Marin County Wildland Fires II: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, CA
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Harrison Raine↗
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Enclosure fire dynamics model
A practical situation of an enclosure fire is presented and why the need for a fire dynamic model is addressed. The difficulties in establishing a model are discussed, along with a brief review of enclosure fire models available. The approximation of the practical situation and the model developed are presented.
Bellan, J.↗
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↗
Quantitative Risk Modeling of Fire on the International Space Station
The International Space Station (ISS) Program has worked to prevent fire events and to mitigate their impacts should they occur. Hardware is designed to reduce sources of ignition, oxygen systems are designed to control leaking, flammable materials are prevented from flying to ISS whenever possible, the crew is trained in fire response, and fire response equipment improvements are sought out and funded. Fire prevention and mitigation are a top ISS Program priority - however, programmatic resources are limited; thus, risk trades are made to ensure an adequate level of safety is maintained onboard the ISS. In support of these risk trades, the ISS Probabilistic Risk Assessment (PRA) team has modeled the likelihood of fire occurring in the ISS pressurized cabin, a phenomenological event that has never before been probabilistically modeled in a microgravity environment. This paper will discuss the genesis of the ISS PRA fire model, its enhancement in collaboration with fire experts, and the results which have informed ISS programmatic decisions and will continue to be used throughout the life of the program.
Castillo, Theresa↗
Verification of the naval oceanic vertical aerosol model during FIRE
The value of Naval Oceanic Vertical Aerosol Model (NOVAM) is illustrated for estimating the non-uniform and non-logarithmic extinction profiles, based on a severe test involving conditions close to and beyond the limits of applicability of NOVAM. A more comprehensive evaluation of NOVAM from the FIRE data is presented, which includes a clear-air case. For further evaluation more data are required on the vertical structure of the extinction in the marine atmospheric boundary layer (MABL), preferably for different meteorological conditions and in different geographic areas (e.g., ASTEX).
Davidson, K. L.↗
Modeling Forest Understory Fires in an Eastern Amazonian Landscape
Forest understory fires are an increasingly important cause of forest impoverishment in Ammonia, but little is known of the landscape characteristics and climatic phenomena that determine their occurrence. We developed empirical functions relating the occurrence of understory fires to landscape features near Paragominas, a 35- yr-old ranching and logging center in eastern Ammonia. An historical sequence of maps of forest understory fire was created based on field interviews With local farmers and Landsat TM images. Several landscape features that might explain spatial variations in the occurrence of understory fires were also mapped and co-registered for each of the sample dates, including: forest fragment size and shape, forest impoverishment through logging and understory fires, source of ignition (settlements and charcoal pits), roads, forest edges, and others. The spatial relationship between forest understory fire and each landscape characteristic was tested by regression analyses. Fire probability models were then developed for various combinations of landscape characteristics. The analyses were conducted separately for years of the El Nino Southern Oscillation (ENSO), which are associated with severe drought in eastern Amazonia, and non-ENS0 years. Most (91 %) of the forest area that burned during the 10-yr sequence caught fire during ENSO years, when severe drought may have increased both forest flammability and the escape of agricultural management fires. Forest understory fires were associated with forest edges, as reported in previous studies from Ammonia. But the strongest predictor of forest fire was the percentage of the forest fragment that had been previously logged or burned. Forest fragment size, distance to charcoal pits, distance to agricultural settlement, proximity to forest edge, and distance to roads were also correlated with forest understory fire. Logistic regression models using information on fragment degradation and distance to ignition sources accurately predicted the location of lss than 80% of the forest fires observed during the ENSO event of 1997- 1998. In this Amazon landscape, forest understory fire is a complex function of several variables that influence both the flammability and ignition exposure of the forest.
Alencar, A. A. C.↗
Full-scale flammability test data for validation of aircraft fire mathematical models
Twenty-five large scale aircraft flammability tests were conducted in a Boeing 737 fuselage at the NASA Johnson Space Center (JSC). The objective of this test program was to provide a data base on the propagation of large scale aircraft fires to support the validation of aircraft fire mathematical models. Variables in the test program included cabin volume, amount of fuel, fuel pan area, fire location, airflow rate, and cabin materials. A number of tests were conducted with jet A-1 fuel only, while others were conducted with various Boeing 747 type cabin materials. These included urethane foam seats, passenger service units, stowage bins, and wall and ceiling panels. Two tests were also included using special urethane foam and polyimide foam seats. Tests were conducted with each cabin material individually, with various combinations of these materials, and finally, with all materials in the cabin. The data include information obtained from approximately 160 locations inside the fuselage.
