Defining fire spread event days for fire-growth modelling

Podur, Justin; Wotton, B. M.

doi:10.1071/wf09001

Cited by 67 publications

(53 citation statements)

References 28 publications

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“…Ultimately the amount of fire on the landscape is driven largely by extremes; a few critical days during which a few critical fires are burning are responsible for much of the area burned (Flannigan and Wotton 2001). Podur and Wotton (2011) found that the 50 % probability of having a day of significant fire growth in the boreal forest region, given that a fire was spreading, occurred when the FFMC was above 92.6. If we see more extremes in the fuel moisture in the future we can expect more days with active fires and more area burned.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, we examine the frequency of extremes or frequency of exceeding a threshold in the fuel moisture as fire activity is driven by extreme fire weather (Wang et al 2015). For example, Podur and Wotton (2011) found that there was almost a 50 % probability of having a day of significant fire growth in the boreal forest region, given that a fire was spreading, occurred when the FFMC was above 92.6Lastly, we interpret the results in terms of future fuel moisture conditions based on temperature and precipitation changes from General Circulation Models (GCMs). (Lawson and Armitage 2008).…”

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confidence: 99%

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Fuel moisture sensitivity to temperature and precipitation: climate change implications

et al. 2015

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The objective of this paper is to examine the sensitivity of fuel moisture to changes in temperature and precipitation and explore the implications under a future climate. We use the Canadian Forest Fire Weather Index System components to represent the moisture content of fine surface fuels (Fine Fuel Moisture Code, FFMC), upper forest floor (duff) layers (Duff Moisture Code, DMC) and deep organic soils (Drought Code, DC). We obtained weather data from 12 stations across Canada for the fire season during the 1971-2000 period and with these data we created a set of modified weather streams from the original data by varying the daily temperatures by 0 to +5°C in increments of 1°C and the daily precipitation from −40 to 40 % in increments of 10 %. The fuel moistures were calculated for all the temperature and precipitation combinations. When temperature increases we find that for every degree of warming, precipitation has to increase by more than 15 % for FFMC, about 10 % for DMC and about 5 % for DC to compensate for the drying caused by warmer temperatures. Also, we find in terms of the number of days equal to or above an FFMC of 91, a critical value for fire spread, that no increase in precipitation amount alone could compensate for a temperature increase of 1°C. Results from three General Circulation Models (GCMs) and three emission scenarios suggest that this sensitivity to temperature increases will result in a future with drier fuels and a higher frequency of extreme fire weather days.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Fuel moisture sensitivity to temperature and precipitation: climate change implications

et al. 2015

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“…Fires may burn for weeks to months, but only achieve significant spread during a few days of high to extreme fire weather (hereafter 'spreadevent days') (Podur and Wotton 2011). We identified the dates of significant spread to inform other parameters in the BP models.…”

Section: Simulation Model: Inputsmentioning

confidence: 99%

Spatial bottom‐up controls on fire likelihood vary across western North America

2012

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Abstract. The unique nature of landscapes has challenged our ability to make generalizations about the effects of bottom-up controls on fire regimes. For four geographically distinct fire-prone landscapes in western North America, we used a consistent simulation approach to quantify the influence of three key bottom-up factors, ignitions, fuels, and topography, on spatial patterns of fire likelihood. We first developed working hypotheses predicting the influence of each factor based on its spatial structure (i.e., autocorrelation) in each of the four study areas. We then used a simulation model parameterized with extensive fire environment data to create high-resolution maps of fire likelihood, or burn probability (BP). To infer the influence of each bottom-up factor within and among study areas, these BP maps were compared to parallel sets of maps in which one of the three bottom-up factors was randomized. Results showed that ignition pattern had a relatively minor influence on the BP across all four study areas, whereas the influence of fuels was large. The influence of topography was the most equivocal among study areas; it had an insignificant influence in one study area and was the dominant control in another. We also found that the relationship between the influence of these factors and their spatial structure appeared nonlinear, which may have important implications for management activities aimed at attenuating the effect of fuels or ignitions on wildfire risk. This comparative study using landscapes with different biophysical and fire regime characteristics demonstrates the importance of employing consistent methodology to pinpoint the influence of bottom-up controls.

