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DOE OSTI2020

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.

Whiteley, J. S.↗

DOE OSTI2020

A redox-active organic cation for safer metallic lithium-based batteries

Safety concerns have severely impeded the practical application of high-energy-density lithium-based batteries. Dendrite growth and overcharging can lead to particularly catastrophic thermal failure. Here we report an organic cation, trisaminocyclopropenium (TAC), as a bi-functional electrolyte additive to suppress dendrite growth and offer reversible overcharge protection for metallic lithium-based batteries. During the Li plating process, TAC cations with aliphatic chains can form a positively charged electrostatic shield around Li protrusions, repelling the approaching Li + and thereby attaining a more uniform plating. A two times longer cycle life of 300 h at 1 mA cm -2 is achieved in a Li|Li symmetric cell in comparison with the control. During the overcharging process, the redox-active TAC can repeatedly shuttle between two electrodes, maintaining the cell voltage within a safe value. A solid protection of 117 cycles (~1640 h) at 0.2 C with a 100% overcharge is achieved in a LiFePO 4 /Li 4 Ti 5 O 12 cell. This study sheds fresh light on the ability of organic cations to build safer batteries.

25 ENERGY STORAGE↗

DOE OSTI2021

DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or iterative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power afforded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. Here, we verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from continuous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases to relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

The evolution of seafloor venting from hydrate-sealed gas reservoirs

Here we use 3D seismic data to show that three rows of seafloor gas mounds can be traced downward to leak points that lie at the hydrate-gas contact within three individual dipping coarse-grained sand bodies in the Terrebonne Basin, Walker Ridge block 313, northern Gulf of Mexico. We predict the overpressure within the sand bodies by assuming that the gas pressure at the vent points equal the least principal stress. We interpret that free gas accumulates at the base of the hydrate stability zone, trapped by the overlying sand which has a high methane hydrate saturation. The free gas accumulates until the gas pressure at the base of the hydrate stability zone reaches the least principal stress in the overlying mudrock, whereupon hydraulic fractures form and fluids are vented to the surface. The warm rising fluids and perhaps localized exothermic formation of hydrate raise the local salinity and temperature. This process progressively shifts the base of the hydrate stability zone to shallower depths and dissociates the hydrate seal within the sand, which creates new leak points and results in the observed migration of the seafloor vents. Within the southwest corner of the Terrebonne Basin, this process has repeated multiple times within the Blue, Orange, and Green sands. Here this study shows how the hydrate stability zone can influence the location of fluid expulsion and in turn be affected by the warm, rising, saline fluids sealed by hydrate.

58 GEOSCIENCES↗

DOE OSTI2026

Cave-based chronology of valley incision in the glaciated Northern Carpathians

While glaciers leave a mark on mountain landscapes, the importance of glacial erosion in mountains at a global scale remains insufficiently constrained despite over a century of research aimed at quantifying the rates and magnitudes of such erosion. Measuring rates of landscape change across the Quaternary in repeatedly glaciated mountain landscapes requires the ability to accurately partition the relative role of glacial versus fluvial processes in forming relief; however, the modification of landforms by surface erosion hinders quantification of process rates over the last few million years. This study presents 32 paired 26 Al and 10 Be cosmogenic nuclide burial ages from allochthonous, quartz-bearing sediment deposited within a paleophreatic karst system in the northern Carpathians. These data quantify Pliocene-to-present timing of valley incision and karst system reactivation, as well as erosion rates over time. Post-burial, muon production-correction yields 24 fully resolved ages, six minimum ages, and two unresolvable ages. Burial ages are interpreted to document the tempo of valley incision via phreatic passage formation and reveal that ∼80% of current valley relief (∼700 m) had developed by approximately 1.6 Ma. Paleoerosion rates derived from this dataset suggest little change in the processes generating sediment washed into the karst networks since the Pliocene. These results suggest that river incision dominated relief creation in Carpathian valleys that bear the imprint of glaciation. Our findings suggest the importance of investigating other mid-latitude mountainous regions worldwide to determine if they have similar erosion and topographic histories.