Kuminecz, J. F.↗
Identification of the Space Shuttle Main Engine dynamic models from firing data
A multi-input multi-output system identification technique is used to obtain linearized point models of the Space Shuttle Main Engine (SSME) at five different operating conditions (power levels) from the engine firing data. This study demonstrates that the open-loop dynamics of the SSME can be successfully modeled from the engine firing data. The identified linear models may be used for fault detection studies and control system design and development.
Saravanan, N.↗
Modeling aerosol losses and clothing effects in fires
The described modification of a physiochemical fire model predicts physical effects in clothed humans exposed to elevated temperatures as well as gas and aerosol concentrations in the atmosphere of an aerospace vehicle. The modifications involve the reduction in aerosol concentrations caused by settling and some improvements in dealing with mass and heat transfer effects. Predicted skin temperatures are found to be within 2 C of experimental values for the cases studied, and model predictions of total body enthalpy seem to correlate with symptomology observed experimentally.
Cagliostro, D. E.↗
The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction
Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.
global fire atlas↗
Modelling the firing pattern of bullfrog vestibular neurons responding to naturalistic stimuli
We have developed a neural system identification method for fitting models to stimulus-response data, where the response is a spike train. The method involves using a general nonlinear optimisation procedure to fit models in the time domain. We have applied the method to model bullfrog semicircular canal afferent neuron responses during naturalistic, broad-band head rotations. These neurons respond in diverse ways, but a simple four parameter class of models elegantly accounts for the various types of responses observed. c1999 Elsevier Science B.V. All rights reserved.
Non-NASA Center↗
The use of computer models to predict temperature and smoke movement in high bay spaces
The Building and Fire Research Laboratory (BFRL) was given the opportunity to make measurements during fire calibration tests of the heat detection system in an aircraft hangar with a nominal 30.4 (100 ft) ceiling height near Dallas, TX. Fire gas temperatures resulting from an approximately 8250 kW isopropyl alcohol pool fire were measured above the fire and along the ceiling. The results of the experiments were then compared to predictions from the computer fire models DETACT-QS, FPETOOL, and LAVENT. In section A of the analysis conducted, DETACT-QS AND FPETOOL significantly underpredicted the gas temperature. LAVENT at the position below the ceiling corresponding to maximum temperature and velocity provided better agreement with the data. For large spaces, hot gas transport time and an improved fire plume dynamics model should be incorporated into the computer fire model activation routines. A computational fluid dynamics (CFD) model, HARWELL FLOW3D, was then used to model the hot gas movement in the space. Reasonable agreement was found between the temperatures predicted from the CFD calculations and the temperatures measured in the aircraft hangar. In section B, an existing NASA high bay space was modeled using the CFD model. The NASA space was a clean room, 27.4 m (90 ft) high with forced horizontal laminar flow. The purpose of this analysis is to determine how the existing fire detection devices would respond to various size fires in the space. The analysis was conducted for 32 MW, 400 kW, and 40 kW fires.
Notarianni, Kathy A.↗
Simulated wildfire burned area over the CONUS during 2001-2020
Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.
Liu, Ye↗
The impact of a 2 X CO2 climate on lightning-caused fires
Future climate change could have significant repercussions for lightning-caused wildfires. Two empirical fire models are presented relating the frequency of lightning fires and the area burned by these fires to the effective precipitation and the frequency of thunderstorm activity. One model deals with the seasonal variations in lightning fires, while the second model deals with the interannual variations of lightning fires. These fire models are then used with the Goddard Institute for Space Studies General Circulation Model to investigate possible changes in fire frequency and area burned in a 2 X CO2 climate. In the United States, the annual mean number of lightning fires increases by 44%, while the area burned increases by 78%. On a global scale, the largest increase in lightning fires can be expected in untouched tropical ecosystems where few natural fires occur today.
Price, Colin↗
Implementing Aerosol Coagulation in the QUIC-Fire/QUIC-SMOKE model
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Earth Sciences↗
Pathways to instability in models of fire propagation
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Earth Sciences↗
Frequency-Based Spatial Correlation Assessments of the Ares I Subscale Acoustic Model Test Firings
The Marshall Space Flight Center has performed a series of test firings to simulate and understand the acoustic environments generated for the Ares I liftoff profiles. Part of the instrumentation package had special sensor groups to assess the acoustic field spatial correlation features for the various test configurations. The spatial correlation characteristics were evaluated for all of the test firings, inclusive of understanding the diffuse to propagating wave amplitude ratios, the acoustic wave decays, and the incident angle of propagating waves across the sensor groups. These parameters were evaluated across the measured frequency spectra and the associated uncertainties for each parameter were estimated.
Kenny, R. Jeremy↗
Frequency-Based Spatial Correlation Assessments of the Ares I Subscale Acoustic Model Test Firings
No abstract available
Kenny, R. Jeremy↗