scholarly journals The Hot-Dry-Windy Index: A New Fire Weather Index

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 279 ◽  
Author(s):  
Alan Srock ◽  
Joseph Charney ◽  
Brian Potter ◽  
Scott Goodrick
Keyword(s):  
Multiple Scales ◽  
Wildland Fire ◽  
Meteorological Data ◽  
Fire Behavior ◽  
Weather Conditions ◽  
Meteorological Variables ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Complex Interactions

Fire weather indices are commonly used by fire weather forecasters to predict when weather conditions will make a wildland fire difficult to manage. Complex interactions at multiple scales between fire, fuels, topography, and weather make these predictions extremely difficult. We define a new fire weather index called the Hot-Dry-Windy Index (HDW). HDW uses the basic science of how the atmosphere can affect a fire to define the meteorological variables that can be predicted at synoptic-and meso-alpha-scales that govern the potential for the atmosphere to affect a fire. The new index is formulated to account for meteorological conditions both at the Earth’s surface and in a 500-m layer just above the surface. HDW is defined and then compared with the Haines Index (HI) for four historical fires. The Climate Forecast System Reanalysis (CFSR) is used to provide the meteorological data for calculating the indices. Our results indicate that HDW can identify days on which synoptic-and meso-alpha-scale weather processes can contribute to especially dangerous fire behavior. HDW is shown to perform better than the HI for each of the four historical fires. Additionally, since HDW is based on the meteorological variables that govern the potential for the atmosphere to affect a fire, it is possible to speculate on why HDW would be more or less effective based on the conditions that prevail in a given fire case. The HI, in contrast, does not have a physical basis, which makes speculation on why it works or does not work difficult because the mechanisms are not clear.

scholarly journals Fire Weather Index application in north-western Italy

2008 ◽  
Vol 2 (1) ◽  
pp. 77-80 ◽  
Author(s):  
D. Cane ◽  
N. Ciccarelli ◽  
F. Gottero ◽  
A. Francesetti ◽  
F. Pelfini ◽  
...  
Keyword(s):  
Forest Fires ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Fire Weather ◽  
Mountain Areas ◽  
Data Set ◽  
Weather Index ◽  
Fire Weather Index ◽  
North Western ◽  
Piedmont Region

Abstract. Piedmont region is located in North-Western Italy and is surrounded by the alpine chain and by the Appennines. The region is covered by a wide extension of forests, mainly in its mountain areas (the forests cover 36% of the regional territory). Forested areas are interested by wildfire events. In the period 1997–2005 Piedmont was interested by an average 387 forest fires per year, covering an average 1926 ha of forest per year. Meteorological conditions like long periods without precipitation contribute to create favourable conditions to forest fire development, while the fire propagation is made easier by the foehn winds, frequently interesting the region in winter and spring particularly. The meteorological danger index FWI (Fire Weather Index) was developed by Van Wagner (1987) for the Canadian Forestry Service, providing a complete description of the behaviour of the different forest components in response to the changing weather conditions. We applied the FWI to the Piedmont region on warning areas previously defined for fire management purposes. The meteorological data-set is based on the data of the very-dense non-GTS network of weather stations managed by Arpa Piemonte. The thresholds for the definition of a danger scenarios system were defined comparing historical FWI data with fires occurred on a 5 years period. The implementation of a prognostic FWI prediction system is planned for the early 2008, involving the use of good forecasts of weather parameters at the station locations obtained by the Multimodel SuperEnsemble post-processing technique.

scholarly journals Fire prediction with logistic regression on territory of Bosnia and Herzegovina

2021 ◽  
Vol 1208 (1) ◽  
pp. 012033
Author(s):  
Mursel Musabašić ◽  
Denis Mušić ◽  
Elmir Babović
Keyword(s):  
Logistic Regression ◽  
Index System ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Supervised Machine Learning ◽  
Fire Weather ◽  
Fire Behaviour ◽  
Weather Index ◽  
Fire Weather Index ◽  
User Input

Abstract The Canadian Fire Weather Index system [1] has been used worldwide by many countries as classic approach in fire prediction. It represents system that account for the effects of fuel moisture and weather conditions on fire behaviour. It numerical outputs are based on calculation of four meteorological elements: air temperature, relative humidity, wind speed and precipitation in last 24h. In this paper meteorological data in combination with Canadian Fire Weather Index system (CFWI) components is used as input to predict fire occurrence using logistic regression model. As logistic regression is a supervised machine learning method it’s based on user input in the form of dataset. Dataset is collected using NASA GES DISC Giovanni web-based application in the form of daily area-averaged time series in period of 31.7.2010 to 31.7.2020, it’s analysed and pre-processed before it is used as input for logit model. CFWI components values are not imported but calculated in run-time based on pre-processed meteorological data. As a result of this research windows application was developed to assist fire managers and all those involved in studying the fire behaviour.