Carpathians↗

DOE OSTI2025

Networked Microgrid Topology Reconfiguration to Promote Fairness in Proactive Load Shedding

Increasing occurrences of natural disasters and grid emergency events consistently challenge the safe and reliable operations of power systems. During such emergency situations, system operators may proactively shed load to mitigate risks. However, uncoordinated implementation of load shedding may disrupt electricity supply and even lead to cascading failures. Meanwhile, it is crucial to address potential biases affecting different customers when executing load shedding. This paper addresses the dynamic topology reconfiguration problem for networked microgrids with distributed energy resources under emergency conditions. Specifically, we propose a novel rolling-horizon optimization model that integrates fairness-aware constraints into the networked microgrid topology reconfiguration. Unlike existing approaches that focus solely on efficiency or apply fairness considerations in static settings, our method explicitly incorporates temporal fairness constraints to restrict repeated or excessive load curtailment for load blocks. Moreover, the fairness-aware constraints are specifically developed for the context of dynamic networked microgrid topology reconfiguration, and are designed to be convex or amenable to linear reformulations, which offers a more tractable alternative to traditional models with non-convex formulations. Numerical studies on a modified IEEE 13-bus system and a larger-sized SMART-DS networked microgrid system demonstrate the performance of the proposed algorithm towards more fairness-aware networked microgrid topology reconfiguration decision-making.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Thermal energy storage composites with preformed expanded graphite matrix and paraffin wax for long-term cycling stability and tailored thermal properties

Harvesting solar energy, preventing hot spots in electronics, transport of temperature-sensitive materials, and capture and repurposing of thermal energy require a latent heat thermal energy storage (TES) system to store/discharge heat repeatedly. For the practical application of phase change material (PCM) composites within TES systems, reliable thermal performance throughout its operational lifetime is essential. Nevertheless, the reliability of thermal conductivity in multi-phase composites over relevant numbers (>10 3 ) of melt/freeze cycles has barely been studied, particularly for composites containing fillers for thermal conductivity enhancement. Here, we introduce a preform-type expanded graphite (EG)/paraffin wax composite possessing highly robust heat transfer and storage properties even after 10,000 melt/freeze cycles. To achieve such excellent reliability, comparative studies on the combined influence of fabrication process, particle size, EG vol%, binder amount, and compaction on both magnitude and robustness of thermal conductivity were undertaken. Our parametric study has yielded a trade-off between thermal conductivity and latent heat. Based on our modeling, 20 vol% EG approaches the case where all EG particles are well-connected thermally while 10 vol% EG is close to loosely connected fillers in the matrix. Thermal conductivity of our paraffin composites containing 20 vol% EG (25.1 W·m -1 ·K -1 ) is highest among other EG/paraffin composites without aligned EG in the literature. After 10,000 thermal cycling, negligible conductivity fading was observed for the 10 vol% EG composite, while reduction in latent heat remained within 10% for all 10, 14, 17 and 20 vol% EG samples. Here we anticipate this work provides insight on suitable recipe for desirable magnitude and robustness of thermal conductivity of EG/paraffin composites.

25 ENERGY STORAGE↗

DOE OSTI2023

Thermal energy storage in concrete utilizing a thermosiphon heat exchanger

The performance of a lab-scale concrete thermal energy storage (TES) module with a 2-kWh thermal capacity is evaluated at temperatures up to 400 °C. The TES module uses conventional normal weight concrete with thermal and mechanical properties that are tailored for use as a solid thermal energy storage media. A thermosiphon heat exchanger is used to provide rapid heat transfer into the concrete module, which is reinforced with a steel pipe outer shell. The charging and discharging cycles are examined to assess the thermal performance of the concrete and thermosiphon. Based on the results of testing, the thermosiphon is able to repeatably charge the concrete module from 300 to 400 °C over a 4-h period. After 2250 total hours of charge and discharge cycles, there was no observed delamination between the concrete and the thermosiphon or the steel jacket reinforcement. Non-destructive measurements of the concrete indicated no significant degradation of the solid storage media. The experimental results are then used to validate a numerical model of the concrete TES module for parametric study. Finally, varying insulation conditions were analyzed using this model to determine practical configurations for regular operation of the proposed concrete TES module.

