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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗
DOE OSTI2025

Metal concentration and leaf spatial distribution assessed by synchrotron µXRF in Brazilian nickel hyperaccumulators

Background and Aims: The limited number of known hyperaccumulator species in tropical regions, including Brazil, has hampered the development of nickel (Ni) agromining. In addition, the mechanisms underlying metal accumulation and distribution in these species remain poorly understood, despite their pivotal role in identifying species with economic potential. Furthermore, this study assessed the accumulation potential and foliar spatial distribution of metals in three Brazilian hyperaccumulator species (Pfaffia sarcophylla, Justicia lanstyakii, and Lippia lupulina) growing in one of the world’s largest mafic–ultramafic complexes. Methods Concentrations of Ni and other metals in leaves were determined using portable X-ray fluorescence (pXRF) and inductively coupled plasma optical emission spectroscopy (ICP-OES). Synchrotron-based X-ray microfluorescence (SR-µXRF) was employed to map the spatial distribution of metals across intact leaves. Results: The results revealed leaf Ni concentrations ranging from 320 to 1,950 mg kg⁻ 1 in P. sarcophylla, 1,640 to 6,810 mg kg⁻ 1 in L. lupulina, and 1,990 to 4,900 mg kg⁻ 1 in J. lanstyakii. Nickel, Mn, and Co exhibited similar distribution patterns, concentrating mainly in the leaf margins and veins across all species, while Ca, K, and Mg co-localization suggests regulatory mechanisms for adaptation. Conclusion: Although these plants exhibited Ni hyperaccumulation, our findings suggest limited commercial potential for agromining. Further research on genetic variability and nutrient assimilation, particularly in P. sarcophylla, is recommended to elucidate the mechanisms underlying Ni accumulation and intraspecific variability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

COVID-19 Data Curation Effort: An Initial Analysis of the Data

During the COVID-19 pandemic of 2020, major case reporting outlets quickly coalesced around two or three primary vendors. Johns Hopkins University and The New York Times were among the more prominent, and all were of great value to the nation, particularly during the uncertain early stages of the pandemic. They primarily focused on three major attributes: number of new cases, deaths, and recovery. Recognizing that many states were reporting very detailed data sets (e.g., hospital beds) at a county level or finer, the ORNL Pandemic Modeling team embarked on a major data curation effort from March to June 2020 for the purpose of capturing this wealth of detailed data. The challenge of curating this data was daunting. The number of attributes reported by the states grew on almost on a weekly basis. States were routinely shifting their web tool strategies away from easily parsable HTML-based formatting to new Tableau and ArcGIS content. This growth in the sheer number of attributes, combined with the unpredictable shifts in data format, meant an aggressive and agile combination of automated scripting and manual scraping was required to capture new daily streams. Further, the team had to scale up staff and widen its approach for capture and storage. As a result, the team collected more than 11 million data points. Following the close of this data collection effort on June 30 th , 2020, the team embarked on a major effort to appraise what had been collected, including an inventory list, spatial completeness, temporal completeness, scale and geographic characteristics, and a determination. A report on this matter was submitted on September 15 th , 2020, titled “DOE COVID-19 Data Curation Effort: Overview of Data Collection Coverage”. Over 2000 unique attributes had been netted over a wide range of spatial scales, including state, county, zip codes, health regions, and census blocks. Over 11 million individual data points were collected across these attributes, and spatial coverage (in total) included all 50 states and multiple territories. What became apparent in the process is that in the absence of any data standards, many states reported a wide variety of unique attributes that were not always compatible with attributes reported in other states. As time continued, states began adding new attributes and offering finer grain detail in some older attributes. This meant that not all data streams existed for the entire time period; in fact, the number tended to increase dramatically towards the end. Often, states would begin an attribute series and then stop altogether. These highly variable and uncertain conditions illuminated the need for harmonization approaches that would reconcile and conflate changing attribute names and detail over time. For example, grouping racial data reported as either Black or African American, depending on the state, into a single harmonized attribute. These choices would make a within-state analysis possible during the time period and lead to potential between-state analytics later on. This was almost entirely a manual decision process, requiring some subjective decision-making at times, to prevent a fragmented, short-lived collection of time series fragments that would offer few insights into trends, patterns, and correlates. This report imports harmonized data for state and county into the World Spatio-Temporal Analytics and Mapping Project (WSTAMP). WSTAMP is a major space-time analysis and visualization tool developed at ORNL for the National Geospatial-Intelligence Agency specifically for this kind of exploratory analysis. WSTAMP offers a rich analytical and graphical environment consisting of a wide range of analytics. These include time series plots, statistical summaries, data mining techniques, trend and pattern detection, and hypothesis generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River basin

