Sensitivity of modeled fire behavior to small perturbations in initial conditions
Abstract not provided.
54 ENVIRONMENTAL SCIENCES↗
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National Renewable Energy Laboratory Wildland Fire Management Plan (August 2022-August 2025): February-August 2022
The National Renewable Energy Laboratory (NREL) is located in a moderate wildfire risk region. The region has a history of damaging wildland fire activity and is also susceptible to changing environmental conditions (drought, elevated temperatures) that may lead to an increase in wildfire frequency and consequence over time. An integrated, site-wide wildland fire management plan is required by the Department of Energy and is to be consistent with the Federal Wildland Fire Management Policy. The NREL Wildland Fire Management Plan (WFMP) provides an integrated approach to reducing risk to wildland fire at NREL. The goals of the WFMP are to protect human health and safety, protect NREL facilities and research, enhance community protection, diminish risk and consequences of wildland fires, and maintain the health of the ecosystem. The WFMP accomplishes these goals through the use of tools such as fire behavior modeling, fire growth modeling, a structure ignition risk assessment, and through the application of robust fire prevention, emergency management, and environmental programs. The NREL WFMP was developed in a collaborative manner with input from regional stakeholders including cities, counties, local fire departments, and other governmental agencies. The WFMP provides for recommendations to reduce overall wildland fire risk to life safety and property at NREL.
54 ENVIRONMENTAL SCIENCES↗
Disturbance and Response Model (DRM)
The DRM is a framework to interpret ecosystem process model output for 3D fire behavior model input and interpret the 3D fire behavior model output for ecosystem process model inpu
Atchley, Adam↗
Forest structural complexity and ignition pattern influence simulated prescribed fire effects
Background: Forest structural characteristics, the burning environment, and the choice of ignition pattern each influence prescribed fire behaviors and resulting fire effects; however, few studies examine the influences and interactions of these factors. Understanding how interactions among these drivers can influence prescribed fire behavior and effects is crucial for executing prescribed fires that can safely and effectively meet management objectives. To analyze the interactions between the fuels complex and ignition patterns, we used FIRETEC, a three-dimensional computational fluid dynamics fire behavior model, to simulate fire behavior and effects across a range of horizontal and vertical forest structural complexities. For each forest structure, we then simulated three different prescribed fires each with a unique ignition pattern: strip-head, dot, and alternating dot. Results: Forest structural complexity and ignition pattern affected the proportions of simulated crown scorch, consumption, and damage for prescribed fires in a dry, fire-prone ecosystem. Prescribed fires in forests with complex canopy structures resulted in increased crown consumption, scorch, and damage compared to less spatially complex forests. The choice of using a strip-head ignition pattern over either a dot or alternating-dot pattern increased the degree of crown foliage scorched and damaged, though did not affect the proportion of crown consumed. We found no evidence of an interaction between forest structural complexity and ignition pattern on canopy fuel consumption, scorch, or damage. Conclusions: We found that forest structure and ignition pattern, two powerful drivers of fire behavior that forest managers can readily account for or even manipulate, can be leveraged to influence fire behavior and the resultant fire effects of prescribed fire. These simulation findings have critical implications for how managers can plan and perform forest thinning and prescribed burn treatments to meet risk management or ecological objectives.
54 ENVIRONMENTAL SCIENCES↗
Modeling spatial patterns of longleaf pine needle dispersal using long-term data
Abstract Background Predicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of available fuels. Leaf litter is a significant canopy-derived fine fuel in fire-maintained forests. Litter dispersal is dependent on foliage production, stand structure, and wind direction, but the relative importance of these factors is unknown. Results Using a 10-year litterfall dataset collected within eighteen 4-ha longleaf pine ( Pinus palustris Mill.) plots varying in canopy spatial pattern, we compared four spatially explicit models of annual needle litter dispersal: a model based only on basal area, an overstory abundance index (OAI) model, both isotropic and anisotropic litter kernel models, and a null model that assumed no spatial relationship. The best model was the anisotropic model (R 2 = 0.656) that incorporated tree size, location, and prevailing wind direction, followed by the isotropic model (R 2 = 0.612), basal area model (R 2 = 0.488), OAI model (R 2 = 0.416), and the null model (R 2 = 0.08). Conclusions As with previous studies, the predictive capability of the litter models was robust when internally validated with a subset of the original dataset (R 2 = 0.196–0.549); however, the models were less robust when challenged with an independent dataset (R 2 = 0.122–0.319) from novel forest stands. Our model validation underscores the need for rigorous tests with independent, external datasets to confirm the validity of litter dispersal models. These models can be used in the application of prescribed fire to estimate fuel distribution and loading, as well as aid in the fine tuning of fire behavior models to better understand fire outcomes across a range of forest canopy structures.
