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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

CO 2 Storage Site Screening Platform Development and CO 2 Storage Resource Analysis in SECARB Offshore Reservoirs Using SAS Viya

A major goal of the SECARB Offshore Partnership (DE-FE0031557) is to screen deep saline aquifers and hydrocarbon reservoirs in the central Gulf of Mexico for CO 2 sequestration and CO 2 -enhanced oil and gas recovery (EOR/EGR) and estimate the corresponding CO 2 storage resources for select reservoirs. CO 2 storage potential associated with offshore CO 2 -EOR is considerable and likely represents “low hanging fruit” for near-term CO 2 storage given the in-place infrastructure in the region. It is for these reasons that this assessment focuses on oil and gas fields. To this end, three major objectives have been completed and include (1) managing geological data derived from different sources, (2) building a reservoir screening platform for CO 2 storage, and (3) ranking the reservoirs based on the estimated CO 2 storage resources. The SAS ® Viya platform was used for data management and analytics. The Viya platform is a cloud service platform that provides data integration, data management, quick analytics, data visualization, machine learning functions, and application programming interfaces (APIs) for multi-programming languages. Different sources of data containing geologic information, reservoir properties, and EOR/EGR information were collected, cleaned, formatted, and loaded into the SAS ® Viya platform for evaluation. The major geological characteristics of both shelf and deep-water areas of the central Gulf were examined and compared to define the appropriate reservoir screening criteria. Next, a CO 2 storage site screening system was built in the SAS ® Viya platform with the pre-defined criteria. Finally, the CO 2 storage resources of the screened reservoirs were calculated and reported at the BOEM field level to identify fields with the highest estimated CO 2 storage resource. The fields with the largest total estimated CO 2 storage resource are located in the Mississippi Canyon protraction area. Due to proximity to the Mississippi Delta (indicative of less infrastructure) and large estimated CO 2 storage resources, future development activities may wish to focus efforts in the Mississippi Canyon protraction area.

02 PETROLEUM↗
DOE OSTI2024

Simplified Approximations of Direct Cumulus Entrainment and Detrainment

Abstract In recent years, direct calculations of simulated cumulus entrainment and detrainment have facilitated new physical insights into these highly elusive but critically important processes. However, these calculations require substantial computational resources that may limit their widespread usage. To facilitate such calculations, two simplified approximations of direct cumulus entrainment and detrainment are examined herein. The first approximation, termed the “semidirect” method, follows a standard bulk approach but makes more realistic assumptions about the sources of entrained and detrained air near the cloud edges. In contrast, the second approximation (the “projection” method) uses the governing equations of motion to project whether grid points near the cloud edge will entrain or detrain as the mean cloud ascends by one grid point. Verification exercises using large-eddy simulations reveal that both methods generally agree better with corresponding direct entrainment/detrainment estimates than the traditional bulk formulation, with the projection method outperforming the semidirect method. The two methods can be used in a synergistic fashion, with the semidirect method helping to optimize the projection method, to suit a wide range of applications. Because the latter incorporates the essential dynamics of entrainment and detrainment at the local scale, it can be used to gain physical insight into the causal mechanisms regulating these complex processes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Interseismic Strain Accumulation between the Colorado Plateau and the Eastern California Shear Zone: Implications for the Seismic Hazard near Las Vegas, Nevada

ABSTRACT Crustal deformation in the central Basin and Range between the Colorado plateau and the Eastern California Shear Zone is active but slow, making it a challenge to assess how strain is distributed and crustal motion transferred. However, knowledge of strain rates is very important, particularly for addressing the seismic hazard for both the Las Vegas urban area and the site of the proposed Yucca Mountain nuclear waste repository, in southern Nevada. Global Positioning System (GPS) data provide important constraints, particularly now that the GPS network in the area has substantially expanded in recent years. However, because deformation is slow, it is important to mitigate any transient tectonic and nontectonic signals to obtain the most accurate long-term interseismic motion and robust estimation of strain rates. We use data from all GPS stations in the region including both long-running continuous and semicontinuous stations. We model and remove postseismic displacements at these stations using source parameters for 41 events, dating back to the 1700 Cascadia megathrust earthquake, which contribute significantly to the deformation field within the central Basin and Range. We also remove correlated noise from the time series with the common-mode component imaging technique. We find that removal of both the postseismic transients and common-mode noise substantially reduces the uncertainties and spatial variation in the velocities. We find east–west extension across the Las Vegas Valley of 0.5–0.6 mm/yr. The interseismic strain rate field, calculated with the final velocities, reveals higher strain rates through southern Nevada than in previous studies, with rates within Las Vegas Valley of 8.5±2.4×10−9 yr−1. Our results also confirm shear along the Pahranagat shear zone, but the estimated amplitude is strongly affected by postseismic relaxation.