Wildfires impact vegetation mortality and productivity and are increasing in intensity, frequency, and spatial area in the western United States. The rates of vegetation recovery after fires play a major role in the reestablishment of biomass and ecosystem functioning (e.g., structure, resilience, and productivity), but such recovery rates are poorly understood. Here we use remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify the resistance and resilience of leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET) to 138 wildfires of various burn severity across the Columbia River basin (CRB) of the Pacific Northwest in 2015. Increasing burn severity caused lower resistance and resilience for all three variables. Resistance and resilience are highest in grasslands, intermediate in savanna, and lowest in needleleaf evergreen forests, consistent with the adaptation of these vegetation types to fire. LAI has consistently lower resistance and resilience than GPP and ET, which is consistent with physical and physiological mechanisms that compensate for reduced LAI. Resilience is influenced by precipitation, vapor pressure deficit (VPD), and burn severity across all three vegetation types; however, burn severity plays a more minor role in grasslands. Increasing wildfire severity will reduce the resistance and resilience and lengthen the recovery time of vegetation structure and fluxes with climate change, with significant consequences for the provision of ecosystem functioning and implications for model predictions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Robust late twenty-first century shift in the regional monsoons in RegCM-CORDEX simulations

Abstract We use an unprecedented ensemble of regional climate model (RCM) projections over seven regional CORDEX domains to provide, for the first time, an RCM-based global view of monsoon changes at various levels of increased greenhouse gas (GHG) forcing. All regional simulations are conducted using RegCM4 at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three General Circulation Models (GCMs), which are part of the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). Each simulation covers the period from 1970 through 2100 under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Regional climate simulations exhibit high fidelity in capturing key characteristics of precipitation and atmospheric dynamics across monsoon regions in the historical period. In the future period, regional monsoons exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length at higher levels of radiative forcing. All regions with substantial delays in the monsoon onset exhibit a decrease in pre-monsoon precipitation, indicating a strong connection between pre-monsoon drying and a shift in the monsoon onset. The weakening of latent heat driven atmospheric warming during the pre-monsoon period delays the overturning of atmospheric subsidence in the monsoon regions, which defers their transitioning into deep convective states. Monsoon changes under the RCP2.6 scenario are mostly within the baseline variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hydropower potential derived from streamflow extremes for Alaska, USA

Alaska is an expansive region known for its abundant natural resources, including thousands of miles of streams and rivers. These rivers represent potential opportunities for future hydropower development that could provide reliable energy supply for local communities. There is limited long-term high temporal resolution streamflow data available for the region, making data-driven estimates of potential hydropower and its variability across the state challenging. This study provides a novel data-driven approach for hydropower capacity estimation across Alaska. We use supervised machine learning to develop a relationship between the daily and peak flow duration curves in order to augment the size of our dataset from 44 sites to 67 sites. We perform a stochastic hydropower estimation across the 67 sites and identify approximately 1000 MW of total potential hydropower capacity distributed across these sites. Our study provides the first step towards more comprehensive hydropower estimation for this critical region, highlighting the need for future work integrating high-resolution spatial data, community needs, and economic constraints in estimates of potential hydropower development in Alaska.

Hydropower↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2026

Correlation-aware binning for small-angle neutron scattering via Gaussian-process inference

Binning in small-angle neutron scattering (SANS) is typically performed empirically, with fixed parameters chosen for convenience rather than statistical optimality. Such practices often fail to balance statistical precision and spatial resolution, leading to inconsistencies across instruments and datasets. Here we establish a correlation-aware framework that determines the optimal bin width from first principles by extending the classical Freedman–Diaconis (FD) rule to account for inter-bin correlations with a Gaussian process. In this formulation, the scattering intensity is treated as a smooth stochastic field whose statistical coherence is described by a covariance matrix. Analytical expressions of errors derived from this model yield closed-form criteria that separate the total deviation into contributions from counting noise, aliasing distortion and curvature-dependent correlation effects. Expressed in reduced variables, the resulting dimensionless error surface reveals a continuous transition from the uncorrelated FD regime to the correlation-dominated limit, providing a unified description of noise suppression and resolution control. Because the formulation depends only on the profile characteristics of scattering intensity I(Q), specifically its average intensity and first- and second-order derivatives, it applies generally to any SANS measurement regardless of sample, instrument or geometry. Experimental validation using small- and ultra-small-angle neutron scattering data confirms the predicted scaling behavior, demonstrating that correlation-aware inference systematically reduces mean-squared error and enables information-efficient reproducible data reduction across materials and instruments.

