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NASA NTRS1999

A Comparison of FIFE Observation with GEOS Assimilated Data Including a Heterogeneous LSM

Several recent studies have shown that much can be learned by comparing grid-point data from a data assimilation system with in-situ observations from field experiments. While the surface heterogeneity is acknowledged in these studies, they lack quantitative representations of the influence of heterogeneity on the near-surface meteorology and surface hydrologic and energy balance. Here, we use the Betts and Ball FIFE site-averaged data. Standard deviations of the site-average will provide an estimate of the FIFE site heterogeneity. Recently, the Mosaic Land-Surface Model (LSM) has been incorporated into the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The Mosaic LSM computes the surface energy and hydrologic balance for nine distinct surface types at each grid-point. Each surface type is proportionally weighted to determine the mean grid point properties. Hence, we can compare modeled and observed grid-point variability in addition to the mean properties. Also, assimilated data sets created with and without the LSM are compared. The results indicate the importance of including quantitative estimates of heterogeneity in the analysis of the land surface hydrology and energy balances in assimilation systems.

Bosilovich, M.↗

NASA NTRS2017

On the Relationship Between Sub-Daily Instantaneous and Daily Total Gross Primary Production: Implications for Interpreting Satellite-Based SIF Retrievals

Spatially and temporally continuous estimation of plant photosynthetic carbon fixation (or gross primary production, GPP) is crucial to our understanding of the global carbon cycle and the impact of climate change. Besides spatial, seasonal and interannual variations, GPP also exhibits strong diurnal variations. Satellite retrieved solar-induced chlorophyll fluorescence (SIF) provides a spatially continuous, but temporally discrete measurement of plant photosynthesis, and has the potential to be used to estimate GPP at global scale. However, it remains unclear whether the seasonal time series of SIF snapshots taken at a fixed time of the day can be used to infer daily total GPP variation at spatial and seasonal scales. In this study, we first used GPP estimates from 135 eddy covariance flux sites, covering a wide range of geographic locations and biome types, to investigate the relationship between the instantaneous GPP (GPP(sub inst)) and daily GPP (GPP(sub daily)) on the seasonal course for different times of the day. Latitudinal and diurnal patterns were found to correspond to variations in photosynthetically active radiation (PAR) and light use efficiency (LUE), respectively. We then used the Soil-Canopy Observation Photosynthesis and Energy Balance (SCOPE) model and the FluxCom GPP product to investigate the instantaneous and daily SIF-GPP relationships at five flux tower sites along a latitudinal gradient and at a global scale for different biome types. The results showed that daily SIF had a stronger linear correlation with daily GPP than instantaneous SIF at the seasonal scale, with an instantaneous to daily SIF conversion factor following the latitudinal and seasonal pattern driven by PAR. Our study highlights the necessity to take the latitudinal and diurnal factors into consideration for SIF-GPP relationship analyses or for physiological phenology analyses based on SIF.

photosynthesis model↗

DOE OSTI2025

Shrub Expansion Can Counteract Carbon Losses From Warming Tundra

Arctic warming is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and tree-line expansion and densification. However, predicting the carbon trajectories of the changing Arctic is challenging due to interacting feedbacks between vegetation composition and structure, and surface characteristics. We conduct a sensitivity analysis of the current-date to 2100 projected surface energy fluxes, soil carbon pools, and CO 2 fluxes to different shrub expansion rates under future emission scenarios (intermediate—RCP4.5, and high—RCP8.5) using the Arctic-focused configuration of E3SM Land Model (ELM). We focus on Trail Valley Creek (TVC), an upland tundra site in the western Canadian Arctic, which is experiencing shrub densification and expansion. We find that shrub expansion did not significantly alter the modeled surface energy and water budgets. However, the carbon balance was sensitive to shrub expansion, which drove higher rates of carbon sequestration as a consequence of higher shrubification rates. Thus, at low shrub expansion rates, the site would become a carbon source, especially under RCP8.5, due to higher temperatures, which deepen the active layer and enhance soil respiration. At higher shrub expansion rates, TVC would become a net CO 2 sink under both Representative Concentration Pathway scenarios due to higher shrub productivity outweighing temperature-driven respiration increase. Our simulations highlight the effect of shrub expansion on Arctic ecosystem carbon fluxes and stocks. We predict that at TVC, shrubification rate would interact with climate change intensity to determine whether the site would become a carbon sink or source under projected future climate.

