Users' needs in fire models

This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Fire modeling and information system technology play an important supporting role in fuel and fire management. Modeling is used to examine alternative fuel treatment options, project potential ecosystem changes, and assess risk to life and property. Models are also used to develop fire prescriptions, conduct prescribed fire operations, and predict fire behavior. Fire models and information systems have greatly influenced fuel assessment methods. As an example, we examine the evolution of technology used to put Rothermel’s fire spread model into application. A review of fire and fuel modeling terminology is given, and the relationship between fire models and fuel models is explained. We review current fire modeling work and the influence that it will have on fuel characterization. Finally, we discuss opportunities and challenges involved in the use of advanced computers, the Internet, Geographic Information Systems (GIS), and remote sensing in fire and fuel management.

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An Inverse Fire Modeling Methodology for the Determination of Fire Size, Fire Location, and Soot Deposition in a Compartment

Volume 7: Fluids and Heat Transfer, Parts A, B, C, and D ◽

10.1115/imece2012-88924 ◽

2012 ◽

Author(s):

Kristopher J. Overholt ◽

Ofodike A. Ezekoye

Keyword(s):

Gas Phase ◽

Solid Phase ◽

Fire Modeling ◽

Fire Intensity ◽

Intermediate Step ◽

Fire Size ◽

Fire Models ◽

Input Parameters ◽

In Fire ◽

Fire Location

Fire models are routinely used in life safety design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths (LODDs) and injuries. In all of these applications, the fire heat release rate (HRR), location of a fire in a compartment, gas-phase soot concentration, and solid-phase soot accumulation are important parameters that govern the evolution of thermal conditions within the fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available, such as fire investigations and LODD reconstructions. Various methods have been reported in literature to determine the size and location of a fire in a compartment using ceiling-mounted detectors [1–4]. A previous study by the authors developed an inverse fire modeling technique to determine the time-varying HRR of fire in a compartment using measured thermocouple data [5]. The work presented in this paper extends the inverse HRR methodology by developing a technique to determine the location of a fire using wall-mounted heat flux sensors or a surrogate such as degradation characteristics of enclosure boundaries that can be collected during post-fire assessments. Additionally, the presence of soot modifies the radiative transfer field in the hot gas layer (gas phase) as well as radiative heat transfer to surfaces (condensed phase). As a detailed history of compartment conditions becomes less available, there is a need for an inversion methodology to accurately recover governing input parameters such as fire size, fire location, and fire burning properties while maintaining an adequate level of accuracy. As an intermediate step using measured fire test data, we can begin to construct an approach to use rich data to invert for fire intensity, fire location, and fire properties such as the amount of soot produced by the fire.

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Historic global biomass burning emissions based on merging satellite observations with proxies and fire models (1750–2015)

10.5194/gmd-2017-32 ◽

2017 ◽

Cited By ~ 4

Author(s):

Margreet J. E. van Marle ◽

Silvia Kloster ◽

Brian I. Magi ◽

Jennifer R. Marlon ◽

Anne-Laure Daniau ◽

...

Keyword(s):

Biomass Burning ◽

Satellite Data ◽

Carbon Emissions ◽

Data Representation ◽

Atmospheric Composition ◽

Full Time ◽

Fire Emissions ◽

Fire Models ◽

Time Period ◽

In Fire

Abstract. Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data has shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emissions estimates based on satellite data starting in 1997 back in time, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies, and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant with 10-year averages varying between 1.8 and 2.3 Pg C year−1. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 % of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emissions estimates are mostly suited for global analyses and will be used in the IPCC CMIP simulations.

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Chemical Modeling of Fire Gases

Journal of Fire Sciences ◽

10.1177/073490419501300304 ◽

1995 ◽

Vol 13 (3) ◽

pp. 214-223 ◽

Cited By ~ 3

Author(s):

Yoshio Tsuchiya

Keyword(s):

Hazard Assessment ◽

Equivalence Ratio ◽

Combustion Products ◽

Chemical Modeling ◽

Incomplete Combustion ◽

Fire Models ◽

Mass Fractions ◽

In Fire ◽

Products Of Incomplete Combustion ◽

Chemical Balances

When a fuel containing C, H, and O burns, CO2 and H2O are the major combustion products and, depending on the conditions, CO, unburnt gasified fuel, and other products of incomplete combustion are produced. In this paper, chemical modeling to calculate rates of generation and mass fractions of various products in reference to the fuel/O2 equivalence ratio is presented. In addition to chemical balances, empirical CO/CO 2 ratios are used. CO is con sidered to be the most significant factor for toxicity hazard assessment in build ing fires. This modeling provides generation rates and mass fractions of CO among other species of gases. This model can be used as a sub-model in fire models for estimating CO.