Geochemistry & Geophysics↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2022

Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<10 3 km 2 ) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. Furthermore, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nuclear Data to Reduce Uncertainties in Reactor Antineutrino Measurements

The large quantities of antineutrinos produced through the decay of fission fragments in nuclear reactors provide an opportunity to study the properties of these particles and investigate their use in reactor monitoring. The reactor antineutrino spectra are measured using specialized, large area detectors that detect antineutrinos through inverse beta decay, electron elastic scattering, or coherent elastic neutrino nucleus scattering; although, inverse beta decay is the only demonstrated method so far. Reactor monitoring takes advantage of the differences in the antineutrino yield and spectra resulting from uranium and plutonium fission providing an opportunity to estimate the fissile material composition in the reactor. Recent experiments reveal a deviation between the measured and calculated antineutrino flux and spectra (the reactor anomaly) indicating either the existence of yet undiscovered neutrino physics, uncertainties in the reactor source term calculation, incorrect nuclear data, or a combination of all three. To address the nuclear data that impact the antineutrino spectrum calculations and measurements, an international group of over 180 experts in antineutrino physics, reactor analysis, detector development, and nuclear data came together during the Workshop on Nuclear Data for Reactor Antineutrino Measurements (WoNDRAM) to discuss nuclear data needs and achieve concordance on a set of recommended priorities for nuclear data improvements. Three topical sessions focused on the reactor source term, the antineutrino spectrum, and the detector response, provided a forum to gain consensus amongst the participants on the most important data improvements to address two goals: 1) understand the reactor anomaly and 2) improve the ability to monitor reactors using antineutrinos. This report summarizes the outcomes of the workshop discussions and the recommendations for nuclear data efforts that reduce reactor antineutrino measurement uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Generating Uncertainty Distributions for Seismic Signal Onset Times

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

58 GEOSCIENCES↗
DOE OSTI2023

Acoustic Research under the Source Physics Experiment

The Source Physics Experiment series is a long-term research and development (R&D) effort under the U.S. Department of Energy’s National Nuclear Security Administration focused on improving the physical understanding of how chemical explosions generate seismoacoustic signals. Beginning in 2011, a series of subsurface chemical explosions in two different and highly contrasting geologies were conducted at the Nevada National Security Site in Nevada, USA with the objective of improving simulation and modeling approaches to explosion identification, yield estimation and other monitoring applications. The two executed phases of the series provide new explosion signature source data from a wide range of geophysical diagnostic equipment; recorded data from the test series is now openly available to the broader seismoacoustic community. This manuscript details the executed test series, deployed seismoacoustic networks, and summarizes major scientific achievements utilizing recorded signatures from the explosive tests.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Meteorological, reference evapotranspiration and estimated transpiration data, July 2020-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling

Abstract. Organic matter (OM) composition plays a central role in microbial respiration of dissolved organic matter and subsequent biogeochemical reactions. Here, a direct connection of organic matter chemistry and thermodynamics to reactive transport simulators has been achieved through the newly developed Lambda-PFLOTRAN workflow tool that succinctly incorporates carbon chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate organic matter degradation and the resulting biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow, executed through a Jupyter notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow and transport code PFLOTRAN. The workflow consists of the following five steps: configuration, thermodynamic (lambda) analysis, sensitivity analysis, parameter estimation, and simulation output and visualization. Two test cases are provided to demonstrate the functionality of the Lambda-PFLOTRAN workflow. The first test case uses laboratory incubation data of temporal oxygen depletion to fit lambda parameters (i.e., maximum utilization rate and microbial carrying capacity). A slightly more complex second test case fits multiple lambda formulation and soil organic matter release parameters to temporal greenhouse gas generation measured during a soil incubation. Overall, the Lambda-PFLOTRAN workflow facilitates upscaling by using molecular-scale characterization to inform biogeochemical processes occurring at larger scales.