Yazbeck, Theresia [The Ohio State Univ., Columbus,↗

NASA NTRS2004

Apollo 16 Mafic Glass: Geochemistry, Provenance, and Implications

Although the Apollo 16 mission landed in the feldspathic lunar highlands, mass-balance models suggest that there is a 5-6% mare component in the mature soils collected at the site. Only one mare basalt greater than 1 cm was found and two surveys of 2-4 mm particles found that less than 1% of this size fraction is mare basalt. Similar surveys of the less than 1 mm size fraction of A16 soils found very little lithic mare basalt, but several percent of basaltic green, yellow, and orange glass. The green glass beads were identified as VLT picritic glass and the orange/yellow glass shards were a mix of high and low Ti mare-like glass, high-Al basaltic glass, and KREEPy glasses. Most previous studies of glasses in the A16 regolith were surveys that identified a high proportion of feldspathic glass because most of the glass is produced by local impacts. Because the number of mafic glasses found was low, few compositional groupings were identified. As part of our ongoing study of the mafic components of the Apollo 16 site, we specifically targeted mafic glasses from Apollo 16, selecting against the more feldspathic glasses. In this way we were able to identify over 300 mafic glasses (greater than 10 wt % FeO). We present here the major- and trace-element chemistry of the main glass groups and discuss the likely provenance of each group.

Zeigler, R. A.↗

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↗

DOE OSTI2025

Observations and modeling reveal that heatwaves reduce photosynthesis, plant carbon reserves, and net carbon uptake

Heatwaves threaten ecosystem carbon balances, yet the mechanisms driving short-term carbon flux responses remain poorly understood. Here, integrating high-frequency eddy covariance (EC) data from 140 global flux tower sites (872 site-years) with detailed process-based modeling, we examine ecosystem responses during and immediately after heatwaves. We show that heatwaves caused a −40% (range [−29%, −128%]) reduction in net ecosystem productivity (NEP) compared to pre-heatwave values, with this reduction persisting over the following two weeks (−38% range [+3%, −154%]). We attributed NEP decreases to photosynthesis decreases more than to ecosystem respiration (RE) increases. Forest sites had greater NEP decreases during heatwaves than non-forest sites, but remained carbon sinks afterwards, indicating resilience. Our modeling analysis of extreme heatwaves at selected EC sites shows that decreased photosynthesis, increased maintenance respiration, and decreased plant non-structural carbon reserves during heatwaves drive carbon cycle changes that persist for weeks. Consistent with phenocam observations, we modeled a reduction in leaf area index caused by reduced non-structural carbon reserves, leading to early leaf senescence and longer-term impacts. Ongoing increases in heatwaves are therefore likely to reduce NEP across a range of ecosystems, exacerbating carbon cycle feedback.

carbon cycle↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1976

Definition study for photovoltaic residential prototype system

A parametric sensitivity study and definition of the conceptual design is presented. A computer program containing the solar irradiance, solar array, and energy balance models was developed to determine the sensitivities of solar insolation and the corresponding solar array output at five sites selected for this study as well as the performance of several solar array/battery systems. A baseline electrical configuration was chosen, and three design options were recommended. The study indicates that the most sensitive parameters are the solar insolation and the inverter efficiency. The baseline PST selected is comprised of a 133 sg m solar array, 250 ampere hour battery, one to three inverters, and a full shunt regulator to limit the upper solar array voltage. A minicomputer controlled system is recommended to provide the overall control, display, and data acquisition requirements. Architectural renderings of two photovoltaic residential concepts, one above ground and the other underground, are presented. The institutional problems were defined in the areas of legal liabilities during and after installation of the PST, labor practices, building restrictions and architectural guides, and land use.