54 ENVIRONMENTAL SCIENCES↗
Data accompanying the publication "Sensitivity of grass fires burning in marginal conditions to atmospheric turbulence" in the Journal of Geophysical Research-Atmospheres
Data accompanying Jonko, Yedinak, Conley, and Linn (2021): "Sensitivity of grass fires burning in marginal conditions to atmospheric turbulence", JGR-Atmospheres. LA-UR-21-24832. The following files were generated from simulations performed with Los Alamos National Laboratory coupled atmosphere-fire behavior model FIRETEC v1.3: -area_burned.csv contains the area burned per second in m^2 for 45 CORE simulations, 20 INIT simulations and 20 BOUND simulations (85 columns), and 600 seconds of simulation (600 rows). -fire_perimeter.csv containing the fire perimeters in m for 45 CORE simulations, 20 INIT simulations and 20 BOUND simulations (85 columns), and 600 seconds of simulation (600 rows). -consumption_per_second.csv containing consumption in kg/s for 45 CORE simulations, 20 INIT simulations and 20 BOUND simulations (85 columns), and 600 seconds of simulation (600 rows). -rate_of_spread.csv containing the distance of the fire front from the ignition line in m for 45 CORE simulations, 20 INIT simulations and 20 BOUND simulations (85 columns), and 600 seconds of simulation (600 rows). -CORE1_u_10m_300_sec.hdf, INIT3_u_10m_300_sec.hdf, and BOUND3_u_10m_300_sec.hdf contain u wind speeds at 10 m height for 300 seconds (3D: time, x, y) of simulation for simulations CORE1, INIT3, and BOUND3, respectively. -CORE.final.fuel.density.hdf contains final fuel densities in the surface layer of the model (3D: ensemble member, x, y) for all 45 CORE simulations. -CORE{1-45}_3D_u_120_sec.hdf: 45 files containing vertically and horizontally resolved u winds for 120 seconds of simulation (4D: time, x, y, z) for the 45 CORE ensemble members. For additional information about this data, please contact the corresponding author at ajonko@lanl.gov.
54 ENVIRONMENTAL SCIENCES↗
Advancing wildfire modeling for improved fire behavior understanding [Slides]
Wildland-Urban Interface (WUI): Region where houses meet or overlap the wildland vegetation. Buffer distance: distance between flammable vegetation and human settlements.
54 ENVIRONMENTAL SCIENCES↗
QUIC-fire: A fast-running simulation tool for prescribed fire planning
Coupled fire-atmospheric modeling tools are increasingly used to understand the complex and dynamic behavior of wildland fires. Multiple research tools linking combustion to fluid flow use Navier-Stokes numerical solutions coupled to a thermodynamic model to understand fire-atmospheric feedbacks, but these computational fluid dynamics approaches require high-performance computing resources. We present a new simulation tool called QUIC-Fire to rapidly solve these feedbacks by coupling the mature 3-D rapid wind solver QUIC-URB to a physics-based cellular automata fire spread model Fire-CA. QUIC-Fire uses 3-D fuels inputs similar to the CFD model FIRETEC, allowing this tool to simulate effects of fuel structure on local winds and fire behavior. Results comparing fire behavior metrics to the computational fluid dynamic model FIRETEC show strong agreement. As a result, QUIC-Fire is the first tool intended to provide an opportunity for prescribed fire planners to compare, evaluate and design burn plans, including complex ignition patterns and coupled fire atmospheric feedbacks.
54 ENVIRONMENTAL SCIENCES↗
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↗
Wildfire towers drive firebrand lofting: insights from coupled fire-atmosphere model simulations
Wildfire behavior is shaped by complex fire dynamics, with firebrands playing a critical role in spot fire ignition and fire spread. While previous studies have explored firebrand generation and transport, the specific role of towers and troughs from wildland fires in the lofting of firebrands remains unquantified. This study addresses that gap by using physics-based coupled fire-atmosphere model simulations to examine how wildfire towers (updrafts) and troughs (downdrafts) influence firebrand lofting. Our results show that the majority of firebrands (78.85%) are lofted from towers, where strong updrafts drive long-range transport. In contrast, only 21.15% of firebrands are lofted within troughs, where downdrafts cause most firebrands to fall near the fireline. We also find that firebrand size significantly influences lofting behavior, with smaller particles (1 mm radius) exhibiting the strongest correlation with updraft intensity. These findings highlight the dominant role of wildfire towers in promoting long-distance firebrand dispersal—an essential factor in rapid wildfire growth and wildland-urban interface (WUI) fire risks. By quantifying the relationship between firebrand lofting and fire-induced atmospheric features, this study provides critical insights to improve spot fire modeling, support mitigation planning, and enhance firefighter and WUI community safety in spot fire-prone regions.
54 ENVIRONMENTAL SCIENCES↗
Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data
High temporal resolution information on burned area is a prerequisite for incorporating bottom-up estimates of wildland fire emissions in regional air transport models and for improving models of fire behavior. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the evolution of nine large wildland fires. For each fire, local input parameters for the kriging model were defined using variogram analysis. The accuracy of the kriging model was assessed using high resolution daily fire perimeter data available from the U.S. Forest Service. We also assessed the temporal reporting accuracy of the MODIS burned area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared to 33% for MCD45A1 and 53% for MCD64A1.