Fire weather index system components for large fires in the Canadian boreal forest

2004 ◽  
Vol 13 (4) ◽  
pp. 391 ◽  
Author(s):  
B. D. Amiro ◽  
K. A. Logan ◽  
B. M. Wotton ◽  
M. D. Flannigan ◽  
J. B. Todd ◽  
...  
Keyword(s):  
Weather Conditions ◽  
Fire Season ◽  
Fire Weather ◽  
Fuel Moisture ◽  
Weather Index ◽  
Fire Weather Index ◽  
System Parameters ◽  
System Components ◽  
Large Fires ◽  
Canadian Boreal Forest

Canadian Fire Weather Index (FWI) System components and head fire intensities were calculated for fires greater than 2 km2 in size for the boreal and taiga ecozones of Canada from 1959 to 1999. The highest noon-hour values were analysed that occurred during the first 21 days of each of 9333 fires. Depending on ecozone, the means of the FWI System parameters ranged from: fine fuel moisture code (FFMC), 90 to 92 (82 to 96 for individual fires); duff moisture code (DMC), 38 to 78 (10 to 140 for individual fires); drought code (DC), 210 to 372 (50 to 600 for individual fires); and fire weather index, 20 to 33 (5 to 60 for individual fires). Fine fuel moisture code decreased, DMC had a mid-season peak, and DC increased through the fire season. Mean head fire intensities ranged from 10 to 28 MW m−1 in the boreal spruce fuel type, showing that most large fires exhibit crown fire behaviour. Intensities of individual fires can exceed 60 MW m−1. Most FWI System parameters did not show trends over the 41-year period because of large inter-annual variability. A changing climate is expected to create future weather conditions more conducive to fire throughout much of Canada but clear changes have not yet occurred.

scholarly journals A high-resolution reanalysis of global fire weather from 1979 to 2018 – Overwintering the Drought Code

2020 ◽  
Author(s):  
Megan McElhinny ◽  
Justin F. Beckers ◽  
Chelene Hanes ◽  
Mike Flannigan ◽  
Piyush Jain
Keyword(s):  
High Resolution ◽  
Fire Behavior ◽  
Fire Season ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Start Up ◽  
System Calculation ◽  
Second Procedure ◽  
Temporal Coverage

Abstract. We present a global high-resolution calculation of the Canadian Fire Weather Index (FWI) System Indices using surface meteorology from the ERA5-HRS reanalysis for 1979–2018. ERA5-HRS represents an improved dataset compared to several other reanalyses in terms of accuracy, as well as spatial and temporal coverage. The FWI calculation is performed using two different procedures for setting the start-up value of the Drought Code (DC) at the beginning of the fire season. The first procedure, which accounts for the effects of inter-seasonal drought, overwinters the DC by adjusting the fall DC value with a fraction of accumulated overwinter precipitation. The second procedure sets the DC to its default start-up value (i.e. 15) at the start of each fire season. We validate the FWI values over Canada using station observations from Environment and Climate Change Canada and find generally good agreement (mean Spearman correlation of 0.77). We also show that significant differences in early season DC and FWI values can occur when the FWI System calculation is started using the overwintered versus default DC values, as is highlighted by an example from 2016 over North America. The FWI System moisture codes and fire behavior indices are made available for both versions of the calculation at https://doi.org/10.5281/zenodo.3626193 (McElhinny et al., 2020), although we recommend using codes and indices calculated with the overwintered DC, unless specific research requirements dictate otherwise.

scholarly journals Fire Activity and Their Relationship with the Global Fire Weather Index Database Components in Guinea

