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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Low-intensity frequent fires in coniferous forests transform soil organic matter in ways that may offset ecosystem carbon losses

The impact of shifting disturbance regimes on soil carbon (C) storage is a key uncertainty in global change research. Wildfires in coniferous forests are becoming more frequent in many regions, potentially causing large C emissions. Repeated low-intensity prescribed fires can mitigate wildfire severity, but repeated combustion may decrease soil C unless compensatory responses stabilize soil organic matter. Here, we tested how 30 years of decadal prescribed burning affected C and nitrogen (N) in plants, detritus, and soils in coniferous forests in the Sierra Nevada mountains, USA. Tree basal area and litter stocks were resilient to fire, but fire reduced forest floor C by 77% (-36.4 Mg C/ha). In mineral soils, fire reduced C that was free from minerals by 41% (-4.4 Mg C/ha) but not C associated with minerals, and only in depths ≤ 5 cm. Fire also transformed the properties of remaining mineral soil organic matter by increasing the proportion of C in a pyrogenic form (from 3.2% to 7.5%) and associated with minerals (from 46% to 58%), suggesting the remaining soil C is more resistant to decomposition. Laboratory assays illustrated that fire reduced microbial CO2 respiration rates by 55% and the activity of eight extracellular enzymes that degrade cellulosic and aromatic compounds by 40–66%. Lower decomposition was correlated with lower inorganic N (-49%), especially ammonium, suggesting N availability is coupled with decomposition. The relative increase in forms of soil organic matter that are resistant to decay or stabilized onto mineral surfaces, and the associated decline in decomposition suggest that low-intensity fires may promote mineral soil C storage in pools with long mean residence times in coniferous forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Resilience in soil bacterial communities of the boreal forest from one to five years after wildfire across a severity gradient

Wildfires can represent a major disturbance to ecosystems, including soil microbial communities belowground. Furthermore, fire regimes are changing in many parts of the world, altering and often increasing fire severity, frequency, and size. The boreal forest and taiga plains ecoregions of northern Canada are characterized by naturally-occurring stand-replacing wildfires on a 40–350 year basis. We previously studied the effects of wildfire on soil microbial communities one year post-fire across 40 sites, spanning a range of burn severity. Here, we return to the same sites five years post-fire to test a series of hypotheses about the effects of fire on bacterial community composition. We ask questions on two themes: which factors control bacterial community composition during post-fire recovery, and how does the importance of different fire-responsive traits change during post-fire recovery? We find the following: Five years post-fire, vegetation community, moisture regime, pH, total carbon, texture, and burned/unburned all remained significant predictors of bacterial community composition with similar predictive value (R 2 ). Bacterial communities became more similar to unburned sites five years post-fire, across the range of severity, suggesting resilience, while general structure of co-occurrence networks remained similar one and five years post-fire. Fast growth potential, as estimated using predicted 16S rRNA copy numbers, was no longer significantly correlated with burn severity five years post-fire, indicating the importance of this trait for structuring bacterial community composition may be limited to relatively short timescales. Many taxa that were enriched in burned sites one year post-fire remained enriched five years post-fire, although the degree to which they were enriched generally decreased. Specific taxa of interest from the genera Massilia, Blastococcus, and Arthrobacter all remained significantly enriched, suggesting that they may have traits that allow them to continue to flourish in the post-fire environment, such as tolerance to increased pH or ability to degrade pyrogenic organic matter. This hypothesis-based work expands our understanding of the post-fire recovery of soil bacterial communities and raises new hypotheses to test in future studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Self-reported health impacts of do-it-yourself air cleaner use in a smoke-impacted community