58 GEOSCIENCES↗
DOE OSTI2023

BeyondPlanck: VII. Bayesian estimation of gain and absolute calibration for cosmic microwave background experiments

We present a Bayesian calibration algorithm for cosmic microwave background (CMB) observations as implemented within the global end-to-end BEYONDPLANCK framework and applied to the Planck Low Frequency Instrument (LFI) data. Following the most recent Planck analysis, we decomposed the full time-dependent gain into a sum of three nearly orthogonal components: one absolute calibration term, common to all detectors, one time-independent term that can vary between detectors, and one time-dependent component that was allowed to vary between one-hour pointing periods. Each term was then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components were sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole-polarization quadrupole degeneracy and processing masks. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of -0.67% (-0.84%) for 30 GHz, 0.12% (-0.04%) for 44 GHz and -0.03% (-0.64%) for 70 GHz, compared to Planck PR4 and Planck 2018, respectively. We note that the BEYONDPLANCK calibration was performed globally, which results in better inter-frequency consistency than previous estimates. Additionally, WMAP observations were used actively in the BEYONDPLANCK analysis, which both breaks internal degeneracies in the Planck data set and results in an overall better agreement with WMAP. Finally, we used a Wiener filtering approach to smoothing the gain estimates. We show that this method avoids artifacts in the correlated noise maps as a result of oversmoothing the gain solution, which is difficult to avoid with methods like boxcar smoothing, as Wiener filtering by construction maintains a balance between data fidelity and prior knowledge. Although our presentation and algorithm are currently oriented toward LFI processing, the general procedure is fully generalizable to other experiments, as long as the Solar dipole signal is available to be used for calibration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Benchmarking Utility-Scale PV Operational Expenses and Project Lifetimes: Results from a Survey of U.S. Solar Industry Professionals

This paper draws on a survey of solar industry professionals and other sources to clarify trends in the expected useful life and operational expenditure (OpEx) of utility-scale photovoltaic (PV) plants in the United States. Solar project developers, sponsors, long-term owners, and consultants have increased project-life assumptions over time, from an average of ~21.5 years in 2007 to ~32.5 years in 2019. Current assumptions range from 25 years to more than 35 years depending on the organization; 17 out of 19 organizations surveyed or reviewed use 30 years or more. Levelized, lifetime OpEx estimates have declined from an average of ~$\$$35/kW DC -yr for projects built in 2007 to an average of ~$\$$17/kW DC -yr in 2019. Across 13 sources, the range in average lifetime OpEx for projects built in 2019 is broad, from $\$$13 to $\$$25/kW DC -yr. Operations and maintenance (O&M) costs—one component of OpEx—have declined precipitously in recent years, to $\$$5-8/kW DC -yr in many cases. Property taxes and land lease costs are highly variable across sites, but on average are—together—of similar magnitude. Other OpEx line items include security, insurance, and asset management. Given 2007-2009 values for not only project life and OpEx but also other drivers of the levelized cost of energy (LCOE, excluding the investment tax credit), the LCOE for utility-scale PV projects built from 2007 through 2009 averaged $\$$305 /MWh. Using 2019 values for all parameters yields an average LCOE of $\$$51 /MWh. The decline in LCOE from $\$$305 /MWh to $\$$51 /MWh was predominantly caused by reductions in up-front expenditures (and, to a much lesser extent, by changes in capacity factors, financing costs, and tax rates), but 9% ($\$$22 /MWh) of the overall decline is due to improvements in project life and OpEx. Project life extensions and OpEx reductions have had similarly sized impacts on LCOE over this period, at $\$$11 /MWh each. Had project life and OpEx not improved over the last decade, LCOE in 2019 would have instead been $\$$73 /MWh—43% higher. Given the limited quantity and comparability of previously available data on these cost drivers, the data and trends presented here may inform assumptions used by electric system planners, modelers, and analysts. The results may also provide useful benchmarks to the solar industry, helping developers and assets owners compare their expectations for project life and OpEx with those of their peers.