2019 ◽  
Vol 8 (2) ◽  
pp. 18
Author(s):  
Mamadou Baïlo Barry ◽  
Daouda Badiane ◽  
Saïdou Moustapha Sall ◽  
Moussa Diakhaté ◽  
Habib Senghor
Keyword(s):  
Statistical Analysis ◽  
Fire Behavior ◽  
Burned Area ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Burned Areas ◽  
Active Fire ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The relationships between the Canadian Fire Weather Index (FWI) System components and the monthly burned area as well as the number of active fire which has taken from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua/TERRA were investigated in 32 Guinean stations between 2003 and 2013. A statistical analysis based on a multi-linear regression model was used to estimate the skills of FWI components on the predictability of burned area and active fire. This statistical analysis gave performances explaining between 16 to 79% of the variance for the burned areas and between 29 and 82% of the variance for the number of fires (P<0.0001) at lag 0. Respectively 16 to 79 % and 29 to 82 % of the variance of the burned areas and variance for the number of fires (P<0.0001) at lag0 can be explained based on the same statistical analysis. All the combinations used gave significant performances to predict the burned areas and active fire on the monthly timescale in all stations excepted Fria and Yomou where the predictability of the burned areas was not obvious. We obtained a significant correlation between the average over all of the stations of burned areas, active fires and FWI composites with percentage of variance between (75 to 84% and 29 to 77%) for active fires and burned areas at lag0 respectively. While for burned area peak (January), the skill of the predictability remains significant only one month in advance, for the active fires, the model remains skilful 1 to 3 months in advance. Results also showed that active fires are more related to fire behavior indices while the burned areas are related to the fine fuel moisture codes. These outcomes have implications for seasonal forecasting of active fire events and burned areas based on FWI components, as significant predictability is found from 1 to 3 months and one month before respectively.

What are the drivers of dangerous fires in Mediterranean France?

2018 ◽  
Vol 27 (3) ◽  
pp. 155 ◽  
Author(s):  
S. Lahaye ◽  
T. Curt ◽  
T. Fréjaville ◽  
J. Sharples ◽  
L. Paradis ◽  
...  
Keyword(s):  
Weather Conditions ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Quantile Regression Model ◽  
Mediterranean Europe ◽  
Main Driver ◽  
Large Fires ◽  
Strong Winds ◽  
Mediterranean France

Wildfire containment is often very challenging for firefighters, especially for large and rapidly spreading fires where the risk of firefighter entrapment is high. However, the conditions leading to these ‘dangerous’ fires are poorly understood in Mediterranean Europe. Here, we analyse reports and interviews of firefighters over the last 40 years in four regions of south-eastern France and investigate the weather conditions that induce large fires, fast-growing fires and fires that are conducive to entrapment. We adopt a quantile regression model to test the effect of weather conditions across different fire sizes and growth rates. The results show that strong winds drive the largest fires everywhere except in Corsica, the southernmost region, where high temperature is the main driver. Strong winds also drive entrapments whereas high temperatures induce rapidly spreading fires. This emphasises that wind-driven fire is the dominant pattern of dangerous fires in France, but it reveals that large ‘convective’ fires can also present considerable danger. Beyond that, the Fire Weather Index appears to be a good predictor of large fires and fires conducive to entrapments. Identifying weather conditions that drive ‘dangerous’ wildfires will provide useful information for fire agencies to better prepare for adverse fire behaviours.

Modification of the Fosberg fire weather index to include drought

2002 ◽  
Vol 11 (4) ◽  
pp. 205 ◽  
Author(s):  
Scott L. Goodrick
Keyword(s):  
Drought Index ◽  
Wildland Fire ◽  
Fire Weather ◽  
Potential Influence ◽  
Weather Index ◽  
Fire Weather Index ◽  
Area Burned ◽  
Availability Factor ◽  
Simple Tool ◽  
The Impact

The Fosberg fire weather index is a simple tool for evaluating the potential influence of weather on a wildland fire based on temperature, relative humidity and wind speed. A modification to this index that includes the impact of precipitation is proposed. The Keetch-Byram drought index is used to formulate a ‘fuel availability’ factor that modifies the response of the fire weather index. Comparisons between the original and modified indices are made using historical fire data from the Florida Division of Forestry. The addition of the fuel availability factor helps increase the utility of the fire weather index as it offers an improved relationship between the index and area burned.

Fire Regime and the Abundance of Red Pine

1993 ◽  
Vol 3 (4) ◽  
pp. 241 ◽  
Author(s):  
MD Flannigan
Keyword(s):  
Fire Regime ◽  
Fire Behavior ◽  
Rare Event ◽  
Weather Conditions ◽  
Red Pine ◽  
Fire Intensity ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Area Burned ◽  
Forward Stepwise

Red pine (Pinus resinosa Ait.) is a fire-dependent species. This study examines the relationship between the fire regime and the abundance of red pine. The fire regime is represented by components of the Canadian Fire Weather Index System and outputs from the Canadian Fire Behavior Prediction System as well as the average area burned and the percentage of conifers of each forest section. Extreme as well as averages values were used in this analysis as a large forest fire is a rare event that can occur on only a few days of the year under extreme fire weather conditions. Results from a forward-stepwise regression explained about 70% of die variance in red pine volume (abundance) data. Variables selected in the regression analysis included extreme headfire intensity, area burned and average drought code. These results suggest that abundance of red pine and other fire affected tree species is directly related to the aspects of the fire regime such as fire intensity.

