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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites

In the United States, more than 2 Gt of coal combustion residuals (i.e., coal ash) are stored in hundreds of disposal units. Recent federal regulations mandate the closure or retrofitting of most coal ash impoundments, presenting significant challenges for waste management. These regulatory pressures also present opportunities to reuse coal ash. However, the quality and quantity of discarded coal ash across the U.S. are not well known, even though this information is crucial for spurring its reuse for conventional and new material applications. This study describes a predictive model for the major element composition of coal ash in reserve at disposal sites of major U.S. coal-fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973 to 2022 and was trained on coal ash composition data, showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe2O3), as power stations changed their source of coal over this time frame. Our approach enables an estimation of chemical composition for ash stored in waste impoundments at individual power stations. Such information can help to delineate the regional market resource potential of supplementary cements for concrete and other material innovations that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

High-Accuracy Simulations to Model Pyrometallurgical Processes in a Secondary Lead Reverberatory Furnace

The US manufacturing industry produces about 1.3 million tons of refined lead each year using secondary sources consisting mainly of lead batteries. ORNL is partnering with Gopher resource, the second largest lead recycling company in the United States, and GTI, to develop a high-fidelity CFD model of a directly fired, reverberatory-style, secondary lead furnace. These High Performance Computing (HPC) simulations are aimed to use first principles modeling for combustion and melting processes of the secondary lead feed while accounting for complex interphase interactions between the gas, solid charge (lead) material, slag, and metal phases. Through validation against operating plant data, this effort will enable significant improvements in design, operational parameters, and energy efficiency, thus improving productivity and refractory lifetime of secondary lead melting furnaces. Estimated savings/reduction of, at least, 1 trillion BTU, 1 million ton/year of greenhouse gas emissions, and $\$50$ million/year to the US lead industry can be expected. ORNL resources and expertise in high-performance computing and multicomponent, multiphase flows were utilized to realize this goal while advancing the understanding of the smelting and melting processes occurring within the furnace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Files and scripts to support manuscript Shuman et al 2023 FATES-SPITFIRE ecosystem assembly across tropics

The dataset includes the parameter and domain files, relevant output files, and scripts to generate simulations and perform analysis with Jupyter notebooks that support the manuscript Shuman, JK et al 2023 “Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0”. We have adapted the fire-behavior and effects module, SPITFIRE, for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture, and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire burned area and its effects, which could fee back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period, but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m-1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m-1. The results support a positive grass-fire feedback across the region, and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Mitigating Satellite-Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF-Chem Model over the Northern sub-Saharan African Region

Largely used in several independent estimates of fire emissions, fire products based on MODIS sensors aboard the Terra and Aqua polar-orbiting satellites have a number of inherent limitations, including (a) inability to detect fires below clouds, (b) significant decrease of detection sensitivity at the edge of scan where pixel sizes are much larger than at nadir, and (c) gaps between adjacent swaths in tropical regions. To remedy these limitations, an empirical method is developed here and applied to correct fire emission estimates based on MODIS pixel level fire radiative power measurements and emission coefficients from the Fire Energetics and Emissions Research (FEER) biomass burning emission inventory. The analysis was performed for January 2010 over the northern sub-Saharan African region. Simulations from WRF-Chem model using original and adjusted emissions are compared with the aerosol optical depth (AOD) products from MODIS and AERONET as well as aerosol vertical profile from CALIOP data. The comparison confirmed an 30-50% improvement in the model simulation performance (in terms of correlation, bias, and spatial pattern of AOD with respect to observations) by the adjusted emissions that not only increases the original emission amount by a factor of two but also results in the spatially continuous estimates of instantaneous fire emissions at daily time scales. Such improvement cannot be achieved by simply scaling the original emission across the study domain. Even with this improvement, a factor of two underestimations still exists in the modeled AOD, which is within the current global fire emissions uncertainty envelope. Plain Language Summary Polar-orbiting satellites sensors, such as MODIS, have limitations in detecting fires under clouds or when viewing angles are large or in the gaps among satellites' different ground swaths. Here we developed an empirical method to mitigate the effect of these limitations in fire emission estimate. The method is applied to a fire emission inventory (FEER) based on MODIS. We show that, with our method, the adjusted emission inventory improves WRF-Chem simulation of smoke transport and distribution.