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Missing climate feedbacks in fire models: limitations and uncertainties in fuel loadings and the role of decomposition in fine fuel succession

10.1002/essoar.10508012.1 ◽

2021 ◽

Author(s):

Erin Hanan ◽

Maureen C Kennedy ◽

Jianning Ren ◽

Morris C Johnson ◽

Alistair Matthew Stuart Smith

Keyword(s):

Climate Feedbacks ◽

Fire Models ◽

In Fire

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Sensitivity of simulated historical burned area to environmental andanthropogenic controls: A comparison of seven fire models

10.5194/bg-2019-42 ◽

2019 ◽

Cited By ~ 1

Author(s):

Lina Teckentrup ◽

Sandy P. Harrison ◽

Stijn Hantson ◽

Angelika Heil ◽

Joe R. Melton ◽

...

Keyword(s):

Land Use ◽

Global Change ◽

Fire Regime ◽

Fire Behavior ◽

Fire Regimes ◽

Burned Area ◽

Strong Response ◽

Fire Models ◽

In Fire ◽

The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers of fire, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1900. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trend in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the strongest differences leading to diverging trajectories are related to the way anthropogenic ignitions and suppression, as well as the effects of land-use on vegetation and fire, are incorporated in individual models. This points to a need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire for global change applications. Only two models show a strong response to CO2 and the response to lightning on global scale is low for all models. The sensitivity to climate shows a spatially heterogeneous response and globally only two models show a significant trend. It was not possible to attribute the climate-induced changes in burned area to model assumptions or specific climatic parameters. However, the strong influence of climate on the inter-annual variability in burned area, shown by all the models, shows that we need to pay attention to the simulation of fire weather but also meteorological influences on biomass accumulation and fuel properties in order to better capture extremes in fire behavior.

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A Simple Method for Determining Kinetic Parameters for Materials in Fire Models

Fire Safety Science ◽

10.3801/iafss.fss.10-765 ◽

2011 ◽

Vol 10 ◽

pp. 765-777 ◽

Cited By ~ 13

Author(s):

R. Lyon ◽

N. Safronava ◽

E. Oztekin

Keyword(s):

Kinetic Parameters ◽

Simple Method ◽

Fire Models ◽

In Fire

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The Importance of Vegetation Build Up for Burnt Area Seasonality

10.5194/egusphere-egu2020-16359 ◽

2020 ◽

Author(s):

Alexander Kuhn-Regnier ◽

Apostolos Voulgarakis ◽

Sandy Harrison ◽

Colin Prentice

Keyword(s):

Cloud Cover ◽

Management Practices ◽

Fire Regimes ◽

Exact Nature ◽

Contributing Factors ◽

Burnt Area ◽

Vegetation Productivity ◽

Fire Models ◽

In Fire ◽

The Relationship

Vegetation build up is a major controlling factor for wildfires globally. The exact nature of the dependency of wildfire activity on past vegetation productivity is still under debate, however. Given the potential future rise in conditions conducive to extremely damaging fires in many regions of the world, controlling factors like this need to be investigated urgently to better understand and manage especially extreme wildfire events. To improve our understanding of wildfires and the advice given to policy makers, a comprehensive understanding of all contributing factors is required. Changes to land management can be controversial and thus concrete evidence is required to assess and modify longstanding management practices and regulations if needed. We therefore used global satellite datasets extending from 2005 to 2011 to assess the relationship between burnt area and various biophysical variables. Vegetation proxy data included vegetation optical depth and the fraction of absorbed photosynthetically activate radiation. Different regions and time periods were analysed separately to isolate regional and temporal effects respectively. The relationship between pre-season vegetation productivity and burnt area was modelled as a regionally and temporally varying weighted sum of past monthly productivity proxies. As expected, significant differences in fire regimes were found across biomes, signified for example by significant shifts in the seasonality of burnt area. Understanding these shifts in the seasonality of both burnt area and the accompanying temporal dependence on past vegetation growth is key to reproducing observed wildfire regimes in fire models. As these relationships were found to vary both temporally and regionally, judicious inclusion of biophysical variables in fire models coupled with algorithms able to capture these relationships is necessary.&#160; However, remotely sensed observations were of different quality in different areas due to inhomogeneous cloud cover patterns, making assessments for much-affected regions like South America and South East Asia especially difficult. Likewise, the found correlation between decreasing cloud cover and increasing burnt area biased our results. Due also to the short time span of the data available in this investigation, these factors warrant further investigation to more fully quantify the temporal and regional relationships at work.

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