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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating the combined effects of light and water availability on the early growth and physiology of Tamarindus indica : Implications for restoration

Abstract Premise The tamarind tree (Tamarindus indica) is a species of significant cultural, economic, and ecological value, with a pantropical distribution. However, the tamarind is experiencing a decline in wild populations in its native range, but the reasons for its decline remain unknown. Methods We examined the critical early life‐history stages for tamarind establishment to understand how varying levels of light and water availability and watering frequency affect its regeneration. Through three greenhouse experiments, we assessed the impact of these resources on the germination, survival, growth, and physiological responses of tamarind seedlings and saplings. Results Water availability was critical for seed germination, but not light levels or pre‐germination treatments. Light was the primary limiting factor for seedling growth. Tamarinds in high light availability grew taller, had more biomass and larger diameter, but the effect of light was modulated by water availability, indicating that there was an interaction between both resources. Water and light affected specific leaf area and leaf dry matter content but not biomass allocation, root‐to‐shoot ratio, or stomatal conductance. Water availability influenced sapling growth, but watering frequency did not, indicating a resilience of tamarind saplings to changes in rainfall periodicity but a sensitivity to total rainfall amounts. Conclusions Our study underscores the importance of considering both light and water availability in tamarind restoration efforts and contribute to understanding plant responses and trade‐offs under different levels of critical resources. Our findings will inform conservation strategies to support the regeneration and long‐term survival ofTamarindus indicain its native habitats.

Plant Sciences↗
DOE OSTI2025

Mobilizing mine lands for biobased decarbonization strategies

Over 163 000 ha of mine lands in Pennsylvania (PA) have the potential to produce willow as a feedstock for renewable energy generation. Each year these lands could produce between 454 000 and 907 000 dry Mg of willow, which can be a feedstock for bioenergy (i.e., biopower) with carbon capture and sequestration (BECCS) or sustainable aviation fuel (SAF) production. We used a spatially explicit model to explore cost-minimized allocation of willow biomass from abandoned mine lands (AML) to be used for BECCS or SAF production and to abate carbon dioxide. The results suggest that between 454 000 and 907 000 dry Mg of biomass can be produced on AML in PA at costs from $94 to $250 per dry Mg. This AML willow biomass could be combined with other available biomass and aggregated to ten facilities to produce 17.9 TWh of power; alternatively, it could produce 958 million liters of jet fuel, 638 million liters of renewable diesel, and 1.289 billion liters of naphtha, along with 0.735 TWh of electricity. From this analysis, in PA, under the BECCS pathway, up to 36 million Mg per year of CO2 could be abated at costs of up to $134 per Mg; under the SAF pathway, up to 17.5 million Mg per year of CO2 could be abated at costs up to $150 per Mg of CO2. The use of mine land willow as a supplemental feedstock for SAF and BECCS would need strong incentive programs if costs of production are to be comparable with production on agricultural land.

Wahlstrom, Mallory [Pennsylvania State University]↗
DOE OSTI2026

Simulating competition in the US bioeconomy to produce hard‐to‐electrify transportation fuels using limited biomass resources

This study presents a novel bioeconomy optimization framework, BiOpt, designed to address critical questions regarding the strategic use of limited US biomass resources for biofuel production. By integrating detailed techno-economic analyses, life cycle assessments, and resource assessment data, BiOpt optimizes resource distributions across competing technologies to maximize economic performance and/or minimize greenhouse gas emissions. Using feedstock scenarios from the 2023 Billion Ton Study, the analysis explores optimal biomass allocations across sustainable aviation fuel, diesel, and marine biofuel conversion pathways given varying production targets and policy incentives. Results demonstrate distinct feedstock preferences and pathway utilizations when prioritizing economic returns vs. emissions reductions. For instance, fats, oils, and greases were highly favored in cost-optimized scenarios, while low-carbon feedstocks such as wet waste dominated greenhouse gas-minimized strategies. The findings underscore the pivotal role of policy incentives and technological advances in shaping biofuel supply chains and provide actionable insights for scaling sustainable biofuel production to decarbonize hard-to-electrify sectors. This framework offers a robust tool for policymakers and stakeholders to evaluate biofuel strategies that balance energy output, economic viability, and environmental impact.

