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 Weather Index Implementation in the Alpine Area

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Daniele Cane ◽  
Nadia Ciccarelli ◽  
Renata Pelosini
Keyword(s):  
Forest Fire ◽  
Fire Weather ◽  
Mountain Areas ◽  
Weather Index ◽  
Local Conditions ◽  
Fire Weather Index ◽  
Fire Propagation ◽  
A New Technique ◽  
Definition Of ◽  
Piedmont Region

Piedmont region is located in North-Western Italy and is surrounded by the alpine chain and by the Apennines. The region is covered by a wide extension of forests, mainly in its mountain areas (the forests cover 36% of the regional territory). In the period 1997–2007, Piedmont gained interest by an average of 378 wildfire events per year, covering an average of 1767 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. We applied the Fire Weather Index FWI (Van Wagner, 1987) to the Piedmont region on warning areas previously defined for fire management purposes (Cane et al., 2008). Here we present a new technique for the definition of thresholds in order to obtain alert levels more suited with the local conditions of the forest fire warning areas. We describe also the implementation of the prognostic FWI prediction system, involving the use of good forecasts of weather parameters at the station locations obtained by the Multimodel SuperEnsemble postprocessing technique.

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

<p>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 – 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).</p><p>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.</p>

scholarly journals Forest Fire Prediction in Northern Sumatera using Support Vector Machine Based on the Fire Weather Index

2020 ◽  
Author(s):  
Darwis Robinson Manalu ◽  
Muhammad Zarlis ◽  
Herman Mawengkang ◽  
Opim Salim Sitompul
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Meteorological Data ◽  
Burned Area ◽  
Support Vector ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Data Mining Approach ◽  
Prevention Activity

Forest fires are a major environmental issue, creating economical and ecological damage while dangering human lives. The investigation and survey for forest fire had been done in Aek Godang, Northern Sumatera, Indonesia. There is 26 hotspot in 2017 close to Aek Godang, North Sumatera, Indonesia. In this study, we use a data mining approach to train and test the data of forest fire and the Fire Weather Index (FWI) from meteorological data. The aim of this study to predict the burned area and identify the forest fire in Aek Godang areas, North Sumatera. The result of this study indicated that Fire fighting and prevention activity may be one reason for the observed lack of correlation. The fact that this dataset exists indicates that there is already some effort going into fire prevention.

Environmental determinants of lightning- v. human-induced forest fire ignitions differ in a temperate mountain region of Switzerland

2010 ◽  
Vol 19 (5) ◽  
pp. 541 ◽  
Author(s):  
Björn Reineking ◽  
Patrick Weibel ◽  
Marco Conedera ◽  
Harald Bugmann
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Weather Conditions ◽  
Forest Composition ◽  
Small Range ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Independent Variables ◽  
Fire Ignitions

Understanding the environmental and human determinants of forest fire ignitions is crucial for landscape management. In this study, we consider lightning- and human-induced fires separately and evaluate the relative importance of weather, forest composition and human activities on the occurrence of forest fire ignitions in the most fire-prone region of Switzerland, the Canton Ticino. Independent variables included 14 drought and fire weather indices, forest composition and human influences. Logistic regression models were used to relate these independent variables to records of forest fires over a 37-year period (1969–2005). We found large differences in the importance of environmental and human controls on forest fire ignitions between lightning- and human-induced events: lightning-induced fires occurred in a small range of weather conditions well captured by the Duff Moisture Code from the Canadian Forest Fire Weather Index System and the LandClim Drought Index, and with negligible influence of distance to human infrastructure, whereas human-induced fires occurred in a wider range of weather conditions well captured by the Angstroem and the Fosberg Fire Weather Index, mainly in deciduous forests, and strongly depending on proximity to human infrastructure. We conclude that the suitability of fire indices can vary dramatically between ignition sources, suggesting that some of these indices are useful within certain regions and fire types only. The ignition source is an important factor that needs to be taken into account by fire managers and when developing models of forest fire occurrence.

