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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The impact of plant‐derived fire management prescriptions on fire‐responsive bird species

Abstract In fire‐prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post‐fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire‐sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi‐arid vegetation types across an environmental gradient in south‐eastern Australia. We conducted four surveys at each of 253 sites across a 75‐year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant‐derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant‐derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire‐responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species‐specific data collected at an appropriate spatial scale to inform management plans.

Makdissi, Rhys↗
DOE OSTI2023

Analysis of Fire-Induced Circulations during the FireFlux2 Experiment

Despite recent advances in both coupled fire modeling and measurement techniques to sample the fire environment, the fire–atmosphere coupling mechanisms that lead to fast propagating wildfires remain poorly understood. This knowledge gap adversely affects fire management when wildland fires propagate unexpectedly rapidly and shift direction due to the fire impacts on local wind conditions. In this work, we utilized observational data from the FireFlux2 prescribed burn and numerical simulations performed with a coupled fire–atmosphere model WRF-SFIRE to assess the small-scale impacts of fire on local micrometeorology under moderate wind conditions (10–12 m/s). The FireFlux2 prescribed burn provided a comprehensive observational dataset with in situ meteorological observations as well as IR measurements of fire progression. To directly quantify the effects of fire–atmosphere interactions, two WRF-SFIRE simulations were executed. One simulation was run in a two-way coupled mode in which the heat and moisture fluxes emitted from the fire were injected into the atmosphere, and the other simulation was performed in a one-way coupled mode for which the atmosphere was not affected by the fire. The difference between these two simulations was used to analyze and quantify the fire impacts on the atmospheric circulation at different sections of the fire front. The fire-released heat fluxes resulted in vertical velocities as high as 10.8 m/s at the highest measurement level (20 m above ground level) gradually diminishing with height and dropping to 7.9 m/s at 5.77 m. The fire-induced horizontal winds indicated the strongest fire-induced flow at the lowest measurement levels (as high as 3.3 m/s) gradually decreasing to less than 1 m/s at 20 m above ground level. The analysis of the simulated flow indicates significant differences between the fire-induced circulation at the fire head and on the flanks. The fire-induced circulation was much stronger near the fire head than at the flanks, where the fire did not produce particularly strong cross-fire flow and did not significantly change the lateral fire progression. However, at the head of the fire the fire-induced winds blowing across the front were the strongest and significantly accelerated fire progression. The two-way coupled simulation including the fire-induced winds produced 36.2% faster fire propagation than the one-way coupled run, and more realistically represented the fire progression.

Benik, Jeremy T.↗
DOE OSTI2026

Effects of slope steepness and cross-slope wind speed on fire spreading behavior for various vegetation

Wildland fire behavior is significantly influenced by environmental factors such as slope steepness, wind speed, and fuel type. Understanding these interactions is critical for improving predictive models and fire management. This study explores how slope steepness and cross-slope wind speed influence fire spread dynamics in various fuel bed types. Simulations are conducted using a physics-based wildland fire model, HIGRAD/FIRETEC, across six slope angles (0–50 %), four cross-slope wind speeds (4–10 m s –1 ), and three fuel bed types (grass, shrubland, and forest). Representative cases are additionally compared with FARSITE fireline evolution. Fire behavior is categorized into distinct propagation types based on spread characteristics and analyzed. The fire propagation angle, which indicates deviation from the wind direction, generally increases with steeper slopes and decreases with stronger cross-slope winds. Secondary upslope propagation is observed in shrubland under moderate slopes, while secondary downwind propagation occurs in all fuel beds at higher wind speeds. These findings highlight fire spread characteristics that differ from predictions by traditional models like Rothermel’s. By capturing complex propagation patterns and dynamics, this study demonstrates the value of a physics-based, atmosphere-fire coupled model for accurate wildland fire prediction. Incorporating secondary propagations and the influence of fuel bed complexities into predictive models can improve the accuracy of fire spread forecasts, enabling more effective fire management and risk mitigation efforts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing carbon storage through proactively managing fire-prone coniferous mountain forests