emissions properties↗
NASA NTRS2013

Unmanned Vehicle Material Flammability Test

Microgravity combustion phenomena have been an active area of research for the past 3 decades however, there have been very few experiments directly studying spacecraft fire safety under low-gravity conditions. Furthermore, none of these experiments have studied sample and environment sizes typical of those expected in a spacecraft fire. All previous experiments have been limited to samples of the order of 10 cm in length and width or smaller. Terrestrial fire safety standards for all other habitable volumes on earth, e.g. mines, buildings, airplanes, ships, etc., are based upon testing conducted with full-scale fires. Given the large differences between fire behavior in normal and reduced gravity, this lack of an experimental data base at relevant length scales forces spacecraft designers to base their designs using 1-g understanding. To address this question a large scale spacecraft fire experiment has been proposed by an international team of investigators. This poster presents the objectives, status and concept of this collaborative international project to examine spacecraft material flammability at realistic scales. The concept behind this project is to utilize an unmanned spacecraft such as Orbital Cygnus vehicle after it has completed its delivery of cargo to the ISS and it has begun its return journey to earth. This experiment will consist of a flame spread test involving a meter scale sample ignited in the pressurized volume of the spacecraft and allowed to burn to completion while measurements are made. A computer modeling effort will complement the experimental effort. Although the experiment will need to meet rigorous safety requirements to ensure the carrier vehicle does not sustain damage, the absence of a crew removes the need for strict containment of combustion products. This will facilitate the examination of fire behavior on a scale that is relevant to spacecraft fire safety and will provide unique data for fire model validation. This will be the first opportunity to examine microgravity flame behavior at scales approximating a spacecraft fire.

Combustion↗
NASA NTRS2013

Determination of Realistic Fire Scenarios in Spacecraft

This paper expands on previous work that examined how large a fire a crew member could successfully survive and extinguish in the confines of a spacecraft. The hazards to the crew and equipment during an accidental fire include excessive pressure rise resulting in a catastrophic rupture of the vehicle skin, excessive temperatures that burn or incapacitate the crew (due to hyperthermia), carbon dioxide build-up or accumulation of other combustion products (e.g. carbon monoxide). The previous work introduced a simplified model that treated the fire primarily as a source of heat and combustion products and sink for oxygen prescribed (input to the model) based on terrestrial standards. The model further treated the spacecraft as a closed system with no capability to vent to the vacuum of space. The model in the present work extends this analysis to more realistically treat the pressure relief system(s) of the spacecraft, include more combustion products (e.g. HF) in the analysis and attempt to predict the fire spread and limiting fire size (based on knowledge of terrestrial fires and the known characteristics of microgravity fires) rather than prescribe them in the analysis. Including the characteristics of vehicle pressure relief systems has a dramatic mitigating effect by eliminating vehicle overpressure for all but very large fires and reducing average gas-phase temperatures.

combustion↗
NASA NTRS1979

Electrets used in measuring rocket exhaust effluents from the space shuttle's solid rocket booster during static test firing, DM-3

The purpose of this experimental research was to compare Marshall Space Flight Center's electrets with Thiokol's fixed flow air samplers during the Space Shuttle Solid Rocket Booster Demonstration Model-3 static test firing on October 19, 1978. The measurement of rocket exhaust effluents by Thiokol's samplers and MSFC's electrets indicated that the firing of the Solid Rocket Booster had no significant effect on the quality of the air sampled. The highest measurement by Thiokol's samplers was obtained at Plant 3 (site 11) approximately 8 km at a 113 degree heading from the static test stand. At sites 11, 12, and 5, Thiokol's fixed flow air samplers measured 0.0048, 0.00016, and 0.00012 mg/m3 of CI. Alongside the fixed flow measurements, the electret counts from X-ray spectroscopy were 685, 894, and 719 counts. After background corrections, the counts were 334, 543, and 368, or an average of 415 counts. An additional electred, E20, which was the only measurement device at a site approximately 20 km northeast from the test site where no power was available, obtained 901 counts. After background correction, the count was 550. Again this data indicate there was no measurement of significant rocket exhaust effluents at the test site.