Tung, Chi-Huan [ORNL] (ORCID:0000000221972074)↗
DOE OSTI2025

Facets of hydro power and future trends in a Nordic Context

Hydropower technologies bolster high penetration of variable renewable energies (VREs) in the net zero emissions scenarios. Nevertheless, there are various challenges to meeting the ambitious goal, such as stability, reliability, resiliency, security, lack of reactive power, voltage support and inertia, large-scale storage deployment and coordination, interconnectedness, demand-side response, higher thermal cycles with increased start/stops, and inadequate Levelized Cost of Energy (LCOE) for system-wise VRE integration and profitability. This survey conducts a bottom-up analysis to unveil the opportunities to utilize hydropower facilities and disentangle the nested problem for intertwining design features, control algorithms, operation, optimization approaches, incentives, services, and market mechanisms using a three-pillar framework perspective: grid owners, power producers, and machine designers. The survey identified emerging trends in real-time and capacity markets, flexible power systems, and enhanced grid capabilities, including advanced voltage support and updated grid codes. These developments present significant opportunities for hydropower, such as achieving super-flexibility through hybridization, expanded reactive power capabilities, and advanced operational modes like a synchronous condenser and power adequator functionalities. These opportunities require novel design philosophies — including new winding, stator, and rotor configurations, optimized ventilation, and active cooling systems — to enhance performance under stressed grid and climate conditions. Finally, integrating climate and energy models for multi-basin optimization with finer spatial and temporal granularity enhances the planning accuracy for water management of hydropower while addressing environmental challenges. The review delivers helpful prospective suggestions and tools that would serve researchers, power engineers, and stakeholders in making decisions about hydropower technologies and services in 2050 and beyond.

13 HYDRO ENERGY↗
DOE OSTI2025

Spatially varying seasonal modulation to tidal stream energy potential due to mixed tidal regimes in the Aleutian Islands, AK

We provide an assessment of the tidal stream energy resource of the Aleutian Islands, Alaska via a validated barotropic tidal numerical model of the region. Eight island passes are identified as energy “hotspots”. The annual mean kinetic energy fluxes, KEF , calculated at each pass vary from 1000 to 11,000 MW, while the annual available energy, AAE , varies from 5 to 42 MWh m −2 . Notable seasonal modulation to monthly power density averages and ranges are noted at some passes and not others. Seasonal adjustment is linked to the semi-annual solar declination cycle which enhances (dampens) diurnal (D 1 ) tidal amplitudes in summer/winter (spring/fall) as well as the time-varying phase lag between D 1 and semidiurnal (D 2 ) fortnightly tidal cycles. Annual variability in monthly mean power density scales with the tidal current form factor, F u , with the largest seasonal change occurring for F u > 1 (D 1 dominated tide). The spread in power density over a month is on average smaller for passes with mixed tides (F u = 1 ) than those with D 1 or D 2 dominance, as changes to fortnightly phase lag become influential to net power density ranges when tides are mixed. This study outlines overlooked, but relevant, long-term modulation to tidal streams in regions with mixed tides.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Size dependence in chord characteristics from simulated and observed continental shallow cumulus

Abstract. In this study we compare long-term Doppler and Raman lidar observations against a full month of large eddy simulations of continental shallow cumulus clouds. The goal is to evaluate if the simulations can reproduce the mean observed vertical velocity and moisture structure of cumulus clouds and their associated subcloud circulations, as well as to establish if these properties depend on the size of the cloud. We propose methods to compare continuous chords of cloud detected from Doppler and Raman lidars with equivalent chords derived from 1D and 3D model output. While the individual chords are highly variable, composites of thousands of observed and millions of simulated chords contain a clear signal. We find that the simulations underestimate cloud size and fraction but successfully reproduce the observed structure of vertical velocity and moisture perturbations. There is a clear scaling of vertical velocity and moisture anomalies below the chords with chord size, but the moisture anomalies are only 1 %–2 % higher than the horizontal mean values. The differences between the observations and simulations are smaller than the difference in sampling the modeled chords in time or space. The shape of the vertical velocity and moisture anomalies from cloud chords sampled spatially from 3D model snapshots is almost perfectly symmetric. In contrast, the chords sampled temporally from the lidar observations and 1D model output have a marked asymmetry, with stronger updrafts and higher moisture anomalies occurring earlier on.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Direct numerical simulation of hypersonic turbulent boundary layers: effect of spatial evolution and Reynolds number