Mountain ecosystems typically serve as carbon (C) sinks. However, studies also suggest that they could be C sources due to climate warming, drought and insect-related mortality, wildfires, and management actions. We applied the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a process-based dynamic vegetation-ecosystem model, to investigate the role of ecosystem management in C storage under Mediterranean climate over the 21 st century. We modified LPJ-GUESS to include implementing mechanical thinning by vegetation size classes, components, and types along with a new mechanistic fire-occurrence model that accounts for wind speed and lightning ignition. Simulations show that mechanical thinning or prescribed fire performed 5-20 years in advance of a high-severity wildfire reduced direct wildfire C emissions by 38-66 %. Our results also show that long-term management actions repeated every 5-20 years, including thinning relatively small trees (diameters up to 7 inches or ∼178 mm), can maintain stable C levels in the forest and lower dead-fuel amounts. We found that, although prescribed fire mitigated wildfire severity, ecosystem C storage from reduced wildfire emissions can be outweighed by the added emissions from the prescribed fire themselves. Thinning plus removing and sequestering the thinned biomass can ensure that forests act as net C sinks through the end of the 21 st century. However, addition of prescribed fire is needed to reduce understory and lower the projected extent of high-severity wildfire. Achieving the competing goals of reducing wildfire and making the Sierra Nevada long-term C sink can be advanced through carefully coordinated thinning, sequestration of thinned biomass, and prescribed fire.

Climate solutions↗
DOE OSTI2026

Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition

Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow

Background: Wildfires are becoming more severe, so we need improved tools to predict them over a wide range of conditions and scales. One approach towards this goal entails the use of coupled fire/atmosphere modelling tools. Although significant progress has been made in advancing their physical fidelity, existing tools have not taken full advantage of emerging programming paradigms and computing architectures to enable high-resolution wildfire simulations. Aims: The aim of this study was to present a new framework that enables landscape-scale wildfire simulations with physical representation of combustion at an affordable cost. Methods: We developed a coupled fire/atmosphere simulation framework using TensorFlow, which enables efficient and scalable computations on Tensor Processing Units. Key Results: Simulation results for a prescribed fire were compared with experimental data. Predicted fire behavior and statistical analysis for fire spread rate, scar area, and intermittency showed overall reasonable agreement. Scalability analysis was performed, showing close to linear scaling. Conclusions: While mesh refinement was shown to have less impact on global quantities, such as fire scar area and spread rate, it benefits predictions of intermittent fire behavior, buoyancy-driven dynamics, and small-scale turbulent motion. Implications: This new simulation framework is efficient in capturing both global quantities and unsteady dynamics of wildfires at high spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating plant physiology into simulation of fire behavior and effects

Summary Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine‐scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine‐scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process‐based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Satellite-Based Assessment of Rocket Launch and Coastal Change Impacts on Cape Canaveral Barrier Island, Florida, USA

The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Enzymatic Biocontrol of Fire Blight ( Erwinia amylovora ) Using an Engineered Glycosyl Hydrolase

Current management of fire blight, caused by Erwinia amylovora , relies heavily on streptomycin a practice that has contributed to the emergence of antibiotic-resistant strains and raised environmental and regulatory concerns. Enzyme-based biocontrol agents offer a promising antibiotic-free approach that combines target specificity with environmental compatibility. This study evaluates CAase, a bacteriophage-derived glycosyl hydrolase, for its ability to disrupt E. amylovora biofilms and reduce disease severity. Biochemical assays and scanning electron microscopy confirmed that CAase efficiently degraded the extracellular polysaccharide (EPS) matrix, releasing cells from biofilms. Gas chromatography–mass spectrometry (GC–MS) linkage analysis of EPS isolated from two Erwinia amylovora strains demonstrated that CAase preferentially cleaves galactan-rich amylovoran produced by strain EA273, while exhibiting only limited activity toward the levan-rich EPS from strain EA1430. Functional assays revealed that CAase reduced bacterial viability by nearly 2 orders of magnitude at higher enzyme concentrations, strongly suppressed surface motility, and induced ultrastructural damage visible by transmission electron microscopy. Importantly, field trials showed that CAase significantly lowered blossom and shoot blight incidence under orchard conditions. These results highlight CAase as a potent enzyme-based strategy for reducing the virulence of E. amylovora and demonstrate its potential as a sustainable alternative to antibiotics in fire blight management.

antibiotic resistance↗
DOE OSTI2024

Electric Vehicle Fire Primer for Fleet Managers

The U.S. Department of Energy's Vehicle Technologies Office provides project assistance through Clean Cities, technical expertise, and funding to help stake-holders implement alternative fuels and electric vehicles (EVs). Fleet managers considering EVs can learn about the potential for EV fires and measures to help reduce fire risk.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Monitoring and Documentation of Forest Management Activities at Los Alamos National Laboratory