Smoke exposure from wildfires or residential wood burning for heat is a public health problem for many communities. Do-It-Yourself (DIY) portable air cleaners (PACs) are promoted as affordable alternatives to commercial PACs, but evidence of their effect on health outcomes is limited. Pilot test an evaluation of the effect of DIY PAC usage on self-reported symptoms, and investigate barriers and facilitators of PAC use, among members of a tribal community that routinely experiences elevated concentrations of fine particulate matter (PM 2.5 ) from smoke. We conducted studies in Fall 2021 (“wildfire study”; N = 10) and Winter 2022 (“wood stove study”; N = 17). Each study included four sequential one-to-two-week phases: 1) initial, 2) DIY PAC usage ≥8 h/day, 3) commercial PAC usage ≥8 h/day, and 4) air sensor with visual display and optional PAC use. We continuously monitored PAC usage and indoor/outdoor PM 2.5 concentrations in homes. Concluding each phase, we conducted phone surveys about participants’ symptoms, perceptions, and behaviors. We analyzed symptoms associated with PAC usage and conducted an analysis of indoor PM 2.5 concentrations as a mediating pathway using mixed effects multivariate linear regression. We categorized perceptions related to PACs into barriers and facilitators of use. No association was observed between PAC usage and symptoms, and the mediation analysis did not indicate that small observed trends were attributable to changes in indoor PM 2.5 concentrations. Small sample sizes hindered the ability to draw conclusions regarding the presence or absence of causal associations. DIY PAC usage was low; loud operating noise was a barrier to use. This research is novel in studying health effects of DIY PACs during wildfire and wood smoke exposures. Such research is needed to inform public health guidance. Recommendations for future studies on PAC use during smoke exposure include building flexibility of intervention timing into the study design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mechanical Solutions Scan Report

Power lines, poles, and towers are the backbone of the United States (U.S.) electric-power grid. These transmission and distribution networks route electricity from generator to loads. The characteristics of these routes are rapidly changing -- trending towards decentralized renewable generation, electric heating, vehicle charging, and large data-center loads. Coupled with aging infrastructure and the increased frequency of extreme weather events, there is concern about the future reliability and transmission capacity of conductors and adjacent components. This scan report seeks to provide an overview of mechanical solutions to challenges caused by extreme weather events associated with components of transmission and distribution infrastructure, including conductor heat sag, ice accumulation, wind, and wildfire. Many options could increase transmission capacity or reliability, and these are at various stages of technological readiness. Some have only been lab tested, while some have been widely deployed in the U.S. or overseas for decades. The solution categories and providers featured in this report are intended to be comprehensive at the time of publication and to serve as a reference for decision-makers concerned about transmission and distribution reliability. There are two other categories of large, complex solutions, which are not covered in this report: replacing existing conductors with advanced conductors and implementing digital grid enhancing technologies. A separate scan report titled “Advanced Conductor Scan Report,” which discusses advanced carbon-core conductors, was published by the Idaho National Laboratory (INL) in 2023. Information on digital technologies, such as dynamic line ratings, power-flow controllers, and other power electronics and communications-based devices, can be found on the Grid- Enhancing Technologies landing page. Mechanical grid-enhancing technologies, or solutions covered in this report, often do not require full equipment replacement and do not rely on digital components. Mechanical technologies are overlooked because they may be older, simpler, or seemingly “more obvious” than digital or carbon-core technologies. However, it is wise to consider mechanical solutions in a thorough evaluation of grid enhancing technology solutions.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task

This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.