Susko, M.↗
NASA NTRSDate not supplied

Identifying Chemical and Optical Smoke Signatures in Suborbital Observations to Improve Model Projections

Biomass Burning (BB) fires are expected to occur more frequently in the future due to climate change, the build‐up of fuels due to fire suppression, and the expansion of the wildland‐urban interface [Schoennagel et al., 2017; Shivdenko & Schepaschenko, 2013; Stevens et al., 2014; Turner et al., 2019; Yue et al., 2015; Tilman et al., 2001]. Accurate knowledge of the abundance, distribution, and optical properties of absorbing aerosols such as BB smoke is essential to understanding regional and global aerosol radiative effects [Boucher et al., 2013]. It is recognized that differing fuel types (e.g., rice straw, ponderosa pine), combustion modes (i.e., flaming, smoldering or both) and temperature/ moisture content produce smoke with differing optical characteristics (e.g., aerosol light scattering and absorption) and component mixing ratios [e.g., BC, OC and inorganic species such as sulfate, potassium, chloride; Streets et al. 2003; Reid et al., 2005a, b; Janhäll et al., 2010; Sayer et al., 2014]. However, climate models often ascribe a fixed set of emission factors [i.e., amount of particulate for a given species released in the atmosphere per unit dry fuel burned; Darmenov and Dasilva, 2013] and optical properties [Hess et al., 1998] to all BB aerosol plumes, inherently limiting accurate representation of the global and regional heterogeneity of BB. The overarching goal of the proposed work is to improve the microphysical and optical representation of different types of Biomass Burning in Chemistry Transport Models. Our strategy is to capitalize on multiple airborne campaigns (e.g., ARCTAS, SEAC4RS, ORACLES, FIREX-AQ), which sampled different types of BB plumes at different aging stages. Models, when improved and combined with satellite, airborne, and ground observations will help address many aerosol science objectives from the NASA Decadal survey [National Academies of Sciences, Engineering, and Medicine. 2018].

Meloe Shenandoah F Kacenelenbogen↗
DOE OSTI2023

Transient Efficiency, Flexibility, and Reliability Optimization of Coal-Fired Power Plants - Final Report

This program developed an advanced model-based monitoring and model-predictive control algorithms for a coal fired power plant (CFPP), and deployed these algorithms in a real-time platform to demonstrate performance benefits for transient flexibility and plant operation efficiency. More specifically, the objectives were successfully achieved through a combination of (i) developing a high-fidelity transient plant model in Apros, which was used as a high-fidelity plant simulation between $100-50\% TMCR$ where TMCR denotes the turbine maximum continuous rating, i.e., baseload, (ii) developing a very fast physics-based reduced-order model (ROM) of the plant, which ran more than $100\times$ faster than real-time, enabling its use as real-time embedded model for model-based estimation (MBE) and model predictive control (MPC) (iii) implementing a real-time MBE based on ROM using a robust unscented Kalman filter (UKF) to continuously tune the ROM to match the measurements from high-fidelity Apros plant model despite significant plant-model mismatch, and thus, obtain a Digital Twin of the plant (iv) designing and implementing a real-time MPC with dual objectives of transient plant load tracking with high ramp rates and minimizing coal consumption, i.e., improving plant efficiency in the baseload-partload operation range of $100-50\% TMCR$. Each key element above was developed and tested individually, and has been reported in corresponding Topical Reports. Finally, all the individual elements were integrated in an overall closed-loop system, that was successfully tested in desktop Simulink test harness simulations with ROM or high-fidelity model as the plant. Thereafter, the Simulink implementation was used to auto-generate C-code and deploy as real-time Docker microservice containers in Linux, and validate that they can run in real-time in the hardware-in-the loop (HIL) setup and produce the same results as in Simulink. The results of the integrated simulation tests in Simulink as well as the real-time HIL deployment are documented in this final report, showing good load tracking for load ramps at $3-4\%/min$ ramp rates, and achieving up to $5.5\%$ reduction in coal relative to baseline operation at $50\% TMCR$ load. The desktop and HIL simulations show successful performance of the overall model based estimation and control solution and achieve the key objectives of the program for flexible, efficient and reliable operation of subcritical coal fired power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Water Network Tool for Resilience (WNTR)

The Water Network Tool for Resilience (WNTR) is an open source Python package designed to simulate and analyze resilience of water distribution networks. The United States Environmental Protection Agency, in partnership with Sandia National Laboratories, developed WNTR to integrate critical aspects of resilience modeling for water distribution networks into a single software framework. The software includes capability to: • Generate water network models • Modify network structure and operations • Assign fragility and survival curves to network components • Model disruptive events such as power outages, earthquakes, fires, pipe breaks, and contamination incidents • Model response and repair strategies • Simulate hydraulics and water quality • Evaluate resilience using a wide range of metrics • Integrate dependency with other critical infrastructure and supply chains • Analyze results and generate graphics SAND2019-450 M

