and grazing treatments with plant community data and soil samples archived over three decades, we demonstrate that human-caused changes to natural disturbance regimes in a tallgrass prairie significantly alter soil C and N cycles through belowground changes associated with shifts in the plant community. Since natural disturbance regimes have been altered in grasslands across the world, our results are relevant for understanding the long-term biogeochemical consequences of these ongoing land use changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using satellite data to assess Hurricane Helene’s impact on vegetation by land use in the CSRA

Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering

If clean energy pathways are to harness massive increases in wind power, innovations with broad geographic viability will be needed to support buildout in diverse locations. However, geodiversity in impact potential is seldom captured in technology assessment. Here we propose a scalable approach to plant-level optimization using artificial intelligence to evaluate land sparing and economic benefits of wake steering at more than 6,800 plausible onshore wind locations in the USA. This emerging controls strategy optimizes plant energy production by directing turbine wakes. On the basis of estimates from our artificial intelligence model trained on engineering wind flow simulations, co-optimizing plant layouts with wake steering can reduce land requirements by an average of 18% per plant (site-specific benefits range from 2% to 34%), subject to errors and uncertainties in the flow model, wind resource estimates, buildout scenario and geographic factors. According to model estimates, wake steering is predicted to increase power production during high-value (relatively low wind) periods, boosting the annual revenue of individual plants by up to US$3.7 million (equivalent to US$13,000 MW-1 yr-1) but producing negligible gains in some settings. Consideration of wake steering’s geographic potential reveals divergent nationwide prospects for improved economics and siting flexibility.

deployment↗
DOE OSTI2021

Land use and turbine technology influences on wind potential in the United States

As clean energy ambitions have expanded, critically evaluating renewable energy supply has become increasingly important to the energy research community and stakeholders. This study examines the onshore wind resource potential for the conterminous United States and its sensitivity to siting constraints and turbine technology innovation. We compile localized regulatory information and use high-resolution data to present multiple siting regimes covering relatively constrained to unconstrained potentials. Our efforts reveal high sensitivity to these variables and sizable uncertainty in the overall wind energy resource potential. Specifically, we find that siting constraints may shift the total capacity available to commercial wind energy by 2.3–15.1 TW. Furthermore, our results illustrate that technology advancement could require larger setbacks from buildings and infrastructure, reducing the total available capacity potential by 20% relative to estimates using current technology, but that this reduction is largely offset by increased generation such that the net effect on generation is 1%. The observed sensitivity to and uncertainty resulting from the variables we analyze suggest there is value in continued study and development of increasingly sophisticated approaches to characterizing wind resource potential.

14 SOLAR ENERGY↗