25 ENERGY STORAGE↗

DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗

DOE OSTI2022

Memory forensic analysis of a programmable logic controller in industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLCs) are used to automate physical processes such as nuclear plants and power grid stations, and are often subject to cyber attacks. As in conventional IT domain, the memory analysis of the PLCs can help answer important forensic questions about the attack, such as the presence of malicious firmware, injection of modified control logic (the program running on the PLC), and manipulation of I/O devices (e.g., sensors and actuators). Unlike conventional IT domain, PLCs have heterogeneous hardware architecture, proprietary firmware and control software, making it challenging to employ a unified framework for their memory forensics. For merely extracting artifacts of forensic importance, reverse-engineering the firmware is a tedious task, and the effort needs to be repeated for every PLC model. As a community, a step-wise approach to tackle this challenge is to analyze the memory of specific PLCs, and subsequently find a generic framework applicable to all PLCs. Our work is a step forward in this direction. By following a methodology that focuses on the functional layer of PLCs instead of reverse engineering the firmware, we analyze the digital forensic artifacts available in a common PLC, Allen-Bradley ControlLogix 1756-L61. Before diving into the memory dump, we analyze the PLC control software to create a list of important artifacts that are sure to exist in the PLC memory dump. The approach employs a setup where PLC control software RSLogix-5000 is connected to the PLC, and the memory dump can be obtained as and when needed. We create test cases that sequentially highlight each category of artifacts, followed by an examination of the resultant impact on memory. After attaining the listed artifacts, we employ conventional string and known data searches to extract interesting information present in this PLC's memory. The memory analysis profile, presented as a Python library and shared with the community, can help a forensic investigator to readily extract forensic artifacts from the same model's controller. The adopted approach may help researchers in creating memory profile of other PLCs, and ultimately formulating a generic PLC memory analysis framework.

Rais, Muhammad Haris↗

DOE OSTI2022

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female 1 donor, and Component C) a 1:3 male 2 :female 2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Finally, our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Geomechanical and flow implications with continued bioconversion of coal to methane: Experiments and modeling

Microbial conversion of coal to methane is a promising technology during transition of coal-based energy from conventional coal mining to natural gas recovery from coal. Significant research advances have been made towards engineering ideal microbial communities and nutrients for bio-stimulation of coal. However, actual field applications require geomechanical and flow behavior characterization of coal during the bioconversion process as well as gas production over the life of the created “biogenic gas reservoirs”. This work presents the results of an experimental investigation to estimate gas/methane production using bioconversion of coal and analyses of variations in bulk modulus, strain and permeability. Bulk modulus of coal demonstrated time-dependent behavior with continued bioconversion of solid coal. The phenomenon was modeled using mass balancing in a closed environment and the logistic equation based biogenic gas production with time. The modeled gas production and changes in modulus of coal showed excellent agreement with the corresponding experimental results. Next, numerical simulation of biogenic conversion of coal in a constant stress in-situ condition, replicating the Huff ‘n Puff method under field conditions, was carried out. The results showed that bioconversion can lead to decrease in effective stress, increase in permeability and decrease in the modulus of coal with time. Repeated recharge of coal with nutrients and recovering the produced gas, that is, sequential cycles of Huff n’ Puff, showed further increase in permeability and decrease in coal strength, potentially leading to coal failure in-situ, further increasing the permeability, thus enhancing the prospect of field application of the technology.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Integrated geological, economic, and risk assessment of underground hydrogen storage

Underground hydrogen storage (UHS) is a promising option to buffer variable renewable power and support the hydrogen economy. Yet this technology is early-stage, and key uncertainties remain about how coupled processes such as geochemical and geomechanical impacts affect long-term storage security. In this paper, we seek to integrate the results from a large, multi-scale research program to quantify core feasibility metrics and situate these studies within ongoing efforts. Through this work, we evaluate hydrogen recoverability during storage by assessing diffusive losses, losses to geochemical interactions and their subsequent impact on geomechanical properties, and losses to residual saturation during injection and withdrawal. Molecular and laboratory studies show that lithologic heterogeneity, pore geometry, and mineral surface chemistry govern hydrogen–rock interactions, controlling loss pathways. High-pressure coreflooding is used to estimate relative permeabilities and measure parameters needed for reservoir-scale sensitivity analyses. These core-scale experiments found early hydrogen breakthrough at low saturations driven by capillary and viscous fingering, which limits pore-space utilization at early times, while repeated injection and withdrawal cycles improve deliverability over time. Technoeconomic factors and purity requirements for end uses are also considered as part of our comprehensive feasibility analysis. Finally, we underscore the importance of geophysical monitoring and tailored injection strategies to maintain integrity and efficiency. Together, these results provide a quantitative foundation for safe, scalable deployment of UHS and highlight priorities for future integrated studies.