Forest management treatments at Los Alamos National Laboratory (LANL) are administered by the Emergency Management Division (EMD-DO) Wildland Fire Program within the Wildland Fire and Forest Management Program (WFFM Program or the Program). Open space forest inventory monitoring is conducted by Forest Health Program staff and students within the Environmental Stewardship Group (EPC-ES). The goals of the integrated Wildland Fire and Forest Management Program are to reduce impacts to Laboratory operations from climate driven events by implementing forest management and healthy forest initiatives such as forest thinning and erosion control. The LANL Wildland Fire Mitigation and Forest Health Plan (EMD PLAN-200, LA-UR-19-25122; LANL 2019) presents treatment standards for LANL property to meet the following goals: 1) Restore and maintain landscapes: LANL landscapes are resilient to disturbances; 2) Develop a fire-adapted community: LANL workforce, neighbors, and infrastructure can withstand a wildland fire without loss of life and property; 3) Ensure wildland fire mitigation implementation: All wildland fire mitigation working group organizations participate in making and implementing safe, effective, efficient risk-based wildland fire management decisions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hanford Site Post-Fire Vegetation Monitoring Report for Calendar Year 2023

The Hanford Site is comprised of an expanse of shrub-steppe habitats that provide exceptional value to plants and animals located on the Hanford Site and in the surrounding greater Columbia Basin. The greatest threat to this habitat is fire-related conversion to a cheatgrass-dominated monoculture. To decrease future fire risk and to disrupt the positive feedback cycle between cheatgrass and fire, land managers can make efforts to restore native vegetation following a fire. The goal of this report is to summarize fires and fire monitoring efforts that occurred in 2023 and analyze vegetation recovery post-fire in both restored and unrestored areas to inform future post-fire response actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hanford Site Post-Fire Vegetation Monitoring Report for Calendar Year 2022

The Hanford Site is comprised of an expanse of shrub-steppe habitats that provide exceptional value to plants and animals located on the Site and in the surrounding greater Columbia Basin. The greatest threat to this habitat is fire-related conversion to a cheatgrass-dominated monoculture. In order to decrease future fire risk and to disrupt the positive feedback cycle between cheatgrass and fire, land managers can make efforts to restore native vegetation following a fire. The goal of this report is to analyze vegetation recovery post-fire in both restored and unrestored areas to inform future post-fire response actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Idaho National Laboratory Site Natural Resources: Wildland Fire Recovery Framework

As pressures from invasive species, climate change, and anthropogenic impacts increase across the landscape in the western U.S., managing wildland fire recovery to promote healthy sagebrush steppe becomes an increasingly important stewardship responsibility. The Idaho National Laboratory (INL) has developed and implemented wildland fire recovery plans to hasten desirable vegetation re-establishment on several individual fires, but lacks an overarching wildland fire recovery strategy, or framework. The intent of this document is to develop the technical approach and scientific basis for wildland fire recovery at the INL Site and to evaluate the tools available to support it in a comprehensive and broadly applicable format. This wildland fire recovery framework will outline the process of assessing the potential impacts of wildland fire on natural resources, present a range of post-fire recovery options, outline an approach for post-fire monitoring, and provide a template for post-fire recovery plans designed to addresses the specific conditions of each wildland fire. There are numerous benefits to developing a wildland fire recovery framework for the INL Site. The first is streamlining the development of post-fire recovery plans for individual fires. A second benefit is more closely aligning INL’s post-fire planning processes with those of other federal agencies. The development of an INL Site fire recovery framework will also allow resource professionals to consider a broader set of recovery tools than they have before because all proposed tools included in the framework were vetted through the process of scoping and stakeholder review. Finally, this framework is a publicly available document that can be used as a basis for communicating and discussing post-fire natural resource recovery objectives with agency collaborators, conservation partners, and other stakeholders. Through the proactive land stewardship principles outlined in this framework, current INL sustainability initiatives can be enhanced, and future INL mission flexibility will be maintained.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

How will future climate change impact prescribed fire across the contiguous United States?

As of 2023, the use of prescribed fire to manage ecosystems accounts for more than 50% of area burned annually across the United States. Prescribed fire is carried out when meteorological conditions, including temperature, humidity, and wind speed are appropriate for its safe and effective application. However, changes in these meteorological variables associated with future climate change may impact future opportunities to conduct prescribed fire. In this study, we combine climate projections with information on prescribed burning windows for ecoregions across the contiguous United States (CONUS) to compute the number of days when meteorological conditions allow for the safe and effective application of prescribed fire under present-day (2006–2015) and future climate (2051–2060) conditions. The resulting projections, which cover 57% of all vegetated area across the CONUS, indicate fewer days with conditions suitable for prescribed burning across ecoregions of the eastern United States due to rising maximum daily temperatures, but opportunities increase in the northern and northwestern United States, driven primarily by rising minimum temperatures and declining wind speeds.

54 ENVIRONMENTAL SCIENCES↗