Villa, Daniel↗
DOE OSTI2022

Modular Adaptive Packing for Integrally Cooled Absorbers

Process intensification is one cornerstone in ION Clean Energy’s (ION) efforts to lowering CO2 capture cost. ION has modeled, designed, and fabricated an innovative gas-liquid contactor known as Modular Adaptive Packing (MAP). During two successful SBIR Phase I and II projects entitled: “Rapid Design and Testing of Novel Gas-Liquid Contacting Devices for Post-Combustion CO2 Capture via 3D-printing”, ION developed and proved this new technology at bench scale. MAP, a 3D-printed lattice-structured packing, combines the absorber gas/liquid contactor with an innovative in-situ heat-exchanger. Thanks to the capabilities of 3D-printing, the lattice structure of MAP contains hollow channels through which coolant water can be pumped to remove the heat of reaction from CO2 absorption. ION refers to this novel method of heat exchange as intracooling. After 25.4 cm (10 in) diameter MAP modules were fabricated, ION built and tested a packing characterization rig at its pilot facility in Boulder, Colorado, U.S.A. To provide baseline results for the characterization rig, ION tested Sulzer’s Mellapak™ 250Y (MP250Y) as a standard structured packing. ION’s MAP was then compared directly to the baseline MP250Y packing to evaluate key indicators including pressure drop, liquid hold-up, and effective area. MAP has a higher pressure drop than MP250Y at the same gas velocities in addition to greater liquid holdup. However, ION found that MAP displays a higher wetting coverage of 93% compared to 65% for MP250Y and reduces shearing forces that result in undesirable droplet formation. Using Optimized Gas Treating’s (OGT) rate-based simulation software ProTreat®, a conceptual evaluation of MAP was modeled for a CO2 absorber using 30 wt% MEA solvent over a range of lean loadings at 90% CO2 capture from a coal-fired power plant. ION modeled a 25-meter column absorber for both the standard MP250Y packing and a hybrid column. The hybrid absorber contained 10 m of MP250Y packing at the top and bottom with the middle 5 meters comprised of MAP. Compared to a traditional intercooled absorber, MAP can remove 22% more heat and increase overall MEA carrying capacity by 4% without increasing overall pressure drop.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Machine learning and artificial intelligence for wildfire prediction

Wildfire ignition, intensity, and spread rates are tightly linked with water cycle extremes. The science of wildfire prediction has traditionally encompassed the use of physical and empirical models to quantify the direction and speed of fire spread, plume injection and fire-aerosol impacts on atmospheric composition, predictions of fire season severity on subseasonal-to-seasonal (S2S) time scales, and assessment of the spatial and temporal patterns of fire risk across landscapes. Together with expanding observation networks, machine learning and artificial intelligence (AI) have the potential to revolutionize the application of such models for fire science, saving lives, protecting critical infrastructure, and providing more accurate estimates of wildfire-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Reusable Solid Rocket Motor - V(RSRMV)Nozzle Forward Nose Ring Thermo-Structural Modeling

During the developmental static fire program for NASAs Reusable Solid Rocket Motor-V (RSRMV), an anomalous erosion condition appeared on the nozzle Carbon Cloth Phenolic nose ring that had not been observed in the space shuttle RSRM program. There were regions of augmented erosion located on the bottom of the forward nose ring (FNR) that measured nine tenths of an inch deeper than the surrounding material. Estimates of heating conditions for the RSRMV nozzle based on limited char and erosion data indicate that the total heat loading into the FNR, for the new five segment motor, is about 40-50% higher than the baseline shuttle RSRM nozzle FNR. Fault tree analysis of the augmented erosion condition has lead to a focus on a thermomechanical response of the material that is outside the existing experience base of shuttle CCP materials for this application. This paper provides a sensitivity study of the CCP material thermo-structural response subject to the design constraints and heating conditions unique to the RSRMV Forward Nose Ring application. Modeling techniques are based on 1-D thermal and porous media calculations where in-depth interlaminar loading conditions are calculated and compared to known capabilities at elevated temperatures. Parameters such as heat rate, in-depth pressures and temperature, degree of char, associated with initiation of the mechanical removal process are quantified and compared to a baseline thermo-chemical material removal mode. Conclusions regarding postulated material loss mechanisms are offered.

Clayton, J. Louie↗
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↗