Aqua↗
Forecast of Wildfire Potential Across California USA Using a Transformer
Wildfires are a major issue facing the United States, a matter further exacerbated by an ever-changing climate. In California alone, wildfires are responsible for billions of dollars in damages and take lives each year. Accurately predicting fire danger conditions allows preparation awareness before wildfires start. Transformers are a class of deep learning models designed to identify patterns in sequential datasets. In recent years, transformers have gained popularity through their impressive performance in natural language processing and other applications of signal recognition. This analysis demonstrates the ability of a transformer with a residual connection to forecast fire danger potential over the state of California. Wildland fire potential index (WFPI) maps collected from the US Geological Survey database from January 1st 2020 to December 31st 2023 were used to tune, train and evaluate the transformer. Meteorological inputs (provided by Daymet daily weather and climatological summaries), the normalized difference vegetation index (NDVI) (calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS)), and outputs from the Scott and Burgman fire behavior fuel models (to characterize maps of fuel types), were used as inputs. Our results show that a transformer can effectively emulate the US Forest Service modeled WFPI maps of California USA for four week long forecasts over the month of July, 2023, with correlations ranging from 0.85 – 0.98.
Limber, Russell [ORNL]↗
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↗
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↗
Emissions Characterization and Smoke Transport of A Prescribed Fire
Under the background of climate change, vast regions of the world will face a hotter and probably drier future that favors ignition and spread of wildfires. This poses additional challenges to the communities who are fighting the ever-larger wildfires. Fire and smoke models are valuable tools to fire managers and decision-makers to mitigate the impact of fires. Existing fire modeling systems bear large uncertainties due to the difficulty in collecting data to characterize fire behavior and smoke transport. More observations are urgently needed for improvement of fire modeling and reduction in model uncertainty. The coordinated prescribed burn experiment, such as the Fire and Smoke Model and Measurement Evaluation Experiment (FSMMEE), will allow the concurrent collection of critical measurements of fuel, fire behavior, smoke, and meteorology to better understand and model fires. In this preliminary investigation, we employed the data from the prescribed burn experiment at Langdon Mountain, Utah, in November 2019, to reconstruct the fire emissions and characterize the smoke transport. The NASA Unified Weather Research and Forecasting Model (NU-WRF) was utilized to assist in identifying the potential pre-fire remote sensing capabilities, such as fuel load, moisture, and fire radiative power, which are helpful to model development and advancement. Along the way, two sets of NU-WRF experiments would be done by applying either a default fire emissions inventory (i.e., NASA’s Quick Fire Emissions Dataset, QFED) or reconstructed fire emissions using the data collected from the prescribed fire. The results would help answer the questions such as “How do reconstructed emissions compare to QFED emissions and their impact on plume transport”.
NU-WRF↗
Analysis of historic fires to determine most frequent challenging events
The fire probabilistic risk assessment framework for nuclear power plants relies on experimental data to determine expected fire behavior or to validate models to predict fire conditions in the plant. To support reducing the uncertainty in this experimental data, a research effort was conducted to identify the most frequent and challenging fire scenarios using historic fire data from nuclear power plants in the United States. To support this effort, an electronic version of the publicly available Updated Fire Event Database developed by Electric Power Research Institute was produced resulting in data on 2111 fire events, 540 events were labelled as being challenging fires with 74.2% of these challenging fire events being due to eleven selected fire types. In conclusion, of these fire types, electrical and electronic equipment, transient combustibles, and liquid fires were the most frequent of the challenging fires. The fire scenario specifics were characterized for each of the eleven selected types and then related to existing fire experiments.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Megafires in a Warming World: What Wildfire Risk Factors Led to California’s Largest Recorded Wildfire
Massive wildfires and extreme fire behavior are becoming more frequent across the westernUnited States, creating a need to better understand how megafire behavior will evolve in our warmingworld. Here, the fire spread model Prometheus is used to simulate the initial explosive growth ofthe 2020 August Complex, which occurred in northern California (CA) mixed conifer forests. Hightemperatures, low relative humidity, and daytime southerly winds were all highly correlated withextreme rates of modeled spread. Fine fuels reached very dry levels, which accelerated simulationgrowth and heightened fire heat release (HR). Model sensitivity tests indicate that fire growth andHR are most sensitive to aridity and fuel moisture content. Despite the impressive early observedgrowth of the fire, shifting the simulation ignition to a very dry September 2020 heatwave predicted a>50% increase in growth and HR, as well as increased nighttime fire activity. Detailed model analysesof how extreme fire behavior develops can help fire personnel prepare for problematic ignitions.
Kevin Varga↗
Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide
Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.
Mu, M.↗