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Figure 1 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 1. Number of known locations infested with Wasmannia auropunctata on Hawaii island between 1999 and 2007. Data sourced from Conant and Hirayama (2000); Motoki et al. (Motoki et al. 2013), P. Conant (pers. com.) and informal reports from Hawaii Department of Agriculture.

Figure 4 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 4. Map of Kauai showing location infested by Wasmannia auropuntata (2012). Currently this site is putatively ant free.

Figure 2 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 2. Location of properties infested with Wasmannia auropunctata in January 2007 prepared by Hawaii Department of Agriculture.

Figure 6 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 6. Locations of known sites on Oahu infested with Wasmannia auropunctata. (currently the infestation in Mililani and the original infestation in Waimanalo are putatively ant-free)

opencc-zeroJun 2022View details →

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Data from: Quantifying the environmental limits to fire spread in grassy ecosystems

Modeling fire spread as an infection process is intuitive: an ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce non-linear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires (n=1131) in grassy ecosystems across a precipitation gradient (496-1442mm mean annual precipitation), and evaluating how these scaled regionally (across 533 sites) and across time (1989-2012, 2016-2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate non-linear fire spread thresholds, but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

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Figure 5 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 5. Locations of all known sites on Maui infested with Wasmannia auropunctata.

opencc-by-4.0Mar 2017View details →

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Making tea using dry leaves, with enclosure to prevent fire from spreading (Difoinarti, N19.94736° E30.49462°)

Use of fire: c) making tea (using dry leaves, including date palms leaves, central part of sociability among farmers during day; here with metal enclosure to prevent fire from spreading) or heating food

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Data from: Quantifying the environmental limits to fire spread in grassy ecosystems

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publicJun 2022View details →

opencc-by-4.0Oct 2019View details →

Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests - Data Set

This data set is a supplementary resource for the article "<a href="http://www.mdpi.com/2571-6255/3/3/33">Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests</a>", published by the peer-reviewed open access journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. It is part of the "<a href="https://www.researchgate.net/project/Fire-Propagation-in-Cable-Tray-Installations">Fire Propagation in Cable Tray Installations</a>" project. The provided data is only a summary of the full data produced for the article, due to its size. This article was previously submitted to the Fire Safety Journal and got eventually rejected. The information is structured into multiple *.rar archieves, which mimic the sub-directory structure created for the work. To be able to run the analysis scripts without much tweaking, extract all archieves into the same directory, with each archieve being a sub-directory in it. The data set is comprised of: <ul> <li>The PROPTI and FDS input files used for the inverse modelling process (IMP) -- the 13* archieves.</li> <li>Full FDS simulation data of the mirco-combustion calorimeter (MCC) simulations of the best parameter sets per generation of the IMP runs, for jacket and insulator materials.</li> <li>Full FDS simulation data of the Cone Calorimeter simulations of the best parameter sets per generation of the IMP runs, for all three (25 kW/m², 50 kW/m², 75 kW/m²) incident heat flux conditions.</li> <li>FDS input files for the MT3 simulations, but full data only for the best parameter sets per IMP run (see below).</li> <li>Jupyter notebooks used for the analysis of the simulation responses including the scripts and plots generated for, and used in, the paper (RunReports).</li> <li>A general information directory, containing the FDS input file templates, experimental data used as target and Python scripts containing helper functions.</li> <li>Videos of a qualitative comparison of the SmokeView animation of the best parameter set in a cable tray simulation against a video from the experiment and an animation of the GAUGE_HEAT_FLUX development for the same simulation over the course of the simulation.</li> </ul> Due to the size of the MT3 simulation data, only the FDS input files for the best parameter sets per generation are uploaded. Complete FDS simulation data is only provieded for the best perameter set of each IMP run, these are : IMP run, best rep. -------------------------------- imp_13b, 110751 imp_13c, 121106 imp_13d, 137738 imp_13e, 97493 imp_13f, 91988 imp_13g, 86988 imp_13h, 17480 imp_13b_1, 19033 imp_13b_2, 25043 imp_13b_3b, 24149 imp_13b_4, 29666 imp_13h_1, 10685 imp_13h_2, 10024 imp_13h_3, 10109 imp_13i, 13253   Note: The individual runs of the IMP are named differently as compared to the labeling used in the paper, as described below: IMP run, label in paper -------------------------------- imp_13b, Tb imp_13c, Ta imp_13d, Tc imp_13e, Ta,b,c imp_13f, Tb,c imp_13g, Ta,c imp_13h, Ta,b,cLA,L1,HC imp_13b_1, TbL1 imp_13b_2, TbPL1 imp_13b_3b, TbPL1L1 imp_13b_4, TbPL2,HT imp_13h_1, Ta,b,cPA,L1,HCL1 imp_13h_2, Ta,b,cPA,L1,HCL2 imp_13h_3, Ta,b,cPA,L1,HCL3 imp_13i, TbPA,L1,HC   Version 2 changes: Added pre-print.   Version 3 changes: Added new files that where produced during the revision process, after the original manuscript got rejected by the Fire Safety Journal. This revision corresponds to the intitial manuscript that was submitted to the journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. The respective Zip archieves are labeled with a "_Revision01".