08 HYDROGEN↗

DOE OSTI2020

Analysis of thermal response of new diagnostic probe in TCV

Thermal response of the new multi-purpose diagnostic probe inside the TCV (Tokamak à Configuration Variable) tokamak is studied. The New Probe Head (NPH) was constructed at the University of Innsbruck and developed in collaboration with various European laboratories within EUROfusion program. The NPH can be used in different MST devices by applying corresponding adapters. Its design, followed by the prototype, has to go through a number of tests (numerical and experimental), before it can be used in the tokamak environment, where it has to withstand repeated exposure to the Scrape-Off-Layer (SOL) plasma. Presented numerical analyses of the NPH thermal response inside the TCV tokamak are performed by the Jožef Stefan Institute.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

HIDRA-MAT liquid metal droplet injector for liquid metal applications in HIDRA

A liquid metal droplet injector was explicitly designed for the Hybrid Illinois Device for Research and Applications Material Analysis Test-stand (HIDRA-MAT) at the University of Illinois Urbana-Champaign to prepare liquid metal plasma-facing components (PFCs) for plasma exposure. The design goals were to create a compact, reliable, and robust design that could apply liquid metal droplets in-vacuo to a variety of samples. The injector was designed for liquid metal use, and results discussed pertain to lithium. The injector can produce lithium droplets of consistent size by utilizing programmable piston movement. Here, droplet formation data is presented for four different piston step sizes (0.125 mm, 0.25 mm, 0.5 mm, and 1 mm) at three different nozzle temperatures (185 °C, 230 °C, and 270 °C). Droplets are formed on the injector nozzle tip and a linear shift mechanism vertically translates the droplet to the substrate for application. This design prevents the droplet from inadvertently detaching off the nozzle and falling to the sample. Application of the liquid metal droplet ensures placement consistency on the substrate and helps avoid damage to components in HIDRA-MAT that should not interact with liquid metals. A description of the liquid lithium droplet creation and application results is given and provides additional insight into the cooling and oxidation of lithium droplets under vacuum conditions. A second nozzle was fabricated and demonstrated repeatability in droplet diameter creation having a variance of ±0.14 mm for droplets created from the same number of piston steps. The end-use for the injector on HIDRA-MAT is to apply liquid metals to PFCs and expose them to HIDRA's plasma with subsequent intershot in-vacuo surface analysis in HIDRA-MAT for plasma-material interaction studies.

42 ENGINEERING↗

DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Functional variability in specific root respiration translates to autotrophic differences in soil respiration in a temperate deciduous forest

CO 2 release from forest soils (R s ) is a prominent flux in the global carbon cycle. Rs is derived from roots (autotrophic respiration, R a ) and microbial (heterotrophic) respiration and is highly dynamic, as it depends on edaphic and environmental conditions as well as root functional traits and microbial community composition. It is unclear how root functional traits affect root and microbial respiration rates; however, their consideration may help parse out the relative contributions of root and microbial respiration to R s . At a temperate forest site, root systems of 3–4 functional root orders and their surrounding surface soil were carefully excavated and placed into custom trays designed to repeatedly measure R s in situ on eight temperate tree species that varied in their root functional strategies and mycorrhizal affinity. R s was measured bi-weekly to monthly for nearly one year using a custom chamber attached to a gas exchange system. R s varied over time, ranging from 0.3 to 12 µmol m -2 s -1 . Comparable root systems of the same species were excised from the soil and specific root respiration rates (R r ) were measured. Rr ranged from 2.5 to 9.0 nmol g -1 s -1 and was negatively correlated with root tissue density and positively related to root tissue nitrogen concentration. Using R r to estimate R a , we estimate that R a accounts for <10%, on average 2–3%, of R s for individual root systems (averaging 1.2 g dry biomass) housed in surrounding soil (average 1.3 kg dry mass) in situ; thus, Ra was roughly 20 times greater than Rh per unit mass. The contribution of R a peaked in the fall and coincided with leaf senescence of the forest canopy. A soil-sterilizing experimental treatment designed to help isolate R a in situ reduced bacterial biomass and shifted fungal community composition, but there was no reduction in Rs of the in-situ root-soil tray systems. The relative R a to R s ratio increased with root functional strategies characterized by greater specific root length and tip abundance, but also to greater root tissue density. The ratio of R a to R s also increased with warmer soil temperatures and decreased slightly with increasing soil moisture. We discuss how incorporating root functional traits as modulators of the autotrophic contribution to R s could be considered when modeling total soil CO 2 efflux from forests.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