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 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.

scholarly journals Proposal of an Iot Solution to Fire Risk Assessment Problem

2020 ◽  
Author(s):  
Ana Bernardo ◽  
Pedro Silva ◽  
Paulo Fazendeiro
Keyword(s):  
Forest Fires ◽  
Fire Risk ◽  
Initial Response ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Keywords Internet ◽  
The Common ◽  
Civic Awareness ◽  
Municipal Level

Several of the fighting weaknesses evidenced by the forest fires tragedies of the last years are rooted in the disconnection between the current technical/scientific resources and the availability of the resulting information to operational agents on the ground. In order to be effective, a pre-emptive response to similar disasters must include the articulation between local authorities at municipal level - in prevention, preparedness and initial response - and the common citizen who is on the field, resides there, and has a deeper knowledge about the field of operation. This work intends to take a first step in the development of a tool that can serve to improve the civic awareness of all and to support the decision-making of the competent authorities. Keywords: Internet of things, Citizen science, Fire weather index

Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System

2008 ◽  
Vol 17 (3) ◽  
pp. 328 ◽  
Author(s):  
A. Carvalho ◽  
M. D. Flannigan ◽  
K. Logan ◽  
A. I. Miranda ◽  
C. Borrego
Keyword(s):  
Forest Fires ◽  
Daily Maximum ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Area Burned ◽  
System Components ◽  
Baseline Information ◽  
Forward Stepwise ◽  
Maximum Temperatures

The relationships among the weather, the Canadian Fire Weather Index (FWI) System components, the monthly area burned, and the number of fire occurrences from 1980 to 2004 were investigated in 11 Portuguese districts that represent respectively 66% and 61% of the total area burned and number of fires in Portugal. A statistical approach was used to estimate the monthly area burned and the monthly number of fires per district, using meteorological variables and FWI System components as predictors. The approach succeeded in explaining from 60.9 to 80.4% of the variance for area burned and between 47.9 and 77.0% of the variance for the number of fires; all regressions were highly significant (P < 0.0001). The monthly mean and the monthly maximum of daily maximum temperatures and the monthly mean and extremes (maximum and 90th percentile) of the daily FWI were selected for all districts, except for Bragança and Porto, in the forward stepwise regression for area burned. For all districts combined, the variance explained was 80.9 and 63.0% for area burned and number of fires, respectively. Our results point to highly significant relationships among forest fires in Portugal and the weather and the Canadian FWI System. The present analysis provides baseline information for predicting the area burned and number of fires under future climate scenarios and the subsequent impacts on air quality.

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.

Wildfires and the Canadian Forest Fire Weather Index system for the Daxing'anling region of China

2011 ◽  
Vol 20 (8) ◽  
pp. 963 ◽  
Author(s):  
Xiaorui Tian ◽  
Douglas J. McRae ◽  
Jizhong Jin ◽  
Lifu Shu ◽  
Fengjun Zhao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Coniferous Forest ◽  
Northern China ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Burnt Area ◽  
Weather Index ◽  
Fire Weather Index

The Canadian Forest Fire Weather Index (FWI) system was evaluated for the Daxing'anling region of northern China for the 1987–2006 fire seasons. The FWI system reflected the regional fire danger and could be effectively used there in wildfire management. The various FWI system components were classified into classes (i.e. low to extreme) for fire conditions found in the region. A total of 81.1% of the fires occurred in the high, very high and extreme fire danger classes, in which 73.9% of the fires occurred in the spring (0.1, 9.5, 33.3 and 33.1% in March, April, May and June). Large wildfires greater than 200 ha in area (16.7% of the total) burnt 99.2% of the total burnt area. Lightning was the main ignition source for 57.1% of the total fires. Result show that forest fires mainly occurred in deciduous coniferous forest (61.3%), grass (23.9%) and deciduous broad leaved forest (8.0%). A bimodal fire season was detected, with peaks in May and October. The components of FWI system were good indicators of fire danger in the Daxing'anling region of China and could be used to build a working fire danger rating system for the region.

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