opencc-by-4.0Dec 2021View details →

Pyroconvection Classification based on Atmospheric Vertical Profiling Correlation with Extreme Fire Spread Observations

1. Isochrones for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca and Sierra Bermeja fires, in shapefile format. Each file has associated an attribute table identifying the hour (in UTC), the affected area, the rate of spread, and direction. Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya) 2. ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested were temperature, relative humidity, U-component of wind, V-component of wind. Source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.bd0915c6 3. Data from sondes launched in fires during the 2021 Spain wildfire campaign. The files are in CSV format, and there are two per fire: the sounding data corrected and the raw flight history. The information provided is Hour (UTC), Wind speed (m/s), Wind direction (true deg), Dew point (C), Latitude, Longitude, Altitude (in m MSL and m AGL), Pressure (Pascal), Speed (m/s), Heading (degrees), Temperature (C), Relative humidity (%), Internal temperature (C), Latitude, Longitude, Rise speed (m/s). Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya) 4. Data from the closest weather station to each fire. The file is an Excel file. The table fields are: fire name, weather station name, day, hour, average temperature (°C), maximum temperature(°C), minimum temperature (°C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m). Source: Meteo.cat, Servei Meteorològic de Catalunya 5. Fire behavior resume for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca, Llançà, Alfarràs and Sierra Bermeja fires (Spain). The differences in the data shown respond to the possibility of launching sondes, recreating isochrones, and observing the plume column during each fire. In those cases where the information was obtained through these three ways, the variables available are: column type, ABL and LCL height (m), sonde ID, rate of spread (km/h), ROS observed / ROS expected ratio, fireline intensity expected and observed (kW/m), and affected area (ha). 6. Photographic registry of the fire plume evolution and a brief description of the pyroconvective moments in the Alfarràs, Martorell, Llançà, Torroella, Santa Coloma de Queralt, Pobla Massaluca, and Sierra Bermeja fires (Spain). Pictures sources: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)

opencc-by-4.0May 2024View details →

Remotely sensed vegetation phenology drives large fires spreading in northwestern Europe

In recent years, an increase in the frequency of large fires has been reported in NW Europe, a region where a deeper understanding of the conditions conducive to dangerous fire behavior is needed. This study builds on recent efforts to characterize rate of spread (ROS) variation in the region and delves into vegetation and climatic drivers. For 58 large fires in this region, we analyzed phenology (using the temporal variation of satellite-measured vegetation indices) and weather, (using as the Canadian Fire Weather Index System). Results suggest that short-term vegetation greenness play an important role in predicting ROS: High greenness correlated non-linearly with low ROS, and fires spreading in the growing season described a drastic reduction in spread. Fire weather did not prove to be a good indicator of fast spread. Contrary to expectations, high danger related to fire weather ratings were associated with low ROS. This indicates the importance of temporal variability of vegetation greenness, the capability of remote sensing in capturing times when an ignition could generate fast-spreading fires, and the need for a fire weather danger rating system tailored to regional conditions.   This is the repository for the clusterd fire events and its isochrones, and ROS vectors.

opencc-by-4.0Sep 2021View details →

Evaluating the performance of fire rate of spread models in northern-European Calluna vulgaris heathlands - Supplemental Material

Supplemental Data analysis tables and raw data from the publication submitted to the journal "Fire" (MDPI) "Evaluating the performance of fire rate of spread models in northern-European Calluna vulgaris heathlands".

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Figure 3 in The History of Little Fire Ant Wasmannia auropunctata Roger in the Hawaiian Islands: Spread, Control, and Local Eradication

Figure 3. Areas of Hawaii island currently infested with Wasmannia auropunctata (2016). (Not all properties in the larger shaded section are infested).