09 BIOMASS FUELS↗
DOE OSTI2025

Root and Leaf Traits of Alfalfa Exhibit Distinct Responses to Soil Microbial Communities and Environmental Stresses

Ongoing climate change is negatively impacting crop productivity globally. Past research has highlighted that a diverse soil microbial community and variation in plant traits for resource acquisition can mitigate the negative impacts of climate change factors on crop productivity. This study investigates the effects of two major environmental stressors—drought and salinity stress, on plant productivity, biomass allocation, and root and leaf trait responses under distinct soil microbial diversities. Our results showed that salinity stress had stronger negative impacts on plant productivity than drought stress. Shoot biomass decreased by 30% and 32.5% under drought and salinity stress, respectively, whereas the root biomass decreased by 32% only under salinity stress. Soil microbial diversity did not affect plant productivity. Next, root traits were mainly impacted by drought and salinity stress, whereas leaf traits were impacted by both environmental stresses and soil microbial diversity. Specific root length and specific root area decreased under drought, and root tissue density was minimal under salinity stress. Root traits were not affected by soil microbial communities. In contrast, the leaf nitrogen content increased, whereas pheophytin content (a breakdown product of chlorophyll) decreased when plants were grown in diverse microbial communities under environmental stresses, especially drought. These results highlight the importance of soil microbial diversity in impacting plant traits in response to environmental stresses. We showed that the soil microbial diversity influences both aboveground and belowground plant traits, indicating the need for better management practices to conserve and promote soil microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

LandScan HD: a high-resolution gridded ambient population methodology for the world

Unwarned population distributions accounting for routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (LSHD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (approximately 90 m). Although LSHD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LSHD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LSHD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LSHD baseline dataset, we contrast unwarned and residential (WorldPop) population distributions by (1) exploring a practical application of flood risk assessment and (2) evaluating their congruence with outcomes of collective human activities (subnational CO 2 emissions). Finally, we discuss plans to address current LSHD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.

Building morphology↗
DOE OSTI2024

Apollo Next Generation Sample Analysis (ANGSA): an Apollo Participating Scientist Program to Prepare the Lunar Sample Community for Artemis

As a first step in preparing for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA). ANGSA was designed to function as a low-cost sample return mission and involved the curation and analysis of samples previously returned by the Apollo 17 mission that remained unopened or stored under unique conditions for 50 years. These samples include the lower portion of a double drive tube previously sealed on the lunar surface, the upper portion of that drive tube that had remained unopened, and a variety of Apollo 17 samples that had remained stored at -27 °C for approximately 50 years. ANGSA constitutes the first preliminary examination phase of a lunar “sample return mission” in over 50 years. It also mimics that same phase of an Artemis surface exploration mission, its design included placing samples within the context of local and regional geology through new orbital observations collected since Apollo and additional new “boots-on-the-ground” observations, data synthesis, and interpretations provided by Apollo 17 astronaut Harrison Schmitt. ANGSA used new curation techniques to prepare, document, and allocate these new lunar samples, developed new tools to open and extract gases from their containers, and applied new analytical instrumentation previously unavailable during the Apollo Program to reveal new information about these samples. Most of the 90 scientists, engineers, and curators involved in this mission were not alive during the Apollo Program, and it had been 30 years since the last Apollo core sample was processed in the Apollo curation facility at NASA JSC. There are many firsts associated with ANGSA that have direct relevance to Artemis. ANGSA is the first to open a core sample previously sealed on the surface of the Moon, the first to extract and analyze lunar gases collected in situ, the first to examine a core that penetrated a lunar landslide deposit, and the first to process pristine Apollo samples in a glovebox at -20 °C. All the ANGSA activities have helped to prepare the Artemis generation for what is to come. The timing of this program, the composition of the team, and the preservation of unopened Apollo samples facilitated this generational handoff from Apollo to Artemis that sets up Artemis and the lunar sample science community for additional successes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Quantifying atmospheric carbon removal at pulp and paper mills: a life cycle assessment across system boundaries