Matzen, Laura E. [Sandia National Laboratories (SN↗
DOE OSTI2024

Airborne LiDAR to Improve Canopy Fuels Mapping for Wildfire Modeling

Increasing conflict between wildfire and the built environment has increased the need for more up-to-date and finer resolution canopy fuels data to improve wildfire modeling and associated risk forecasts. The US Forest Service and US Department of the Interior’s LANDFIRE product, which provides 30-m resolution canopy fuels data for the entire US, is one of the most widely used sources of fuels data. However, the last complete mapping effort for LANDFIRE is based on 2016 conditions, and subsequent updates reflect disturbances 1-2 years behind the release year. Airborne systems equipped with Light Detection and Ranging (LiDAR) sensors can be deployed to actively sense canopy structure and estimate canopy fuels data (cover, height, base height, bulk density) at finer resolutions. Canopy base height (CBH) and canopy bulk density (CBD) are difficult to measure both in the field and in LiDAR point clouds. Still, they are important for accurately modeling crown fires, which are often intense and difficult to contain. Additionally, point cloud datasets are large, and calculations require efficient utilization of computational resources. To address these challenges, we are working on an approach that uses openly available National Ecological Observatory Network (NEON) airborne LiDAR data, with calculations processed in the R programming language and parallelized through the lidR package. CBH and CBD are often derived from tree height, diameter at breast height, and species-specific allometries using the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). We aim to test if airborne LiDAR can estimate CBH and CBD without the use of empirical equations. Reliable estimates of canopy fuels data directly from airborne LiDAR could streamline quick, fine-resolution updates for use in wildfire behavior models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Opportunities for wave energy in bulk power system operations

Wave energy resources have high, yet largely untapped potential as candidate generation technology. In this paper, we perform a data-driven analysis to characterize the impact of wave energy integration on bulk-scale power systems and market operations. Through data-driven sensitivity studies centered on an optimization-based production cost modeling formulation, our work characterizes the inflection point beyond which wave integration starts impacting power system operations, considering present day transmission infrastructure. Furthermore, our analysis also considers the joint effects of wave energy integration and system-wide transmission expansion. Finally, potential resilience scenarios such as wildfire-driven transmission contingencies and heat wave events are investigated, whereby the contributions of grid-integrated wave energy in alleviating the effects of the resilience events are analyzed. As our demonstration test bed, we consider a reduced-order network topology for the U.S. Western Interconnection with wave energy generation integrated at carefully selected sites across the coastal areas of Washington, Oregon, and northern California. Our results indicate that over a representative year of operations, wave energy integration systematically reduces locational marginal prices (LMPs) of energy and price volatility, especially during periods of high wave resource availability (winter months for the U.S. west coast). Average, maximum, and minimum of hourly LMPs over a typical year of operation was reduced by 2.95, 51.28, and 1.13 $\$$/MWh respectively (over a baseline scenario with no wave energy integration), when the selected network model had a total of 5000 MW wave power installed capacity during the representative year of study. The effects of wave energy integration can remain localized with existing transmission infrastructure (identified to be most pronounced in the Pacific Northwest region in the example we studied). However, with concurrent transmission expansion, the impacts of wave energy integration are likely to have a higher geographical spread. Our results also indicate that wave energy may be able to assist power system operations during resilience events such as major transmission contingencies and heat wave events, although such benefits might be dependent on factors such as proximity of affected area to wave resources, availability of adequate resource potential and adequate transmission capacity.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Identifying Characteristics of Wildfire Towers and Troughs

Wildfire behavior is dictated by the complex interaction of numerous physical phenomena including dynamic ambient and fire-induced winds, heat transfer, aerodynamic drag on the wind by the fuel and combustion. These phenomena create complex feedback effects between the fire and its surroundings. In this study, we aim to study the mechanisms by which buoyant flame dynamics along with vortical motions and instabilities control wildfire propagation. Specifically, this study employs a suite of simulations conducted with the physics-based coupled fire-atmosphere behavior model (FIRETEC). The simulations are initialized with a fire line and the fires are allowed to propagate on a grass bed, where the fuel heights and wind conditions are varied systematically. Flow variables are extracted to identify the characteristics of the alternating counter-rotational vortices, called towers and troughs, that drive convective heat transfer and fire spread. These vortices have previously been observed in wildfires and laboratory fires, and have also been observed to arise spontaneously in FIRETEC due to the fundamental physics incorporated in the model. However, these past observations have been qualitative in nature and no quantitative studies can be found in the literature which connected these coherent structures fundamental to fire behavior with the constitutive flow variables. To that end, a variety of state variables are examined in the context of these coherent structures under various wind profile and grass height conditions. Identification of various correlated signatures and fire-atmosphere feedbacks in simulations provides a hypothesis that can be tested in future observational or experimental efforts, potentially assisting experimental design, and can aid in the interpretation of data from in situ detectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Wildfire-Power Grid Interactions: Feedback, Impacts, Monitoring, Modeling, and Mitigation Strategies