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“…A large percentage of the area is burned in just a few days with such weather conditions (Rothermel et al 1994;Flannigan and Wotton 2001;Scotto et al 2014). These days are called "spreadevent days" (Parisien et al 2005;Podur and Wotton 2011), or "spread days" (Wang et al 2014), and can be used to estimate potential fire danger (Parisien et al 2013;Wang et al 2015Wang et al , 2016. Thus, predicting the spatial and temporal occurrence of spread days over a large area would be a valuable tool for fire management and prediction.…”

Section: Introductionmentioning

confidence: 99%

Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada

et al. 2017

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Abstract:Wildfires burn an average of 2 million hectares per year in Canada, most of which can be attributed to only a few days of severe fire weather. These "spread days" are often associated with large-scale weather systems. We used extreme threshold values of three Canadian Fire Weather Index System (CFWIS) variables -the fine fuel moisture code (FFMC), initial spread index (ISI), and fire weather index (FWI) -as a proxy for spread days. Then we used self-organizing maps (SOMs) to predict spread days, with sea-level pressure and 500 hPa geopotential height as predictors. SOMs require many input parameters, and we performed an experiment to optimize six key parameters. For each month of the fire season (May-August), we also tested whether SOMs performed better when trained with only one month or with neighbouring months as well. Good performance (AUC of 0.8) was achieved for FFMC and ISI, while nearly good performance was achieved for FWI. To our knowledge, this is the first study to develop a machine-learning model for extreme fire weather that could be deployed in real time.Key words: wildland fire, fire danger, fire regimes, SOM, weather.Résumé : Les feux, dont la plupart peuvent être imputés à seulement quelques jours durant lesquels les conditions météorologiques sont propices aux incendies forestiers sévères, détruisent en moyenne deux millions d'hectares de forêt par année au Canada. Ces jours propices à la propagation des feux sont souvent associés à de vastes systèmes météorologiques. Nous avons utilisé des valeurs seuils extrêmes pour trois variables de la méthode canadienne de l'indice forêt-météo (MCIFM) : l'indice du combustible léger (ICL), l'indice de propagation initiale (IPI) et l'indice forêt-météo (IFM) en tant que substituts pour les jours propices à la propagation des feux. Ensuite, nous avons utilisé des cartes autoorganisables (SOM) pour prédire les jours propices à la propagation des feux avec comme prédicteurs la pression au niveau de la mer et une hauteur du géopotentiel de 500 hPa. Les SOM exigent plusieurs paramètres d'entrée et nous avons effectué une expérience pour optimiser six paramètres clés. Pour chaque mois de la saison des feux (mai-août), nous avons aussi testé si les SOM étaient plus performantes lorsqu'elles étaient entraînées avec seulement un mois ou en incluant aussi les mois voisins. Une bonne performance (AUC de 0,8) a été obtenue pour ICL et IPI, alors qu'une performance satisfaisante a été obtenue pour IFM. À notre connaissance, il s'agit de la première étude qui élabore un modèle d'apprentissage automatique pour des conditions météorologiques extrêmes propices aux incendies forestiers qui peut être déployé en temps réel. [Traduit par la Rédaction] Mots-clés : feux de forêt, danger d'incendie, régime des feux, cartes autoorganisables (SOM), conditions météorologiques.

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Defining fire spread event days for fire-growth modelling

Cited by 67 publications

References 28 publications

Fuel moisture sensitivity to temperature and precipitation: climate change implications

Fuel moisture sensitivity to temperature and precipitation: climate change implications

Spatial bottom‐up controls on fire likelihood vary across western North America

Automated prediction of extreme fire weather from synoptic patterns in northern Alberta, Canada

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