Direct numerical simulations (DNS) are performed to investigate the spatial evolution of flat-plate zero-pressure-gradient turbulent boundary layers over long streamwise domains ( >300δ i , with δ i the inflow boundary-layer thickness) at three different Mach numbers, 2.5, 4.9 and 10.9, with the surface temperatures ranging from quasiadiabatic to highly cooled conditions. The settlement of turbulence statistics into a fully developed equilibrium state of the turbulent boundary layer has been carefully monitored, either based on the satisfaction of the von Kármán integral equation or by comparing runs with different inflow turbulence generation techniques. The generated DNS database is used to characterize the streamwise evolution of multiple important variables in the high-Mach-number, cold-wall regime, including the skin friction, the Reynolds analogy factor, the shape factor, the Reynolds stresses, and the fluctuating wall quantities. The data confirm the validity of many classic and newer compressibility transformations at moderately high Reynolds numbers (up to friction Reynolds number Re τ ≈1200 ) and show that, with proper scaling, the sizes of the near-wall streaks and superstructures are insensitive to the Mach number and wall cooling conditions. The strong wall cooling in the hypersonic cold-wall case is found to cause a significant increase in the size of the near-wall turbulence eddies (relative to the boundary-layer thickness), which leads to a reduced-scale separation between the large and small turbulence scales, and in turn to a lack of an outer peak in the spanwise spectra of the streamwise velocity in the logarithmic region.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

The surface of the Great Lakes interacts with the atmosphere, influencing the weather and climate over the Great Lakes Region (GLR). However, most climate models were not designed with sufficient emphasis on lake–atmosphere interactions, which could potentially cause model biases over the GLR. To understand how lake surface temperature (LST) affects the regional summer climate over the GLR, we conducted twin experiments using the Weather Research and Forecasting model at a spatial resolution of 4 km using two different LST data sets as the bottom boundary condition over the Great Lakes. In this work, our simulations include 10 ensemble members for the summer of 2018 and a single multiyear run for the summers of 2014–2020. Results show that variations in LST influence atmospheric temperature and moisture at a local scale—while affecting the convective environment and precipitation processes over a much larger spatial scale. In particular, an LST that is only 1°C–3°C warmer (depending on the lake) increases near-surface air temperature by 1.93°C and 0.97°C over Lake Superior and Lake Erie, respectively, and increases evaporation over the lakes by 0.23 and 1.1 mm day –1 . The warmer LST reduces mesoscale convective precipitation upstream of the GLR; however, it increases isolated deep convective precipitation and nonconvective precipitation downstream of GLR due to increased local instability and enhancement of moisture transport. Our analyses confirm the robustness of these impacts, which is at least 2 times larger than the model internal variability and is seen across all simulated summer seasons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SDOM (Storage Deployment Optimization Model) [SWR-21-73]

SDOM is designed to accurately represent the operation of energy storage across different timescales, including long-duration and seasonal applications, and the spatiotemporal diversity and complementarity among VRE sources. SDOM uses an hourly temporal resolution, a fine spatial resolution for VRE sources, and a 1-year optimization window. SDOM assumes that all builds of VRE are accompanied by sufficient additional transmission capacity to allow full utilization of these additional resources. Nuclear, hydropower, and other renewable generation (e.g., biomass and geothermal energy sources) are fixed based on operational data (time series) for a given year; thus, SDOM minimizes total system cost using conventional generators as balancing units and using VRE and storage technologies to achieve a user-defined carbon-free or renewable energy target. The total system cost includes capital costs, fixed operation-and-maintenance (FO&M) costs, variable operation-and-maintenance (VO&M) costs, and fuel cost for power generation and storage technologies.

Guerra Fernandez, Omar Jose↗
DOE OSTI2022

Advantages of a variable‐resolution global climate model in reproducing the seasonal evolution of East Asian summer monsoon

Abstract The East Asian summer monsoon (EASM) is unique among monsoon systems that it features meridional evolution of the summer monsoon. In this study, we evaluate the performances of a Variable‐Resolution Community Earth System Model (VR‐CESM) regionally refined over eastern China (14 km) in reproducing the seasonal evolution of EASM precipitation over China. Compared with reference datasets, VR‐CESM shows better performance than the corresponding globally uniform coarse‐resolution model CESM (quasi‐uniform 1°), especially over western China where complex local topography exists. The northward monsoon migration is closely related to low‐level southerly flows and vertical moisture advection, which are more reasonably simulated in VR‐CESM. The four critical timings of the EASM (monsoon onset, withdrawal, peak, and duration) are also better captured in VR‐CESM than in CESM. The corresponding spatial Pearson correlation coefficients of the four critical timings with respect to reference datasets are about 0.1 higher in VR‐CESM than those in CESM. Both models are most accurate in simulating monsoon onset and least accurate at simulating the monsoon peak. The overestimated zonal thermal contrast in CESM is responsible for the earlier monsoon onset and excessive precipitation in September over the Yangtze River valley. Finer resolution in VR‐CESM, especially over the Tibetan Plateau (TP), appears to be a main factor in simulating better zonal thermal contrast and seasonal evolution of the EASM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