The pulp and paper industry is a promising yet underexplored platform for large-scale carbon dioxide removal (CDR) due to its use of biogenic feedstocks and production of concentrated CO 2 emissions from point sources. This study presents the first comprehensive life cycle assessment (LCA) of retrofitting an amine-based carbon capture and storage (CCS) system into a representative virgin kraft pulp and paper mill in the Southeastern U.S. We evaluate carbon removal across five system configurations, applying both static and dynamic LCA methods under multiple functional units: CO 2 captured, biomass input, and paper output. Results show that CCS retrofits can convert a conventional mill from a net emitter into a net carbon sink, with total removal efficiencies from 17% to 92% (metric tonnes of CO 2 removed per metric tonne of CO 2 available for removal under selected boundary conditions). When carbon removal is normalized to the quantity of biogenic CO 2 captured—a narrow, gate-to-gate system boundary that considers only CCS facility emissions—removal efficiencies reached as high as 92%. The use of such narrow boundaries aligns with precedents in traditional LCA methodology, where gate-to-gate assessments are commonly applied to isolate process-level performance and allocate emissions accordingly, providing a consistent basis for comparison across technologies. Under broader cradle-to-grave boundaries—which begin tracking carbon at the point of its physical removal from the atmosphere via photosynthesis in the forest, and extend to include upstream forest operations, mill-wide emissions, and downstream product decomposition—efficiencies declined, ranging from 17% to 46% under static assumptions and dropping to 12% when accounting for dynamic biogenic carbon fluxes over time. These results underscore how system boundary definitions influence reported outcomes, while also illustrating the complementary roles of narrow and broad perspectives for different decision-making contexts.

09 BIOMASS FUELS↗
DOE OSTI2026

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating advanced nuclear fission technologies for future decarbonized power grids

Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $\$5.7$–$\$7.3$/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $\$6.0$/W–$\$7.7$/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall ~32 % at 100 GW of cumulative capacity and ~51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. In conclusion, these findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.

Capacity expansion↗
DOE OSTI2026

Hybrid Power Purchase Agreements for Flexible 24/7 Energy Delivery – A Comprehensive Review of Current Practices and Research Pathways

Power Purchase Agreements (PPAs) are becoming increasingly preferred among large energy consumers, such as data centers, to secure cost-effective energy and meet accelerating demand growth. Traditionally, variable renewable energy (VRE)-based PPAs operate on a pay-as-produced basis, balancing supply and demand for a relatively longer duration (e.g., annually). However, the focus is shifting toward matching supply and demand on an hourly basis to fully meet energy needs. This shift requires the integration of flexible energy resources, such as hydropower, thermal generation, and energy storage, to complement VRE sources like wind and solar, forming the foundation for 24/7 PPA. This work contributes by: (i) reviewing emerging market trends and current practices in PPA procurement, supported by data on PPA prices and technology portfolios; (ii) synthesizing the existing literature on modeling approaches for contract pricing, quantities, hybrid resource procurement, and risk management in 24/7 PPA design, while identifying key research gaps; and (iii) proposing an integrated 24/7 PPA design framework along with two contracting mechanisms from the perspectives of both PPA providers and consumers. The proposed framework highlights critical modeling challenges, risk-allocation issues, and future research opportunities for 24/7 PPA design.

24/7↗
DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2025

Linking groundwater variability to ecosystem carbon and water use efficiencies across India

Carbon use efficiency (CUE) and water use efficiency (WUE) are important indicators of ecosystem health, reflecting the balance between carbon uptake and allocation, and the relationship between carbon assimilation and water loss. Although India shows large spatiotemporal variation in water table depth (WTD), and increasing groundwater stress, the influence of WTD on ecosystem functioning remains underexplored. This study uses satellite-based, modeled, and in-situ datasets to (1) quantify variations in CUE and WUE under shallow (SWTD) and deep (DWTD) WTD conditions across six homogeneous meteorological regions (HMRs), (2) evaluate temporal roles of gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) in driving these efficiencies, and (3) examine temporal responses to WTD shifts. SWTD regions generally showed 12 to 18 % higher GPP, 10 to 15 % higher NPP, and 10 to 20 % higher ET than DWTD zones, especially in semi-arid croplands and forests, leading to 8 to 12 % higher CUE and WUE. However, in humid and heavily irrigated regions such as northeastern and hilly India, CUE was up to 10 % higher in DWTD zones, possibly due to reduced respiration and better soil aeration compared to SWTD areas affected by waterlogging. During the Kharif (wet) season, DWTD croplands in humid zones had higher efficiencies, while in the Rabi (dry) season, SWTD croplands in northern India benefited from irrigation and cooler temperatures. These results highlight strong influence of WTD on carbon and water use processes and support the need for region-specific groundwater strategies.