Sensitivity of the CFFDRS Fire Weather Index parameters for Indian weather conditions

2021 ◽  
Author(s):  
Anasuya Barik ◽  
Somnath Baidya Roy
Keyword(s):  
Forest Fires ◽  
Fire Hazard ◽  
Fire Behavior ◽  
Fire Danger ◽  
Model Parameters ◽  
Fire Weather ◽  
Weather Index ◽  
Climatic Zones ◽  
Fire Weather Index ◽  
Fire Model

&lt;p&gt;The Canadian Forest Fire Danger Rating System (CFFDRS) is used to assess and predict the fire behavior in various forest ecosystems all over the world. The Fire Weather Index (FWI) module of the CFFDRS models the relationship between meteorology and forest fires. It was observed in our earlier study that the values of the FWI and its related parameters were considerably different from the other countries that use the model for their operational fire weather simulation. In this study we evaluate the model performance over Indian climate for a period of 10 years 1996-2005 under various weather scenarios. The daily meteorological data from ECMWF&amp;#8217;s ERA5 reanalysis has been used as inputs to the fire model and the active fire data from MODIS Terra and Aqua satellites over the study period has been used to evaluate the capability of model to simulate fire danger. As India has many different climatic zones, we evaluated the behavior fire model parameters over 5 forest zones namely Himalayan, Deciduous, Western Ghats, Thorn forests and North Eastern forests based on the Roy et al. 2016 Land Use Land Cover data and Koppen climatic zones.&amp;#160; The analysis was narrowed down over only the forest areas of the zones so as to remove any chances of including the non-forest fires detected by the satellite. Results show that the FWI shows a strong correlation with forest fires if the model is correctly spun up and appropriately calibrated. A spin up time of minimum 60 days was found to be appropriate for stabilization of FWI components like Duff Moisture Code (DMC) and Drought Code (DC). Sensitivity studies showed that temperature and relative humidity are the key controlling factors of forest fires over India and that the parameters depict high interannual seasonality due to relatively lower values during the Indian monsoon season.&lt;/p&gt;&lt;p&gt;This study is one of the first attempts to use fire models to simulate fire behavior over India. It can serve as a launchpad for further work on fire hazard prediction and effects of climate change on fire hazard in India.&lt;/p&gt;

Evaluation of the reliability of the high-resolution WRF fire danger forecasts in Poland

2021 ◽  
Author(s):  
Marta Gruszczynska ◽  
Alan Mandal ◽  
Grzegorz Nykiel ◽  
Tomasz Strzyzewski ◽  
Weronika Wronska ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Human Life ◽  
Meteorological Data ◽  
Fire Service ◽  
Fire Danger ◽  
Economic Losses ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index

&lt;p&gt;Fires negatively affect the composition and structure of fauna and flora, as well as the quality of air, soils and water. They cause economic losses and pose a risk to human life. Poland is at the forefront of European countries in terms of forest fires. Therefore, Institute of Meteorology and Water Management - National Research Institute (IMWM-NIR) implemented fire danger forecast system based on high-resolution (2.5 km) Weather Research and Forecast (WRF) model. Forecasted meteorological data are used to calculate parameters of Canadian Forest Fire Weather Index (FWI) System: Fire Weather Index (FWI), Initial Spread Index (ISI), Buildup Index (BUI), Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC). Each parameter is presented in one of the classes corresponding to the fire danger &amp;#8211; from low to extreme. In this way, a daily 24- and 48-hour fire danger forecasts are generated for the whole area of Poland and presented on IMWM-NIR meteorological website (meteo.imgw.pl).&lt;/p&gt;&lt;p&gt;In this presentation we show analyses of reliability of implemented FWI system. For this purpose, data reprocessing from March to September 2019 were made. Also data on fires occurrence on forest lands: time of occurrence, characteristics and location, from the resources of the State Fire Service were collected. Finally, for the selected period, we obtained a dataset of about 8 thousand events for which we assigned values of FWI parameters. Generally, based on our analysis, correlation between number of fires and averaged value of FWI amounted over 0.8. We found out, the correlation coefficient calculated for regions differ. The correlation is higher in central and northern Poland compared to the eastern part of the country, which also correspond to the number of fires. This may be related to the different forest structure - there is a higher proportion of broadleaf forests in the east. The comparison of 24- and 48-hour forecasts showed that they have similar reliability.&lt;/p&gt;

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