Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications

Wotton, B. M.

doi:10.1007/s10651-007-0084-2

Cited by 213 publications

(205 citation statements)

References 29 publications

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“…This reflects the fact that the most problematic fire activity occurs during extremely dry periods, which are relatively infrequent in most parts of Canada. We examined the number of days where FFMC values exceeded 84 and 91 (corresponding to roughly to 17 and 10 % moisture content in pine litter respectively), which represent reasonable thresholds for fire ignition and vigorous fire spread respectively (Wotton 2009). For the DMC we examined frequency of days where the DMC exceeded 20, indicating that the upper layer of the decaying forest floor has dried to a moisture content that could sustain combustion (Van Wagner 1972;Wotton 2009).…”

Section: Methodsmentioning

confidence: 99%

“…We examined the number of days where FFMC values exceeded 84 and 91 (corresponding to roughly to 17 and 10 % moisture content in pine litter respectively), which represent reasonable thresholds for fire ignition and vigorous fire spread respectively (Wotton 2009). For the DMC we examined frequency of days where the DMC exceeded 20, indicating that the upper layer of the decaying forest floor has dried to a moisture content that could sustain combustion (Van Wagner 1972;Wotton 2009). For the DC we examined the number of days that exceeded a value of 400, another common threshold used operationally to identify potential for deep sustained smouldering and increased wildfire mop-up difficulty.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fuel moisture sensitivity to temperature and precipitation: climate change implications

et al. 2015

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“…One of these models, the Canadian Forest Fire Danger Rating System (CFFDRS; Wotton, 2009), is composed of four subsystems, each one comprising the use of different variables in their respective calculation. One of the sub-systems of the CFFDRS is the Canadian Fire Weather Index (FWI).…”

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confidence: 99%

“…One of the sub-systems of the CFFDRS is the Canadian Fire Weather Index (FWI). The FWI uses daily observations of temperature, relative humidity, wind speed, and 24-h accumulated rainfall to estimate the MC of dead fuels and fuel layers of forest soils for three fuel size classes (f ine, medium and large fuels, Wotton, 2009). In the constitution of the FWI, fine dead surface fuels are represented by the Fine Fuel Moisture Code (FFMC; Van Wagner, 1987), while the Duff Moisture Code (DMC), and the Drought Code (DC) correspond to medium and large fuels, respectively (Forestry Canada Fire Danger Group, 1992).…”

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confidence: 99%

Ignition probability of fine dead surface fuels of native Patagonian forests or Argentina

Bianchi

Defossé

2014

For. syst.

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Aim of study: The Canadian Forest Fire Weather Index (FWI) is being implemented all over the world. This index is being adapted to the Argentinean ecosystems since the year 2000. With the objective of calibrating the Fine Fuel Moisture Code (FFMC) of the FWI system to Patagonian forests, we studied the relationship between ignition probability and fine dead surface fuel moisture content (MC) as an indicator of potential fire ignition.Area of study: The study area is located in northwestern Patagonia, Argentina, and comprised two main forest types (cypress and ñire) grown under a Mediterranean climate, with a dry summer and precipitations during winter and autumn (~500-800 mm per year).Material and Methods: We conducted lab ignition tests fires to determine the threshold of fine dead fuel ignition at different MC levels. Moisture content of dead fine surface fuels in the field was measured every 10-15 days from November to March for three seasons. We calculated the FFMC during these seasons and correlated it with the measured MC by applying a logistic regression model. We combined the results of the ignition tests and of the regressions to suggest FFMC categories for estimating fire danger in Patagonian forests.Main results: The ignition threshold occurred at MC values of 21.5 and 25.0% for cypress and ñire sites, respectively. The MC measured varied from 7.3 to 129.6%, and the calculated FFMC varied between 13.4 and 92.6. Highly significant regressions resulted when FFMC was related to MC. The ignition threshold corresponded to a FFMC=85. We proposed to divide the FFMC scale in three fire danger categories: Low (FFMC≤85), High (85<FFMC≤89) and Extreme (FFMC>89).Research highlights: Our results provide a useful tool for predicting fire danger in these ecosystems, and are a contribution to the development of the Argentinean Fire Danger Rating and a reference for similar studies in other countries where the FWI is being implemented.Keywords: Austrocedrus chilensi; Nothofagus antarctica; wildfire; fire behavior; fuel moisture; fire weather index.

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“…FWI component itself is used not only for generally determining the fire severity of many fuel types, but also for explaining the fire danger [42,43]. As for FFMC component, it is generally used as the indicator of fire outbreaks and the potential human-induced fire outbreak danger [43][44][45][46][47][48][49]. In addition to these two indices, it was also determined that there is a close relationship between ISI and DSR and the weather conditions in another study carried out in New Zealand.…”

Section: Resultsmentioning

confidence: 99%

Drought-Forest Fire Relationships

Varol¹,

Ertuğrul²,

Özel³

2017

Mediterranean Identities - Environment, Society, Culture

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This study was carried out to determine the methods that bear the most realistic results in predicting the number of fires and burned area under the climate conditions in future. Different indices and statistical methods were used in predicting the burned area and the number of fires. With this aim, in addition to the indices used in estimating the climate, Machine Learning and multivariate adaptive regression spline (MARS) models are also used in predicting these factors. According to the results obtained in several studies, the relationship between the drought and fire indices burned area and the number of fires changes from region to region. While better results are obtained in predicting the burned area and the number of fires via the drought indices being used in this study and the MARS models that the combinations of these indices use, it is seen that a 30-39% success was achieved for predicting the amount of burned area via Machine Learning methods (Kernel Nearest Neighbor (kNN), Recursive Partitioning and Regression Trees (RPART), Support Vector Machine (SVM) and RF), and this success ranges widely from 8 to 41% in terms of the number of fires. RPART, of these four algorithms, performed the best in fire prediction, but kNN was the worst.

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Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications

Cited by 213 publications

References 29 publications

Fuel moisture sensitivity to temperature and precipitation: climate change implications

Fuel moisture sensitivity to temperature and precipitation: climate change implications

Ignition probability of fine dead surface fuels of native Patagonian forests or Argentina

Drought-Forest Fire Relationships

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