Wildfires are increasingly interacting with electric power systems through a two-way hazard chain: fires damage grid assets and trigger cascading outages, while grid faults can ignite new fires under hot, dry, and windy conditions. This review synthesizes the state of knowledge across five domains: (i) physical impacts of flames, heat, and smoke on lines, towers, insulators, and substations; (ii) power-infrastructure-initiated ignitions via conductor clash, high-impedance faults, and corona discharge; (iii) widespread blackouts and disproportionate societal impacts; (iv) multi-scale monitoring spanning laboratory tests, in-situ and grid-integrated sensors, and Earth observation; (v) coupled modeling that links fire behavior with grid operations; and (vi) technological and strategic mitigation pathways spanning prevention, response, and recovery. We integrate these domains into a novel 'feedback-aware' socio-technical framework. Through a longitudinal analysis (2005-2025) of global incidents, we identify that while vegetation contact remains the most frequent ignition source, aging infrastructure failure has emerged as a critical driver of catastrophic 'mega-fires'. We further identify persistent gaps, including limited interoperability of high-frequency grid and environmental data, scarce real-time data assimilation, and under-developed equity metrics for outage management. We conclude by outlining a research agenda to (1) deploy interoperable sensing architectures, (2) advance feedback-coupled fire-grid simulations, and (3) evaluate mitigation portfolios through techno-economic and fairness lenses. Recognizing wildfire-grid interactions as coupled socio-technical systems is essential for protecting infrastructure and communities and for ensuring reliable, sustainable electricity in a changing world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Predicting wind-driven spatial deposition through simulated color images using deep autoencoders

Abstract For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Decline in Seasonal Snow during a Projected 20-Year Dry Spell

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence, and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections, we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a “stress test” for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find that 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in the area burned, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems, we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47–58% of historical values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Experimental and Modeled Assessment of Interventions to Reduce PM2.5 in a Residence during a Wildfire Event

Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Rapid Spaceborne Mapping of Wildfire Retardant Drops for Active Wildfire Management

Aerial application of fire retardant is a critical tool for managing wildland fire spread. Retardant applications are carefully planned to maximize fire line effectiveness, improve firefighter safety, protect high-value resources and assets, and limit environmental impact. However, topography, wind, visibility, and aircraft orientation can lead to differences between planned drop locations and the actual placement of the retardant. Information on the precise placement and areal extent of the dropped retardant can provide wildland fire managers with key information to (1) adaptively manage event resources, (2) assess the effectiveness of retardant slowing or stopping fire spread, (3) document location in relation to ecologically sensitive areas; and perform or validate cost-accounting for drop services. This study uses Sentinel-2 satellite data and commonly used machine learning classifiers to test an automated approach for detecting and mapping retardant application. We show that a multiclass model (retardant, burned, unburned, and cloud artifact classes) outperforms a single-class retardant model and that image differencing (post-application minus pre-application) outperforms single-image models. Compared to the random forest and support vector machine, the gradient boosting model performed the best with an overall accuracy of 0.88 and an F1 Score of 0.76 for fire retardant, though results were comparable for all three models. Our approach maps the full areal extent of the dropped retardant within minutes of image availability, rather than linear representations currently mapped by aerial GPS surveys. The development of this capability allows for the rapid assessment of retardant effectiveness and documentation of placement in relation to sensitive environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Acute wood smoke exposure is associated with cell-specific hippocampal transcriptomic responses in an accelerated ovarian failure mouse model