Imamura, M. S.↗

DOE OSTI2024

Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales

Evapotranspiration (ET) is a pivotal component in catchment-scale water balance and is essential for informed watershed management. Nevertheless, uncertainties in ET observation or modeling have been hindering effective water resources management. This study addresses this gap by establishing a robust, generalized linear relationship between ET and gross primary productivity (GPP) at the catchment scale. We test the linearity of the relationships between monthly GPP and ET data at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., yielding Pearson’s r ≥ 0.6 for 97% of the 380 catchments. We then develop a regionalization strategy to parameterize this GPP-ET relationship at the catchment scale by identifying and utilizing the linkages between the parameter values and extensively available hydroclimatic and landscape data. We demonstrate the efficacy of the proposed GPP-ET relationship and parameter regionalization strategy by their combined predictive capacity, where the predicted monthly GPP matches well with remote-sensing-based GPP product, achieving Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. In addition, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, suggesting that the new relationship is transferable across the site, catchment, and regional scales. Furthermore, our findings are valuable for improving remote-sensing-based estimation of monthly ET and diagnosing coupled water–carbon simulations in land surface and Earth system models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

A catchment scale water balance model for FIFE

A catchment scale water balance model is presented and used to predict evaporation from the King's Creek catchment at the First ISLSCP Field Experiment site on the Konza Prairie, Kansas. The model incorporates spatial variability in topography, soils, and precipitation to compute the land surface hydrologic fluxes. A network of 20 rain gages was employed to measure rainfall across the catchment in the summer of 1987. These data were spatially interpolated and used to drive the model during storm periods. During interstorm periods the model was driven by the estimated potential evaporation, which was calculated using net radiation data collected at site 2. Model-computed evaporation is compared to that observed, both at site 2 (grid location 1916-BRS) and the catchment scale, for the simulation period from June 1 to October 9, 1987.

Famiglietti, J. S.↗

DOE OSTI2024

MRS Modelling in OpenFAST

Multi-rotor wind turbines, where more than one rotor is connected to a single support structure, are increasing in popularity due to their balance of power generation with component size, weight, and cost. Multi-rotor turbines are also common in marine energy, where deployment sites tend to be depth-constrained, limiting the maximum size of a rotor. To support the industry and research communities in designing and modeling multi-rotor turbines, the open-source turbine modeling tool OpenFAST, developed and supported by the National Renewable Energy Laboratory, is being extended to model multi-rotor wind and marine turbines. This presentation gives an overview of the current state of OpenFAST, planned changes to enable coupled aero-hydro-servo-elastic dynamic modeling of multi-rotor turbines, and examples of the types of turbines that OpenFAST will support.

marine energy↗

DOE OSTI2026

Evapotranspiration partitioning estimates from 8 methods from 47 NEON sites, 2019-2021

This dataset provides daily estimates of evapotranspiration (ET) and the transpiration-to-evapotranspiration ratio (T/ET) across 47 terrestrial National Ecological Observatory Network (NEON) sites spanning diverse environmental and biome conditions in the United States across three years of data (2019-2021). Daily ET is reported in both energy units (MJ m⁻² day⁻¹) and equivalent water depth (mm day⁻¹), assuming a constant latent heat of vaporization of 2.45 MJ/kg. The primary method uses a hybrid recurrent neural network–Penman–Monteith framework (RNN-PM), which integrates physically based surface energy balance constraints with data-driven learning to partition ET into transpiration and evaporation components. Model inputs include in situ meteorological observations (air temperature, vapor pressure deficit, wind speed, and radiation) combined with satellite-derived land surface temperature, leaf area index, and soil moisture. For benchmarking and uncertainty assessment, T/ET estimates from seven additional models are included: Priestley-Taylor Jet Propulsion Laboratory (PT-JPL), Penman-Monteith (P-M), Two-Source Energy Balance (TSEB), Support Vector Regression (SVR), and Categorical Boosting (CatBoost), among others—spanning empirical, machine-learning, and process-based approaches (see methods section or linked publication for detailed descriptions). Data Package Contents: The dataset a csv files containing daily ET and T/ET estimates for each site and model, along with associated metadata files these variables. Data can be accessed using common spreadsheet software (e.g., Microsoft Excel, LibreOffice) or programming environments such as R or Python. Together, these data support cross-site comparisons of ecosystem water use, evaluation of ET partitioning methods, and development of improved land–atmosphere exchange models.