Carbon use efficiency↗
DOE OSTI2025

Experimental investigation of flow distribution in enhanced geothermal systems with deep eutectic solvent

Geothermal energy has been recognized as a valuable alternative to fossil fuels and nuclear power, as it is renewable and reliable. Enhanced Geothermal Systems (EGSs) have the potential to expand geothermal energy production by enabling access to previously untapped geothermal resources. Geothermal short-circuiting poses a significant challenge to EGS development, leading to reduced heat extraction. Deep Eutectic Solvent (DES) exhibits favorable thermal and rheological properties, making it a candidate for geothermal applications. Here, this paper examines Choline Chloride-Based Deep Eutectic Solvent (DES) as a working fluid in geothermal applications and its potential to mitigate geothermal short-circuiting. Hydraulic experiments using a dual fracture flow loop were conducted at high temperatures. The results showed that DES exhibited higher differential pressure behavior compared to water. Flow distribution results revealed that DES enhances flow allocation within the small fracture, particularly when a temperature difference exists between fractures. Specifically, DES increased flow distribution by an average of 11% when the temperature difference was 85°C, and by 13% when the difference was 45°C, relative to water. These findings suggest that DES responds to thermal fracture differences, making it a potential remedy to address geothermal short-circuiting.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Life cycle analysis of hydrogen production via methane pyrolysis using plasma arc

Steam methane reforming of natural gas is the primary method of producing hydrogen in the United States, accounting for 95% of all hydrogen produced there. Methane pyrolysis, an alternative production pathway that decomposes natural gas into solid carbon and hydrogen, both eliminates CO 2 emissions associated with methane reforming and allows for additional income from carbon black. A life-cycle inventory of this process has been developed using ASPEN Plus to model the methane pyrolysis (plasma arc) process. From well to gate, hydrogen production via methane pyrolysis produces 2.78 kg CO 2 e/kg H 2 of greenhouse gas emissions using mass allocation of emissions between hydrogen and carbon black coproducts. The well-to-gate emissions are mainly driven by electricity consumption (∼38 kW h/kg H 2 ), which accounts for 81% of the emissions; if renewable electricity is used, well-to-gate emissions can be reduced to −0.448 kg CO 2 e/kg H 2 .

08 HYDROGEN↗
DOE OSTI2026

Evaluating CO 2 mitigation strategies in SAF biorefineries: Techno-economic and life cycle analysis

The aviation sector requires scalable decarbonization strategies, and lignocellulosic sustainable aviation fuel (SAF) represents a promising pathway. This study comparatively evaluates the techno-economic analysis and life cycle assessment (LCA) of three CO 2 management strategies integrated within a U.S.-based gasification–Fischer–Tropsch SAF biorefinery: (i) catalytic hydrogenation of captured CO 2 to methanol, (ii) geological CO 2 sequestration, and (iii) mineralization to sodium bicarbonate (NaHCO 3 ). Techno-economic analysis indicates that methanol synthesis requires approximately 26% higher capital investment and 33% higher operating costs than mineralization. Although methanol co-production generates the highest gross revenue, NaHCO 3 production reduces the SAF minimum selling price by approximately 38% relative to both methanol synthesis and geological sequestration pathways, reflecting a more balanced cost allocation through mineral co-product valorization. Geological sequestration lowers operating costs by nearly 50% compared with methanol synthesis but remains highly dependent on carbon credit mechanisms. LCA reveals substantial divergence in climate performance. Relative to methanol synthesis, sequestration improves net greenhouse gas performance by approximately 163%, transitioning the system from net-positive to net-negative emissions. Mineralization further enhances carbon mitigation, achieving roughly 85% greater carbon reduction than sequestration and over sixfold improvement relative to methanol synthesis within the defined system boundary. Sensitivity analysis identified hydrogen price, co-product market value, and process emissions as dominant drivers. Under baseline assumptions, CO 2 mineralization is found to offer the most balanced pathway.

Carbon capture and storage↗
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]↗