Background Wildfire events are increasing in frequency and intensity, and aging individuals demonstrate heightened biological susceptibility to air pollution exposures including increased risk of neurological sequelae. Declining ovarian hormones levels that occur with aging in females along with associated systemic physiological and inflammatory changes may contribute to increased cerebral vulnerability to air pollution, representing a potential but underexplored mechanism. Menopause and the menopausal transition represent a period of profound physiological change that affects cardiovascular, neurological, and immune health. Methods We tested whether peri-menopausal–like hormonal status amplifies hippocampal responses to acute wood smoke (WS) using an ovary-intact, 4-vinylcyclohexene diepoxide (VCD) model of moderate accelerated ovarian failure (AOF) in female C57BL/6 mice. Animals were exposed to HEPA-filtered air (FA) or WS for 4 h/day over 2 consecutive days (∼0.5 mg/m³). Exposure characterization confirmed a complex mixture of combustion products with significant levels of both trace metals and gas release during WS exposure. Results Spatial transcriptomics (10x Visium; n = 4 sections/group) with automated cell-type annotation identified astrocytes, GABAergic and glutamatergic neurons, oligodendrocytes, revealed cell type-specific transcriptional alterations following WS exposure. Distinct transcriptional patterns were observed across all identified neuronal and glial cell populations. Conclusion Together, these findings define a cell-type specific transcriptomic framework describing how WS exposure and ovarian hormone decline interact to influence hippocampal responses and identify potential cellular pathways relevant to hippocampal vulnerability.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

The drivers and predictability of wildfire re-burns in the western United States (US)

Evidence is mounting that the effectiveness of using prescribed burns as a management tactic may be diminishing due to the higher incidence of wildfire re-burns. The development of predictive models of re-burns is thus essential to better understand their primary drivers so that forest management practices can be updated to account for these events. First, we assess the potential for human activity as a driver of re-burns by evaluating re-burn trends both within and outside of the wildland–urban interface (WUI) of the western US. Next, we investigate the predictability of re-burns through the application of both random forest and the explanatory machine learning non-negative matrix factorization using k-means clustering (NMFk) algorithms to predict re-burn occurrence over California based on a number of climate factors. Our findings indicate that while most states showed increasing trends within the WUI when trends were conducted over longer moving windows (e.g. 20 years), California was the only state where the rate of increase was consistently higher in the WUI, indicating a stronger potential for human activity as a driver in that location. Furthermore, we find model performance was found to be robust over most of California (Testing F1 scores = 0.688), although results were highly variable based on EPA level III Ecoregion (F1 scores = 0.0–0.778). Insights provided from this study will lead to a better understanding of climate and human activity drivers of re-burns and how these vary at broad spatial scales so that improvements in forest management practices can be tuned according to the level of change that is expected for a given region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Satellite-based Investigation of Power-Line Vegetation Encroachment in the US (SILVANUS)

Rapid wide-area assessments of vegetation encroachment on transmission and distribution line rights-of-way (ROW) is a highly desirable capability for understanding risks to the power grid during severe weather and wildfire events. Conventional assessments are time-consuming and expensive due to the need for in-situ inspections and the use of aerial assets. Performing conventional assessments on a wide area would require immense resources and time that might not be available within the horizon of an expected adverse event. Developing a capability to accurately assess vegetation encroachment into ROWs will enable faster analysis of potential grid vulnerabilities in NAERM. This project sought to develop a prototype capability for rapid ROW vegetation encroachment assessments by using Puerto Rico as a test case. Puerto Rico is a heavily forested island territory frequently impacted by tropical cyclones that threaten the electric grid by downing trees across transmission and distribution lines. Multispectral satellite imagery (MSI) enable very high resolution (i.e., 0.5 - 2 meter) assessment of vegetation conditions at scale and with revisit times appropriate for regular monitoring (e.g., weekly to quarterly, depending on cloud cover) of the entire island. Synthetic aperture radar (SAR) data from satellites was also investigated as solution to the cloud-cover issue as they are active sensors that emit and receive a microwave signal rather than relying on solar illumination, and are therefore unaffected by cloud cover and can collect data during day or night. Finally, MSI-derived digital surface models (DSMs) map the height of objects relative to sea level, and were assessed for their ability to estimate the height of forest canopies relative to coincident transmission lines. The results of the mapping investigation are described in this report.

24 POWER TRANSMISSION AND DISTRIBUTION↗