14 SOLAR ENERGY↗
DOE OSTI2022

Recent Improvements in the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) has significantly evolved since the first release of the point source database in 1993. The NSRDB has been widely used by the solar energy industry to provide long-term time-series solar resource data for various project phases. The NSRDB represents the state of the art in the satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This poster provides an overview of (1) the improved spatiotemporal resolution; (2) on-demand services and their applications; (3) future Improvements, such as a new direct normal irradiance model and new methods to gap-fill missing data using physics-guided machine learning; (4) data quality; and (5) data dissemination.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗
DOE OSTI2024

Getting allometry right at the Oak Ridge free‐air CO 2 enrichment experiment: Old problems and new opportunities for global change experiments

Societal Impact Statement Free‐air CO 2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO 2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments. Summary Long‐term field experiments to elucidate forest responses to rising atmospheric CO 2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO 2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO 2 . At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Forecasting distributed energy resources adoption for power systems

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity - the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Harmonising the land-use flux estimates of global models and national inventories for 2000–2020

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr —1 ) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr —1 for the period 2000–2020, and NGHGIs, which estimate a sink of —1.9 GtCO 2 yr —1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr —1 ), with differences also for deforestation (2.4 GtCO 2 yr —1 ), for fluxes from other land uses (1.0 GtCO 2 yr —1 ) and to a lesser extent for fluxes from organic soils (0.2 GtCO 2 yr —1 ). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (—6.4 GtCO 2 yr —1 ) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr —1 ) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic

The COVID-19 pandemic highlighted the importance of human behavior in mitigating the spread of disease. Nonetheless, human behavior is often overlooked in models of disease spread, particularly by underutilizing real-world data. We address this by estimating probabilities that individuals engage in behaviors that influence SARS-CoV-2 transmission risk during the COVID-19 pandemic, between September 2020 and June 2022. These behaviors include wearing a mask, using public transportation, spending time with others, avoiding contact with others, and going to work. Our estimates account for the age and sex of individuals and are generated for every county in the United States. We utilized multiple open-source datasets and United States Census data to produce these estimates. Multiple datasets were used for validation, showing our estimates demonstrated comparable accuracy and robustness. Our estimates aid in understanding human behavior dynamics during the COVID-19 pandemic and could be used to inform monthly or longer-term behavior in simulations of COVID-19. Moreover, the methods presented can be applied to other behaviors and features for future simulations of infectious disease.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Structural, Criticality, and Radiation Dose Calculations to support SNF Loading into a DOE Standard Canister

The DOE Standard Canister Demonstration Project includes the development of an internal support structure (ISS) for the 4.6-m long and 45.7-cm diameter canister. This study presents structural evaluations, criticality safety assessments, and dose rate calculations conducted within the scope of the ISS design process to support smooth canister loading operations and to ensure safe storage, transportation, and disposal of Peach Bottom 1 Core II (PB2) and Fort St. Vrain (FSV) SNF, currently stored at the CPP-603 facility of the INL. The ISS includes a 316L stainless steel basket that holds twelve PB2 SNF rods. Further, six individual, 78.7-cm long, 316L stainless steel columns are equally spaced and welded to the inner canister wall at the lower end of the shell. These columns represent the FSV basket and can hold one FSV SNF element. An A92014 T6 aluminum spacer disc is bolted to the bottom plate of the PB2 basket to vertically restrain the FSV SNF element after the PB2 basket is placed inside the canister on top of the FSV basket. The structural evaluations of the ISS followed applicable ASME BPVC.III.3 guidelines and included finite element (FE) analyses of the PB2 basket structure; analyses of welds and bolds; buckling analyses of selected components, and acceptability assessments of the expected basket deformations under loading operations. The criticality safety assessments used the Monte Carlo N-Particle (MCNP) software architecture version 6.2 including ENDF/B-V continuous energy cross-section libraries, considering intact SNF in a single storage overpack or two multi-storage overpack configurations, and intact or failed SNF configured for disposal. The dose rate computations are based on source terms taken from the DOE Spent Fuel Database. The isotopic composition was decay corrected for the year 2022 using the ORIGEN module in the SCALE suite. A 19-group photon spectrum and 27-group neutron-source spectra were generated and used in MCNP to calculate estimated dose-equivalent rates, both on DOE Standard Canister contact and at a radial distance of from the canister surface. The results of this study indicate a structurally sound system that can uphold its criticality safety functions throughout its intended operational phases. Further, they increase confidence that sufficient radiological protection is technically achievable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