EARTH SCIENCE > ATMOSPHERE↗

NASA NTRS1978

User's instructions for the erythropoiesis regulatory model

The purpose of the model provides a method to analyze some of the events that could account for the decrease in red cell mass observed in crewmen returning from space missions. The model is based on the premise that erythrocyte production is governed by the balance between oxygen supply and demand at a renal sensing site. Oxygen supply is taken to be a function of arterial oxygen tension, mean corpuscular hemoglobin concentration, oxy-hemoglobin carrying capacity, hematocrit, and blood flow. Erythrocyte destruction is based on the law of mass action. The instantaneous hematocrit value is derived by integrating changes in production and destruction rates and accounting for the degree of plasma dilution.

Source record↗

NASA NTRS2011

Variation of Accumulation Rates Over the Last Eight Centuries on the East Antarctic Plateau Derived from Volcanic Signals in Ice Cores

Volcanic signatures in ice-core records provide an excellent means to date the cores and obtain information about accumulation rates. From several ice cores it is thus possible to extract a spatio-temporal accumulation pattern. We show records of electrical conductivity and sulfur from firn cores from the Norwegian-USA scientific traverse during the International Polar Year 2007-2009 (IPY) through East Antarctica. Major volcanic eruptions are identified and used to assess century-scale accumulation changes. The largest changes seem to occur in the most recent decades with accumulation over the period 1963- 2007/08 being up to 25 % different from the long-term record. There is no clear overall trend, some sites show an increase in accumulation over the period 1963 to present while others show a decrease. Almost all of the sites above 3200 m above sea level (asl) suggest a decrease. These sites also show a significantly lower accumulation value than large-scale assessments both for the period 1963 to present and for the long-term mean at the respective drill sites. The spatial accumulation distribution is influenced mainly by elevation and distance to the ocean (continentality), as expected. Ground-penetrating radar data around the drill sites show a spatial variability within 10-20 % over several tens of kilometers, indicating that our drill sites are well representative for the area around them. Our results are important for large-scale assessments of Antarctic mass balance and model validation.

Anschuetz, H.↗

NASA NTRS1973

Urban and regional land use analysis: CARETS and Census Cities experiment package

The author has identified the following significant results. The arrival of the so-called energy crisis makes the portion of this experiment dealing with land use climatology of more immediate significance than before, since in addition to helping to understand the processes of climatic change associated with urbanization, the knowledge obtained may be useful in assigning an energy balance impact factor to proposed changes in land use in and around cities. Thermal maps derived from S-192 data are to be used as a measure of the energy being radiated into space from the mosaic of different surfaces in and around the city. While presenting excellent spatial sampling potential for a metropolitan area tests site, the Skylab data permit a very poor temporal sampling opportunity, owing to the large number of factors beyond the investigator's control that determine when data will be taken over a given test site. The strategy is to augment the thermal maps derived from S-192 with a modeling technique which enables the simulation of a number of components of the surface energy balance, calculated at regular time intervals throughout the day or year. Preliminary tests on the performance of the model are still underway, using airborne MSS data from NASA aircraft flights. Results look extremely promising.

Alexander, R. H.↗

DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗

DOE OSTI2025

"Hidden" hydrothermal technical potential & technoeconomics: Revealing permeability & fluids with more data

Historical hydrothermal estimates have largely relied on temperature or heat flow estimates ignoring the need for natural flowing fluids. More accurate hydrothermal estimates require some indication of permeability and fluids that naturally exist in the subsurface. This paper describes a novel approach that includes proxies of permeability and fluids in hydrothermal estimates by leveraging the relatively data-rich Great Basin. Specifically, nameplate capacities (megawatts) of operating geothermal plants, negative (0 megawatt) locations and 48 geophysical and geologic features are used to used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictions. Additionally, this work inputs the XGBoost-based hydrothermal predictions into the Renewable Energy Potential (reV) model to quantify technical capacity, its uncertainty and techno-economics. Compared to historical hydrothermal estimates, these predictions adhere to the 37 operating geothermal plants and negative locations. We present a method for subsampling the negative sites to bring the labels into balance that uses the geologic domain knowledge to proportionally represent negatives. Overall, the distributions of the hydrothermal technical capacity and the site levelized cost of energy are respectively much tighter, lower and more accurate than the previous estimates for the Great Basin, as they include geological and geophysical surrogates for permeability and fluids. Percentile (50th and 90th, median and high estimate, respectively) models provide bookends for these metrics.

13 HYDRO ENERGY↗