FilDReaMS: II. Application to the analysis of the relative orientations between filaments and the magnetic field in four Herschel fields

Context. Both simulations and observations of the interstellar medium show that the study of the relative orientations between filamentary structures and the magnetic field can bring new insight into the role played by magnetic fields in the formation and evolution of filaments and in the process of star formation. Aims. We provide a first application of FilDReaMS, the new method presented in the companion paper to detect and analyze filaments in a given image. The method relies on a template that has the shape of a rectangular bar with variable width. Our goal is to investigate the relative orientations between the detected filaments and the magnetic field. Methods. We apply FilDReaMS to a small sample of four Herschel fields (G210, G300, G82, G202) characterized by different Galactic environments and different evolutionary stages. First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred from Planck polarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H2 column density. Results. We find preferential relative orientations in the four Herschel fields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H 2 column density ≃ 1.1 × 10 21 cm -2 and 1.4 × 10 21 cm -2 , respectively, consistent with the results of previous studies. Conclusions. Our results confirm the existence of a coupling between magnetic fields at cloud scales and filaments at smaller scale. They also illustrate the potential of combining Herschel and Planck observations, and they call for further statistical analyses with our dedicated method.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Multi-Mechanism Flood Hazard Assessment: Critical Review of Current Practice and Approaches

This report documents the initial findings from the Nuclear Regulatory commission (NRC)-sponsored research project Methods for Estimating Joint Probabilities of Coincident and Correlated Flooding Mechanisms for Nuclear Power Plant Flood Hazard Assessments.1 This research project is a part of NRC’s Probabilistic Flood Hazard Assessment (PFHA) Research Program and will aid the development of guidance on the use of PFHA methods to evaluate infrastructure safety for existing and proposed US nuclear power plants (NPPs). More specifically, this project intends to provide technical background for the development of flood hazard curves for multi-mechanism floods (MMFs). MMFs are flood events caused by more than one flooding mechanism (e.g., flood events due to the simultaneous occurrence of precipitation-induced river flooding and storm surge). Project activities include three main tasks: Task 1—Survey of current concepts and methods in assessing MMF hazards; Task 2—Critical assessment of selected methods and approaches for quantifying probabilistic MMF hazard risk; Task 3—Development of example case studies to illustrate best practices for quantifying probabilistic MMF hazard risk The initial findings from Tasks 1 and 2 are documented in this report. Task 1 comprised a survey of approaches and methods that have been applied to understand and assess flood hazards due to MMFs. Task 2 involved a critical review of the selected approaches and methods. To that end, the scope of this report includes documentation of (1) a reconnaissance-level survey of the current state of concepts and practice for MMF hazard assessment; (2) a generalized MMF assessment framework to address the distinctions among various types of flood-forcing phenomena, flood mechanisms (grouped into three mechanism types), and flood severity metrics; (3) a wide-ranging survey of approaches and methods that have been applied to various flooding phenomena and settings; and (4) a critical assessment of MMF hazard assessment methods. Studies were identified involving MMFs related to coastal flooding mechanisms, fluvial (rivers/streams) flooding mechanisms, and associated combinations of coastal and fluvial flooding mechanisms. Studies were also identified that address MMFs involving coastal and fluvial flooding mechanisms as well as coastal flooding mechanisms along with extreme precipitation (without specific attribution to fluvial or pluvial mechanisms). The studies identified for review in this report included assessments at varying spatial scales (from local to global) with differing geographic regions of focus using both observed and synthetic data. The majority of studies identified and reviewed were site-specific assessments focusing on relatively short return periods. Studies considered a range of flood severity metrics, made differing assumptions regarding the occurrence of extrema, and used multiple statistical techniques; the use of copulas for the development of joint distributions was a particularly popular analysis technique. The literature review highlighted the differences among existing studies relative to terminology used, means of presenting results, framework and techniques employed, and level of sophistication regarding the number and types of variables considered. Despite the significant diversity in existing studies, the review identified several promising techniques that will be considered in future work under this project, including the development of joint distributions for MMFs using copula and Bayesian-motivated approaches.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO 2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr −1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr −1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10 3 ± 0.9×10 3 km 3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Nelson, Jacob A.↗